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"<h2 style=\"color:Blue;\"> Advance Pandas Trick and Techniques </h2>\n",
"\n",
"----\n",
"\n",
"<h3 style=\"color:Blue;\">Index of learning</h3> <a id=\"00\"> </a>\n",
"\n",
"\n",
"---\n",
"1. [**Basic Pandas Reading Method**](#1)\n",
"1. [**Advance Pandas Reading Methods**](#2)\n",
" 1. [**Manipulating Column & Index Locations and Names**](#21)\n",
" 1. [**Data Parsing options**](#22)\n",
" 1. [**Reading data from excel files**](#23)\n",
" 1. [**Reading data from some other popular formats**](#24)\n",
"1. [**Apply multiple filter criteria to a pandas DataFrame**](#3)\n",
"1. [**Changing the datatype of a Pandas Series**](#4)\n",
"1. [**Filter rows of a pandas DataFrame by column value**](#5)\n",
"1. [**Selecting multiple rows and columns from a pandas DataFrame**](#6)\n",
"1. [**Sorting a pandas DataFrame or a Series**](#7)\n",
"1. [**Using pandas Series data structure to select a subset of the data**](#8)\n",
"1. [**Using string methods in pandas**](#9)\n",
"1. [**Using the axis parameter in pandas**](#10)\n",
"1. [**Applying a function to a pandas Series or DataFrame** ](#11)\n",
"1. [**Handling SettingWithCopyWarning**](#12)\n",
"1. [**Handling missing values in pandas**](#13)\n",
"1. [**Indexing in pandas dataframes**](#14)\n",
"1. [**Merging and concatenating multiple data frames into one** ](#15)\n",
"1. [**Modifying a Pandas Dataframe inplace**](#16)\n",
"1. [**Removing columns from a pandas DataFrame**](#17)\n",
"1. [**Renaming columns in a pandas DataFrame**](#18)\n",
"1. [**Using groupby method**](#19)\n",
"1. [**Work with dates and times data**](#20)\n",
"1. [**Choosing the colors for the plots**](#211)\n",
"1. [**Controlling plot aesthetics**](#221)\n",
"1. [**Plotting categorical data**](#231)\n",
"1. [**Plotting with data aware grids**](#241)\n",
" \n",
"---\n"
]
},
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"%%html\n",
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"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
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{
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"source": [
"# 1.Basic Pandas Reading Method <a id=\"1\"> </a> \n",
" ---\n",
" [**Go to top**](#00)\n",
" \n",
" \n",
" \n",
"  "
]
},
{
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"execution_count": 3,
"metadata": {
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"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)\n",
"warnings.simplefilter(action='ignore', category=RuntimeWarning)\n",
"warnings.simplefilter(action='ignore', category=UserWarning)"
]
},
{
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" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
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" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
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" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
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" <th>Votes3044M</th>\n",
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" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
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"df = pd.read_csv(\"../input/datasetsdifferent-format/IMDB.csv\", encoding=\"ISO-8859-1\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "874f13ff80642477b5c340131cef545b32c58246"
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"source": [
"# 2.Advance Pandas Reading Methods <a id=\"2\"></a>\n",
"---\n",
" [**Go to top**](#00)\n",
" \n",
" \n",
" \n",
"* [**Advance Pandas Reading Methods**](#2)\n",
" * [**Manipulating Column & Index Locations and Names**](#21)\n",
" * [**Data Parsing options**](#22)\n",
" * [**Reading data from excel files**](#23)\n",
" * [**Reading data from some other popular formats**](#24)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ef5847c6c5b2ddc6c9a77e183f861ead8dfe87b9"
},
"source": [
"> ### 2.1 Manipulating Columns & Index Location and Names <a id=\"21\"></a>\n",
"\n",
"### 1. No Header and No Columns\n",
"* There is **no header*** and **no columns** while reading csv file here and used `encoding` because file in `ISO-8859-1` format"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
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{
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" <th>35</th>\n",
" <th>36</th>\n",
" <th>37</th>\n",
" <th>38</th>\n",
" <th>39</th>\n",
" <th>40</th>\n",
" <th>41</th>\n",
" <th>42</th>\n",
" <th>43</th>\n",
" <th>44</th>\n",
" <th>45</th>\n",
" <th>46</th>\n",
" <th>47</th>\n",
" <th>48</th>\n",
" <th>49</th>\n",
" <th>50</th>\n",
" <th>51</th>\n",
" <th>52</th>\n",
" <th>53</th>\n",
" <th>54</th>\n",
" <th>55</th>\n",
" <th>56</th>\n",
" <th>57</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>X</td>\n",
" <td>Title</td>\n",
" <td>Rating</td>\n",
" <td>TotalVotes</td>\n",
" <td>Genre1</td>\n",
" <td>Genre2</td>\n",
" <td>Genre3</td>\n",
" <td>MetaCritic</td>\n",
" <td>Budget</td>\n",
" <td>Runtime</td>\n",
" <td>CVotes10</td>\n",
" <td>CVotes09</td>\n",
" <td>CVotes08</td>\n",
" <td>CVotes07</td>\n",
" <td>CVotes06</td>\n",
" <td>CVotes05</td>\n",
" <td>CVotes04</td>\n",
" <td>CVotes03</td>\n",
" <td>CVotes02</td>\n",
" <td>CVotes01</td>\n",
" <td>CVotesMale</td>\n",
" <td>CVotesFemale</td>\n",
" <td>CVotesU18</td>\n",
" <td>CVotesU18M</td>\n",
" <td>CVotesU18F</td>\n",
" <td>CVotes1829</td>\n",
" <td>CVotes1829M</td>\n",
" <td>CVotes1829F</td>\n",
" <td>CVotes3044</td>\n",
" <td>CVotes3044M</td>\n",
" <td>CVotes3044F</td>\n",
" <td>CVotes45A</td>\n",
" <td>CVotes45AM</td>\n",
" <td>CVotes45AF</td>\n",
" <td>CVotes1000</td>\n",
" <td>CVotesUS</td>\n",
" <td>CVotesnUS</td>\n",
" <td>VotesM</td>\n",
" <td>VotesF</td>\n",
" <td>VotesU18</td>\n",
" <td>VotesU18M</td>\n",
" <td>VotesU18F</td>\n",
" <td>Votes1829</td>\n",
" <td>Votes1829M</td>\n",
" <td>Votes1829F</td>\n",
" <td>Votes3044</td>\n",
" <td>Votes3044M</td>\n",
" <td>Votes3044F</td>\n",
" <td>Votes45A</td>\n",
" <td>Votes45AM</td>\n",
" <td>Votes45AF</td>\n",
" <td>VotesIMDB</td>\n",
" <td>Votes1000</td>\n",
" <td>VotesUS</td>\n",
" <td>VotesnUS</td>\n",
" <td>Domestic</td>\n",
" <td>Foreign</td>\n",
" <td>Worldwide</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96</td>\n",
" <td>20000000</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8</td>\n",
" <td>7.9</td>\n",
" <td>8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82</td>\n",
" <td>18000000</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72</td>\n",
" <td>8000000</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 ... 56 57\n",
"0 X Title ... Foreign Worldwide\n",
"1 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"2 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"3 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"4 4 About Time (2013) ... 71777528 $87,100,449 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", header=None)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "08294d75e254363a4538f82483e9c6c354e19c53"
},
"source": [
"### 2.Specify a different row as header\n",
"* Read specific **rows as header** which is working as **column name**\n",
"* In the result, row 2 become header of dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_uuid": "750fcf0945cb137ca4db9c504a16f48532a943d8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2</th>\n",
" <th>127 Hours (2010)</th>\n",
" <th>7.6</th>\n",
" <th>297075</th>\n",
" <th>Adventure</th>\n",
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" <th>44600</th>\n",
" <th>745</th>\n",
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" <th>170</th>\n",
" <th>133336</th>\n",
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" <th>7.6.1</th>\n",
" <th>7.6.2</th>\n",
" <th>7.9</th>\n",
" <th>7.9.1</th>\n",
" <th>7.9.2</th>\n",
" <th>7.7</th>\n",
" <th>7.8</th>\n",
" <th>7.7.1</th>\n",
" <th>7.5</th>\n",
" <th>7.5.1</th>\n",
" <th>7.5.2</th>\n",
" <th>7.3</th>\n",
" <th>7.3.1</th>\n",
" <th>7.5.3</th>\n",
" <th>7.6.3</th>\n",
" <th>7</th>\n",
" <th>7.7.2</th>\n",
" <th>7.6.4</th>\n",
" <th>$18,335,230</th>\n",
" <th>42403567</th>\n",
" <th>$60,738,797</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6</td>\n",
" <td>Argo (2012)</td>\n",
" <td>7.7</td>\n",
" <td>486840</td>\n",
" <td>Action</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>86.0</td>\n",
" <td>44500000.0</td>\n",
" <td>120 min</td>\n",
" <td>43875</td>\n",
" <td>89490</td>\n",
" <td>171495</td>\n",
" <td>115165</td>\n",
" <td>37332</td>\n",
" <td>12630</td>\n",
" <td>4992</td>\n",
" <td>2910</td>\n",
" <td>2020</td>\n",
" <td>6941</td>\n",
" <td>334838</td>\n",
" <td>67910</td>\n",
" <td>971</td>\n",
" <td>795</td>\n",
" <td>162</td>\n",
" <td>178794</td>\n",
" <td>146371</td>\n",
" <td>30643</td>\n",
" <td>163795</td>\n",
" <td>136391</td>\n",
" <td>24948</td>\n",
" <td>36215</td>\n",
" <td>28817</td>\n",
" <td>6752</td>\n",
" <td>740</td>\n",
" <td>70110</td>\n",
" <td>229137.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>$136,025,503</td>\n",
" <td>96300000</td>\n",
" <td>$232,325,503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7</td>\n",
" <td>Arrival (2016)</td>\n",
" <td>8.0</td>\n",
" <td>370842</td>\n",
" <td>Drama</td>\n",
" <td>Mystery</td>\n",
" <td>Sci-Fi</td>\n",
" <td>81.0</td>\n",
" <td>47000000.0</td>\n",
" <td>116 min</td>\n",
" <td>55533</td>\n",
" <td>87850</td>\n",
" <td>109536</td>\n",
" <td>65440</td>\n",
" <td>26913</td>\n",
" <td>10556</td>\n",
" <td>5057</td>\n",
" <td>3083</td>\n",
" <td>2194</td>\n",
" <td>4734</td>\n",
" <td>237437</td>\n",
" <td>46272</td>\n",
" <td>1943</td>\n",
" <td>1544</td>\n",
" <td>376</td>\n",
" <td>126301</td>\n",
" <td>101741</td>\n",
" <td>23163</td>\n",
" <td>111985</td>\n",
" <td>95005</td>\n",
" <td>15227</td>\n",
" <td>24027</td>\n",
" <td>20118</td>\n",
" <td>3440</td>\n",
" <td>537</td>\n",
" <td>42062</td>\n",
" <td>163774.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.6</td>\n",
" <td>8.6</td>\n",
" <td>8.4</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>7.3</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$100,546,139</td>\n",
" <td>102842047</td>\n",
" <td>$203,388,186</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"0 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"1 4 About Time (2013) ... 71777528 $87,100,449 \n",
"2 5 Amour (2012) ... 13100000 $19,839,492 \n",
"3 6 Argo (2012) ... 96300000 $232,325,503 \n",
"4 7 Arrival (2016) ... 102842047 $203,388,186 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../input/datasetsdifferent-format/IMDB.csv\", encoding = \"ISO-8859-1\", header=2)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "579d6d098ca78625931c91013b093d5e6b2ccbf1"
},
"source": [
"### 3.Specify a column as index\n",
"* Read specify column as index for dataframe\n",
"* In the result, title column become Index column of dataframe"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_uuid": "eaa4eb0bb9c5a3c63bd996147d1ac4632ef1e47a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Title</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12 Years a Slave (2013)</th>\n",
" <td>1</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127 Hours (2010)</th>\n",
" <td>2</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50/50Â (2011)</th>\n",
" <td>3</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>About Time (2013)</th>\n",
" <td>4</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Amour (2012)</th>\n",
" <td>5</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Rating ... Foreign Worldwide\n",
"Title ... \n",
"12 Years a Slave (2013) 1 8.1 ... 131061209 $187,733,202 \n",
"127 Hours (2010) 2 7.6 ... 42403567 $60,738,797 \n",
"50/50Â (2011) 3 7.7 ... 4173591 $39,187,783 \n",
"About Time (2013) 4 7.8 ... 71777528 $87,100,449 \n",
"Amour (2012) 5 7.9 ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 57 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../input/datasetsdifferent-format/IMDB.csv\", encoding = \"ISO-8859-1\", index_col=\"Title\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "9b059a5914a12924ebb48694c933b5c80e22eaf3"
},
"source": [
"### 4.Choose only a subset of columns to be read\n",
"* Subset specific columns from the dataframe while reading file\n",
"* In the result, subset the ` Title, Genre1, Genre2, Budget` columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_uuid": "f4aa41dc2b262bc2a2eebef760bbba9f2791ba0a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Title</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Budget</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>20000000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>127 Hours (2010)</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>18000000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>50/50Â (2011)</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>8000000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>About Time (2013)</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>12000000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Amour (2012)</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>8900000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Title Genre1 Genre2 Budget\n",
"0 12 Years a Slave (2013) Biography Drama 20000000.0\n",
"1 127 Hours (2010) Adventure Biography 18000000.0\n",
"2 50/50Â (2011) Comedy Drama 8000000.0\n",
"3 About Time (2013) Comedy Drama 12000000.0\n",
"4 Amour (2012) Drama Romance 8900000.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../input/datasetsdifferent-format/IMDB.csv\", encoding = \"ISO-8859-1\", usecols=['Title','Genre1','Genre2','Budget'])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "33993b9f2fca1e51dd0bea77f946e61b107d6bfc"
},
"source": [
"### 5.Handling Missing and NA data\n",
"\n",
"***Missing Value format :*** NaN: ”, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘nan’`.\n",
"\n",
"* Handling Missing value while reading data.\n",
"* In the result, dataframe handle the result which contain `nan` kind missing value"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_uuid": "7dbad608ce9b01300bca013668eae61d2a2828aa"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(117, 58)\n"
]
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", na_values=['nan'])\n",
"display(df.head())\n",
"print(df.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b65eef0d3d330b1608bcd7afad673711ca53de54"
},
"source": [
"### 6.Choose whether to skip over blank rows or not\n",
"\n",
"* you choose whether to skip over blank rows while reading data\n",
"* In the result, you can see that we have skipped blank rows.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_uuid": "10ab61642b1b00e58ea82fa2535e35864653eba7"
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>Genre1</th>\n",
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" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
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" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", skip_blank_lines=False)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ee0eda98b03f87df896b6b249eab34457a758741"
},
"source": [
"> ### 2.2 Data Parsing options <a id=\"22\"> </a>\n",
"\n",
"### 1. Skip Rows\n",
"* We can skip the rows by reading the dataset\n",
"* In the result, you can see that row number `1,3,7` are skipped from the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_uuid": "0007420c303217d629eb1fb838e5706fa1b1166c"
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
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" <th>VotesM</th>\n",
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" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
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" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
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" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6</td>\n",
" <td>Argo (2012)</td>\n",
" <td>7.7</td>\n",
" <td>486840</td>\n",
" <td>Action</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>86.0</td>\n",
" <td>44500000.0</td>\n",
" <td>120 min</td>\n",
" <td>43875</td>\n",
" <td>89490</td>\n",
" <td>171495</td>\n",
" <td>115165</td>\n",
" <td>37332</td>\n",
" <td>12630</td>\n",
" <td>4992</td>\n",
" <td>2910</td>\n",
" <td>2020</td>\n",
" <td>6941</td>\n",
" <td>334838</td>\n",
" <td>67910</td>\n",
" <td>971</td>\n",
" <td>795</td>\n",
" <td>162</td>\n",
" <td>178794</td>\n",
" <td>146371</td>\n",
" <td>30643</td>\n",
" <td>163795</td>\n",
" <td>136391</td>\n",
" <td>24948</td>\n",
" <td>36215</td>\n",
" <td>28817</td>\n",
" <td>6752</td>\n",
" <td>740</td>\n",
" <td>70110</td>\n",
" <td>229137.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>$136,025,503</td>\n",
" <td>96300000</td>\n",
" <td>$232,325,503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9</td>\n",
" <td>Before Midnight (2013)</td>\n",
" <td>7.9</td>\n",
" <td>106553</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>3000000.0</td>\n",
" <td>109 min</td>\n",
" <td>16953</td>\n",
" <td>22109</td>\n",
" <td>31439</td>\n",
" <td>19251</td>\n",
" <td>8142</td>\n",
" <td>3412</td>\n",
" <td>1649</td>\n",
" <td>1033</td>\n",
" <td>826</td>\n",
" <td>1745</td>\n",
" <td>67076</td>\n",
" <td>23823</td>\n",
" <td>208</td>\n",
" <td>138</td>\n",
" <td>66</td>\n",
" <td>43312</td>\n",
" <td>30016</td>\n",
" <td>12857</td>\n",
" <td>37072</td>\n",
" <td>28401</td>\n",
" <td>8189</td>\n",
" <td>7479</td>\n",
" <td>5891</td>\n",
" <td>1470</td>\n",
" <td>447</td>\n",
" <td>12382</td>\n",
" <td>59116.0</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>7.4</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.4</td>\n",
" <td>7.2</td>\n",
" <td>8.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$8,114,627</td>\n",
" <td>3061842</td>\n",
" <td>$11,176,469</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"1 4 About Time (2013) ... 71777528 $87,100,449 \n",
"2 5 Amour (2012) ... 13100000 $19,839,492 \n",
"3 6 Argo (2012) ... 96300000 $232,325,503 \n",
"4 9 Before Midnight (2013) ... 3061842 $11,176,469 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", skiprows = [1,3,7])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "bd774460f6a6df8f91736f9969be5eca18c67017"
},
"source": [
"### 2.Skip rows from footer or from end of the file\n",
"\n",
"* We can skip the rows from the footer.\n",
"* In the result, "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_uuid": "e8f0a40f628ab1087eb755d14d4d005b50db4697"
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>X</th>\n",
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" <th>Genre1</th>\n",
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" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>117</td>\n",
" <td>X-Men: First Class (2011)</td>\n",
" <td>7.8</td>\n",
" <td>556713</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>65.0</td>\n",
" <td>160000000.0</td>\n",
" <td>132 min</td>\n",
" <td>64428</td>\n",
" <td>96219</td>\n",
" <td>200144</td>\n",
" <td>129352</td>\n",
" <td>41945</td>\n",
" <td>12861</td>\n",
" <td>4799</td>\n",
" <td>2349</td>\n",
" <td>1448</td>\n",
" <td>3182</td>\n",
" <td>382107</td>\n",
" <td>80444</td>\n",
" <td>2075</td>\n",
" <td>1612</td>\n",
" <td>443</td>\n",
" <td>223309</td>\n",
" <td>176821</td>\n",
" <td>44428</td>\n",
" <td>185909</td>\n",
" <td>157332</td>\n",
" <td>26094</td>\n",
" <td>30217</td>\n",
" <td>25051</td>\n",
" <td>4691</td>\n",
" <td>780</td>\n",
" <td>87542</td>\n",
" <td>257681.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.3</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$146,408,305</td>\n",
" <td>207215819</td>\n",
" <td>$353,624,124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>118</td>\n",
" <td>Zootopia (2016)</td>\n",
" <td>8.1</td>\n",
" <td>309474</td>\n",
" <td>Animation</td>\n",
" <td>Adventure</td>\n",
" <td>Comedy</td>\n",
" <td>78.0</td>\n",
" <td>150000000.0</td>\n",
" <td>108 min</td>\n",
" <td>53626</td>\n",
" <td>70912</td>\n",
" <td>102352</td>\n",
" <td>57261</td>\n",
" <td>16719</td>\n",
" <td>4539</td>\n",
" <td>1467</td>\n",
" <td>733</td>\n",
" <td>496</td>\n",
" <td>1386</td>\n",
" <td>176202</td>\n",
" <td>52345</td>\n",
" <td>2362</td>\n",
" <td>1641</td>\n",
" <td>706</td>\n",
" <td>119637</td>\n",
" <td>87499</td>\n",
" <td>30813</td>\n",
" <td>75474</td>\n",
" <td>61358</td>\n",
" <td>13034</td>\n",
" <td>12353</td>\n",
" <td>9959</td>\n",
" <td>2151</td>\n",
" <td>518</td>\n",
" <td>35975</td>\n",
" <td>122844.0</td>\n",
" <td>8.0</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>8.3</td>\n",
" <td>8.7</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>$341,268,248</td>\n",
" <td>682515947</td>\n",
" <td>$1,023,784,195</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X ... Worldwide\n",
"112 117 ... $353,624,124 \n",
"113 118 ... $1,023,784,195 \n",
"\n",
"[2 rows x 58 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After Skipping the Rows\n"
]
},
{
"data": {
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"<div>\n",
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" <th></th>\n",
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" <th>Title</th>\n",
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" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>115</td>\n",
" <td>Wreck-It Ralph (2012)</td>\n",
" <td>7.7</td>\n",
" <td>295125</td>\n",
" <td>Animation</td>\n",
" <td>Adventure</td>\n",
" <td>Comedy</td>\n",
" <td>72.0</td>\n",
" <td>165000000.0</td>\n",
" <td>NaN</td>\n",
" <td>41980</td>\n",
" <td>50262</td>\n",
" <td>96477</td>\n",
" <td>67934</td>\n",
" <td>24894</td>\n",
" <td>7748</td>\n",
" <td>2724</td>\n",
" <td>1190</td>\n",
" <td>703</td>\n",
" <td>1226</td>\n",
" <td>190983</td>\n",
" <td>50202</td>\n",
" <td>1663</td>\n",
" <td>1182</td>\n",
" <td>467</td>\n",
" <td>120962</td>\n",
" <td>90759</td>\n",
" <td>29003</td>\n",
" <td>90203</td>\n",
" <td>74767</td>\n",
" <td>14148</td>\n",
" <td>13706</td>\n",
" <td>11356</td>\n",
" <td>2112</td>\n",
" <td>614</td>\n",
" <td>44962</td>\n",
" <td>129487.0</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$189,422,889</td>\n",
" <td>281800000</td>\n",
" <td>$471,222,889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>116</td>\n",
" <td>X-Men: Days of Future Past (2014)</td>\n",
" <td>8.0</td>\n",
" <td>560736</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>74.0</td>\n",
" <td>200000000.0</td>\n",
" <td>132 min</td>\n",
" <td>91765</td>\n",
" <td>127521</td>\n",
" <td>183578</td>\n",
" <td>104658</td>\n",
" <td>33027</td>\n",
" <td>10059</td>\n",
" <td>3710</td>\n",
" <td>1903</td>\n",
" <td>1225</td>\n",
" <td>3301</td>\n",
" <td>370835</td>\n",
" <td>71008</td>\n",
" <td>3038</td>\n",
" <td>2403</td>\n",
" <td>614</td>\n",
" <td>220178</td>\n",
" <td>179039</td>\n",
" <td>39094</td>\n",
" <td>158607</td>\n",
" <td>135392</td>\n",
" <td>20927</td>\n",
" <td>26834</td>\n",
" <td>22460</td>\n",
" <td>3884</td>\n",
" <td>710</td>\n",
" <td>67889</td>\n",
" <td>229049.0</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.6</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>8.1</td>\n",
" <td>7.9</td>\n",
" <td>$233,921,534</td>\n",
" <td>513941241</td>\n",
" <td>$747,862,775</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X ... Worldwide\n",
"113 115 ... $471,222,889 \n",
"114 116 ... $747,862,775 \n",
"\n",
"[2 rows x 58 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(2)\n",
"print(\"After Skipping the Rows\")\n",
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", skipfooter=2, engine='python')\n",
"df.tail(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0b64a249ab7243461062d00e86fb97c5ee21b3b6"
},
"source": [
"### 3.Reading only a subset of the file or a certain number of rows\n",
"\n",
"* We are also Reading only a subset of the file or a certain number of rows while reading whole dataset file.\n",
"* In the result, we can see the shape of the data before and after."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_uuid": "39dc72f18ba37cd62d186860d5e7cfbfca0acad2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before Shape: (115, 58)\n",
"After Selecting 100 Rows\n",
"After Shape: (100, 58)\n"
]
}
],
"source": [
"print(\"Before Shape:\",df.shape)\n",
"print(\"After Selecting 100 Rows\")\n",
"df = pd.read_csv('../input/datasetsdifferent-format/IMDB.csv', encoding = \"ISO-8859-1\", nrows=100)\n",
"print(\"After Shape:\",df.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "cc48f56dd7a7c3c24e75d303ddd313877f85bd4d"
},
"source": [
"> ### 2.3.Reading data from excel files <a id=\"23\"></a>\n",
"\n",
"### 1.Basic Excel read\n",
"* Basic Excel file reading with default sheet number"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_uuid": "892798063ece437ac8ad6c5c7e29eb5b807452ea"
},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
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" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
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" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "e3bf37852aee430f60c2b4579421c93a7432a56e"
},
"source": [
"## Advanced read options\n",
"\n",
"`pandas.read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, dtype=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)`\n",
"\n",
"***Reference:*** [Pandas Doc](http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.read_excel.html)\n",
"\n",
"### 2.Which Sheet to read?\n",
"* We can select which sheet which we have to read."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_uuid": "8d233c938e9366f4eba90ae1ec943a123635f497"
},
"outputs": [
{
"data": {
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" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=0)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8ae73a56d8cc6ff09da94d8e306d9301d403acb2"
},
"source": [
"### 3.Reading data from multiple sheets in an excel file\n",
"* Find out the sheet list of the excel file"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_uuid": "0a20ca9bff983a3cf4235747b7c14f4bcc693027"
},
"outputs": [
{
"data": {
"text/plain": [
"['movies', 'by genre']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_excel = pd.ExcelFile('../input/datasetsdifferent-format/IMDB.xlsx')\n",
"df_excel.sheet_names"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_uuid": "3f68aa745dc513893d650899da432ca8bb0bf115"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
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" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
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" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
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" <td>1490</td>\n",
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" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
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" <td>8.5</td>\n",
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" <td>8.0</td>\n",
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" <td>7.7</td>\n",
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" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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" <td>224519.0</td>\n",
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" <td>8.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
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" <td>127 Hours (2010)</td>\n",
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" <td>Drama</td>\n",
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" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
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" <td>28394</td>\n",
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" <td>567</td>\n",
" <td>170</td>\n",
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" <td>12400</td>\n",
" <td>2261</td>\n",
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" <td>7.5</td>\n",
" <td>7.6</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
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" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
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" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
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" <td>7.6</td>\n",
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" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
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" <td>7.6</td>\n",
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" <td>7.6</td>\n",
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" <td>7.8</td>\n",
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" <td>6.9</td>\n",
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" <td>7.7</td>\n",
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" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
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" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
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" <td>534</td>\n",
" <td>995</td>\n",
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" <td>1490</td>\n",
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" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = df_excel.parse('movies')\n",
"df2 = df_excel.parse('by genre')\n",
"df1.head()\n",
"df2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "f91f240361730172a6fc594cb94a13d8daa3e206"
},
"source": [
"### 4.Choose Header or column labels\n",
"\n",
"* we can also select header or columns labels from the `read_excel()` function"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_uuid": "e5c5560acc7253f9ff7a1cf3953c73517857aa22"
},
"outputs": [
{
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" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6</td>\n",
" <td>Argo (2012)</td>\n",
" <td>7.7</td>\n",
" <td>486840</td>\n",
" <td>Action</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>86.0</td>\n",
" <td>44500000.0</td>\n",
" <td>120 min</td>\n",
" <td>43875</td>\n",
" <td>89490</td>\n",
" <td>171495</td>\n",
" <td>115165</td>\n",
" <td>37332</td>\n",
" <td>12630</td>\n",
" <td>4992</td>\n",
" <td>2910</td>\n",
" <td>2020</td>\n",
" <td>6941</td>\n",
" <td>334838</td>\n",
" <td>67910</td>\n",
" <td>971</td>\n",
" <td>795</td>\n",
" <td>162</td>\n",
" <td>178794</td>\n",
" <td>146371</td>\n",
" <td>30643</td>\n",
" <td>163795</td>\n",
" <td>136391</td>\n",
" <td>24948</td>\n",
" <td>36215</td>\n",
" <td>28817</td>\n",
" <td>6752</td>\n",
" <td>740</td>\n",
" <td>70110</td>\n",
" <td>229137.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>$136,025,503</td>\n",
" <td>96300000</td>\n",
" <td>$232,325,503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>Arrival (2016)</td>\n",
" <td>8.0</td>\n",
" <td>370842</td>\n",
" <td>Drama</td>\n",
" <td>Mystery</td>\n",
" <td>Sci-Fi</td>\n",
" <td>81.0</td>\n",
" <td>47000000.0</td>\n",
" <td>116 min</td>\n",
" <td>55533</td>\n",
" <td>87850</td>\n",
" <td>109536</td>\n",
" <td>65440</td>\n",
" <td>26913</td>\n",
" <td>10556</td>\n",
" <td>5057</td>\n",
" <td>3083</td>\n",
" <td>2194</td>\n",
" <td>4734</td>\n",
" <td>237437</td>\n",
" <td>46272</td>\n",
" <td>1943</td>\n",
" <td>1544</td>\n",
" <td>376</td>\n",
" <td>126301</td>\n",
" <td>101741</td>\n",
" <td>23163</td>\n",
" <td>111985</td>\n",
" <td>95005</td>\n",
" <td>15227</td>\n",
" <td>24027</td>\n",
" <td>20118</td>\n",
" <td>3440</td>\n",
" <td>537</td>\n",
" <td>42062</td>\n",
" <td>163774.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.6</td>\n",
" <td>8.6</td>\n",
" <td>8.4</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>7.3</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$100,546,139</td>\n",
" <td>102842047</td>\n",
" <td>$203,388,186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9</td>\n",
" <td>Before Midnight (2013)</td>\n",
" <td>7.9</td>\n",
" <td>106553</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>3000000.0</td>\n",
" <td>109 min</td>\n",
" <td>16953</td>\n",
" <td>22109</td>\n",
" <td>31439</td>\n",
" <td>19251</td>\n",
" <td>8142</td>\n",
" <td>3412</td>\n",
" <td>1649</td>\n",
" <td>1033</td>\n",
" <td>826</td>\n",
" <td>1745</td>\n",
" <td>67076</td>\n",
" <td>23823</td>\n",
" <td>208</td>\n",
" <td>138</td>\n",
" <td>66</td>\n",
" <td>43312</td>\n",
" <td>30016</td>\n",
" <td>12857</td>\n",
" <td>37072</td>\n",
" <td>28401</td>\n",
" <td>8189</td>\n",
" <td>7479</td>\n",
" <td>5891</td>\n",
" <td>1470</td>\n",
" <td>447</td>\n",
" <td>12382</td>\n",
" <td>59116.0</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>7.4</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.4</td>\n",
" <td>7.2</td>\n",
" <td>8.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$8,114,627</td>\n",
" <td>3061842</td>\n",
" <td>$11,176,469</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"0 4 About Time (2013) ... 71777528 $87,100,449 \n",
"1 5 Amour (2012) ... 13100000 $19,839,492 \n",
"2 6 Argo (2012) ... 96300000 $232,325,503 \n",
"3 7 Arrival (2016) ... 102842047 $203,388,186 \n",
"4 9 Before Midnight (2013) ... 3061842 $11,176,469 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=1, header=3)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "13fd726b3eff8b3bff121aafb75a41b661ffaf81"
},
"source": [
"### 5.No header\n",
"* We can set `header = None` for not seeing header"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_uuid": "e141cd99952beebdfc5cf54efd844c819983da8b"
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
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" <th>25</th>\n",
" <th>26</th>\n",
" <th>27</th>\n",
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" <th>43</th>\n",
" <th>44</th>\n",
" <th>45</th>\n",
" <th>46</th>\n",
" <th>47</th>\n",
" <th>48</th>\n",
" <th>49</th>\n",
" <th>50</th>\n",
" <th>51</th>\n",
" <th>52</th>\n",
" <th>53</th>\n",
" <th>54</th>\n",
" <th>55</th>\n",
" <th>56</th>\n",
" <th>57</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>X</td>\n",
" <td>Title</td>\n",
" <td>Rating</td>\n",
" <td>TotalVotes</td>\n",
" <td>Genre1</td>\n",
" <td>Genre2</td>\n",
" <td>Genre3</td>\n",
" <td>MetaCritic</td>\n",
" <td>Budget</td>\n",
" <td>Runtime</td>\n",
" <td>CVotes10</td>\n",
" <td>CVotes09</td>\n",
" <td>CVotes08</td>\n",
" <td>CVotes07</td>\n",
" <td>CVotes06</td>\n",
" <td>CVotes05</td>\n",
" <td>CVotes04</td>\n",
" <td>CVotes03</td>\n",
" <td>CVotes02</td>\n",
" <td>CVotes01</td>\n",
" <td>CVotesMale</td>\n",
" <td>CVotesFemale</td>\n",
" <td>CVotesU18</td>\n",
" <td>CVotesU18M</td>\n",
" <td>CVotesU18F</td>\n",
" <td>CVotes1829</td>\n",
" <td>CVotes1829M</td>\n",
" <td>CVotes1829F</td>\n",
" <td>CVotes3044</td>\n",
" <td>CVotes3044M</td>\n",
" <td>CVotes3044F</td>\n",
" <td>CVotes45A</td>\n",
" <td>CVotes45AM</td>\n",
" <td>CVotes45AF</td>\n",
" <td>CVotes1000</td>\n",
" <td>CVotesUS</td>\n",
" <td>CVotesnUS</td>\n",
" <td>VotesM</td>\n",
" <td>VotesF</td>\n",
" <td>VotesU18</td>\n",
" <td>VotesU18M</td>\n",
" <td>VotesU18F</td>\n",
" <td>Votes1829</td>\n",
" <td>Votes1829M</td>\n",
" <td>Votes1829F</td>\n",
" <td>Votes3044</td>\n",
" <td>Votes3044M</td>\n",
" <td>Votes3044F</td>\n",
" <td>Votes45A</td>\n",
" <td>Votes45AM</td>\n",
" <td>Votes45AF</td>\n",
" <td>VotesIMDB</td>\n",
" <td>Votes1000</td>\n",
" <td>VotesUS</td>\n",
" <td>VotesnUS</td>\n",
" <td>Domestic</td>\n",
" <td>Foreign</td>\n",
" <td>Worldwide</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96</td>\n",
" <td>20000000</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8</td>\n",
" <td>7.9</td>\n",
" <td>8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82</td>\n",
" <td>18000000</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72</td>\n",
" <td>8000000</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 ... 56 57\n",
"0 X Title ... Foreign Worldwide\n",
"1 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"2 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"3 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"4 4 About Time (2013) ... 71777528 $87,100,449 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=1, header=None)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "de562cd865358ff0204833a4f8e39fff69f4674e"
},
"source": [
"### 6.Skip Rows at the beginning of the file\n",
"* Skip the rows"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_uuid": "5efd37ea4d594331b30954a4c7ce6231ca53430e",
"scrolled": true
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>7</th>\n",
" <th>Arrival (2016)</th>\n",
" <th>8</th>\n",
" <th>370842</th>\n",
" <th>Drama</th>\n",
" <th>Mystery</th>\n",
" <th>Sci-Fi</th>\n",
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" <th>1544</th>\n",
" <th>376</th>\n",
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" <th>7.9</th>\n",
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" <th>8.6</th>\n",
" <th>8.6.1</th>\n",
" <th>8.4</th>\n",
" <th>8.2</th>\n",
" <th>8.2.1</th>\n",
" <th>8.1</th>\n",
" <th>7.8</th>\n",
" <th>7.8.1</th>\n",
" <th>7.8.2</th>\n",
" <th>7.6</th>\n",
" <th>7.6.1</th>\n",
" <th>7.7</th>\n",
" <th>8.3</th>\n",
" <th>7.3</th>\n",
" <th>8.2</th>\n",
" <th>7.9.1</th>\n",
" <th>$100,546,139</th>\n",
" <th>102842047</th>\n",
" <th>$203,388,186</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9</td>\n",
" <td>Before Midnight (2013)</td>\n",
" <td>7.9</td>\n",
" <td>106553</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>3000000.0</td>\n",
" <td>109 min</td>\n",
" <td>16953</td>\n",
" <td>22109</td>\n",
" <td>31439</td>\n",
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" <td>1033</td>\n",
" <td>826</td>\n",
" <td>1745</td>\n",
" <td>67076</td>\n",
" <td>23823</td>\n",
" <td>208</td>\n",
" <td>138</td>\n",
" <td>66</td>\n",
" <td>43312</td>\n",
" <td>30016</td>\n",
" <td>12857</td>\n",
" <td>37072</td>\n",
" <td>28401</td>\n",
" <td>8189</td>\n",
" <td>7479</td>\n",
" <td>5891</td>\n",
" <td>1470</td>\n",
" <td>447</td>\n",
" <td>12382</td>\n",
" <td>59116.0</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>7.4</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.4</td>\n",
" <td>7.2</td>\n",
" <td>8.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$8,114,627</td>\n",
" <td>3061842</td>\n",
" <td>$11,176,469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>Big Hero 6 (2014)</td>\n",
" <td>7.8</td>\n",
" <td>315485</td>\n",
" <td>Animation</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>74.0</td>\n",
" <td>165000000.0</td>\n",
" <td>NaN</td>\n",
" <td>50311</td>\n",
" <td>61304</td>\n",
" <td>103726</td>\n",
" <td>65681</td>\n",
" <td>22389</td>\n",
" <td>6830</td>\n",
" <td>2251</td>\n",
" <td>1036</td>\n",
" <td>539</td>\n",
" <td>1439</td>\n",
" <td>187383</td>\n",
" <td>58731</td>\n",
" <td>2446</td>\n",
" <td>1571</td>\n",
" <td>855</td>\n",
" <td>128237</td>\n",
" <td>91744</td>\n",
" <td>35122</td>\n",
" <td>84098</td>\n",
" <td>68040</td>\n",
" <td>14796</td>\n",
" <td>13974</td>\n",
" <td>11304</td>\n",
" <td>2400</td>\n",
" <td>525</td>\n",
" <td>36702</td>\n",
" <td>131818.0</td>\n",
" <td>7.7</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>8.7</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.3</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>$222,527,828</td>\n",
" <td>435290784</td>\n",
" <td>$657,818,612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>11</td>\n",
" <td>Birdman or (The Unexpected Virtue of Ignorance...</td>\n",
" <td>7.8</td>\n",
" <td>448725</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>NaN</td>\n",
" <td>88.0</td>\n",
" <td>18000000.0</td>\n",
" <td>119 min</td>\n",
" <td>60209</td>\n",
" <td>94476</td>\n",
" <td>121637</td>\n",
" <td>80828</td>\n",
" <td>38373</td>\n",
" <td>19161</td>\n",
" <td>10116</td>\n",
" <td>6750</td>\n",
" <td>5378</td>\n",
" <td>11807</td>\n",
" <td>292808</td>\n",
" <td>63310</td>\n",
" <td>1891</td>\n",
" <td>1538</td>\n",
" <td>334</td>\n",
" <td>178850</td>\n",
" <td>142244</td>\n",
" <td>34666</td>\n",
" <td>129547</td>\n",
" <td>108049</td>\n",
" <td>19457</td>\n",
" <td>26016</td>\n",
" <td>21166</td>\n",
" <td>4329</td>\n",
" <td>656</td>\n",
" <td>52288</td>\n",
" <td>203731.0</td>\n",
" <td>7.8</td>\n",
" <td>7.5</td>\n",
" <td>8.5</td>\n",
" <td>8.6</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.2</td>\n",
" <td>7.3</td>\n",
" <td>7.0</td>\n",
" <td>7.8</td>\n",
" <td>7.1</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>$42,340,598</td>\n",
" <td>60874496</td>\n",
" <td>$103,215,094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12</td>\n",
" <td>Black Swan (2010)</td>\n",
" <td>8.0</td>\n",
" <td>587893</td>\n",
" <td>Drama</td>\n",
" <td>Thriller</td>\n",
" <td>NaN</td>\n",
" <td>79.0</td>\n",
" <td>13000000.0</td>\n",
" <td>108 min</td>\n",
" <td>93798</td>\n",
" <td>136615</td>\n",
" <td>174500</td>\n",
" <td>97826</td>\n",
" <td>40319</td>\n",
" <td>16993</td>\n",
" <td>9084</td>\n",
" <td>6065</td>\n",
" <td>3981</td>\n",
" <td>8726</td>\n",
" <td>356707</td>\n",
" <td>143077</td>\n",
" <td>1112</td>\n",
" <td>583</td>\n",
" <td>516</td>\n",
" <td>244970</td>\n",
" <td>159567</td>\n",
" <td>82856</td>\n",
" <td>204465</td>\n",
" <td>156163</td>\n",
" <td>45352</td>\n",
" <td>35111</td>\n",
" <td>27022</td>\n",
" <td>7459</td>\n",
" <td>802</td>\n",
" <td>86552</td>\n",
" <td>306578.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.5</td>\n",
" <td>8.6</td>\n",
" <td>8.4</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>$106,954,678</td>\n",
" <td>222443368</td>\n",
" <td>$329,398,046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>13</td>\n",
" <td>Boyhood (2014)</td>\n",
" <td>7.9</td>\n",
" <td>290327</td>\n",
" <td>Drama</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.0</td>\n",
" <td>4000000.0</td>\n",
" <td>165 min</td>\n",
" <td>49673</td>\n",
" <td>62055</td>\n",
" <td>76838</td>\n",
" <td>52238</td>\n",
" <td>23789</td>\n",
" <td>10431</td>\n",
" <td>4906</td>\n",
" <td>3071</td>\n",
" <td>2248</td>\n",
" <td>5086</td>\n",
" <td>183807</td>\n",
" <td>51558</td>\n",
" <td>1393</td>\n",
" <td>995</td>\n",
" <td>381</td>\n",
" <td>123006</td>\n",
" <td>92639</td>\n",
" <td>29076</td>\n",
" <td>81594</td>\n",
" <td>65261</td>\n",
" <td>15118</td>\n",
" <td>17881</td>\n",
" <td>13995</td>\n",
" <td>3567</td>\n",
" <td>559</td>\n",
" <td>36433</td>\n",
" <td>134679.0</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.2</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>$25,352,281</td>\n",
" <td>19143000</td>\n",
" <td>$44,495,281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>14</td>\n",
" <td>Bridge of Spies (2015)</td>\n",
" <td>7.6</td>\n",
" <td>223756</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>Thriller</td>\n",
" <td>81.0</td>\n",
" <td>40000000.0</td>\n",
" <td>142 min</td>\n",
" <td>15757</td>\n",
" <td>32840</td>\n",
" <td>83322</td>\n",
" <td>63800</td>\n",
" <td>19183</td>\n",
" <td>5178</td>\n",
" <td>1657</td>\n",
" <td>735</td>\n",
" <td>419</td>\n",
" <td>878</td>\n",
" <td>152707</td>\n",
" <td>23978</td>\n",
" <td>846</td>\n",
" <td>732</td>\n",
" <td>104</td>\n",
" <td>76784</td>\n",
" <td>64810</td>\n",
" <td>11177</td>\n",
" <td>70780</td>\n",
" <td>61525</td>\n",
" <td>8196</td>\n",
" <td>18494</td>\n",
" <td>15504</td>\n",
" <td>2667</td>\n",
" <td>545</td>\n",
" <td>24273</td>\n",
" <td>105678.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.4</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>$72,313,754</td>\n",
" <td>93164594</td>\n",
" <td>$165,478,348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>15</td>\n",
" <td>Captain America: Civil War (2016)</td>\n",
" <td>7.9</td>\n",
" <td>431555</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>75.0</td>\n",
" <td>250000000.0</td>\n",
" <td>147 min</td>\n",
" <td>81893</td>\n",
" <td>90156</td>\n",
" <td>117188</td>\n",
" <td>79377</td>\n",
" <td>32782</td>\n",
" <td>12322</td>\n",
" <td>5095</td>\n",
" <td>2994</td>\n",
" <td>1989</td>\n",
" <td>7786</td>\n",
" <td>264239</td>\n",
" <td>43818</td>\n",
" <td>3572</td>\n",
" <td>2865</td>\n",
" <td>683</td>\n",
" <td>148991</td>\n",
" <td>124124</td>\n",
" <td>23355</td>\n",
" <td>105069</td>\n",
" <td>91345</td>\n",
" <td>12135</td>\n",
" <td>19151</td>\n",
" <td>16351</td>\n",
" <td>2459</td>\n",
" <td>593</td>\n",
" <td>48777</td>\n",
" <td>153638.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.3</td>\n",
" <td>8.3</td>\n",
" <td>8.6</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>8.1</td>\n",
" <td>7.7</td>\n",
" <td>$408,084,349</td>\n",
" <td>745220146</td>\n",
" <td>$1,153,304,495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>16</td>\n",
" <td>Captain America: The Winter Soldier (2014)</td>\n",
" <td>7.8</td>\n",
" <td>552706</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>70.0</td>\n",
" <td>170000000.0</td>\n",
" <td>136 min</td>\n",
" <td>84943</td>\n",
" <td>103896</td>\n",
" <td>169440</td>\n",
" <td>120197</td>\n",
" <td>44124</td>\n",
" <td>14639</td>\n",
" <td>5571</td>\n",
" <td>2735</td>\n",
" <td>1932</td>\n",
" <td>5248</td>\n",
" <td>360615</td>\n",
" <td>66751</td>\n",
" <td>3765</td>\n",
" <td>2900</td>\n",
" <td>844</td>\n",
" <td>208526</td>\n",
" <td>170111</td>\n",
" <td>36456</td>\n",
" <td>150264</td>\n",
" <td>129500</td>\n",
" <td>18637</td>\n",
" <td>28922</td>\n",
" <td>24313</td>\n",
" <td>4103</td>\n",
" <td>720</td>\n",
" <td>72120</td>\n",
" <td>213180.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.3</td>\n",
" <td>8.2</td>\n",
" <td>8.5</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>8.1</td>\n",
" <td>7.5</td>\n",
" <td>$259,766,572</td>\n",
" <td>454497695</td>\n",
" <td>$714,264,267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>17</td>\n",
" <td>Captain Fantastic (2016)</td>\n",
" <td>7.9</td>\n",
" <td>115194</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>NaN</td>\n",
" <td>72.0</td>\n",
" <td>5000000.0</td>\n",
" <td>NaN</td>\n",
" <td>16165</td>\n",
" <td>24762</td>\n",
" <td>39686</td>\n",
" <td>22429</td>\n",
" <td>7134</td>\n",
" <td>2255</td>\n",
" <td>982</td>\n",
" <td>542</td>\n",
" <td>419</td>\n",
" <td>832</td>\n",
" <td>71760</td>\n",
" <td>19138</td>\n",
" <td>447</td>\n",
" <td>329</td>\n",
" <td>112</td>\n",
" <td>40918</td>\n",
" <td>30740</td>\n",
" <td>9707</td>\n",
" <td>36357</td>\n",
" <td>29410</td>\n",
" <td>6414</td>\n",
" <td>8123</td>\n",
" <td>6521</td>\n",
" <td>1433</td>\n",
" <td>351</td>\n",
" <td>10694</td>\n",
" <td>56956.0</td>\n",
" <td>7.9</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.2</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>8.5</td>\n",
" <td>6.8</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>$5,879,736</td>\n",
" <td>n/a</td>\n",
" <td>$5,879,736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18</td>\n",
" <td>Captain Phillips (2013)</td>\n",
" <td>7.8</td>\n",
" <td>350818</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>Thriller</td>\n",
" <td>83.0</td>\n",
" <td>55000000.0</td>\n",
" <td>134 min</td>\n",
" <td>37461</td>\n",
" <td>70216</td>\n",
" <td>133266</td>\n",
" <td>76657</td>\n",
" <td>21791</td>\n",
" <td>6099</td>\n",
" <td>2051</td>\n",
" <td>1062</td>\n",
" <td>707</td>\n",
" <td>1517</td>\n",
" <td>247889</td>\n",
" <td>41602</td>\n",
" <td>995</td>\n",
" <td>838</td>\n",
" <td>147</td>\n",
" <td>131052</td>\n",
" <td>110723</td>\n",
" <td>19092</td>\n",
" <td>114418</td>\n",
" <td>98191</td>\n",
" <td>14686</td>\n",
" <td>24670</td>\n",
" <td>20178</td>\n",
" <td>4053</td>\n",
" <td>633</td>\n",
" <td>43042</td>\n",
" <td>165981.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.4</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>$107,100,855</td>\n",
" <td>111690956</td>\n",
" <td>$218,791,811</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 7 ... $203,388,186 \n",
"0 9 ... $11,176,469 \n",
"1 10 ... $657,818,612 \n",
"2 11 ... $103,215,094 \n",
"3 12 ... $329,398,046 \n",
"4 13 ... $44,495,281 \n",
"5 14 ... $165,478,348 \n",
"6 15 ... $1,153,304,495 \n",
"7 16 ... $714,264,267 \n",
"8 17 ... $5,879,736 \n",
"9 18 ... $218,791,811 \n",
"\n",
"[10 rows x 58 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=1, skiprows=7)\n",
"df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "caea4db03dbaf4eb71c6a358bf229a64f75b6855"
},
"source": [
"### 7.Skip rows from the end of the file"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_uuid": "2066ad264aaf7583b824c7283fcd31f0070d9588"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>109</td>\n",
" <td>True Grit (2010)</td>\n",
" <td>7.6</td>\n",
" <td>257670</td>\n",
" <td>Adventure</td>\n",
" <td>Drama</td>\n",
" <td>Western</td>\n",
" <td>80.0</td>\n",
" <td>38000000.0</td>\n",
" <td>110 min</td>\n",
" <td>21094</td>\n",
" <td>40901</td>\n",
" <td>91825</td>\n",
" <td>67175</td>\n",
" <td>23055</td>\n",
" <td>7191</td>\n",
" <td>2678</td>\n",
" <td>1305</td>\n",
" <td>779</td>\n",
" <td>1672</td>\n",
" <td>197105</td>\n",
" <td>27125</td>\n",
" <td>381</td>\n",
" <td>340</td>\n",
" <td>35</td>\n",
" <td>89394</td>\n",
" <td>76864</td>\n",
" <td>11720</td>\n",
" <td>104201</td>\n",
" <td>91807</td>\n",
" <td>11163</td>\n",
" <td>25641</td>\n",
" <td>21885</td>\n",
" <td>3369</td>\n",
" <td>747</td>\n",
" <td>53749</td>\n",
" <td>137672.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.3</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$171,243,005</td>\n",
" <td>81033922</td>\n",
" <td>$252,276,927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>110</td>\n",
" <td>Tucker and Dale vs Evil (2010)</td>\n",
" <td>7.6</td>\n",
" <td>138624</td>\n",
" <td>Comedy</td>\n",
" <td>Horror</td>\n",
" <td>NaN</td>\n",
" <td>65.0</td>\n",
" <td>5000000.0</td>\n",
" <td>NaN</td>\n",
" <td>16572</td>\n",
" <td>19818</td>\n",
" <td>44460</td>\n",
" <td>35863</td>\n",
" <td>13456</td>\n",
" <td>4588</td>\n",
" <td>1684</td>\n",
" <td>855</td>\n",
" <td>479</td>\n",
" <td>848</td>\n",
" <td>106144</td>\n",
" <td>15113</td>\n",
" <td>219</td>\n",
" <td>198</td>\n",
" <td>20</td>\n",
" <td>52889</td>\n",
" <td>45169</td>\n",
" <td>7232</td>\n",
" <td>56379</td>\n",
" <td>49634</td>\n",
" <td>6156</td>\n",
" <td>8861</td>\n",
" <td>7645</td>\n",
" <td>1072</td>\n",
" <td>540</td>\n",
" <td>26213</td>\n",
" <td>73918.0</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>8.2</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.1</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>$223,838</td>\n",
" <td>4525678</td>\n",
" <td>$4,749,516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>111</td>\n",
" <td>Tyrannosaur (2011)</td>\n",
" <td>7.6</td>\n",
" <td>26016</td>\n",
" <td>Drama</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>65.0</td>\n",
" <td>1000000.0</td>\n",
" <td>NaN</td>\n",
" <td>2060</td>\n",
" <td>4083</td>\n",
" <td>9078</td>\n",
" <td>6754</td>\n",
" <td>2468</td>\n",
" <td>755</td>\n",
" <td>310</td>\n",
" <td>146</td>\n",
" <td>111</td>\n",
" <td>251</td>\n",
" <td>19827</td>\n",
" <td>3649</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>7314</td>\n",
" <td>5920</td>\n",
" <td>1338</td>\n",
" <td>12497</td>\n",
" <td>10628</td>\n",
" <td>1724</td>\n",
" <td>3311</td>\n",
" <td>2784</td>\n",
" <td>480</td>\n",
" <td>4</td>\n",
" <td>2231</td>\n",
" <td>18173</td>\n",
" <td>NaN</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>5.8</td>\n",
" <td>6.5</td>\n",
" <td>7.4</td>\n",
" <td>7.6</td>\n",
" <td>NaN</td>\n",
" <td>$22,321</td>\n",
" <td>n/a</td>\n",
" <td>$22,321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>112</td>\n",
" <td>Warrior (2011)</td>\n",
" <td>8.2</td>\n",
" <td>361049</td>\n",
" <td>Action</td>\n",
" <td>Drama</td>\n",
" <td>Sport</td>\n",
" <td>71.0</td>\n",
" <td>25000000.0</td>\n",
" <td>140 min</td>\n",
" <td>74983</td>\n",
" <td>96953</td>\n",
" <td>106673</td>\n",
" <td>52972</td>\n",
" <td>16668</td>\n",
" <td>5727</td>\n",
" <td>2353</td>\n",
" <td>1205</td>\n",
" <td>1050</td>\n",
" <td>2479</td>\n",
" <td>270734</td>\n",
" <td>31075</td>\n",
" <td>673</td>\n",
" <td>583</td>\n",
" <td>84</td>\n",
" <td>153824</td>\n",
" <td>136536</td>\n",
" <td>16000</td>\n",
" <td>117636</td>\n",
" <td>105144</td>\n",
" <td>11019</td>\n",
" <td>15201</td>\n",
" <td>12960</td>\n",
" <td>1990</td>\n",
" <td>586</td>\n",
" <td>45342</td>\n",
" <td>176397.0</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.5</td>\n",
" <td>8.5</td>\n",
" <td>8.7</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.1</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>$13,657,115</td>\n",
" <td>9400000</td>\n",
" <td>$23,057,115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>113</td>\n",
" <td>What We Do in the Shadows (2014)</td>\n",
" <td>7.6</td>\n",
" <td>87975</td>\n",
" <td>Comedy</td>\n",
" <td>Horror</td>\n",
" <td>NaN</td>\n",
" <td>76.0</td>\n",
" <td>1600000.0</td>\n",
" <td>NaN</td>\n",
" <td>10485</td>\n",
" <td>14507</td>\n",
" <td>28608</td>\n",
" <td>20735</td>\n",
" <td>7696</td>\n",
" <td>2802</td>\n",
" <td>1200</td>\n",
" <td>721</td>\n",
" <td>450</td>\n",
" <td>781</td>\n",
" <td>57028</td>\n",
" <td>15840</td>\n",
" <td>268</td>\n",
" <td>209</td>\n",
" <td>56</td>\n",
" <td>32406</td>\n",
" <td>23869</td>\n",
" <td>8125</td>\n",
" <td>31707</td>\n",
" <td>25592</td>\n",
" <td>5689</td>\n",
" <td>6013</td>\n",
" <td>4740</td>\n",
" <td>1138</td>\n",
" <td>389</td>\n",
" <td>12341</td>\n",
" <td>45062.0</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.8</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>6.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>$3,469,224</td>\n",
" <td>2794000</td>\n",
" <td>$6,263,224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>114</td>\n",
" <td>Whiplash (2014)</td>\n",
" <td>8.5</td>\n",
" <td>492285</td>\n",
" <td>Drama</td>\n",
" <td>Music</td>\n",
" <td>NaN</td>\n",
" <td>88.0</td>\n",
" <td>3300000.0</td>\n",
" <td>107 min</td>\n",
" <td>110404</td>\n",
" <td>161864</td>\n",
" <td>132656</td>\n",
" <td>56007</td>\n",
" <td>16577</td>\n",
" <td>6031</td>\n",
" <td>2937</td>\n",
" <td>1859</td>\n",
" <td>1263</td>\n",
" <td>2723</td>\n",
" <td>308900</td>\n",
" <td>71066</td>\n",
" <td>2878</td>\n",
" <td>2200</td>\n",
" <td>660</td>\n",
" <td>205839</td>\n",
" <td>161853</td>\n",
" <td>41944</td>\n",
" <td>123712</td>\n",
" <td>102839</td>\n",
" <td>19018</td>\n",
" <td>23345</td>\n",
" <td>19072</td>\n",
" <td>3812</td>\n",
" <td>590</td>\n",
" <td>49868</td>\n",
" <td>213952.0</td>\n",
" <td>8.5</td>\n",
" <td>8.4</td>\n",
" <td>9.0</td>\n",
" <td>9.1</td>\n",
" <td>8.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.3</td>\n",
" <td>8.3</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>8.7</td>\n",
" <td>8.0</td>\n",
" <td>8.6</td>\n",
" <td>8.4</td>\n",
" <td>$13,092,000</td>\n",
" <td>35890041</td>\n",
" <td>$48,982,041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>115</td>\n",
" <td>Wreck-It Ralph (2012)</td>\n",
" <td>7.7</td>\n",
" <td>295125</td>\n",
" <td>Animation</td>\n",
" <td>Adventure</td>\n",
" <td>Comedy</td>\n",
" <td>72.0</td>\n",
" <td>165000000.0</td>\n",
" <td>NaN</td>\n",
" <td>41980</td>\n",
" <td>50262</td>\n",
" <td>96477</td>\n",
" <td>67934</td>\n",
" <td>24894</td>\n",
" <td>7748</td>\n",
" <td>2724</td>\n",
" <td>1190</td>\n",
" <td>703</td>\n",
" <td>1226</td>\n",
" <td>190983</td>\n",
" <td>50202</td>\n",
" <td>1663</td>\n",
" <td>1182</td>\n",
" <td>467</td>\n",
" <td>120962</td>\n",
" <td>90759</td>\n",
" <td>29003</td>\n",
" <td>90203</td>\n",
" <td>74767</td>\n",
" <td>14148</td>\n",
" <td>13706</td>\n",
" <td>11356</td>\n",
" <td>2112</td>\n",
" <td>614</td>\n",
" <td>44962</td>\n",
" <td>129487.0</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$189,422,889</td>\n",
" <td>281800000</td>\n",
" <td>$471,222,889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>116</td>\n",
" <td>X-Men: Days of Future Past (2014)</td>\n",
" <td>8.0</td>\n",
" <td>560736</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>74.0</td>\n",
" <td>200000000.0</td>\n",
" <td>132 min</td>\n",
" <td>91765</td>\n",
" <td>127521</td>\n",
" <td>183578</td>\n",
" <td>104658</td>\n",
" <td>33027</td>\n",
" <td>10059</td>\n",
" <td>3710</td>\n",
" <td>1903</td>\n",
" <td>1225</td>\n",
" <td>3301</td>\n",
" <td>370835</td>\n",
" <td>71008</td>\n",
" <td>3038</td>\n",
" <td>2403</td>\n",
" <td>614</td>\n",
" <td>220178</td>\n",
" <td>179039</td>\n",
" <td>39094</td>\n",
" <td>158607</td>\n",
" <td>135392</td>\n",
" <td>20927</td>\n",
" <td>26834</td>\n",
" <td>22460</td>\n",
" <td>3884</td>\n",
" <td>710</td>\n",
" <td>67889</td>\n",
" <td>229049.0</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.6</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.2</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>8.1</td>\n",
" <td>7.9</td>\n",
" <td>$233,921,534</td>\n",
" <td>513941241</td>\n",
" <td>$747,862,775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>117</td>\n",
" <td>X-Men: First Class (2011)</td>\n",
" <td>7.8</td>\n",
" <td>556713</td>\n",
" <td>Action</td>\n",
" <td>Adventure</td>\n",
" <td>Sci-Fi</td>\n",
" <td>65.0</td>\n",
" <td>160000000.0</td>\n",
" <td>132 min</td>\n",
" <td>64428</td>\n",
" <td>96219</td>\n",
" <td>200144</td>\n",
" <td>129352</td>\n",
" <td>41945</td>\n",
" <td>12861</td>\n",
" <td>4799</td>\n",
" <td>2349</td>\n",
" <td>1448</td>\n",
" <td>3182</td>\n",
" <td>382107</td>\n",
" <td>80444</td>\n",
" <td>2075</td>\n",
" <td>1612</td>\n",
" <td>443</td>\n",
" <td>223309</td>\n",
" <td>176821</td>\n",
" <td>44428</td>\n",
" <td>185909</td>\n",
" <td>157332</td>\n",
" <td>26094</td>\n",
" <td>30217</td>\n",
" <td>25051</td>\n",
" <td>4691</td>\n",
" <td>780</td>\n",
" <td>87542</td>\n",
" <td>257681.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.3</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.3</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$146,408,305</td>\n",
" <td>207215819</td>\n",
" <td>$353,624,124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>118</td>\n",
" <td>Zootopia (2016)</td>\n",
" <td>8.1</td>\n",
" <td>309474</td>\n",
" <td>Animation</td>\n",
" <td>Adventure</td>\n",
" <td>Comedy</td>\n",
" <td>78.0</td>\n",
" <td>150000000.0</td>\n",
" <td>108 min</td>\n",
" <td>53626</td>\n",
" <td>70912</td>\n",
" <td>102352</td>\n",
" <td>57261</td>\n",
" <td>16719</td>\n",
" <td>4539</td>\n",
" <td>1467</td>\n",
" <td>733</td>\n",
" <td>496</td>\n",
" <td>1386</td>\n",
" <td>176202</td>\n",
" <td>52345</td>\n",
" <td>2362</td>\n",
" <td>1641</td>\n",
" <td>706</td>\n",
" <td>119637</td>\n",
" <td>87499</td>\n",
" <td>30813</td>\n",
" <td>75474</td>\n",
" <td>61358</td>\n",
" <td>13034</td>\n",
" <td>12353</td>\n",
" <td>9959</td>\n",
" <td>2151</td>\n",
" <td>518</td>\n",
" <td>35975</td>\n",
" <td>122844.0</td>\n",
" <td>8.0</td>\n",
" <td>8.3</td>\n",
" <td>8.4</td>\n",
" <td>8.3</td>\n",
" <td>8.7</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>$341,268,248</td>\n",
" <td>682515947</td>\n",
" <td>$1,023,784,195</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X ... Worldwide\n",
"107 109 ... $252,276,927 \n",
"108 110 ... $4,749,516 \n",
"109 111 ... $22,321 \n",
"110 112 ... $23,057,115 \n",
"111 113 ... $6,263,224 \n",
"112 114 ... $48,982,041 \n",
"113 115 ... $471,222,889 \n",
"114 116 ... $747,862,775 \n",
"115 117 ... $353,624,124 \n",
"116 118 ... $1,023,784,195 \n",
"\n",
"[10 rows x 58 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=1, ski_footer=10)\n",
"df.tail(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "dd6dc7c428fd94d9dcae0e284ead219aa5fc9f4f"
},
"source": [
"### 8.Choose Columns\n",
"* we can choose column from the excel file"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_uuid": "c278735e5e7dba13cfd6fe404a0bf1f140183dfd"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title Rating\n",
"0 1 12 Years a Slave (2013) 8.1\n",
"1 2 127 Hours (2010) 7.6\n",
"2 3 50/50 (2011) 7.7\n",
"3 4 About Time (2013) 7.8\n",
"4 5 Amour (2012) 7.9"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name= 0, usecols=2)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "de629441fc6b8e92d4fbfaebc65174af631ddf21"
},
"source": [
"### 9.Column Names"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_uuid": "51f7cc408c97eeec0870ebb60b97d530c0267b64"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title Rating\n",
"0 1 12 Years a Slave (2013) 8.1\n",
"1 2 127 Hours (2010) 7.6\n",
"2 3 50/50 (2011) 7.7\n",
"3 4 About Time (2013) 7.8\n",
"4 5 Amour (2012) 7.9"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=0, usecols = 2, names=['X','Title', 'Rating'], )\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "55e85355e5ff998b77506eacdde582fa7b382d4c"
},
"source": [
"### 10.Set an Index while reading data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_uuid": "692892ef2ab621063fd916016da68fb7f91b4b0c"
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
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" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
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" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
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" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Title</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>12 Years a Slave (2013)</th>\n",
" <td>1</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127 Hours (2010)</th>\n",
" <td>2</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50/50 (2011)</th>\n",
" <td>3</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>About Time (2013)</th>\n",
" <td>4</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Amour (2012)</th>\n",
" <td>5</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Rating ... Foreign Worldwide\n",
"Title ... \n",
"12 Years a Slave (2013) 1 8.1 ... 131061209 $187,733,202 \n",
"127 Hours (2010) 2 7.6 ... 42403567 $60,738,797 \n",
"50/50 (2011) 3 7.7 ... 4173591 $39,187,783 \n",
"About Time (2013) 4 7.8 ... 71777528 $87,100,449 \n",
"Amour (2012) 5 7.9 ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 57 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name=0, index_col='Title')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8a1013a08809f0470c29de85fe93fc57c6bd04ec"
},
"source": [
"### 11.Handle missing data while reading"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_uuid": "2cc691a70994aeb7563ace2057bbcca177cc0bc7"
},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
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" <th>CVotesnUS</th>\n",
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" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
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" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel('../input/datasetsdifferent-format/IMDB.xlsx', sheet_name= 0, na_values=['nan']) ## as per missing value\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "45ea1503c41eb295b902791fbeff5fbee68f6512"
},
"source": [
"> ### 2.4.Reading data from some other popular formats <a id=\"24\"></a>\n",
"### 1.Reading JSON data into Pandas"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_uuid": "a2ebcc0cc977623af067c778fbc7c4434b6c4ffa"
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Budget</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Rating</th>\n",
" <th>Runtime</th>\n",
" <th>Title</th>\n",
" <th>TotalVotes</th>\n",
" <th>Votes1000</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AF</th>\n",
" <th>Votes45AM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>VotesM</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18F</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Worldwide</th>\n",
" <th>X</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20000000</td>\n",
" <td>4739</td>\n",
" <td>1785</td>\n",
" <td>2420</td>\n",
" <td>4021</td>\n",
" <td>9603</td>\n",
" <td>27231</td>\n",
" <td>83070</td>\n",
" <td>161460</td>\n",
" <td>126223</td>\n",
" <td>75556</td>\n",
" <td>664</td>\n",
" <td>200910</td>\n",
" <td>45301</td>\n",
" <td>153669</td>\n",
" <td>138762</td>\n",
" <td>23895</td>\n",
" <td>112943</td>\n",
" <td>29252</td>\n",
" <td>5726</td>\n",
" <td>23072</td>\n",
" <td>82012</td>\n",
" <td>313823</td>\n",
" <td>1837</td>\n",
" <td>457</td>\n",
" <td>1363</td>\n",
" <td>53328</td>\n",
" <td>224519</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96</td>\n",
" <td>8.1</td>\n",
" <td>134 min</td>\n",
" <td>12 Years a Slave�(2013)</td>\n",
" <td>496092</td>\n",
" <td>7.7</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.4</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>$187,733,202</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18000000</td>\n",
" <td>2059</td>\n",
" <td>1161</td>\n",
" <td>1930</td>\n",
" <td>3796</td>\n",
" <td>9403</td>\n",
" <td>28394</td>\n",
" <td>78451</td>\n",
" <td>98845</td>\n",
" <td>44110</td>\n",
" <td>28939</td>\n",
" <td>649</td>\n",
" <td>133336</td>\n",
" <td>26152</td>\n",
" <td>106007</td>\n",
" <td>102120</td>\n",
" <td>14304</td>\n",
" <td>86609</td>\n",
" <td>14895</td>\n",
" <td>2261</td>\n",
" <td>12400</td>\n",
" <td>44600</td>\n",
" <td>212866</td>\n",
" <td>745</td>\n",
" <td>170</td>\n",
" <td>567</td>\n",
" <td>38478</td>\n",
" <td>169745</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82</td>\n",
" <td>7.6</td>\n",
" <td>94 min</td>\n",
" <td>127 Hours�(2010)</td>\n",
" <td>297075</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$60,738,797</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8000000</td>\n",
" <td>1202</td>\n",
" <td>634</td>\n",
" <td>1109</td>\n",
" <td>2381</td>\n",
" <td>7545</td>\n",
" <td>24252</td>\n",
" <td>71485</td>\n",
" <td>99524</td>\n",
" <td>47501</td>\n",
" <td>28304</td>\n",
" <td>555</td>\n",
" <td>132350</td>\n",
" <td>34765</td>\n",
" <td>96269</td>\n",
" <td>94745</td>\n",
" <td>18163</td>\n",
" <td>75394</td>\n",
" <td>12829</td>\n",
" <td>2681</td>\n",
" <td>9912</td>\n",
" <td>58348</td>\n",
" <td>188925</td>\n",
" <td>506</td>\n",
" <td>153</td>\n",
" <td>348</td>\n",
" <td>46947</td>\n",
" <td>147849</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72</td>\n",
" <td>7.7</td>\n",
" <td>100 min</td>\n",
" <td>50/50�(2011)</td>\n",
" <td>283935</td>\n",
" <td>7.0</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.7</td>\n",
" <td>7.4</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$39,187,783</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12000000</td>\n",
" <td>1182</td>\n",
" <td>664</td>\n",
" <td>1084</td>\n",
" <td>2210</td>\n",
" <td>5673</td>\n",
" <td>16542</td>\n",
" <td>45487</td>\n",
" <td>70850</td>\n",
" <td>43170</td>\n",
" <td>38556</td>\n",
" <td>475</td>\n",
" <td>92940</td>\n",
" <td>34126</td>\n",
" <td>57778</td>\n",
" <td>67477</td>\n",
" <td>16222</td>\n",
" <td>50212</td>\n",
" <td>13973</td>\n",
" <td>3026</td>\n",
" <td>10690</td>\n",
" <td>58098</td>\n",
" <td>126718</td>\n",
" <td>654</td>\n",
" <td>321</td>\n",
" <td>325</td>\n",
" <td>20450</td>\n",
" <td>111670</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NA</td>\n",
" <td>7.8</td>\n",
" <td>123 min</td>\n",
" <td>About Time�(2013)</td>\n",
" <td>225412</td>\n",
" <td>6.9</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.8</td>\n",
" <td>7.5</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>8.2</td>\n",
" <td>8.3</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$87,100,449</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8900000</td>\n",
" <td>995</td>\n",
" <td>534</td>\n",
" <td>710</td>\n",
" <td>1188</td>\n",
" <td>2585</td>\n",
" <td>5945</td>\n",
" <td>14187</td>\n",
" <td>22942</td>\n",
" <td>15944</td>\n",
" <td>11093</td>\n",
" <td>391</td>\n",
" <td>28593</td>\n",
" <td>8167</td>\n",
" <td>20107</td>\n",
" <td>28691</td>\n",
" <td>6269</td>\n",
" <td>21990</td>\n",
" <td>7425</td>\n",
" <td>1490</td>\n",
" <td>5803</td>\n",
" <td>16719</td>\n",
" <td>49808</td>\n",
" <td>121</td>\n",
" <td>24</td>\n",
" <td>95</td>\n",
" <td>7959</td>\n",
" <td>46138</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td></td>\n",
" <td>94</td>\n",
" <td>7.9</td>\n",
" <td>127 min</td>\n",
" <td>Amour�(2012)</td>\n",
" <td>76121</td>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>6.6</td>\n",
" <td>7.8</td>\n",
" <td>8.6</td>\n",
" <td>8.5</td>\n",
" <td>8.7</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$19,839,492</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Budget CVotes01 CVotes02 CVotes03 ... VotesUS VotesnUS Worldwide X\n",
"0 20000000 4739 1785 2420 ... 8.3 8 $187,733,202 1\n",
"1 18000000 2059 1161 1930 ... 7.7 7.6 $60,738,797 2\n",
"2 8000000 1202 634 1109 ... 7.9 7.6 $39,187,783 3\n",
"3 12000000 1182 664 1084 ... 7.8 7.7 $87,100,449 4\n",
"4 8900000 995 534 710 ... 7.9 7.8 $19,839,492 5\n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies_json = pd.read_json('../input/datasetsdifferent-format/IMDB.json')\n",
"movies_json.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8b4c9ad82bfe20ed21eb28183ffd751f102c9495"
},
"source": [
"### 2.Reading HTML data"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_uuid": "364096de4343fd462eebf1041823c4a1e432b35e"
},
"outputs": [],
"source": [
"df = pd.read_html('../input/datasetsdifferent-format/IMDB.html')\n",
"# df"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "5fe67038e4aa9ce51904b41cdb99a72f81632b2d"
},
"source": [
"### 3.Read pickle file"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_uuid": "584732d7f0660d5d78b22a0a316c75b4b6cb53cd"
},
"outputs": [
{
"data": {
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" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
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" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96.0</td>\n",
" <td>20000000.0</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519.0</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82.0</td>\n",
" <td>18000000.0</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50Â (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72.0</td>\n",
" <td>8000000.0</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849.0</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NaN</td>\n",
" <td>12000000.0</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" <td>8900000.0</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138.0</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50Â (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_pickle('../input/datasetsdifferent-format/IMDB.p')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7348b59b3fd1fada9e0b4b17f0a42ec5ad2dc634"
},
"source": [
"### 4.Read SQL file"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_uuid": "b40768bf9b94c37e8faa41b64c2103bf95989b0d"
},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_uuid": "815927487c93eea72212c13e5dd6c5c33fae7871"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Title</th>\n",
" <th>Rating</th>\n",
" <th>TotalVotes</th>\n",
" <th>Genre1</th>\n",
" <th>Genre2</th>\n",
" <th>Genre3</th>\n",
" <th>MetaCritic</th>\n",
" <th>Budget</th>\n",
" <th>Runtime</th>\n",
" <th>CVotes10</th>\n",
" <th>CVotes09</th>\n",
" <th>CVotes08</th>\n",
" <th>CVotes07</th>\n",
" <th>CVotes06</th>\n",
" <th>CVotes05</th>\n",
" <th>CVotes04</th>\n",
" <th>CVotes03</th>\n",
" <th>CVotes02</th>\n",
" <th>CVotes01</th>\n",
" <th>CVotesMale</th>\n",
" <th>CVotesFemale</th>\n",
" <th>CVotesU18</th>\n",
" <th>CVotesU18M</th>\n",
" <th>CVotesU18F</th>\n",
" <th>CVotes1829</th>\n",
" <th>CVotes1829M</th>\n",
" <th>CVotes1829F</th>\n",
" <th>CVotes3044</th>\n",
" <th>CVotes3044M</th>\n",
" <th>CVotes3044F</th>\n",
" <th>CVotes45A</th>\n",
" <th>CVotes45AM</th>\n",
" <th>CVotes45AF</th>\n",
" <th>CVotes1000</th>\n",
" <th>CVotesUS</th>\n",
" <th>CVotesnUS</th>\n",
" <th>VotesM</th>\n",
" <th>VotesF</th>\n",
" <th>VotesU18</th>\n",
" <th>VotesU18M</th>\n",
" <th>VotesU18F</th>\n",
" <th>Votes1829</th>\n",
" <th>Votes1829M</th>\n",
" <th>Votes1829F</th>\n",
" <th>Votes3044</th>\n",
" <th>Votes3044M</th>\n",
" <th>Votes3044F</th>\n",
" <th>Votes45A</th>\n",
" <th>Votes45AM</th>\n",
" <th>Votes45AF</th>\n",
" <th>VotesIMDB</th>\n",
" <th>Votes1000</th>\n",
" <th>VotesUS</th>\n",
" <th>VotesnUS</th>\n",
" <th>Domestic</th>\n",
" <th>Foreign</th>\n",
" <th>Worldwide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>12 Years a Slave (2013)</td>\n",
" <td>8.1</td>\n",
" <td>496092</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>History</td>\n",
" <td>96</td>\n",
" <td>20000000</td>\n",
" <td>134 min</td>\n",
" <td>75556</td>\n",
" <td>126223</td>\n",
" <td>161460</td>\n",
" <td>83070</td>\n",
" <td>27231</td>\n",
" <td>9603</td>\n",
" <td>4021</td>\n",
" <td>2420</td>\n",
" <td>1785</td>\n",
" <td>4739</td>\n",
" <td>313823</td>\n",
" <td>82012</td>\n",
" <td>1837</td>\n",
" <td>1363</td>\n",
" <td>457</td>\n",
" <td>200910</td>\n",
" <td>153669</td>\n",
" <td>45301</td>\n",
" <td>138762</td>\n",
" <td>112943</td>\n",
" <td>23895</td>\n",
" <td>29252</td>\n",
" <td>23072</td>\n",
" <td>5726</td>\n",
" <td>664</td>\n",
" <td>53328</td>\n",
" <td>224519</td>\n",
" <td>8.1</td>\n",
" <td>8.1</td>\n",
" <td>8.4</td>\n",
" <td>8.4</td>\n",
" <td>8.5</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.2</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>8.0</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>8.0</td>\n",
" <td>7.7</td>\n",
" <td>8.3</td>\n",
" <td>8</td>\n",
" <td>$56,671,993</td>\n",
" <td>131061209</td>\n",
" <td>$187,733,202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>127 Hours (2010)</td>\n",
" <td>7.6</td>\n",
" <td>297075</td>\n",
" <td>Adventure</td>\n",
" <td>Biography</td>\n",
" <td>Drama</td>\n",
" <td>82</td>\n",
" <td>18000000</td>\n",
" <td>94 min</td>\n",
" <td>28939</td>\n",
" <td>44110</td>\n",
" <td>98845</td>\n",
" <td>78451</td>\n",
" <td>28394</td>\n",
" <td>9403</td>\n",
" <td>3796</td>\n",
" <td>1930</td>\n",
" <td>1161</td>\n",
" <td>2059</td>\n",
" <td>212866</td>\n",
" <td>44600</td>\n",
" <td>745</td>\n",
" <td>567</td>\n",
" <td>170</td>\n",
" <td>133336</td>\n",
" <td>106007</td>\n",
" <td>26152</td>\n",
" <td>102120</td>\n",
" <td>86609</td>\n",
" <td>14304</td>\n",
" <td>14895</td>\n",
" <td>12400</td>\n",
" <td>2261</td>\n",
" <td>649</td>\n",
" <td>38478</td>\n",
" <td>169745</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.5</td>\n",
" <td>7.3</td>\n",
" <td>7.3</td>\n",
" <td>7.5</td>\n",
" <td>7.6</td>\n",
" <td>7.0</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>$18,335,230</td>\n",
" <td>42403567</td>\n",
" <td>$60,738,797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>50/50 (2011)</td>\n",
" <td>7.7</td>\n",
" <td>283935</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td>72</td>\n",
" <td>8000000</td>\n",
" <td>100 min</td>\n",
" <td>28304</td>\n",
" <td>47501</td>\n",
" <td>99524</td>\n",
" <td>71485</td>\n",
" <td>24252</td>\n",
" <td>7545</td>\n",
" <td>2381</td>\n",
" <td>1109</td>\n",
" <td>634</td>\n",
" <td>1202</td>\n",
" <td>188925</td>\n",
" <td>58348</td>\n",
" <td>506</td>\n",
" <td>348</td>\n",
" <td>153</td>\n",
" <td>132350</td>\n",
" <td>96269</td>\n",
" <td>34765</td>\n",
" <td>94745</td>\n",
" <td>75394</td>\n",
" <td>18163</td>\n",
" <td>12829</td>\n",
" <td>9912</td>\n",
" <td>2681</td>\n",
" <td>555</td>\n",
" <td>46947</td>\n",
" <td>147849</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.4</td>\n",
" <td>7.4</td>\n",
" <td>7.5</td>\n",
" <td>7.4</td>\n",
" <td>7.0</td>\n",
" <td>7.9</td>\n",
" <td>7.6</td>\n",
" <td>$35,014,192</td>\n",
" <td>4173591</td>\n",
" <td>$39,187,783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>About Time (2013)</td>\n",
" <td>7.8</td>\n",
" <td>225412</td>\n",
" <td>Comedy</td>\n",
" <td>Drama</td>\n",
" <td>Fantasy</td>\n",
" <td>NA</td>\n",
" <td>12000000</td>\n",
" <td>123 min</td>\n",
" <td>38556</td>\n",
" <td>43170</td>\n",
" <td>70850</td>\n",
" <td>45487</td>\n",
" <td>16542</td>\n",
" <td>5673</td>\n",
" <td>2210</td>\n",
" <td>1084</td>\n",
" <td>664</td>\n",
" <td>1182</td>\n",
" <td>126718</td>\n",
" <td>58098</td>\n",
" <td>654</td>\n",
" <td>325</td>\n",
" <td>321</td>\n",
" <td>92940</td>\n",
" <td>57778</td>\n",
" <td>34126</td>\n",
" <td>67477</td>\n",
" <td>50212</td>\n",
" <td>16222</td>\n",
" <td>13973</td>\n",
" <td>10690</td>\n",
" <td>3026</td>\n",
" <td>475</td>\n",
" <td>20450</td>\n",
" <td>111670</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.2</td>\n",
" <td>8.1</td>\n",
" <td>8.3</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.6</td>\n",
" <td>7.6</td>\n",
" <td>7.7</td>\n",
" <td>7.6</td>\n",
" <td>7.5</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>6.9</td>\n",
" <td>7.8</td>\n",
" <td>7.7</td>\n",
" <td>$15,322,921</td>\n",
" <td>71777528</td>\n",
" <td>$87,100,449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Amour (2012)</td>\n",
" <td>7.9</td>\n",
" <td>76121</td>\n",
" <td>Drama</td>\n",
" <td>Romance</td>\n",
" <td></td>\n",
" <td>94</td>\n",
" <td>8900000</td>\n",
" <td>127 min</td>\n",
" <td>11093</td>\n",
" <td>15944</td>\n",
" <td>22942</td>\n",
" <td>14187</td>\n",
" <td>5945</td>\n",
" <td>2585</td>\n",
" <td>1188</td>\n",
" <td>710</td>\n",
" <td>534</td>\n",
" <td>995</td>\n",
" <td>49808</td>\n",
" <td>16719</td>\n",
" <td>121</td>\n",
" <td>95</td>\n",
" <td>24</td>\n",
" <td>28593</td>\n",
" <td>20107</td>\n",
" <td>8167</td>\n",
" <td>28691</td>\n",
" <td>21990</td>\n",
" <td>6269</td>\n",
" <td>7425</td>\n",
" <td>5803</td>\n",
" <td>1490</td>\n",
" <td>391</td>\n",
" <td>7959</td>\n",
" <td>46138</td>\n",
" <td>7.8</td>\n",
" <td>7.9</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>8.5</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>7.9</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>7.9</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>8.1</td>\n",
" <td>6.6</td>\n",
" <td>7.2</td>\n",
" <td>7.9</td>\n",
" <td>7.8</td>\n",
" <td>$6,739,492</td>\n",
" <td>13100000</td>\n",
" <td>$19,839,492</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Title ... Foreign Worldwide\n",
"0 1 12 Years a Slave (2013) ... 131061209 $187,733,202 \n",
"1 2 127 Hours (2010) ... 42403567 $60,738,797 \n",
"2 3 50/50 (2011) ... 4173591 $39,187,783 \n",
"3 4 About Time (2013) ... 71777528 $87,100,449 \n",
"4 5 Amour (2012) ... 13100000 $19,839,492 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn = sqlite3.connect(\"../input/datasetsdifferent-format/IMDB.sqlite\")\n",
"df = pd.read_sql_query(\"SELECT * FROM IMDB;\", conn)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "fbe7564a083556b731fd8b0ccc972e66d7ff2315"
},
"source": [
"### 5.Read data from clipboard"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_uuid": "0f56f90b33ffaee4a950199d8286f12a88fb4388"
},
"outputs": [],
"source": [
"# df = pd.read_clipboard()\n",
"# # df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "17eb6411108be9e6c96fe4983826509020d66d57"
},
"source": [
"# 3.Apply multiple filter criteria to a pandas DataFrame<a id=\"3\"></a>\n",
"---\n",
" [**Go to top**](#00)\n",
" \n",
" \n",
" ### In this section, you will learn\n",
"1. Filter using `&` **AND Operator.**\n",
"1. Filter using `|` **OR Operator.**\n",
"1. Filtering using *`isin`* **method**\n",
"1. Using ***`isin` method*** with multiple conditions\n",
" \n",
"### 1.Read in the dataset"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_uuid": "c5a1d7b807ffd9a07f768827d83025f14db09d24"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data_zillow.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "413795c3fa6a2947e100b90001d2f2ac9178c651"
},
"source": [
"### 2. FIlter Based on Multiple Condition"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_uuid": "1b41aa04401ea26bca1613144a5642ca47274793"
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1132</th>\n",
" <td>2017-05-31</td>\n",
" <td>18375</td>\n",
" <td>Great Neck</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Nassau</td>\n",
" <td>1132</td>\n",
" <td>1235800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2405</th>\n",
" <td>2017-05-31</td>\n",
" <td>54333</td>\n",
" <td>Scarsdale</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Westchester</td>\n",
" <td>2405</td>\n",
" <td>1468100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2619</th>\n",
" <td>2017-05-31</td>\n",
" <td>47495</td>\n",
" <td>Rye</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Westchester</td>\n",
" <td>2619</td>\n",
" <td>1736400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3032</th>\n",
" <td>2017-05-31</td>\n",
" <td>25725</td>\n",
" <td>Manhasset</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Nassau</td>\n",
" <td>3032</td>\n",
" <td>1483400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3064</th>\n",
" <td>2017-05-31</td>\n",
" <td>18955</td>\n",
" <td>Larchmont</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Westchester</td>\n",
" <td>3064</td>\n",
" <td>1052200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"1132 2017-05-31 18375 Great Neck ... Nassau 1132 1235800\n",
"2405 2017-05-31 54333 Scarsdale ... Westchester 2405 1468100\n",
"2619 2017-05-31 47495 Rye ... Westchester 2619 1736400\n",
"3032 2017-05-31 25725 Manhasset ... Nassau 3032 1483400\n",
"3064 2017-05-31 18955 Larchmont ... Westchester 3064 1052200\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow[(data_zillow['Zhvi'] > 1000000) & (data_zillow['State'] == 'NY')].head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_uuid": "955854dc68d314209ad4d77486b8d765521fcb2d"
},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-05-31</td>\n",
" <td>54296</td>\n",
" <td>San Diego</td>\n",
" <td>CA</td>\n",
" <td>San Diego</td>\n",
" <td>San Diego</td>\n",
" <td>6</td>\n",
" <td>572100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2017-05-31</td>\n",
" <td>33839</td>\n",
" <td>San Jose</td>\n",
" <td>CA</td>\n",
" <td>San Jose</td>\n",
" <td>Santa Clara</td>\n",
" <td>8</td>\n",
" <td>877400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017-05-31</td>\n",
" <td>20330</td>\n",
" <td>San Francisco</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>San Francisco</td>\n",
" <td>10</td>\n",
" <td>1194300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID ... SizeRank Zhvi\n",
"0 2017-05-31 6181 ... 0 672400\n",
"1 2017-05-31 12447 ... 1 629900\n",
"6 2017-05-31 54296 ... 6 572100\n",
"8 2017-05-31 33839 ... 8 877400\n",
"10 2017-05-31 20330 ... 10 1194300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow[((data_zillow['State'] == 'CA') | (data_zillow['State'] == 'NY'))].head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_uuid": "56e2d0101d7f1561c1d6ce99aa1b5b5a24452ab2"
},
"outputs": [
{
"data": {
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" <th>6</th>\n",
" <td>2017-05-31</td>\n",
" <td>54296</td>\n",
" <td>San Diego</td>\n",
" <td>CA</td>\n",
" <td>San Diego</td>\n",
" <td>San Diego</td>\n",
" <td>6</td>\n",
" <td>572100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2017-05-31</td>\n",
" <td>12970</td>\n",
" <td>Newark</td>\n",
" <td>NJ</td>\n",
" <td>New York</td>\n",
" <td>Essex</td>\n",
" <td>63</td>\n",
" <td>232800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>2017-05-31</td>\n",
" <td>25320</td>\n",
" <td>Jersey City</td>\n",
" <td>NJ</td>\n",
" <td>New York</td>\n",
" <td>Hudson</td>\n",
" <td>72</td>\n",
" <td>380000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>2017-05-31</td>\n",
" <td>51405</td>\n",
" <td>Chula Vista</td>\n",
" <td>CA</td>\n",
" <td>San Diego</td>\n",
" <td>San Diego</td>\n",
" <td>85</td>\n",
" <td>486900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"6 2017-05-31 54296 San Diego ... San Diego 6 572100\n",
"63 2017-05-31 12970 Newark ... Essex 63 232800\n",
"72 2017-05-31 25320 Jersey City ... Hudson 72 380000\n",
"85 2017-05-31 51405 Chula Vista ... San Diego 85 486900\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zillow_filter = data_zillow['Metro'].isin(['New York','San Diego'])\n",
"data_zillow[zillow_filter].head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_uuid": "24f583af9ea5055c9e8b1d0a20e895419fe3e94a"
},
"outputs": [
{
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" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName State Metro County SizeRank Zhvi\n",
"0 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"1 NaN NaN NaN CA NaN NaN NaN NaN\n",
"2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"3 NaN NaN NaN NaN NaN NaN NaN NaN\n",
"4 NaN NaN NaN NaN NaN NaN NaN NaN"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zillow_filter1 = data_zillow.isin({'State': ['CA'], 'Metro': ['San Francisco']})\n",
"data_zillow[zillow_filter1].head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ea28db8b7db9d86d9f80423ed46e840f397d166a"
},
"source": [
"# 4.Changing the datatype of a Pandas Series <a id=\"4\"></a>\n",
"---\n",
"[**Go to Top**](#00)\n",
"\n",
"\n",
"### In this section you will learn\n",
"1. Changes Data int to float\n",
"2. Changing datatype while reading data\n",
"3. Converting string to datetime\n",
"\n",
"\n",
"#### 1.Read Dataset"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_uuid": "93a0db48fed56c47d60a2e9057fee6d7aa6211c9"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data_zillow.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3087f29893ef44d873e10f5fa5c687170daf91e7"
},
"source": [
"#### 2. Changes Data int to float"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_uuid": "dc65e1403cd5ef423af7e4246a2b6f00dc9f2430"
},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"RegionID int64\n",
"RegionName object\n",
"State object\n",
"Metro object\n",
"County object\n",
"SizeRank int64\n",
"Zhvi int64\n",
"dtype: object"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"_uuid": "fbf40f0362b01c6dd98e496b2fef071ca9655166"
},
"outputs": [],
"source": [
"data_zillow['Zhvi'] = data_zillow.Zhvi.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"_uuid": "e24a30d90c5426575338eb43adc16b0e55f1cb1e"
},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"RegionID int64\n",
"RegionName object\n",
"State object\n",
"Metro object\n",
"County object\n",
"SizeRank int64\n",
"Zhvi float64\n",
"dtype: object"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "37d880da6ffad779f60fd944081adc25fc86e78f"
},
"source": [
"### 3.Changing datatype while reading data\n",
"* By using `dtype` parameter in reading function we can change data types of any column as per below example"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"_uuid": "b1fcbfadb1b5d29e9e7eb78c444670c89d8dfb04"
},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"RegionID int64\n",
"RegionName object\n",
"State object\n",
"Metro object\n",
"County object\n",
"SizeRank int64\n",
"Zhvi float64\n",
"dtype: object"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow1 = pd.read_csv('../input/datasetsdifferent-format/data-zillow.csv', sep=',', dtype={'Zhvi':float})\n",
"data_zillow1.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "20c7cc9721e0dcd1f892966016ee83e9ff281e6d"
},
"source": [
"### 4.Converting string to datetime\n",
"* we can also change *`date`* data type by using `pd.to_datetime()`"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"_uuid": "e0627d753f78ab8b0c0863de30984639b9b79ca3"
},
"outputs": [
{
"data": {
"text/plain": [
"0 2017-05-31\n",
"1 2017-05-31\n",
"2 2017-05-31\n",
"3 2017-05-31\n",
"4 2017-05-31\n",
"Name: Date, dtype: datetime64[ns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(data_zillow1.Date,infer_datetime_format=True).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8a447e68c5abd9d1b2158fa2aa651b9606894d2d"
},
"source": [
"# 5.Filter rows of a pandas DataFrame by column value <a id=\"5\"></a>\n",
"---\n",
" [**Go to top**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you will learn\n",
"1. Filtering Method by using `filter()`\n",
"2. Filtering Method by Regular expression in `filter()` function\n",
"3. Filter data using boolean indexing\n",
"4. An alternative way to filter\n",
"\n",
"#### 1. Read Dataset"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"_uuid": "c6d6c02657f0d6ae2603c6b18fb313176b831cc4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "f74b48baf55db3db6dfdbb2cecb212b53cf86810"
},
"source": [
"#### 2.Filter columns by Different Ways\n",
"* Filtering Method by using `filter()`\n",
"* Filtering Method by Regular expression in `filter()` function\n",
"* Filter data using boolean indexing\n",
"* An alternative way to filter"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"_uuid": "8fa14aea076261376b133eb4069bae03b365b24c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NV</td>\n",
" <td>Las Vegas</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Metro\n",
"0 NY New York\n",
"1 CA Los Angeles-Long Beach-Anaheim\n",
"2 IL Chicago\n",
"3 PA Philadelphia\n",
"4 AZ Phoenix\n",
"5 NV Las Vegas"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_data = data.filter(items=['State', 'Metro'])\n",
"filtered_data.head(6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "2e093e9211d2739bdcb5300acda873bb52930c6f"
},
"source": [
"#### 3.Filter columns by regular expression using filter()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"_uuid": "8c35fce48a46ee20f3f226e5f6120772b7e9d369"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RegionID RegionName\n",
"0 6181 New York\n",
"1 12447 Los Angeles\n",
"2 17426 Chicago\n",
"3 13271 Philadelphia\n",
"4 40326 Phoenix"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_data = data.filter(regex='Region', axis=1)\n",
"filtered_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "9b46d2af12421b848afdfe9bf8f9455ff7ae1cc2"
},
"source": [
"#### 4.Filter data using boolean indexing"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"_uuid": "141892021173267ed9cd2edbe7c0c7231a4f37bd"
},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 True\n",
"2 False\n",
"3 False\n",
"4 False\n",
"Name: Zhvi, dtype: bool"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"price_filter_series = data['Zhvi'] > 500000\n",
"price_filter_series.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"_uuid": "7784a5c079b2340c38bcbc77d71d306bdd804e45"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
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" </thead>\n",
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" <th>0</th>\n",
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" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-05-31</td>\n",
" <td>54296</td>\n",
" <td>San Diego</td>\n",
" <td>CA</td>\n",
" <td>San Diego</td>\n",
" <td>San Diego</td>\n",
" <td>6</td>\n",
" <td>572100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2017-05-31</td>\n",
" <td>33839</td>\n",
" <td>San Jose</td>\n",
" <td>CA</td>\n",
" <td>San Jose</td>\n",
" <td>Santa Clara</td>\n",
" <td>8</td>\n",
" <td>877400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017-05-31</td>\n",
" <td>20330</td>\n",
" <td>San Francisco</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>San Francisco</td>\n",
" <td>10</td>\n",
" <td>1194300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID ... SizeRank Zhvi\n",
"0 2017-05-31 6181 ... 0 672400\n",
"1 2017-05-31 12447 ... 1 629900\n",
"6 2017-05-31 54296 ... 6 572100\n",
"8 2017-05-31 33839 ... 8 877400\n",
"10 2017-05-31 20330 ... 10 1194300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[price_filter_series].head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "e678f9ce3a5e3ae498c4144bb5ce20bb06bcc2f1"
},
"source": [
"#### 5.An alternative way to filter"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"_uuid": "d6a04e810b53ef415c45b0fc154cfe7d0173bc72"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017-05-31</td>\n",
" <td>20330</td>\n",
" <td>San Francisco</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>San Francisco</td>\n",
" <td>10</td>\n",
" <td>1194300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>2017-05-31</td>\n",
" <td>54626</td>\n",
" <td>Sunnyvale</td>\n",
" <td>CA</td>\n",
" <td>San Jose</td>\n",
" <td>Santa Clara</td>\n",
" <td>181</td>\n",
" <td>1509300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td>2017-05-31</td>\n",
" <td>13713</td>\n",
" <td>Santa Clara</td>\n",
" <td>CA</td>\n",
" <td>San Jose</td>\n",
" <td>Santa Clara</td>\n",
" <td>234</td>\n",
" <td>1071500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>238</th>\n",
" <td>2017-05-31</td>\n",
" <td>16992</td>\n",
" <td>Berkeley</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>Alameda</td>\n",
" <td>238</td>\n",
" <td>1102000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>2017-05-31</td>\n",
" <td>13699</td>\n",
" <td>San Mateo</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>San Mateo</td>\n",
" <td>308</td>\n",
" <td>1198300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID ... SizeRank Zhvi\n",
"10 2017-05-31 20330 ... 10 1194300\n",
"181 2017-05-31 54626 ... 181 1509300\n",
"234 2017-05-31 13713 ... 234 1071500\n",
"238 2017-05-31 16992 ... 238 1102000\n",
"308 2017-05-31 13699 ... 308 1198300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data.Zhvi >= 1000000].head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "62bdcf35796634d18a388848e0bd9dd571470e84"
},
"source": [
"# 6.Selecting multiple rows and columns from a pandas DataFrame <a id=\"6\"> </a>\n",
"---\n",
" [**Go to top**](#00)\n",
" \n",
" \n",
"### In this Section you can learn:\n",
"\n",
"1. Select single row, single column\n",
"1. Select single row, multiple columns\n",
"1. Select single row, all columns\n",
"1. Select multiple rows, single column\n",
"1. Select multiple rows and multiple contiguous columns\n",
"1. Select multiple rows and multiple non-contiguous columns\n",
"1. Select multiple rows and all columns\n",
"1. Select non-contiguous rows\n",
"1. Selecting rows based on a specific column's value\n",
"1. Selecting all rows for a specific column based on a value of another column\n",
"\n",
"### 1.Read dataset"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"_uuid": "62a4ff323d8368f403fdaad9c7adf68f9a5f6360"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data_zillow.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0171cdcb1d3eb13990638e38aacaa1972fc1de51"
},
"source": [
"### 2.Select single row, single column"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"_uuid": "2c0a3e9aaba52d4f07dce5bb28945a8e14ece0d0"
},
"outputs": [
{
"data": {
"text/plain": [
"'Dallas-Fort Worth'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[7, 'Metro']"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"_uuid": "beb3d57d8ba0cffbc47aaa6a86cf2c51c0f881b9"
},
"outputs": [
{
"data": {
"text/plain": [
"'Dallas-Fort Worth'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.iloc[7,4]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "593675eedf7c58de6effa7f28be47b92ed9b55f2"
},
"source": [
"### 3.Select single row, multiple columns"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"_uuid": "59da74f120a44865d5cc6bc66e034b4c02d60b2c"
},
"outputs": [
{
"data": {
"text/plain": [
"Metro Dallas-Fort Worth\n",
"County Dallas\n",
"Name: 7, dtype: object"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[7, ['Metro', 'County']]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"_uuid": "74ce54f0cee66073d0de3b225258160123a9552d"
},
"outputs": [
{
"data": {
"text/plain": [
"Metro Dallas-Fort Worth\n",
"County Dallas\n",
"Name: 7, dtype: object"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.iloc[7, [4,5]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b77ccba774d2d0197b35164dd332377392ed824a"
},
"source": [
"### 4.Select single row, all columns"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"_uuid": "76ebe4edd971cb606987152e428a19afd4035172"
},
"outputs": [
{
"data": {
"text/plain": [
"Date 2017-05-31\n",
"RegionID 10221\n",
"RegionName Austin\n",
"State TX\n",
"Metro Austin\n",
"County Travis\n",
"SizeRank 11\n",
"Zhvi 321600\n",
"Name: 11, dtype: object"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[11, :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "28850cf25cf9a821d30336841253d722a6a4db1e"
},
"source": [
"### 5.Select multiple rows, single column"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"_uuid": "1e2c1c80b775f7259ca073d8d011bf3d542fa4db"
},
"outputs": [
{
"data": {
"text/plain": [
"101 Winston-Salem\n",
"102 Los Angeles-Long Beach-Anaheim\n",
"103 Richmond\n",
"104 Miami-Fort Lauderdale\n",
"105 Ventura\n",
"Name: Metro, dtype: object"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[101:105, 'Metro']"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "d314c190a15e3cf9353fd324602b50c46fa9b66c"
},
"source": [
"### 6.Select multiple rows and multiple contiguous columns\n",
"\n",
"* **In `loc`** we pass the column label to fetch data.\n",
"* **In `iloc`** we pass the number to fetch data."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"_uuid": "a705c5d817ed2153211c4a1125eeba7a0b80e338"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>OH</td>\n",
" <td>Canton</td>\n",
" <td>Stark</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>LA</td>\n",
" <td>New Orleans</td>\n",
" <td>Jefferson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>CA</td>\n",
" <td>Santa Maria-Santa Barbara</td>\n",
" <td>Santa Barbara</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Metro County\n",
"201 OH Canton Stark\n",
"202 LA New Orleans Jefferson\n",
"203 CA Santa Maria-Santa Barbara Santa Barbara\n",
"204 CA Los Angeles-Long Beach-Anaheim Los Angeles"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[201:204, \"State\":\"County\"]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"_uuid": "3d1d15375f4ebc1f3fd899ebe55eb1604174d202"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>OH</td>\n",
" <td>Canton</td>\n",
" <td>Stark</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>LA</td>\n",
" <td>New Orleans</td>\n",
" <td>Jefferson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>CA</td>\n",
" <td>Santa Maria-Santa Barbara</td>\n",
" <td>Santa Barbara</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Metro County\n",
"201 OH Canton Stark\n",
"202 LA New Orleans Jefferson\n",
"203 CA Santa Maria-Santa Barbara Santa Barbara\n",
"204 CA Los Angeles-Long Beach-Anaheim Los Angeles"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.iloc[201:205, 3:6]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "a8966749d269438d65df99633fed40840507a819"
},
"source": [
"### 7.Select multiple rows and multiple non-contiguous columns"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"_uuid": "dfb6a748589c4cde8ad1820cbbdfc29f56ee0664"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>Canton</td>\n",
" <td>OH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>Metairie</td>\n",
" <td>LA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>Santa Maria</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>Inglewood</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>Orange</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RegionName State\n",
"201 Canton OH\n",
"202 Metairie LA\n",
"203 Santa Maria CA\n",
"204 Inglewood CA\n",
"205 Orange CA"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[201:205, ['RegionName', 'State']]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "84529df0072f54388a4018afe81f7213283684c2"
},
"source": [
"### 8.Select multiple rows and all columns"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"_uuid": "6824c8aa1473fa11c7082474566ed223449fa0aa"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>2017-05-31</td>\n",
" <td>51260</td>\n",
" <td>Canton</td>\n",
" <td>OH</td>\n",
" <td>Canton</td>\n",
" <td>Stark</td>\n",
" <td>201</td>\n",
" <td>94400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>2017-05-31</td>\n",
" <td>5914</td>\n",
" <td>Metairie</td>\n",
" <td>LA</td>\n",
" <td>New Orleans</td>\n",
" <td>Jefferson</td>\n",
" <td>202</td>\n",
" <td>232700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>2017-05-31</td>\n",
" <td>47570</td>\n",
" <td>Santa Maria</td>\n",
" <td>CA</td>\n",
" <td>Santa Maria-Santa Barbara</td>\n",
" <td>Santa Barbara</td>\n",
" <td>203</td>\n",
" <td>354600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>2017-05-31</td>\n",
" <td>45888</td>\n",
" <td>Inglewood</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>204</td>\n",
" <td>470600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>2017-05-31</td>\n",
" <td>33252</td>\n",
" <td>Orange</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Orange</td>\n",
" <td>205</td>\n",
" <td>652000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"201 2017-05-31 51260 Canton ... Stark 201 94400\n",
"202 2017-05-31 5914 Metairie ... Jefferson 202 232700\n",
"203 2017-05-31 47570 Santa Maria ... Santa Barbara 203 354600\n",
"204 2017-05-31 45888 Inglewood ... Los Angeles 204 470600\n",
"205 2017-05-31 33252 Orange ... Orange 205 652000\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[201:205, :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7144be8f55095681af806b286a9f2499d6ae9d4e"
},
"source": [
"### 9.Select non-contiguous rows"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"_uuid": "c5685535872165c7e49c70c2e29320cf580ef6b7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2017-05-31</td>\n",
" <td>18959</td>\n",
" <td>Las Vegas</td>\n",
" <td>NV</td>\n",
" <td>Las Vegas</td>\n",
" <td>Clark</td>\n",
" <td>5</td>\n",
" <td>216500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017-05-31</td>\n",
" <td>20330</td>\n",
" <td>San Francisco</td>\n",
" <td>CA</td>\n",
" <td>San Francisco</td>\n",
" <td>San Francisco</td>\n",
" <td>10</td>\n",
" <td>1194300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID ... SizeRank Zhvi\n",
"0 2017-05-31 6181 ... 0 672400\n",
"5 2017-05-31 18959 ... 5 216500\n",
"10 2017-05-31 20330 ... 10 1194300\n",
"\n",
"[3 rows x 8 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[[0,5,10], :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "585ffc5545c57c1059aabee355875fd098ae03b5"
},
"source": [
"### 10.Selecting rows based on a specific column's value"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"_uuid": "2ed5994091954d738fc8579cd49562ac3705d204"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"\n",
"[1 rows x 8 columns]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[data_zillow.County==\"Queens\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b50e6cfebafdd7410b9c1ba4fa75d89cad72db68"
},
"source": [
"### 11.Selecting all rows for a specific column based on a value of another column"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"_uuid": "18b0420a6eaf28da301625a37fe871c1fb89fcd8"
},
"outputs": [
{
"data": {
"text/plain": [
"0 Queens\n",
"63 Essex\n",
"72 Hudson\n",
"138 Westchester\n",
"176 Passaic\n",
"Name: County, dtype: object"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.loc[data_zillow.Metro==\"New York\", \"County\"].head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "2f7dc2e8ca7d307e7bc13f3052c1bac5f530f436"
},
"source": [
"# 7.Sorting a pandas DataFrame or a Series <a id=\"7\"></a>\n",
"---\n",
"[**Go to top**](#00)\n",
"\n",
"\n",
"\n",
"### In this section you can learn:\n",
"\n",
"1. Simple sort\n",
"1. Changing the sort order\n",
"1. Sort by more than one column\n",
"1. Sort by multiple columns and mixed ascending order\n",
"1. Sort a Series\n",
"\n",
"### 1.Read dataset"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"_uuid": "a589700f06ff02dddae557afba57609b23ef8e82"
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data_zillow.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "004616e4f857e4d1cd5c7a31833fa9954dccfe8e"
},
"source": [
"### 2.Simple sort\n",
"* Sort the value by using column name"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"_uuid": "6a547e6f11ca4f14707c366e30bb81d062f96e80"
},
"outputs": [
{
"data": {
"text/html": [
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" <th>RegionName</th>\n",
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" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9851</th>\n",
" <td>2017-05-31</td>\n",
" <td>48458</td>\n",
" <td>Westport</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>9851</td>\n",
" <td>144600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4996</th>\n",
" <td>2017-05-31</td>\n",
" <td>36873</td>\n",
" <td>Elma</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>4996</td>\n",
" <td>175200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5090</th>\n",
" <td>2017-05-31</td>\n",
" <td>35514</td>\n",
" <td>Hoquiam</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>5090</td>\n",
" <td>95700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9401</th>\n",
" <td>2017-05-31</td>\n",
" <td>33215</td>\n",
" <td>Ocean Shores</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>9401</td>\n",
" <td>152400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9149</th>\n",
" <td>2017-05-31</td>\n",
" <td>18370</td>\n",
" <td>Grayland</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>9149</td>\n",
" <td>143900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"9851 2017-05-31 48458 Westport ... Grays Harbor 9851 144600\n",
"4996 2017-05-31 36873 Elma ... Grays Harbor 4996 175200\n",
"5090 2017-05-31 35514 Hoquiam ... Grays Harbor 5090 95700\n",
"9401 2017-05-31 33215 Ocean Shores ... Grays Harbor 9401 152400\n",
"9149 2017-05-31 18370 Grayland ... Grays Harbor 9149 143900\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_zillow.sort_values('Metro').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7b7b9b398f259c8b9361709a7405f1bf70424fcf"
},
"source": [
"### 3.Changing the sort order\n",
"* Sorting the value basis on the descending order"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"_uuid": "74060e2001dbfb9eb8f63d4818d57f8c8c4a9951"
},
"outputs": [
{
"data": {
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8064</th>\n",
" <td>2017-05-31</td>\n",
" <td>19538</td>\n",
" <td>Nashport</td>\n",
" <td>OH</td>\n",
" <td>Zanesville</td>\n",
" <td>Muskingum</td>\n",
" <td>8064</td>\n",
" <td>153800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10271</th>\n",
" <td>2017-05-31</td>\n",
" <td>15262</td>\n",
" <td>Hopewell</td>\n",
" <td>OH</td>\n",
" <td>Zanesville</td>\n",
" <td>Muskingum</td>\n",
" <td>10271</td>\n",
" <td>138700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10373</th>\n",
" <td>2017-05-31</td>\n",
" <td>49730</td>\n",
" <td>Norwich</td>\n",
" <td>OH</td>\n",
" <td>Zanesville</td>\n",
" <td>Muskingum</td>\n",
" <td>10373</td>\n",
" <td>145100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5423</th>\n",
" <td>2017-05-31</td>\n",
" <td>53527</td>\n",
" <td>New Concord</td>\n",
" <td>OH</td>\n",
" <td>Zanesville</td>\n",
" <td>Muskingum</td>\n",
" <td>5423</td>\n",
" <td>138300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7595</th>\n",
" <td>2017-05-31</td>\n",
" <td>17815</td>\n",
" <td>Dresden</td>\n",
" <td>OH</td>\n",
" <td>Zanesville</td>\n",
" <td>Muskingum</td>\n",
" <td>7595</td>\n",
" <td>118400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"8064 2017-05-31 19538 Nashport ... Muskingum 8064 153800\n",
"10271 2017-05-31 15262 Hopewell ... Muskingum 10271 138700\n",
"10373 2017-05-31 49730 Norwich ... Muskingum 10373 145100\n",
"5423 2017-05-31 53527 New Concord ... Muskingum 5423 138300\n",
"7595 2017-05-31 17815 Dresden ... Muskingum 7595 118400\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted = data_zillow.sort_values('Metro', ascending=False)\n",
"sorted.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "a28385554e6e61a0e5909b0c975f2075a2958695"
},
"source": [
"### 4.Sort by more than one column"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"_uuid": "fb15d1ebc7d0406ae6a6201a2681804768059882"
},
"outputs": [
{
"data": {
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" <th>Zhvi</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2073</th>\n",
" <td>2017-05-31</td>\n",
" <td>30116</td>\n",
" <td>Aberdeen</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>2073</td>\n",
" <td>127800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4568</th>\n",
" <td>2017-05-31</td>\n",
" <td>56078</td>\n",
" <td>Montesano</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>4568</td>\n",
" <td>182000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4996</th>\n",
" <td>2017-05-31</td>\n",
" <td>36873</td>\n",
" <td>Elma</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>4996</td>\n",
" <td>175200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5090</th>\n",
" <td>2017-05-31</td>\n",
" <td>35514</td>\n",
" <td>Hoquiam</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>5090</td>\n",
" <td>95700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7108</th>\n",
" <td>2017-05-31</td>\n",
" <td>6275</td>\n",
" <td>Oakville</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>7108</td>\n",
" <td>186900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"2073 2017-05-31 30116 Aberdeen ... Grays Harbor 2073 127800\n",
"4568 2017-05-31 56078 Montesano ... Grays Harbor 4568 182000\n",
"4996 2017-05-31 36873 Elma ... Grays Harbor 4996 175200\n",
"5090 2017-05-31 35514 Hoquiam ... Grays Harbor 5090 95700\n",
"7108 2017-05-31 6275 Oakville ... Grays Harbor 7108 186900\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted = data_zillow.sort_values(by=['Metro','County'])\n",
"sorted.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "18dd9b184d34bf47a3129c3caab1852ec74c32eb"
},
"source": [
"### 5.Sort by multiple columns and mixed ascending order"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"_uuid": "871b4d89addc1741d79ab58dda35798fa369a2a4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7108</th>\n",
" <td>2017-05-31</td>\n",
" <td>6275</td>\n",
" <td>Oakville</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>7108</td>\n",
" <td>186900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4568</th>\n",
" <td>2017-05-31</td>\n",
" <td>56078</td>\n",
" <td>Montesano</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>4568</td>\n",
" <td>182000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4996</th>\n",
" <td>2017-05-31</td>\n",
" <td>36873</td>\n",
" <td>Elma</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>4996</td>\n",
" <td>175200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8420</th>\n",
" <td>2017-05-31</td>\n",
" <td>19269</td>\n",
" <td>McCleary</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>8420</td>\n",
" <td>170700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9401</th>\n",
" <td>2017-05-31</td>\n",
" <td>33215</td>\n",
" <td>Ocean Shores</td>\n",
" <td>WA</td>\n",
" <td>Aberdeen</td>\n",
" <td>Grays Harbor</td>\n",
" <td>9401</td>\n",
" <td>152400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"7108 2017-05-31 6275 Oakville ... Grays Harbor 7108 186900\n",
"4568 2017-05-31 56078 Montesano ... Grays Harbor 4568 182000\n",
"4996 2017-05-31 36873 Elma ... Grays Harbor 4996 175200\n",
"8420 2017-05-31 19269 McCleary ... Grays Harbor 8420 170700\n",
"9401 2017-05-31 33215 Ocean Shores ... Grays Harbor 9401 152400\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted = data_zillow.sort_values(by=['Metro','County', 'Zhvi'], \n",
" ascending=[True, True, False])\n",
"sorted.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "9acb5996c97db4fe17dc08300bdf824116f6033e"
},
"source": [
"### 6.Sort a Series\n",
"\n",
"* 1.Let's create a Series object"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"_uuid": "a8e2f43674423ca056544339ced4d67c55281867"
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions = data_zillow.RegionID\n",
"type(regions)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "12794c9bdfc00907d20a5ff05a983597845dde5b"
},
"source": [
"**Let's sort the series¶**\n",
"* **1.Original Series**"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"_uuid": "e9877f445f344622bbeb484e3705ce2a955788a0"
},
"outputs": [
{
"data": {
"text/plain": [
"0 6181\n",
"1 12447\n",
"2 17426\n",
"3 13271\n",
"4 40326\n",
"Name: RegionID, dtype: int64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "87a2abb1524e7585f6b3e88e1eea6f8e7b10ef0c"
},
"source": [
"* **2.Sorted**"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"_uuid": "0b80c1a3bf6b8b32f32c90aff466af290fa1f3e1"
},
"outputs": [
{
"data": {
"text/plain": [
"3043 3301\n",
"4159 3304\n",
"4986 3305\n",
"1762 3310\n",
"3116 3312\n",
"Name: RegionID, dtype: int64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions.sort_values().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "84158ba6779216a8939efaba339167dd92a2544d"
},
"source": [
"# 8.Using pandas Series data structure to select a subset of the data <a id=\"8\"></a>\n",
"---\n",
"[**Go to top**](#00)\n",
"\n",
"\n",
"\n",
"### In this Section, you will learn below topics\n",
"\n",
"1. Select data\n",
" * Select a Series with bracket notation\n",
"2. DataFrame vs Series\n",
" * Multi Column Selection - Series or DataFrame\n",
" * Select using dot notation\n",
"3. Creating a new series by selection"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "946e7c43d811837fae81b0668a6986dd26dd7550"
},
"source": [
"### 1.Read Dataset"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"_uuid": "323a868a7b32c613cc6a340833972dbc81e69435"
},
"outputs": [],
"source": [
"data = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"_uuid": "75823353510522d724e4f5ab61e3831fd26f6f9e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "23ed758912e499e705c7fce070c3545dc9af1ca4"
},
"source": [
"### 2.Select data\n",
"* **Select a Series with bracket notation**"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"_uuid": "3fca5ee8f633d53b0a7b83cdb34b3dd9e552db1d"
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions = data['RegionName']\n",
"type(regions)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"_uuid": "bba401187ba6815a159538a3ead2afe03a0eb7e0"
},
"outputs": [
{
"data": {
"text/plain": [
"0 New York\n",
"1 Los Angeles\n",
"2 Chicago\n",
"3 Philadelphia\n",
"4 Phoenix\n",
"Name: RegionName, dtype: object"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "5336e7deb3f385de384b71d962f98487fe328ad7"
},
"source": [
"### 3.DataFrame vs Series\n",
"* **Multi Column Selection - Series or DataFrame**"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"_uuid": "afb08b96760753602cd00c7cd92efe043f7ba93a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RegionName State\n",
"0 New York NY\n",
"1 Los Angeles CA\n",
"2 Chicago IL\n",
"3 Philadelphia PA\n",
"4 Phoenix AZ"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"region_n_state = data[['RegionName', 'State']]\n",
"region_n_state.head()"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"_uuid": "7f7137cc9766d9f714e3d0cb5aee3d7c5a5ce5a7"
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(region_n_state)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3b280401fb54a2caaaa28595cb789afea4ec880a"
},
"source": [
"* **Select using dot notation**"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"_uuid": "2c7b9b1f1151d8bf28e0c3d90901f067b2c9662d"
},
"outputs": [
{
"data": {
"text/plain": [
"0 NY\n",
"1 CA\n",
"2 IL\n",
"3 PA\n",
"4 AZ\n",
"Name: State, dtype: object"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.State.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b60b50c6f145871b82b983ecf22dfc05e438d6a1"
},
"source": [
"### 4.Creating a new series by selection"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"_uuid": "d19d04e9c13ba9d3e091cb13bc22d4bdd4334a28"
},
"outputs": [],
"source": [
"data['Address'] = data.County + ', ' + data.Metro + ', ' + data.State"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"_uuid": "82b3975782a8bf36a205efd8df590ef501d7c5b3"
},
"outputs": [
{
"data": {
"text/plain": [
"0 Queens, New York, NY\n",
"1 Los Angeles, Los Angeles-Long Beach-Anaheim, CA\n",
"2 Cook, Chicago, IL\n",
"3 Philadelphia, Philadelphia, PA\n",
"4 Maricopa, Phoenix, AZ\n",
"Name: Address, dtype: object"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.Address.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "56b7705c00a973b0051b6336101a506c7c8347f6"
},
"source": [
"# 9.Using string methods in pandas <a id=\"9\"></a>\n",
"---\n",
"[**Go To Top**](#00)\n",
"\n",
"### In this section, you will learn\n",
"1. Check for a substring\n",
"2. Make values of a series or column uppercase\n",
"3. Make values lowercase\n",
"4. Get the length of each value in a column\n",
"5. Remove all whitespace from the beginning\n",
"6. Replace parts of a column's values"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b1e2434a65c2f9ff8143c8b78e2e67970a7e7fb8"
},
"source": [
"### 1. Read dataset"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"_uuid": "76386e29ed05257ce80d9222717cebdafb366a1b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "42bf238215d5ff64632d733160c71d478de6ec41"
},
"source": [
"### 2.Check for a substring"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"_uuid": "ade0cd23b629fe45650d933eabdec1107116b81d"
},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"Name: RegionName, dtype: bool"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.RegionName.str.contains('New').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "013788b12a95c8342f46b981b8723aba96adc5d0"
},
"source": [
"### 3.Make values of a series or column uppercase"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"_uuid": "329f44fdaf6c41a2a3cf26c6f2bb74b2899925df"
},
"outputs": [
{
"data": {
"text/plain": [
"0 NEW YORK\n",
"1 LOS ANGELES\n",
"2 CHICAGO\n",
"3 PHILADELPHIA\n",
"4 PHOENIX\n",
"Name: RegionName, dtype: object"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.RegionName.str.upper().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "45d353dfef0585bf8214ff34682d0254a2ad09b7"
},
"source": [
"### 4.Make values lowercase\n"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"_uuid": "83d33939208be8f3b4475c19c64731e7fb22309a"
},
"outputs": [
{
"data": {
"text/plain": [
"0 new york\n",
"1 los angeles\n",
"2 chicago\n",
"3 philadelphia\n",
"4 phoenix\n",
"Name: RegionName, dtype: object"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.RegionName.str.lower().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "78117d56357462279874977341b3345241c52b52"
},
"source": [
"### 5.Get the length of each value in a column"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"_uuid": "decbb956f0df489935f3c5569bd9320f09458bc2"
},
"outputs": [
{
"data": {
"text/plain": [
"0 6\n",
"1 11\n",
"2 4\n",
"3 12\n",
"4 8\n",
"Name: County, dtype: int64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.County.str.len().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8fc1665b41988b6f642373311e749ecd3b4cdf5e"
},
"source": [
"### 6.Remove all whitespace from the beginning"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"_uuid": "292284730d084ff3b9363afe24e9fc9d9b34e2b3"
},
"outputs": [
{
"data": {
"text/plain": [
"0 New York\n",
"1 Los Angeles\n",
"2 Chicago\n",
"3 Philadelphia\n",
"4 Phoenix\n",
"Name: RegionName, dtype: object"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.RegionName.str.lstrip().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "95d22b4ca2591826ade6be510375391515e4b471"
},
"source": [
"### 7.Replace parts of a column's values"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"_uuid": "0c38dbad17108078192d3e10c62b28619fdc6278"
},
"outputs": [
{
"data": {
"text/plain": [
"0 NewYork\n",
"1 LosAngeles\n",
"2 Chicago\n",
"3 Philadelphia\n",
"4 Phoenix\n",
"Name: RegionName, dtype: object"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.RegionName.str.replace(' ', '').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "a913de2eb8531a68ab989277f963d7ddc40d54c1"
},
"source": [
"# 10.Using the axis parameter in pandas<a id=\"10\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Usage of axis parameter\n",
"2. axis usage examples\n",
" * axis = 0\n",
" * axis = 1\n",
" * use labels instead of 0 and 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "5b848b4dfd71b4e71c324be89258bbcba44da8ce"
},
"source": [
"### 1.Read Dataset"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"_uuid": "7acf3f24c653cc4c05515dd9b69852d5ae32ef73"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_table('../input/datasetsdifferent-format/data-zillow.csv', sep=',')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "650fbe79df832ab3f9b929cc8dbb3009f956e5ea"
},
"source": [
"### 2.Usage of axis parameter"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"_uuid": "8fdabcdc1b4bbb1316406a5249c3276d83b51c32"
},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Zhvi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"_uuid": "9f6516b7ee88954c7e01b83988059277ac82a2db"
},
"outputs": [
{
"data": {
"text/plain": [
"[RangeIndex(start=0, stop=10830, step=1),\n",
" Index(['Date', 'RegionID', 'RegionName', 'State', 'Metro', 'County',\n",
" 'SizeRank', 'Zhvi'],\n",
" dtype='object')]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.axes"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b5748ea09979ed1f104c0e1510f7ec7a8c378af5"
},
"source": [
"### 1.**axis = 0**"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"_uuid": "0362bfd8a85f9f94b37408f1453390e8a8f0b401"
},
"outputs": [
{
"data": {
"text/plain": [
"RegionID 84344.818837\n",
"SizeRank 5414.500000\n",
"Zhvi 250307.590028\n",
"dtype: float64"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.mean(axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3cb2c4cf0b13166cdc2485e899c267f9db731a13"
},
"source": [
"### 2.axis = 1"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"_uuid": "19b7f672d56b307f39f1f851d50e25de4a166f86"
},
"outputs": [
{
"data": {
"text/plain": [
"0 226193.666667\n",
"1 214116.000000\n",
"2 80042.666667\n",
"3 50191.333333\n",
"4 83876.666667\n",
"dtype: float64"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.mean(axis=1).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "90d27f42336c1349fcc8b32e7c852c7694c9c40f"
},
"source": [
"### 3.use labels instead of 0 and 1"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"_uuid": "42aecaf3aba29bee84c38f6b51adfbbc2a8ab2e3"
},
"outputs": [
{
"data": {
"text/plain": [
"RegionID 84344.818837\n",
"SizeRank 5414.500000\n",
"Zhvi 250307.590028\n",
"dtype: float64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.mean(axis='rows')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"_uuid": "d6f0b08af0b3ef63d5904e999210550d81e0a86f"
},
"outputs": [
{
"data": {
"text/plain": [
"0 226193.666667\n",
"1 214116.000000\n",
"2 80042.666667\n",
"3 50191.333333\n",
"4 83876.666667\n",
"dtype: float64"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.mean(axis='columns').head()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"_uuid": "83420020fc839458af19316d5da310e082ebe4ed"
},
"outputs": [
{
"data": {
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" <th>2</th>\n",
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" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2017-05-31</td>\n",
" <td>18959</td>\n",
" <td>Las Vegas</td>\n",
" <td>NV</td>\n",
" <td>Las Vegas</td>\n",
" <td>Clark</td>\n",
" <td>5</td>\n",
" <td>216500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Zhvi\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"5 2017-05-31 18959 Las Vegas ... Clark 5 216500\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.drop(0, axis=0).head()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"_uuid": "dd51d2e3bbba27b39138b3c8a4d5a20626ebd6cd"
},
"outputs": [
{
"data": {
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" <th>Metro</th>\n",
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" <th>0</th>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RegionID RegionName State ... County SizeRank Zhvi\n",
"0 6181 New York NY ... Queens 0 672400\n",
"1 12447 Los Angeles CA ... Los Angeles 1 629900\n",
"2 17426 Chicago IL ... Cook 2 222700\n",
"3 13271 Philadelphia PA ... Philadelphia 3 137300\n",
"4 40326 Phoenix AZ ... Maricopa 4 211300\n",
"\n",
"[5 rows x 7 columns]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.drop('Date', axis=1).head()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"_uuid": "2c96a6ecb8507f9d61fa4e4e3013395ddcdb7605"
},
"outputs": [
{
"data": {
"text/html": [
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" <tr>\n",
" <th>1</th>\n",
" <td>12447</td>\n",
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" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" RegionID RegionName State ... County SizeRank Zhvi\n",
"0 6181 New York NY ... Queens 0 672400\n",
"1 12447 Los Angeles CA ... Los Angeles 1 629900\n",
"2 17426 Chicago IL ... Cook 2 222700\n",
"3 13271 Philadelphia PA ... Philadelphia 3 137300\n",
"4 40326 Phoenix AZ ... Maricopa 4 211300\n",
"\n",
"[5 rows x 7 columns]"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.drop('Date', axis=1).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b780dad1ddbb809bb4970064e285b7294c4f30f6"
},
"source": [
"# 11.Applying a function to a pandas Series or DataFrame<a id=\"11\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Apply functions using apply()\n",
"2. Apply functions using applymap()\n",
"3. Applying our own functions"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "62729c46bdde975e817d8cd5f559eaa25a0d0069"
},
"source": [
"### 1.Read dataset"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"_uuid": "f084a0bb05334f3d87d25550691da33203670fdf"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
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" <th>0</th>\n",
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" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-titanic.csv')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "74d7fa3380758da28468e34abb395a24c266a1ae"
},
"source": [
"### 2.Apply functions using apply()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"_uuid": "187ec6b6f4dbf781aebd24ba413ecb8eeb36db6e"
},
"outputs": [
{
"data": {
"text/plain": [
"0 braund, mr. owen harris\n",
"1 cumings, mrs. john bradley (florence briggs th...\n",
"2 heikkinen, miss. laina\n",
"3 futrelle, mrs. jacques heath (lily may peel)\n",
"4 allen, mr. william henry\n",
"Name: Name, dtype: object"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_lower = lambda x: x.lower()\n",
"data.Name.apply(func_lower).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8bfb7e56dcf1cfdd18b67f3966a2064837e031fb"
},
"source": [
"### 3.Apply functions using applymap()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"_uuid": "3a2d5a252cefc2354e4b0bc591b20e5625d5c104"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Pclass</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>484.0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1444.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>676.0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1225.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1225.0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Pclass\n",
"0 484.0 9\n",
"1 1444.0 1\n",
"2 676.0 9\n",
"3 1225.0 1\n",
"4 1225.0 9"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['Age', 'Pclass']].applymap(np.square).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "c327a4138c18d2efa5aeff0c4ab5483a34d50c60"
},
"source": [
"### 3.Applying our own functions"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"_uuid": "a2a3dca203741e47cc3f38627d5721ba269c0350"
},
"outputs": [],
"source": [
"def my_func(i):\n",
" return i + 20"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"_uuid": "3ade8dff119165079e8b4ac524de87d49955941b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Pclass</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>42.0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>58.0</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>46.0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>55.0</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>55.0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Pclass\n",
"0 42.0 23\n",
"1 58.0 21\n",
"2 46.0 23\n",
"3 55.0 21\n",
"4 55.0 23"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['Age', 'Pclass']].applymap(my_func).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b219146024ecc80dac4a12aeb0a307647a117409"
},
"source": [
"# 12.Handling SettingWithCopyWarning<a id=\"12\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. A SettingWithCopyWarning scenario\n",
"2. Handling the SettingWithCopyWarning"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "044a8a5f763fd288b8f66159a86eb6fd56d83c2d"
},
"source": [
"### 1.A SettingWithCopyWarning scenario"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"_uuid": "8bec8adf2f445f714e4a7381cec0cbfa9316fbee"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:4405: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self[name] = value\n"
]
}
],
"source": [
"data[data.Age.isnull()].Age = data.Age.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "939cfa644669a17da37532518abc0db59c59cbb3"
},
"source": [
"### 2.Handling the SettingWithCopyWarning"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"_uuid": "50faa074dd2c4c1431ad4a32ca119a537795b2a0"
},
"outputs": [
{
"data": {
"text/plain": [
"5 NaN\n",
"17 NaN\n",
"19 NaN\n",
"26 NaN\n",
"28 NaN\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data.Age.isnull()].Age.head()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"_uuid": "233ad0d276a2b9ed32462011ef12881a80213ee5"
},
"outputs": [],
"source": [
"data.loc[data.Age.isnull(), 'Age'] = data.Age.mean"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"_uuid": "7978b6f3b9a9819bfa7c91e42ac2f3f7507b0a19"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
"Index: []"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data.Age.isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "19f59d3c81aa929297708dbc3bb2ec98e3417d2d"
},
"source": [
"# 13.Handling missing values in pandas<a id=\"13\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Missing Records \n",
" 1. Find out total records in the dataset\n",
" 1. Number of valid records per column\n",
"2. Dropping missing records\n",
" 1. Drop all records that have one or more missing values\n",
" 1. Drop only those rows that have all records missing\n",
"3. Fill in missing data\n",
" 1. Fill in missing data with zeros\n",
" 1. Fill in missing data with a mean of the values from other rows"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"_uuid": "ea32197e3c192da841ce31923793e9b3052a62e4"
},
"outputs": [],
"source": [
"data = pd.read_csv(\"../input/datasetsdifferent-format/data-titanic.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "c7ca85d63e5972f24a4b87b065fa4bafc178d225"
},
"source": [
"### 1. Missing Records \n",
"1. **Find out total records in the dataset**"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"_uuid": "3e6271ae4ff42998a2eedb87ee7f02f7def78089"
},
"outputs": [
{
"data": {
"text/plain": [
"(891, 12)"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3cd684c000fdb34a0ad644a3492d07c0548b0247"
},
"source": [
"2. **Number of valid records per column**"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"_uuid": "180e92074bcd20e0f33826ba1ba95c8362a94962"
},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 891\n",
"Survived 891\n",
"Pclass 891\n",
"Name 891\n",
"Sex 891\n",
"Age 714\n",
"SibSp 891\n",
"Parch 891\n",
"Ticket 891\n",
"Fare 891\n",
"Cabin 204\n",
"Embarked 889\n",
"dtype: int64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.count()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "05885e972eb1aaa034504a193721425b96cb248c"
},
"source": [
"### 2. Dropping missing records\n",
"\n",
"1. **Drop all records that have one or more missing values**\n"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"_uuid": "f7de03f21884b47dca8d7f938756dea84baab6e2"
},
"outputs": [
{
"data": {
"text/plain": [
"(183, 12)"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_missing_dropped = data.dropna()\n",
"data_missing_dropped.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "037775db411f832eadc6f24e8eb41af794bb0c42"
},
"source": [
"2. **Drop only those rows that have all records missing**"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"_uuid": "89253caae0e10ca237cccb9233aaae886828f3b9"
},
"outputs": [
{
"data": {
"text/plain": [
"(891, 12)"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_all_missing_dropped = data.dropna(how=\"all\")\n",
"data_all_missing_dropped.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3c30936e71f13c7c575ffef77d9f0a0d6c5c1c1e"
},
"source": [
"### 3. Fill in missing data\n",
" \n",
"1. **Fill in missing data with zeros**"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"_uuid": "7b086ead1e2c6056ff2ab2c89f7d650adb273577"
},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 891\n",
"Survived 891\n",
"Pclass 891\n",
"Name 891\n",
"Sex 891\n",
"Age 891\n",
"SibSp 891\n",
"Parch 891\n",
"Ticket 891\n",
"Fare 891\n",
"Cabin 891\n",
"Embarked 891\n",
"dtype: int64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_filled_zeros = data.fillna(0)\n",
"data_filled_zeros.count()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "72acb9bbbc7b6e0b4d4da444c233eb94a0a74eac"
},
"source": [
"2. **Fill in missing data with a mean of the values from other rows**"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"_uuid": "9707b243646e60627633dd97799c553ca8f24d48"
},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 891\n",
"Survived 891\n",
"Pclass 891\n",
"Name 891\n",
"Sex 891\n",
"Age 891\n",
"SibSp 891\n",
"Parch 891\n",
"Ticket 891\n",
"Fare 891\n",
"Cabin 204\n",
"Embarked 889\n",
"dtype: int64"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_filled_in_mean = data.copy()\n",
"data_filled_in_mean.Age.fillna(data.Age.mean(), inplace=True)\n",
"data_filled_in_mean.count()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "240939ed7a03b5e831dac34a8d1f2e7d48face2c"
},
"source": [
"# 14.Indexing in pandas dataframes<a id=\"14\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Default Index\n",
"2. Set an Index post reading of data\n",
"3. Set an Index while reading data\n",
"4. Selection using Index\n",
"5. Reset Index"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"_uuid": "c91dcf2965d02c0b8047688974b7385e88288b11"
},
"outputs": [],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-titanic.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "d94121fb0d915104cc60ff6c9bf37306991723e0"
},
"source": [
"### 1.Default Index"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"_uuid": "bd4fdaad8ff83b3eced90fb79504d52195cdbac4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "6ae91401ff9f46e352172d4fb34d087da10911a7"
},
"source": [
"### 2. Set an Index post reading of data"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"_uuid": "49f04023c64b165e23454840fd89e350e56ab264"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Braund, Mr. Owen Harris</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heikkinen, Miss. Laina</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Braund, Mr. Owen Harris 1 ... S\n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Heikkinen, Miss. Laina 3 ... S\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"\n",
"[5 rows x 11 columns]"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.set_index('Name').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "94ae9a5a794ef9462fa03e1ceccb4306656d5016"
},
"source": [
"### 3. Set an Index while reading data"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"_uuid": "7e0fe8838eb66fa2696ff4a59fad80bdc68176c9"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
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" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
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" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Braund, Mr. Owen Harris</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heikkinen, Miss. Laina</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Braund, Mr. Owen Harris 1 ... S\n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Heikkinen, Miss. Laina 3 ... S\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"\n",
"[5 rows x 11 columns]"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-titanic.csv', index_col=3)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "4633edbff177eafc27d28f8b4a8431b5171a2d3a"
},
"source": [
"### 4. Selection using Index"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"_uuid": "4d1264022da5ca65d3f84d26eae66bfe04201b1f"
},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 1\n",
"Survived 0\n",
"Pclass 3\n",
"Sex male\n",
"Age 22\n",
"SibSp 1\n",
"Parch 0\n",
"Ticket A/5 21171\n",
"Fare 7.25\n",
"Cabin NaN\n",
"Embarked S\n",
"Name: Braund, Mr. Owen Harris, dtype: object"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.loc['Braund, Mr. Owen Harris',:]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0c96097582281be77a84791c2eac5b933c626ab8"
},
"source": [
"### 5. Reset Index"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"_uuid": "59f597ce7a1f4f97e0ede149035252d9a54232f4"
},
"outputs": [],
"source": [
"data.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"_uuid": "5bec37b4ea3aee8149751505cacbe8b246efd4d7"
},
"outputs": [
{
"data": {
"text/html": [
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" <th>Sex</th>\n",
" <th>Age</th>\n",
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" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name ... Embarked\n",
"0 Braund, Mr. Owen Harris ... S\n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... ... C\n",
"2 Heikkinen, Miss. Laina ... S\n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) ... S\n",
"4 Allen, Mr. William Henry ... S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "c877b36e639fb37d0cb33c47a75051ac927070d9"
},
"source": [
"# 15.Merging and concatenating multiple data frames into one<a id=\"15\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Concatenate Dataset DataFrames\n",
"2. Concatenate using append()\n",
"3. Concatenate on columns\n",
"4. Merging DataFrames\n",
"5. Left outer merge\n",
"6. Right outer merge\n",
"7. Full outer merge"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "d4778912dcb549a9628d5c9631d31f1e4a9b6cee"
},
"source": [
"### 1. Concatenate Dataset DataFrames\n"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"_uuid": "64a4397c872c4da6c87e687af394126cb6db70e6"
},
"outputs": [],
"source": [
"dataset1 = pd.DataFrame({'Age': ['32', '26', '29'],\n",
" 'Sex': ['F', 'M', 'F'],\n",
" 'State': ['CA', 'NY', 'OH']},\n",
" index=['Jane', 'John', 'Cathy'])\n",
" \n",
"dataset2 = pd.DataFrame({'Age': ['34', '23', '24', '21'],\n",
" 'Sex': ['M', 'F', 'F', 'F'],\n",
" 'State': ['AZ', 'OR', 'CA', 'WA']},\n",
" index=['Dave', 'Kris', 'Xi', 'Jo'])"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"_uuid": "bf3f6d94018ee8d6957fe59f67e8c0db779ef4d5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th>Sex</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jane</th>\n",
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" <tr>\n",
" <th>John</th>\n",
" <td>26</td>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cathy</th>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dave</th>\n",
" <td>34</td>\n",
" <td>M</td>\n",
" <td>AZ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kris</th>\n",
" <td>23</td>\n",
" <td>F</td>\n",
" <td>OR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xi</th>\n",
" <td>24</td>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jo</th>\n",
" <td>21</td>\n",
" <td>F</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Sex State\n",
"Jane 32 F CA\n",
"John 26 M NY\n",
"Cathy 29 F OH\n",
"Dave 34 M AZ\n",
"Kris 23 F OR\n",
"Xi 24 F CA\n",
"Jo 21 F WA"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat([dataset1, dataset2])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ebca9041c04ba97a409b10ca05c7e9d63fb8ea76"
},
"source": [
"### 2. Concatenate using append()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"_uuid": "fec8d4c508f50d2909e6e2722227330c09043383"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>Sex</th>\n",
" <th>State</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jane</th>\n",
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" <td>CA</td>\n",
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" <tr>\n",
" <th>John</th>\n",
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" <td>M</td>\n",
" <td>NY</td>\n",
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" <tr>\n",
" <th>Cathy</th>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dave</th>\n",
" <td>34</td>\n",
" <td>M</td>\n",
" <td>AZ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kris</th>\n",
" <td>23</td>\n",
" <td>F</td>\n",
" <td>OR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xi</th>\n",
" <td>24</td>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jo</th>\n",
" <td>21</td>\n",
" <td>F</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Sex State\n",
"Jane 32 F CA\n",
"John 26 M NY\n",
"Cathy 29 F OH\n",
"Dave 34 M AZ\n",
"Kris 23 F OR\n",
"Xi 24 F CA\n",
"Jo 21 F WA"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset1.append(dataset2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b742b03b80842138fe6b0d94c18f80308fa851cd"
},
"source": [
"### 3. Concatenate on columns\n"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"_uuid": "c00a9eefe0597423206ad605b3ab3081a6fe488d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>City</th>\n",
" <th>Work Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jane</th>\n",
" <td>32</td>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>SF</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>John</th>\n",
" <td>26</td>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>NY</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cathy</th>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Columbus</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Sex State City Work Status\n",
"Jane 32 F CA SF No\n",
"John 26 M NY NY Yes\n",
"Cathy 29 F OH Columbus Yes"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset1 = pd.DataFrame({'Age': ['32', '26', '29'],\n",
" 'Sex': ['F', 'M', 'F'],\n",
" 'State': ['CA', 'NY', 'OH']},\n",
" index=['Jane', 'John', 'Cathy'])\n",
"\n",
"dataset2 = pd.DataFrame({'City': ['SF', 'NY', 'Columbus'],\n",
" 'Work Status': ['No', 'Yes', 'Yes']},\n",
" index=['Jane', 'John', 'Cathy'])\n",
"\n",
"\n",
"pd.concat([dataset1, dataset2], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "9b00fda156ce029913258a97c359684589e740f8"
},
"source": [
"### 4. Merging DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"_uuid": "be24ce790b850058039192352401eba19387123b"
},
"outputs": [
{
"data": {
"text/html": [
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" <td>No</td>\n",
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" <th>1</th>\n",
" <td>John</td>\n",
" <td>26</td>\n",
" <td>M</td>\n",
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" <td>NY</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>Cathy</td>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Columbus</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Name Age Sex State City Work Status\n",
"0 Jane 32 F CA SF No\n",
"1 John 26 M NY NY Yes\n",
"2 Cathy 29 F OH Columbus Yes"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset1 = pd.DataFrame({'Name': ['Jane', 'John', 'Cathy', 'Sarah'],\n",
" 'Age': ['32', '26', '29', '23'],\n",
" 'Sex': ['F', 'M', 'F', 'F'],\n",
" 'State': ['CA', 'NY', 'OH', 'TX']})\n",
"\n",
"dataset2 = pd.DataFrame({'Name': ['Jane', 'John', 'Cathy', 'Rob'],\n",
" 'City': ['SF', 'NY', 'Columbus', 'Austin'],\n",
" 'Work Status': ['No', 'Yes', 'Yes', 'Yes']})\n",
"\n",
"pd.merge(dataset1, dataset2, on='Name', how='inner')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "af2d844c0322be00375ad36cd06204874797bf68"
},
"source": [
"### 5. Left outer merge"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"_uuid": "65cee7d88741a07d6c91debd85c99f44d8d34ad0"
},
"outputs": [
{
"data": {
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" <td>NY</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Cathy</td>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Columbus</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sarah</td>\n",
" <td>23</td>\n",
" <td>F</td>\n",
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"</div>"
],
"text/plain": [
" Name Age Sex State City Work Status\n",
"0 Jane 32 F CA SF No\n",
"1 John 26 M NY NY Yes\n",
"2 Cathy 29 F OH Columbus Yes\n",
"3 Sarah 23 F TX NaN NaN"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(dataset1, dataset2, on='Name', how='left')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8f3eb0a6dc738ecbcb0a632874649f7bc22617f3"
},
"source": [
"### 6. Right outer merge"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"_uuid": "6444c3a81886e643817b6ef4b6915449e738c8f0"
},
"outputs": [
{
"data": {
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" <td>NY</td>\n",
" <td>NY</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Cathy</td>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Columbus</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Rob</td>\n",
" <td>NaN</td>\n",
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" <td>Austin</td>\n",
" <td>Yes</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Name Age Sex State City Work Status\n",
"0 Jane 32 F CA SF No\n",
"1 John 26 M NY NY Yes\n",
"2 Cathy 29 F OH Columbus Yes\n",
"3 Rob NaN NaN NaN Austin Yes"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(dataset1, dataset2, on='Name', how='right')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "934197307fc81866012f68cfba9cb2a79601d062"
},
"source": [
"### 7. Full outer merge"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"_uuid": "ed462abb8da36b6db5c3359002634cb5990c96e0"
},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>John</td>\n",
" <td>26</td>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>NY</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Cathy</td>\n",
" <td>29</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Columbus</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sarah</td>\n",
" <td>23</td>\n",
" <td>F</td>\n",
" <td>TX</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rob</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Austin</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Age Sex State City Work Status\n",
"0 Jane 32 F CA SF No\n",
"1 John 26 M NY NY Yes\n",
"2 Cathy 29 F OH Columbus Yes\n",
"3 Sarah 23 F TX NaN NaN\n",
"4 Rob NaN NaN NaN Austin Yes"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(dataset1, dataset2, on='Name', how='outer')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "80a9f24149a1b275f338fb790a428fa65c40a474"
},
"source": [
"# 16.Modifying a Pandas Dataframe inplace<a id=\"16\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Modify without inplace\n",
"2. Modify inplace\n",
"3. inplace not required for very method"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"_uuid": "2be34010ca01b82e196940e77b6ba7fa891eb58f"
},
"outputs": [],
"source": [
"top_movies = pd.read_table('../input/datasetsdifferent-format/data-movies-top-grossing.csv', sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"_uuid": "31f2f42bdc95d6f0df94d9b2d9f72bd2572c52b4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Title</th>\n",
" <th>Worldwide gross</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Avatar</td>\n",
" <td>$2,787,965,087</td>\n",
" <td>2009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Titanic</td>\n",
" <td>$2,186,772,302</td>\n",
" <td>1997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>$2,068,223,624</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Jurassic World</td>\n",
" <td>$1,671,713,208</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>The Avengers</td>\n",
" <td>$1,518,812,988</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Title Worldwide gross Year\n",
"0 1 Avatar $2,787,965,087 2009\n",
"1 2 Titanic $2,186,772,302 1997\n",
"2 3 Star Wars: The Force Awakens $2,068,223,624 2015\n",
"3 4 Jurassic World $1,671,713,208 2015\n",
"4 5 The Avengers $1,518,812,988 2012"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_movies.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "dbe6de69de84aebe54c9e5b90137686af2367698"
},
"source": [
"### 1.Modify without inplace"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"_uuid": "aa3a589d3cacd173160e4ab41ed319bb4c0b6646"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
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" <th>Worldwide gross</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Rank</th>\n",
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" <th>2</th>\n",
" <td>Titanic</td>\n",
" <td>$2,186,772,302</td>\n",
" <td>1997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>$2,068,223,624</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Jurassic World</td>\n",
" <td>$1,671,713,208</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>The Avengers</td>\n",
" <td>$1,518,812,988</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Title Worldwide gross Year\n",
"Rank \n",
"1 Avatar $2,787,965,087 2009\n",
"2 Titanic $2,186,772,302 1997\n",
"3 Star Wars: The Force Awakens $2,068,223,624 2015\n",
"4 Jurassic World $1,671,713,208 2015\n",
"5 The Avengers $1,518,812,988 2012"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_movies.set_index('Rank').head()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"_uuid": "78f2f099e83d56e611f5e72e2d4bea4890f2fa55"
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>Rank</th>\n",
" <th>Title</th>\n",
" <th>Worldwide gross</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
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" <td>2009</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Titanic</td>\n",
" <td>$2,186,772,302</td>\n",
" <td>1997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>$2,068,223,624</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Jurassic World</td>\n",
" <td>$1,671,713,208</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>The Avengers</td>\n",
" <td>$1,518,812,988</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Title Worldwide gross Year\n",
"0 1 Avatar $2,787,965,087 2009\n",
"1 2 Titanic $2,186,772,302 1997\n",
"2 3 Star Wars: The Force Awakens $2,068,223,624 2015\n",
"3 4 Jurassic World $1,671,713,208 2015\n",
"4 5 The Avengers $1,518,812,988 2012"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_movies.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "d7f287b63175e029edb46e3752d15d39b0298cb3"
},
"source": [
"### 2.Modify inplace"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"_uuid": "9cd60cf21b731e33d02621363dd1696ed781ceac"
},
"outputs": [],
"source": [
"top_movies.set_index('Rank', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"_uuid": "cf006af8d2caa042f7a7d77760c7678dbf22ad78"
},
"outputs": [
{
"data": {
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" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Rank</th>\n",
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" <tr>\n",
" <th>1</th>\n",
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" <td>2009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" <td>1997</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>$2,068,223,624</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Jurassic World</td>\n",
" <td>$1,671,713,208</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>The Avengers</td>\n",
" <td>$1,518,812,988</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Title Worldwide gross Year\n",
"Rank \n",
"1 Avatar $2,787,965,087 2009\n",
"2 Titanic $2,186,772,302 1997\n",
"3 Star Wars: The Force Awakens $2,068,223,624 2015\n",
"4 Jurassic World $1,671,713,208 2015\n",
"5 The Avengers $1,518,812,988 2012"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_movies.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "e7590d95d8c4bd92a3a81f7ddadbf9edb561d0d1"
},
"source": [
"\n",
"### 3.inplace not required for very method"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"_uuid": "c6ba1222cd5d7ca86baa4e3738dc1ffbf4d124e7"
},
"outputs": [
{
"data": {
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"<style scoped>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Title</th>\n",
" <th>Worldwide gross</th>\n",
" <th>Release Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Rank</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>2009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Titanic</td>\n",
" <td>$2,186,772,302</td>\n",
" <td>1997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>$2,068,223,624</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Jurassic World</td>\n",
" <td>$1,671,713,208</td>\n",
" <td>2015</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>The Avengers</td>\n",
" <td>$1,518,812,988</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Title Worldwide gross Release Year\n",
"Rank \n",
"1 Avatar $2,787,965,087 2009\n",
"2 Titanic $2,186,772,302 1997\n",
"3 Star Wars: The Force Awakens $2,068,223,624 2015\n",
"4 Jurassic World $1,671,713,208 2015\n",
"5 The Avengers $1,518,812,988 2012"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_movies.rename(columns = {'Year': 'Release Year'}).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "6c0231e4bc9d9046c6ff70c0169416784f680a69"
},
"source": [
"# 17.Removing columns from a pandas DataFrame <a id=\"17\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Remove one column\n",
"2. Remove more than one column\n",
"3. Remove row(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0c27babb013188fb3bf535129a6b26fb32af1c07"
},
"source": [
"### 1.Remove one column"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"_uuid": "f3b8046794477494b6015b11b86eb6354b4c3e8b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Braund, Mr. Owen Harris</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heikkinen, Miss. Laina</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Braund, Mr. Owen Harris 1 ... S\n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Heikkinen, Miss. Laina 3 ... S\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"\n",
"[5 rows x 11 columns]"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-titanic.csv', index_col=3)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"_uuid": "62cd9b619226d98730e0b707b171496a48be0faf"
},
"outputs": [],
"source": [
"data.drop('Ticket', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"_uuid": "24ed3693a49e899f38a4d3ca1aee2089447eb5d4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Braund, Mr. Owen Harris</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heikkinen, Miss. Laina</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Braund, Mr. Owen Harris 1 ... S\n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Heikkinen, Miss. Laina 3 ... S\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"\n",
"[5 rows x 10 columns]"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "92f05b2c741cb0af27fc3c4fc4bc384dddb77928"
},
"source": [
"### 2.Remove more than one column"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"_uuid": "8a01198341eaf828007f2a221fb53c2642669d3c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Braund, Mr. Owen Harris</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heikkinen, Miss. Laina</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Braund, Mr. Owen Harris 1 ... S\n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Heikkinen, Miss. Laina 3 ... S\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.drop(['Parch', 'Fare'], axis=1, inplace=True)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "16a5e3418d703eacc3f080c35af0789d9f585b3f"
},
"source": [
"### 3.Remove row(s)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"_uuid": "132146b2e3471bb8f186e34e1133ca36c3aa5f73"
},
"outputs": [],
"source": [
"data.drop(['Braund, Mr. Owen Harris', 'Heikkinen, Miss. Laina'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"_uuid": "9ac61480f4ccb1e3ee1d6827cf014cd142bb0007"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allen, Mr. William Henry</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Moran, Mr. James</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>McCarthy, Mr. Timothy J</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId ... Embarked\n",
"Name ... \n",
"Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C\n",
"Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S\n",
"Allen, Mr. William Henry 5 ... S\n",
"Moran, Mr. James 6 ... Q\n",
"McCarthy, Mr. Timothy J 7 ... S\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "52579047844c6045f74bc88afe0c296f83505733"
},
"source": [
"# 18.Renaming columns in a pandas DataFrame <a id=\"18\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Rename columns while reading the data\n",
"2. Rename columns using rename method \n",
" 1. Read in the dataset again \n",
" 2. Rename\n",
"3. Rename all columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "f251685179b1422f9d701bb491fcf9a0f2bdf2ef"
},
"source": [
"### 1.Rename columns while reading the data"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"_uuid": "054a502e1f47fc5623057f6336a38b610742adbf"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Region ID</th>\n",
" <th>Region Name</th>\n",
" <th>State</th>\n",
" <th>City</th>\n",
" <th>County</th>\n",
" <th>Size Rank</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Date</td>\n",
" <td>RegionID</td>\n",
" <td>RegionName</td>\n",
" <td>State</td>\n",
" <td>Metro</td>\n",
" <td>County</td>\n",
" <td>SizeRank</td>\n",
" <td>Price</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Region ID Region Name ... County Size Rank Price\n",
"0 Date RegionID RegionName ... County SizeRank Price\n",
"1 2017-05-31 6181 New York ... Queens 0 672400\n",
"2 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"3 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"4 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_columns = ['Date', 'Region ID', 'Region Name', 'State',\n",
" 'City', 'County', 'Size Rank','Price']\n",
"data = pd.read_csv('../input/datasetsdifferent-format/data-zillow1.csv', names = list_columns)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "b3b904d313bafc80cd2d7bddc0c7905316ea63ac"
},
"source": [
"### 2.Rename columns using rename method\n",
"1. **Read in the dataset again**"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"_uuid": "18797cfa4e90c95b519a2553a7bfe3c6986143cb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Price\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-zillow1.csv')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "881b4c87f918cc88090a4de2a204c326f76d799b"
},
"source": [
"2. **Rename**"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"_uuid": "d6b8eedbe9aea64f15200c1a8ebd986b0e278311"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'RegionID', 'RegionName', 'State', 'Metro', 'County',\n",
" 'SizeRank', 'Price'],\n",
" dtype='object')"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"_uuid": "93bc39e18df54d181af4a41c107e4ab63b71fbd7"
},
"outputs": [],
"source": [
"data.rename(columns={'RegionName':'Region', 'Metro':'City'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"_uuid": "0594eea707a3f91da75efe6fe8e6c0104bf6304f"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'RegionID', 'Region', 'State', 'City', 'County', 'SizeRank',\n",
" 'Price'],\n",
" dtype='object')"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "62fcd30a3893e1c41c5c750e354af6381db43cbb"
},
"source": [
"### 3.Rename all columns"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"_uuid": "efdeb10a186cc08baf7b3707a4b7450b645f43fa"
},
"outputs": [],
"source": [
"data.columns = ['Date', 'Region ID', 'Region Name', 'State',\n",
" 'City', 'County', 'Size Rank','Price']"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "91b6c91ff2c68acd847c7e17faf58cbfedad66af"
},
"source": [
"# 19.Using groupby method <a id=\"19\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Get Mean price for every State\n",
"2. Split the data into groups\n",
"3. Apply a function on each group and combine the results\n",
"4. Get Descriptive statistics by Groups(States)\n",
"5. Group by data on State and Region\n",
"6. Get the number of records per State\n",
"7. Group by Columns\n",
"8. Iterate over Groups"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"_uuid": "680791116e8b32a8b1113ee61f112daa78f5415c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>RegionID</th>\n",
" <th>RegionName</th>\n",
" <th>State</th>\n",
" <th>Metro</th>\n",
" <th>County</th>\n",
" <th>SizeRank</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-05-31</td>\n",
" <td>6181</td>\n",
" <td>New York</td>\n",
" <td>NY</td>\n",
" <td>New York</td>\n",
" <td>Queens</td>\n",
" <td>0</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-05-31</td>\n",
" <td>12447</td>\n",
" <td>Los Angeles</td>\n",
" <td>CA</td>\n",
" <td>Los Angeles-Long Beach-Anaheim</td>\n",
" <td>Los Angeles</td>\n",
" <td>1</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-05-31</td>\n",
" <td>17426</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>Chicago</td>\n",
" <td>Cook</td>\n",
" <td>2</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-31</td>\n",
" <td>13271</td>\n",
" <td>Philadelphia</td>\n",
" <td>PA</td>\n",
" <td>Philadelphia</td>\n",
" <td>Philadelphia</td>\n",
" <td>3</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-31</td>\n",
" <td>40326</td>\n",
" <td>Phoenix</td>\n",
" <td>AZ</td>\n",
" <td>Phoenix</td>\n",
" <td>Maricopa</td>\n",
" <td>4</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date RegionID RegionName ... County SizeRank Price\n",
"0 2017-05-31 6181 New York ... Queens 0 672400\n",
"1 2017-05-31 12447 Los Angeles ... Los Angeles 1 629900\n",
"2 2017-05-31 17426 Chicago ... Cook 2 222700\n",
"3 2017-05-31 13271 Philadelphia ... Philadelphia 3 137300\n",
"4 2017-05-31 40326 Phoenix ... Maricopa 4 211300\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('../input/datasetsdifferent-format/data-zillow1.csv')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "40f98ea89a62a40263102bbedee918c4c0fd4581"
},
"source": [
"### 1.Get Mean price for every State"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"_uuid": "3ca4482d813565afbce6cd830f7f709d5844a35e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AK</th>\n",
" <td>237783.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>137645.637584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AR</th>\n",
" <td>136331.707317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AZ</th>\n",
" <td>232353.921569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CA</th>\n",
" <td>617425.392297</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price\n",
"State \n",
"AK 237783.333333\n",
"AL 137645.637584\n",
"AR 136331.707317\n",
"AZ 232353.921569\n",
"CA 617425.392297"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data = data[['State', 'Price']].groupby('State').mean()\n",
"grouped_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "e5c503ac2d0aa70f7bf78eca9b129afb9efc02e7"
},
"source": [
"### 2.Split the data into groups"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"_kg_hide-output": true,
"_uuid": "83d1f18fa0cea7b12c84a8fd388e000e32b32aed"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NY</td>\n",
" <td>672400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CA</td>\n",
" <td>629900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>IL</td>\n",
" <td>222700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>PA</td>\n",
" <td>137300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AZ</td>\n",
" <td>211300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NV</td>\n",
" <td>216500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>CA</td>\n",
" <td>572100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>TX</td>\n",
" <td>164700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>FL</td>\n",
" <td>152300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>TX</td>\n",
" <td>321600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>MI</td>\n",
" <td>41500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>OH</td>\n",
" <td>128300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>TN</td>\n",
" <td>81100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NC</td>\n",
" <td>183800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>MA</td>\n",
" <td>554600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>WA</td>\n",
" <td>670300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>MD</td>\n",
" <td>121100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>CO</td>\n",
" <td>383200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>DC</td>\n",
" <td>555900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>TN</td>\n",
" <td>228500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>WI</td>\n",
" <td>107900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>AZ</td>\n",
" <td>164800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>OR</td>\n",
" <td>417900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>OK</td>\n",
" <td>132700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NE</td>\n",
" <td>152100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NM</td>\n",
" <td>189600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>MO</td>\n",
" <td>121600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>VA</td>\n",
" <td>259500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>CO</td>\n",
" <td>251200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>GA</td>\n",
" <td>208100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>IN</td>\n",
" <td>108800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>NY</td>\n",
" <td>439700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>MS</td>\n",
" <td>52800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>KS</td>\n",
" <td>262600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>OR</td>\n",
" <td>232900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>NM</td>\n",
" <td>140300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>CT</td>\n",
" <td>164100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>DE</td>\n",
" <td>223200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>SC</td>\n",
" <td>129900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>UT</td>\n",
" <td>202100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>CT</td>\n",
" <td>161200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td>MT</td>\n",
" <td>204800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>NH</td>\n",
" <td>215500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>ID</td>\n",
" <td>155100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>ND</td>\n",
" <td>217800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>IA</td>\n",
" <td>131300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>WY</td>\n",
" <td>205000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>NH</td>\n",
" <td>251600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>MS</td>\n",
" <td>92500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>453</th>\n",
" <td>AR</td>\n",
" <td>111500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>454</th>\n",
" <td>RI</td>\n",
" <td>223000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>519</th>\n",
" <td>DE</td>\n",
" <td>135500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>HI</td>\n",
" <td>633700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>ND</td>\n",
" <td>263500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>MT</td>\n",
" <td>160500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>WY</td>\n",
" <td>178900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>842</th>\n",
" <td>AK</td>\n",
" <td>221000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1007</th>\n",
" <td>WV</td>\n",
" <td>99400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3545</th>\n",
" <td>WV</td>\n",
" <td>96200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7646</th>\n",
" <td>ME</td>\n",
" <td>73900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>96 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" State Price\n",
"0 NY 672400\n",
"1 CA 629900\n",
"2 IL 222700\n",
"3 PA 137300\n",
"4 AZ 211300\n",
"5 NV 216500\n",
"6 CA 572100\n",
"7 TX 164700\n",
"9 FL 152300\n",
"11 TX 321600\n",
"12 MI 41500\n",
"13 OH 128300\n",
"14 TN 81100\n",
"15 NC 183800\n",
"17 MA 554600\n",
"18 WA 670300\n",
"19 MD 121100\n",
"20 CO 383200\n",
"21 DC 555900\n",
"22 TN 228500\n",
"23 WI 107900\n",
"24 AZ 164800\n",
"25 OR 417900\n",
"26 OK 132700\n",
"27 NE 152100\n",
"28 NM 189600\n",
"33 MO 121600\n",
"34 VA 259500\n",
"35 CO 251200\n",
"36 GA 208100\n",
"... ... ...\n",
"137 IN 108800\n",
"138 NY 439700\n",
"145 MS 52800\n",
"148 KS 262600\n",
"174 OR 232900\n",
"183 NM 140300\n",
"185 CT 164100\n",
"189 DE 223200\n",
"194 SC 129900\n",
"199 UT 202100\n",
"206 CT 161200\n",
"229 MT 204800\n",
"255 NH 215500\n",
"275 ID 155100\n",
"290 ND 217800\n",
"302 IA 131300\n",
"368 WY 205000\n",
"390 NH 251600\n",
"417 MS 92500\n",
"453 AR 111500\n",
"454 RI 223000\n",
"519 DE 135500\n",
"547 HI 633700\n",
"550 ND 263500\n",
"561 MT 160500\n",
"572 WY 178900\n",
"842 AK 221000\n",
"1007 WV 99400\n",
"3545 WV 96200\n",
"7646 ME 73900\n",
"\n",
"[96 rows x 2 columns]"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data = data[['State', 'Price']].groupby('State')\n",
"grouped_data.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"_uuid": "74676b8214edccfbf69982ce10f0cee05a5825b9"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 150,
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"_uuid": "903f0d3e0b8197c413e4e35072e23bb466a411fa"
},
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"source": []
},
{
"cell_type": "code",
"execution_count": 150,
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"_uuid": "ea398ecdbcc6d8726cf5ce5df3c20cccb74a9dd0"
},
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},
{
"cell_type": "code",
"execution_count": 150,
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"_uuid": "7d48fe2602686c67b0dc6d02aa53e2cac9f6e728"
},
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},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"_uuid": "6f6f3242a1b184eb4b2a877e089bc123145a9940"
},
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},
{
"cell_type": "code",
"execution_count": 150,
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"_uuid": "b783fa494291fd526713bd3d95d5a94d771af44f"
},
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},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"_uuid": "1b106b3a3d6c855ea756290f103dfa90dd0fc3ae"
},
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"source": []
},
{
"cell_type": "code",
"execution_count": 150,
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"_uuid": "73fa5af2a93ddc79695d7ffdf68089fe8334dd89"
},
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},
{
"cell_type": "code",
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"_uuid": "a59863aac530be5ef509ad165aed45f192b246a5"
},
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},
{
"cell_type": "code",
"execution_count": 150,
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{
"cell_type": "code",
"execution_count": 150,
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{
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{
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"source": []
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"_uuid": "7c86ab9ef8a37d37078b113592c40e0856b58402"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8781b6bdac3c8297550211212502a70d7993363f"
},
"source": [
"### 3.Apply a function on each group and combine the results"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"_uuid": "f294ed2bc08f427ec94117ee933850b6bd352543"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AK</th>\n",
" <td>237783.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>137645.637584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AR</th>\n",
" <td>136331.707317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AZ</th>\n",
" <td>232353.921569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CA</th>\n",
" <td>617425.392297</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price\n",
"State \n",
"AK 237783.333333\n",
"AL 137645.637584\n",
"AR 136331.707317\n",
"AZ 232353.921569\n",
"CA 617425.392297"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data.mean().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "c5ec0b023db358e4f3e64be250186883946a949f"
},
"source": [
"### 4.Get Descriptive statistics by Groups(States)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"_uuid": "5e6a4a59532dcb79e17c7a656d4e7f0c513a7e7e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">Price</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AK</th>\n",
" <td>12.0</td>\n",
" <td>237783.333333</td>\n",
" <td>41433.711205</td>\n",
" <td>175800.0</td>\n",
" <td>211700.0</td>\n",
" <td>222850.0</td>\n",
" <td>254950.0</td>\n",
" <td>323100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>149.0</td>\n",
" <td>137645.637584</td>\n",
" <td>72538.539135</td>\n",
" <td>44700.0</td>\n",
" <td>103900.0</td>\n",
" <td>126400.0</td>\n",
" <td>155800.0</td>\n",
" <td>598900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AR</th>\n",
" <td>82.0</td>\n",
" <td>136331.707317</td>\n",
" <td>42370.537394</td>\n",
" <td>65300.0</td>\n",
" <td>108175.0</td>\n",
" <td>128750.0</td>\n",
" <td>155050.0</td>\n",
" <td>268800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AZ</th>\n",
" <td>102.0</td>\n",
" <td>232353.921569</td>\n",
" <td>173068.589203</td>\n",
" <td>81500.0</td>\n",
" <td>148875.0</td>\n",
" <td>211950.0</td>\n",
" <td>258425.0</td>\n",
" <td>1611700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CA</th>\n",
" <td>701.0</td>\n",
" <td>617425.392297</td>\n",
" <td>604628.412673</td>\n",
" <td>74400.0</td>\n",
" <td>277000.0</td>\n",
" <td>453500.0</td>\n",
" <td>720200.0</td>\n",
" <td>6343800.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price ... \n",
" count mean ... 75% max\n",
"State ... \n",
"AK 12.0 237783.333333 ... 254950.0 323100.0\n",
"AL 149.0 137645.637584 ... 155800.0 598900.0\n",
"AR 82.0 136331.707317 ... 155050.0 268800.0\n",
"AZ 102.0 232353.921569 ... 258425.0 1611700.0\n",
"CA 701.0 617425.392297 ... 720200.0 6343800.0\n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data.describe().head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "f9dd60e9e45889cd484d393e86f2998070308557"
},
"source": [
"### 5.Group by data on State and Region"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"_uuid": "c593862cc80afa26bb467c28284c4290fab23873"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th>RegionName</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">AK</th>\n",
" <th>Anchor Point</th>\n",
" <td>175800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anchorage</th>\n",
" <td>293900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fairbanks</th>\n",
" <td>221000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Juneau</th>\n",
" <td>323100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kenai</th>\n",
" <td>206500.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price\n",
"State RegionName \n",
"AK Anchor Point 175800.0\n",
" Anchorage 293900.0\n",
" Fairbanks 221000.0\n",
" Juneau 323100.0\n",
" Kenai 206500.0"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data = data[['State',\n",
" 'RegionName', \n",
" 'Price']].groupby(['State','RegionName']).mean()\n",
"grouped_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ad75a696ad9ad900080591936f83ca156547dfbb"
},
"source": [
"### 6.Get the number of records per State"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"_uuid": "daa4a9c149b05adb143c5f9fdf79383f422a228b"
},
"outputs": [
{
"data": {
"text/plain": [
"State\n",
"AK 12\n",
"AL 149\n",
"AR 82\n",
"AZ 102\n",
"CA 701\n",
"dtype: int64"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_data = data.groupby(['State']).size()\n",
"grouped_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3bf8f0e279a32206c1bc6f6d560b799b5761bc8e"
},
"source": [
"### 7.Group by Columns"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"_kg_hide-output": true,
"_uuid": "22e15f3bd5e04e9d6095f27bf1402a3c9ad258df"
},
"outputs": [],
"source": [
"grouped_data = data.groupby(data.dtypes, axis=1)\n",
"# list(grouped_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7ec5956bca29419aeec5405eb15aec3ef6f90df8"
},
"source": [
"### 8.Iterate over Groups"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"_uuid": "40d1f0fb681cf1361c8313cbcec4f5313030b23b"
},
"outputs": [],
"source": [
"# for state, grouped_data in data.groupby('State'):\n",
"# print(state, '\\n', grouped_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "406201335624a1eecb0ba756eb1dfc8ce086e637"
},
"source": [
"# 20.Work with dates and times data <a id=\"20\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. let's first convert our date column to datetime\n",
"2. Let's set index to the date column\n",
"3. Filter and select time series Data\n",
"4. Get properties of date-time series data"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ad9db367ed7ce00168d0e75ccdc11bffa7da6fd2"
},
"source": [
"### 1.let's first convert our date column to datetime"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"_uuid": "3cfa298bfacb5edf42263707a1de63ee04f2d686"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DOB</th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1976-06-01</td>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Jane</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1980-09-23</td>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1984-03-30</td>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Cathy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991-12-31</td>\n",
" <td>M</td>\n",
" <td>OR</td>\n",
" <td>Jo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994-10-2</td>\n",
" <td>M</td>\n",
" <td>TX</td>\n",
" <td>Sam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1973-11-11</td>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Tai</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DOB Sex State Name\n",
"0 1976-06-01 F CA Jane\n",
"1 1980-09-23 M NY John\n",
"2 1984-03-30 F OH Cathy\n",
"3 1991-12-31 M OR Jo\n",
"4 1994-10-2 M TX Sam\n",
"5 1973-11-11 F CA Tai"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = pd.DataFrame({'DOB': ['1976-06-01', '1980-09-23', '1984-03-30', '1991-12-31', '1994-10-2', '1973-11-11'],\n",
" 'Sex': ['F', 'M', 'F', 'M', 'M', 'F'],\n",
" 'State': ['CA', 'NY', 'OH', 'OR', 'TX', 'CA'],\n",
" 'Name': ['Jane', 'John', 'Cathy', 'Jo', 'Sam', 'Tai']})\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"_uuid": "00e01cd3d8ff09aeb8eb5a0b3b88525d69d4fde4"
},
"outputs": [
{
"data": {
"text/plain": [
"DOB object\n",
"Sex object\n",
"State object\n",
"Name object\n",
"dtype: object"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"_uuid": "9a25af8566ccaa80905ba24754b045d7b3a442da"
},
"outputs": [],
"source": [
"dataset.DOB = pd.to_datetime(dataset.DOB)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"_uuid": "3fa6192a0fa60f4e38ce96854136703534da9a8b"
},
"outputs": [
{
"data": {
"text/plain": [
"DOB datetime64[ns]\n",
"Sex object\n",
"State object\n",
"Name object\n",
"dtype: object"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "201617691e6b61854160fdfc79341cf8cca39657"
},
"source": [
"### 2.Let's set index to the date column"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"_uuid": "d6d8b0ddaca8e65d4474a6c1ecc2c5b480af3c08"
},
"outputs": [],
"source": [
"dataset.set_index('DOB', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"_uuid": "baac800ecdc0abf0f3b92f39b296992e16b5a5ff"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DOB</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1976-06-01</th>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Jane</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-09-23</th>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984-03-30</th>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Cathy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991-12-31</th>\n",
" <td>M</td>\n",
" <td>OR</td>\n",
" <td>Jo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994-10-02</th>\n",
" <td>M</td>\n",
" <td>TX</td>\n",
" <td>Sam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973-11-11</th>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Tai</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex State Name\n",
"DOB \n",
"1976-06-01 F CA Jane\n",
"1980-09-23 M NY John\n",
"1984-03-30 F OH Cathy\n",
"1991-12-31 M OR Jo\n",
"1994-10-02 M TX Sam\n",
"1973-11-11 F CA Tai"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "46a8b030bc9c118b188c6528fe8f09764331a258"
},
"source": [
"### 3.Filter and select time series Data"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"_uuid": "c6dc828b3306a4533665df839897b912d00f305f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
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" <th>Name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DOB</th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1980-09-23</th>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex State Name\n",
"DOB \n",
"1980-09-23 M NY John"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['1980']"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"_uuid": "bc4112803f8e0ac0c6ab4af7e917f970d207203f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DOB</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980-09-23</th>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984-03-30</th>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Cathy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991-12-31</th>\n",
" <td>M</td>\n",
" <td>OR</td>\n",
" <td>Jo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994-10-02</th>\n",
" <td>M</td>\n",
" <td>TX</td>\n",
" <td>Sam</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex State Name\n",
"DOB \n",
"1980-09-23 M NY John\n",
"1984-03-30 F OH Cathy\n",
"1991-12-31 M OR Jo\n",
"1994-10-02 M TX Sam"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['1980':]"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"_uuid": "bd0dd8ffec076cd9bf0ae07d46d00d0d2ef4691a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DOB</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1976-06-01</th>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Jane</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-09-23</th>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973-11-11</th>\n",
" <td>F</td>\n",
" <td>CA</td>\n",
" <td>Tai</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex State Name\n",
"DOB \n",
"1976-06-01 F CA Jane\n",
"1980-09-23 M NY John\n",
"1973-11-11 F CA Tai"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset[:'1980']"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"_uuid": "6db3cf6c78056e896de48a27f9eb42129bfbaeef"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>State</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DOB</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980-09-23</th>\n",
" <td>M</td>\n",
" <td>NY</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984-03-30</th>\n",
" <td>F</td>\n",
" <td>OH</td>\n",
" <td>Cathy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex State Name\n",
"DOB \n",
"1980-09-23 M NY John\n",
"1984-03-30 F OH Cathy"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(dataset['1980':'1984'])\n",
"dataset.reset_index(inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "a7ee8354ea6a871c0771f742c21b9a20a735ec7c"
},
"source": [
"### 4.Get properties of date-time series data"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"_uuid": "66770eb5818b751ea0e4a5f39f2ff00e849983b8"
},
"outputs": [
{
"data": {
"text/plain": [
"0 153\n",
"1 267\n",
"2 90\n",
"3 365\n",
"4 275\n",
"5 315\n",
"Name: DOB, dtype: int64"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.DOB.dt.dayofyear"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"_uuid": "4a3c170f7ce93185ca02e63c72d39ab975376709"
},
"outputs": [
{
"data": {
"text/plain": [
"0 Tuesday\n",
"1 Tuesday\n",
"2 Friday\n",
"3 Tuesday\n",
"4 Sunday\n",
"5 Sunday\n",
"Name: DOB, dtype: object"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.DOB.dt.weekday_name"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7ca27b11d576c37de6a858959d766cb4ca318ce5"
},
"source": [
"# 21.Choosing the colors for the plots <a id=\"211\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Color Palettes\n",
"2. Look at how these colors look on a plot\n",
"3. Change the color palette\n",
"4. Impact on the plot\n",
"5. seaborn palettes\n",
"6. matplotlib colormaps as color palettes\n",
"7. Let's set the palette to a matplotlib colormap\n",
"8. Impact on the plot\n",
"9. Building custom color palettes\n",
"10. Let's see how the plot has changed"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"_uuid": "3f01ce87ab64d504a3f3f2c78c811c19c309783a"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"_uuid": "1905d0955e8c6ac278ec93bcc21072ade4af5ee1"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>beer_servings</th>\n",
" <th>spirit_servings</th>\n",
" <th>wine_servings</th>\n",
" <th>total_litres_of_pure_alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>89</td>\n",
" <td>132</td>\n",
" <td>54</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andorra</td>\n",
" <td>245</td>\n",
" <td>138</td>\n",
" <td>312</td>\n",
" <td>12.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Angola</td>\n",
" <td>217</td>\n",
" <td>57</td>\n",
" <td>45</td>\n",
" <td>5.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country ... total_litres_of_pure_alcohol\n",
"0 Afghanistan ... 0.0\n",
"1 Albania ... 4.9\n",
"2 Algeria ... 0.7\n",
"3 Andorra ... 12.4\n",
"4 Angola ... 5.9\n",
"\n",
"[5 rows x 5 columns]"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/data-alcohol.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "6fe753936d55df6c5f52499ae791941ad4a2a5fe"
},
"source": [
"### 1. Color Palettes"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"_uuid": "58d4db02168f058ba50c0a33754ad0ccffa6718d"
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(sns.color_palette())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "500d473161642155f8a2a92e4840ac114bfc1afd"
},
"source": [
"### 2.Look at how these colors look on a plot"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"_uuid": "39e43519a1a60effe578d8ec7aebdba9c5bc86ac"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (15,8))\n",
"sns.set()\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "ea47638fc9f2602c00ebfb1864fc3a52bbffa084"
},
"source": [
"### 3. Change the color palette"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"_uuid": "9dd35f4075898f525a08d767cbec18803ed04561"
},
"outputs": [],
"source": [
"sns.set_palette(\"bright\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "749f3b7bd11d7fd8a75ad57d3dd39a90cf823ec6"
},
"source": [
"### 4. Impact on the plot"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"_uuid": "6b66473d19da8a439373cea4fd662b56fb11e769"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (15,8))\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8f543d3b341609a616ef67e751a18e9a68debc04"
},
"source": [
"### 5. seaborn palettes"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"_uuid": "7f20a42913a493573c39f1fc5b849c268428ea70"
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(sns.color_palette(\"deep\", 7))\n",
"sns.palplot(sns.color_palette(\"muted\", 7))\n",
"sns.palplot(sns.color_palette(\"pastel\", 7))\n",
"\n",
"sns.palplot(sns.color_palette(\"bright\", 7))\n",
"sns.palplot(sns.color_palette(\"dark\", 7))\n",
"sns.palplot(sns.color_palette(\"colorblind\", 7))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "dc1def773ac8561ab75b5c890168ce95f428fd0f"
},
"source": [
"### 6. matplotlib colormaps as color palettes"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"_uuid": "7494f9284274a1974f9286864cbde3d1af4fb7f4"
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(sns.color_palette(\"RdBu\", 7))\n",
"sns.palplot(sns.color_palette(\"Blues_d\", 7))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0785273fc541cdb2c442b5693e8ee055899c1923"
},
"source": [
"### 7. Let's set the palette to a matplotlib colormap"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"_uuid": "737791f09cfa858a87602fcb17a96953ffc735ee"
},
"outputs": [],
"source": [
"sns.set_palette(\"Blues_d\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "04fd24c235edfe8b903abd441ff387a8afc68fc1"
},
"source": [
"### 8. Impact on the plot"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"_uuid": "68ead4cfe19783677e31bf4b7ba19f486113e290"
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (15,8))\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0eaf3c037139d44d978aa5be97ed5be1567afeff"
},
"source": [
"### 9. Building custom color palettes"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"_uuid": "519e93cf69bda9f295852d0691c5d7df1de6a160"
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"my_palette = ['#4B0082', '#0000FF', '#00FF00', '#FFFF00', '#FF7F00', '#FF0000']\n",
"sns.set_palette(my_palette)\n",
"sns.palplot(sns.color_palette())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "33dd2673277c1f11b4c09511c255c8e92098a2c6"
},
"source": [
"### 10. Let's see how the plot has changed"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"_uuid": "6fbf116079bbf7d4d858dc73ed79d15e91e4cf37"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (15,8))\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "cc3381713177b3c63d695118a53ccca7916fbc17"
},
"source": [
"# 22.Controlling plot aesthetics <a id=\"221\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. First plot with seaborn\n",
"2. Changing the plot style with set_style\n",
"\t1. Set plot background to a white grid\n",
"\t1. Set the plot background to dark\n",
"\t1. Set the background to white\n",
"\t1. Adding 'ticks\n",
"3. Customizing the styles\n",
"\t1. Style parameters\n",
"4. Plotting Context Presets\n",
"\t1. Plotting Context Preset - paper\n",
"\t1. Plotting Preset - talk\n",
"\t1. Plotting Preset - poster"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "22e56ecfc3f49dd1787b3b4e21089f152188e1ed"
},
"source": [
"### 1. First plot with seaborn"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"_uuid": "d3724c36033c696346882ddf49fa2ad3e034cf71"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>beer_servings</th>\n",
" <th>spirit_servings</th>\n",
" <th>wine_servings</th>\n",
" <th>total_litres_of_pure_alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>89</td>\n",
" <td>132</td>\n",
" <td>54</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andorra</td>\n",
" <td>245</td>\n",
" <td>138</td>\n",
" <td>312</td>\n",
" <td>12.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Angola</td>\n",
" <td>217</td>\n",
" <td>57</td>\n",
" <td>45</td>\n",
" <td>5.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country ... total_litres_of_pure_alcohol\n",
"0 Afghanistan ... 0.0\n",
"1 Albania ... 4.9\n",
"2 Algeria ... 0.7\n",
"3 Andorra ... 12.4\n",
"4 Angola ... 5.9\n",
"\n",
"[5 rows x 5 columns]"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"df = pd.read_csv('../input/datasetsdifferent-format/data-alcohol.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"_uuid": "f8b4307ad537d4245c7f2192ee5b1de0897ea78e"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb885df9dd8>"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df.beer_servings)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "995ae29fb621054d29813cc4ede7079690306412"
},
"source": [
"### 2. Changing the plot style with set_style\n",
"#### 1. Set plot background to a white grid"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"_uuid": "1ea0d67b849067246516084d59e0d8e0ef5279a4"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"sns.set_style(\"whitegrid\")\n",
"sns.lmplot(x='beer_servings', y='wine_servings', data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "973689716f722ccfd20a8e4c38bc943a1247f3d7"
},
"source": [
"#### 2. Set the plot background to dark"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"_uuid": "f52477138027967aca725ff0acff98128532fc86"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"sns.set_style(\"dark\")\n",
"sns.lmplot(x='beer_servings', y='wine_servings', data=df, fit_reg=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0a0bb68407d3b995188f47d834d7b5afcdc0885c"
},
"source": [
"#### 3.Set the background to white"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"_uuid": "850e0d73f88dac879f226bdde3b1517348d24655"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# sns.set()\n",
"sns.set_style(\"white\")\n",
"plt.figure(figsize=(15,8))\n",
"sns.swarmplot(x='country', y='wine_servings', data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8677bc95a455d6e0d804aa366a7dd709f4fb03aa"
},
"source": [
"#### 4.Adding 'ticks"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"_uuid": "33f9b624f590f7f993c6c46cfa35e1761daace77"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,8))\n",
"sns.set_style(\"ticks\")\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "3aa34f5278090d078eafe0faad52188d2e67c3cb"
},
"source": [
"### 3.Customizing the styles\n",
"#### 1.Style parameters"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"_uuid": "1ad852fb636a7a16f4c9ea39b19fed8e60bf7f1c"
},
"outputs": [
{
"data": {
"text/plain": [
"{'axes.facecolor': 'white',\n",
" 'axes.edgecolor': '.15',\n",
" 'axes.grid': False,\n",
" 'axes.axisbelow': True,\n",
" 'axes.labelcolor': '.15',\n",
" 'figure.facecolor': 'white',\n",
" 'grid.color': '.8',\n",
" 'grid.linestyle': '-',\n",
" 'text.color': '.15',\n",
" 'xtick.color': '.15',\n",
" 'ytick.color': '.15',\n",
" 'xtick.direction': 'out',\n",
" 'ytick.direction': 'out',\n",
" 'lines.solid_capstyle': 'round',\n",
" 'patch.edgecolor': 'w',\n",
" 'image.cmap': 'rocket',\n",
" 'font.family': ['sans-serif'],\n",
" 'font.sans-serif': ['Arial',\n",
" 'DejaVu Sans',\n",
" 'Liberation Sans',\n",
" 'Bitstream Vera Sans',\n",
" 'sans-serif'],\n",
" 'patch.force_edgecolor': True,\n",
" 'xtick.bottom': True,\n",
" 'xtick.top': False,\n",
" 'ytick.left': True,\n",
" 'ytick.right': False,\n",
" 'axes.spines.left': True,\n",
" 'axes.spines.bottom': True,\n",
" 'axes.spines.right': True,\n",
" 'axes.spines.top': True}"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sns.axes_style()"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"_uuid": "e842cd83a6c71930e5db29364c83790f6997aae2"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,8))\n",
"sns.set_style(\"ticks\", {\"axes.facecolor\": \".1\"})\n",
"sns.boxplot(data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "9b88183d1c4b6e5354c95cad1d6a69f19c1392de"
},
"source": [
"### 4.Plotting Context Presets\n",
"#### 1.Plotting Context Preset - paper"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"_uuid": "5f817e3d91922aee9283ecc8af87a6216d326a1f"
},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 1080x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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wc+ZMbNu2Da+88gr8fj/uv/9+7NixI6MfiBBCtCArDIGQhEA4mpXkG5NyEo5Go7jxxhuxePFimM3m+PefeuqpUe/j8Xji/28ymSAIAvbu3YtNmzYBAFavXo2mpiZwHIfa2lo4HA44HA5IkoRwOAyLxZLJz0QIIVmjKAzBsISBUHaTb0zKSfj73/9+xk/S29uL7du348UXX8Qf//jH+Gja5XLh7Nmz6Ovrg8vlit/e5XLB6/UOSeKjmchpHUHgDXPah2JJjmJJjmJJLpuxyLICf1hCMBgBJwpwOlI7D5HmulzqSXj58uVpPrQqGAxi06ZN2Lp1K8rLy2Gz2eKjXJ/Ph9LSUpSWlsLnu7jVzOfzwe12p/T4E9mOQltrkqNYkks3Fi2L3eTz66KlbMTCGEMoom43k5X0h74cB1RXOVO+/bhJ+F//9V/x0ksv4aqrrkr67x999NGo95UkCQ888ADWr1+PxYsXAwCWLFmCDz/8ENdddx127tyJBx98EDU1NWhpaUEgEIDf74cgCDQVQfIaFbvJPwwM4YgCXzACWdZg3mEU4ybhl156CcDYyXY0b731Fvbs2QO/34+XX34Zq1atwt13342HHnoIv/zlL9HQ0IDZs2cDADZs2IC77roLHMdhy5YtaT8XIUaRb22SqDylesptIBBBNIfJNybl6Ygf/vCHuOGGG9DY2AhRTO1ut956K2699dYR33/xxRdHfG/NmjVYs2ZNquEQYlhaFtKJOdXWj9Nt3gknzmIfscdOuUUGD1roIeUkfN111+Htt9/Gf/zHf2DJkiW4/vrrcdVVV8FkMmkZHyF5R+si9m/vOo2PDpwHU9iEEme+jdizafgpNz2lXEVt1apVeOKJJ/DOO+/g5ptvxptvvonGxkYtYyMkL2nZJimWOG1mccL9+oqxPKUkK/AORHChL2yIBAykWTvC6/Xivffew5///GecP38e69at0youQgBk77I717QqYp+YOBWZTWiqI5/aTk1Utk+5ZVPKSfjb3/42enp6cO211+IHP/gB5syZo2VchGTtslsv6RS7SXVxLJt1fYuhPKVWp9yyKaUkrCgKli1bhnvvvVfreAgBcPGyu8RmgsJYQc9XprM4Fkuc8Q8nTCxxFmrbKYUxBEPanXLLppSSMM/z+Otf/0pJmORMNi+7jSyTxbGvN9ZgxaKpaDmXnWmaQipPKcsKAiEJ/mAUstGz76CUpyOWLl2Kp59+GjfccMOQIj5UT5hooVja6WS6na222gW3La0lnYIWO+UWlAPoD0T0DictKf8WDx06BADYu3dv/HtUT5hoJduX3UZVTItjWhh+ys3lzL/XLeUk/Otf/1rLOAgZIduX3UZUDItj2tD3lFs2pZyET58+jSeeeALd3d347W9/i2PHjuHDDz/E3XffrWV8pMgVw2V3oS6OacUIp9yyKeXDGj/84Q9x7733QpIkAMDs2bPxhz/8QbPACDGi1g4f9p/ozuhwxFimVzmwaNYkSsBjiMoyen0hXOgPxxNwW7cfh0/34nyPX+foMpfyECMUCmHBggXxrzmOgyCkVl+TkELw+7+dwF8+OV20dRb0Mlovtw/3nUPTkc7472PZnCr8XyvrdIszUymPhKuqqnD48GFwg61EX3/99SFNPwkZjVajx1xq7fDhvaZWWMzChI8Lk9TIioJ+fwQX+kMIDUvAbd1+NB3phNkkwGIWYTYJaDraibNd+ff7SHkk/Oijj+Lxxx9HV1cXrr76aixduhSPPPKIlrGRAlAoVbrUrWScppXRiCqVU259/kjSrX29/WG4rPm1hpBytBUVFWP2kyNkuEKq0qVuJWO0lUxD6fRyKy0xJ93aV+bKv2YQKU9HPPHEExgYGEAkEsH69euxcuVK/O53v9MyNpLnCqlK1wyPE9cum6FJZbRipzB15NvTF4IvmNox4ymTSrBsThUikoxwREJEkrFsThWmVer3+2jr9uP3H57EI79sSut+KY+Ed+/ejS1btuDtt99GXV0dfv7zn+O2227DN77xjbSDJcWh0A4i/NPqWZg9xUVbyTC04FCmjTUn2stt1RVTcekMN7wDEbgdZlRXlGQUx0TIioKDp3rxycF2tLSrfTKd9vRqrKechMPhMCKRCN555x3cfvvtsNls6UVLik4hHkQopDoLmRo+z39dQw2+umjKqLcfXiEum73cqitKdEm+A8EoPj3cgU8Pd6Lfrx6TnuFxoLF+MuZfUp7WY6WchG+77TasWrUK8+fPR0NDA9ra2lBSkvsfnuQXOohQWJLN87/X1IrZU1xJf7eJCdss8rj68im4YvakvD3ldrZzALsOtqP5ZA9khUEUOCy+dBIa6ydj6uBUCJdmz/u06gl/+9vfjn9dXV095CjzK6+8gjvvvDO9ZydFgUaPhSPpPL/MJd0lEkvYrhIz7FYRwYiEPze1oqLUqsvoNVOSrODAlxew62B7fEtiaYkZV87zYOmcKjhsE2vxlvFeDo7jhjT8/N3vfkdJmJACl3yenyWd5/f6I3C7LOABXOgPQ2EMYIB3IJIXSbjfH4lPOQwEowCA2monGusnY15tOQQ+zSHvKLK2oY7lSe1OQkjmks3zX39lzYhRsCQpMIs8evtCEAR+yMKs22HWI/SUMMbQ2qFOORz48gIUxmASeCydU4XGeo8mHx5ZS8JcuhMhhJC8NHyef8GlVfB6AwDUI8aBkIRQWILbYcHiSyvRdLQznrCXzaky5Cg4KiloPtmNXQc70Nat1qEoc1rQMM+DJZdVwa7hARAaCRNC0jZ8nn+0RppG2EY2Fu9AGLsPdaDpcCcCYbU42ayppWio92DOjLL43LeW0krCsiyjs7MT1dXVI/7tm9/8ZtaCIoTkB1lh8A6E0d0XGvWQhV7byEbDGMOp8/3YdaADh05fAGPqzo0r53nQUO+Bpyyzfc+ZSjkJ/+1vf8OTTz6JQCCAv/71rzhy5Aiee+45PPPMMwCAf/qnf9IsSEKIscROuflDUTgdVsM30wTUOsSfH1enHNovqNMnFS4rGuo9WHJZJaxmfWpOpPys27Ztw6uvvhrfATFnzhycOnVKs8AIIcajMIZgWEYgjxppXugPYfehDuw52olgWAYAXDrdjcZ6D2ZPd4PXeT0r5SQsiiIcjotzQDQHTEjxYIwhGJHVLsYZHDHONcYYTpzrw64DHTja2gsGwGISsGL+ZDTUezCp1DgnflNOwosWLcLLL7+MaDSK/fv3Y/v27bjqqqvGvE80GsX69etx4sQJPPbYY7jhhhvw7LPP4p133kF5eTkmTZqEp59+GgDwwQcf4IUXXgDHcXj44YexcOHCif1khJAJy+YR41wIRSTsOtCOXQfb0d2nFoqqdNvQWO/BFbMrYTEbrxFFykl48+bNeOONN1BXV4f/+q//QmNjI9auXTv2g4sinnnmGbz22mtDvr9x40bccMMN8a9lWca2bdvwyiuvwO/34/7778eOHTvS/FHy26m2fpxuK9yGliTfMISiCvx50kiz2xvEroMd2He8C6GIDI4D5taUoXH+ZNRNcRl6C23KSVgQBKxdu3bcxJuI4zhUVVWN+P7zzz+Pl19+GXfccQduvvlmtLS0oLa2Fg6HAw6HA5IkIRwOw2LJv9qgmXh71+mLrd3zuPA5KQSDXYyDUUQlYzfSVBjDsTNe7DrQjuNn+wAAdquIqxdWo6HegzJnflTrSzkJf/zxx3j22WfR1tYGWZbj3//oo4/SesI777wTGzduhM/nw7e+9S0sWbIEfX19cLlc8du4XC54vV54PJ5xHy/TMnoAIAj8hO6fDafa+vHRgfOwWUTwnHqq6KMD57Fi0VTUVrvGfwANGOF1iaFYktMilmA4ioGABBkMNpsZqc6a8gIPVw4TXiAUxScH27Fz/zl0e9Uph6mVJVi1aCqWz6+GmIO9vWNJ99lTTsI//vGP8fTTT2PevHkTGtqXlZUBAJxOJxoaGnDs2DFMmzYNPp8vfhufzwe3253S48VO6mTC7bZP6P7ZcLrNC6Yw8BwHSVZHHkxhaDnn1a3VuxFelxiKJblsxhKR1AW3cIYt5F1OK/p92hfq7+gNYNeBduw/3o2IpIDngPmXlKOxfjJqJzvVejY8l5NYxsJxQHWVM+Xbp/xXPmnSJNTX12cUVCKfzwen0wlJkrB//378y7/8C6ZPn46WlhYEAgH4/X4IglA0UxGJBVEA5H3hc5I/JEmBLxRFOCKPf2OdKArDkdZefHygHV+29QNQpxxWzJ+MK+d5UOrI/zyRchKuq6vDd77zHaxevRpm88UCHLfddtuY99u0aRMOHDgAu92O5uZm9PX14eTJk5BlGTfffDNmzpwJANiwYQPuuusucByHLVu2ZPjj5J9YQZT4nDDyv/A5MbaorCAQlEZ0MDaSQCiKPUe68MmhdngH1KLpUyeVoHH+ZCy4pAImMeXObIaXchL2eDzweDzo6+tL6wm2bduW0u3WrFmDNWvWpPXYheLrjTVYsWgqWs6puyMYY9h/opt2SpCsGq2+g5G0dfvxycF27D/RDUlmEHgOC+sqsGL+ZEyvchh6l0OmUk7CGzZs0DKOoldb7YLbJhZMi3hiHKm0kNeTrCg41KJOOZyO9WmzmbB8ngfL5lbBZTdu6ctsGDcJ/+d//icefPBBPPjgg0k/hZ566ilNAitGhdQinugvsb6DEZPvWH3a6meWQxQKZ8phLOMm4euuuw4A8NWvfhWSJGHq1Kl0ZFkjY7WIpyRMUmX0+g6j9WlrqJ+sa8t6vYybhOfPnw8AOHXqFPbu3Yv29nbMmTMHixcvxhVXXKF5gMWk0FrEk9xiDAhGJEPWd9C6T1sq2rr96PMbr65xynPCGzduBAAMDAzg9ddfx4svvoienh4cOHBAs+CKTSG2iCfaY2AIhWUMhKKGq+/QH4jg00Pa92kbz4f7zqHpSGd8rWXZnCqsumJqTp57PCkn4ccffxxHjx6FxWLBwoUL8cQTT+Dyyy/XMraiRC3iSaqMWlxntD5ty+ZUoUGjPm1jaev2o+lIJ8ymi2stTUc7cekMtyFGxCkn4bNnzyISiWDGjBmYNm0apk+fjpIS/X+AQkQt4snYGAKhKC70hQxVXEfPPm1j6fNHkq61GKXrc8qvynPPPQcAaGlpwfvvv48777wTsiynXTuCEC21dvhwwRcu2KuIiCRjIBiFNcoMk4Av9IfwflPriD5tjfUeXJajPm1jKS0xJ11rMUrX55ST8Jtvvol9+/bh888/h8ViwfXXX4/FixdrGVteKvQkYGSFvMdaVhQMBC6ecrNqtFab6uIVYwxfnu/HJwbp0zaWKZNKsGxOleZdnzlOfQ0sZjGtue6Uk3BHRwduvvlmPPzww7Bq9Q7Ic4WcBIyuUPdYKwpDIJybvb6pLF5FojL2n+jGrgPt6OgNAlCLpi+fW6Vrn7bxaNX1meMAs0mAzSzCbOLjrZLSGf2n/Irdc8896UdYRAo1CeSLQttjnet2QuMtXl3oD+GTQx3Yc6QTocjQPm1L5k3GwEBY8xgnKltdn3meg8UkwGIWYBb4CU+3GPNjKw9pmQRoimN8hbLHWq8dD8kWr8AYDrX04t2mMzja6o33afvK/Mm4MqFPm96NMnNB4DlYzSIsZh4mQUA2f2RKwlmiVRKgKY7U5P8e68GOFjq1E0pcvAIAfygKf0jC+5+dBQBUldnQEOvTZjJen7Zs4wAIAhefZhBFHlza5dpTQ0k4S7RIAsU8xREb/ddOdadc3D5f91jHdjxEMiyqng1TJpVgXm0Zmo52IRKR4yUu86VPWzZwHCAKPKyDidckcEi/T0b6KAlnUbaTQKHNc6YqcfTP8Ryuml+d8ug/n/ZYS7ICX1DfourJ+rSZRR7zLynHmiXT8qZPW6ZiOxpiiVcU+JwXO6IknGXZTAKFMs+ZjuGjf57jCm70LysM/mBU17q+wbCEz46qRdMv9KuLapPL7WicPxmXz6qAWSzcKQee42Ay8SN2NADQ5fdBSdjA8n+eM31GGv1ne0FUYQzBkIQBHUtLdlwIYNfBduw73o1orE/bzHI0zr/Yp60QxXY0WM0CTCJvqMVESsIGl6/znJkyyug/2YLoHTfOzeixGNTSknpVNxutT9tXCqhPWzJDdzTwhv0B8/q0AAAgAElEQVSAoSScB/JpnnOiho/+OZ7L6ei/tcOHo2e8eH/vWThspiELoisWTU2zAzZDKKLAH9Rnx0MgFEXTkU7sPtRR8H3aYnieg8NmUhfWNNzRkE2UhInhJI7+09kdMVGx0W8kKqM/EAEY4HKY41Mi3d4g3LZUWpnru93sfI9fbQ2f0Kft8lkVaKxPrU+bUevujkbgOdgs6vxuZbkdfX1BvUNKCyVhYkix0b/bbYfXG9D8+RIXBAWBgy8URX8gDJtFgCDwYAAmuW3jPApDKKrAH4wiKuV2u1msT9uuA+1omUCfNiPX3U2UmHjNIo/YVjKjTjmMhZIwIRi6IGjmBThtJvgCUQTCEswmASsvn4LaateoHwjhqLrXN9fJN5t92oxedzcx8ebLVEMqKAkTgpELgk67GTzH4YYrZ+CyGWWjzknrtdf3dHs/3vu0Nat92oxYd3fI4loBJd5ElIQJQfLtgF9dMg3XLp2e9PYKY/AHc9tGXus+bUapu1sMiTcRJWFCBqWyHZANdjIeCEah5Cj79vsj8SmHWJ+2WdNKsWxOVVb7tOWq7m4yxZZ4E1ESJiTB6NsB1VNu3f2hnFQ3i/Vp+/hAOw6eutinbemcKrVjRW0F+n2hrD+vVnV3k0kskFNsiTcRJWFCxpHYUkjrBDxan7Yr53mwNEd92rJVd3c4jgNEnoPVYoJZ5GESc1Mgx+goCRMyioiknnILD1Y307KhjHcgjN2HOkb0aWuo92COAfq0ZYrjAFOsQI6ojnj1OrJtVJom4Wg0ivXr1+PEiRN47LHHcMMNN+DChQt46KGH4Pf7sWLFCmzcuBEA8MEHH+CFF14Ax3F4+OGHsXDhQi1DI2RUkqTAF9J+xwNjDKfO92NXkj5tdVNcEAQe7sHDIvkkVpnMZjHBJPJD5qwpAY+kaRIWRRHPPPMMXnvttfj3fvGLX+Ab3/gGbrzxRnzve9/DiRMnMHPmTGzbtg2vvPIK/H4/7r//fuzYsUPL0AgZQZIV+EMSQmEJWuaKZH3aKlxWNNR7sOSySuw+2IF3drca/sBEosTEO7wyGRmbpkmY4zhUVVUN+d7evXuxadMmAMDq1avR1NQEjuNQW1sLh8MBh8MBSZIQDodhsRRmYRFiLLnabpasT9tl091onD8Zs6aVgue4rB2YyMXR41hJyNISC6wiR4k3QzmfEw4EAvFuzS6XC2fPnkVfXx9cLlf8Ni6XC16vFx6PZ9zHc7szb60tCPyE7p9NFEtyWsYSr+sbikAwCXCO07aHF3i40ixyzhjDkdO9+HDfORz8skdtV28WcM3iabh60RRUDWsNf7rTD2Fw7hRQt27JCkNExpDnHiuWv3zaio+/aAPHcWCMYcWCKbhu+Yy04h4NxwMWswirSYDFpB7pFgQesn1ie5SzxUjv3VTlPAnbbLb4KNfn86G0tBSlpaXw+Xzx2/h8Prjd7pQebyJ1BXJVlyAVFEtyWsSSaTNNl9Oa8rawcETG3mNq0fQur3qfSrcNjfOH9mk7cqpnyIjVxAOypCDKcfGRsKwoMAsY8tyjxdLW7cdH+88NGUl/9Pk51FRltuMhvqPBLMJkGiwJKSuIyAoiIXXPcqG/XzJRWZlKoSdVzpPwkiVL8OGHH+K6667Dzp078eCDD6KmpgYtLS0IBALw+/0QBIGmIogmwlEFA8GIZjUeur1B7DrYgb3HuhCOyuC40fu0jVYsZyIHJrJx9Dg2v2uhHQ05oXkS3rRpEw4cOAC73Y7m5mbcfffdeOihh/DLX/4SDQ0NmD17NgBgw4YNuOuuu8BxHLZs2aJ1WKTIRGUZA4GL282yKVmfNptFwNULq9FQ70nap22sud+JHJjI9Ogxz3EwD3aeMELLn2KieRLetm3biO+9+OKLI763Zs0arFmzRutwSJGRFQUDQW12PEykT9t4I9ZMD0ykc/Q4dlQ4dmKNFtb0QYc1SEHScsdDNvq0aVksZ7SRNAeAp6PChkNJmBQUrZppKgrD58e78P6eMyP6tC2f54E7zT5tWhfLiY2kOQ4QBbWzsLqwRkeFjYaSMBlTtjsOa4UxhmAk+800A6Eo9hxRpxyy3adNq2I5HIfBzsLiiBNrxHgoCZNRJes4/PXGGr3DGkZtppnudrPxtHX7setgOz5P6NO2dE4Vll5WmVKftlSlO/cbO4Qx1eOEK6GYD89xsJgFWMwCzAKfd0edixklYZJUYs+1xI7DC+oqDDIiZghHWVa3m8mKgoOnerHrYDtOJ+nTNs3j0qR8ZKoSt7TxAocr53iwZul0WM0CLazlMUrCJKnEnmvAxdX7nv6Q7kl4eHWziRqtT1tD/WTMT7NP21gmcpS4rduPfce7UOa0wGYVIfA8Dpy6gOXzPCgtMcKHIskUJWGS1PCea7HV+wqXhvUcxyHJg9vNIlJWHu9M5wA+Odie1T5to8m0i3Hs4ATHcyh1mMFxHAaCUQg8j2BYQndfKOuxktyiJEySStZzbeXlU3QZBcuKAn9QQjAiTXjHgyQr+OLLHuw60I6zXWrR9Gz2aUtmvKI8w0fIQ/bvDs7v+gJR9A1Eh0wP6f2hSLKDkjAZVSo917SkMAbvQBjdfaEJJ99+fwS7D6tF02N92mZWO9FYPxlzs9inLZmxDmYca/Vi/4luWM0CLCYRS+dUYc3SqSP27w7/UOR4TrcPRZJdlITJmEbvuaYdZbCZZiAYRYnDknECHq1P27I5VWio9+SsjfvwgxlMYTCbBJhFDi3tPpS7rJAUhlBYwnufncFlM9xJX/PED8XaqW64bfTnWwiK9rfY2uHDifM+WASORhMGka29vvE+bQfa0dajVtQqc1rQMM+DJRr2aRtt4W3KpBIsn1uFL05dgM0sguc5LLmsEk67Bd6BMKwWNR4uhcXP2IeiUaqFkYkryiQc2/8qiDxkSTHo/tdikp29vqP1aWus9+Ayjfu0JVt4W33FVIiCOr/7/6y8BCsWVKOnP4Rypzq109rhS2vxM/HgTL7VzCWjK7oknLj/1WwSEOFlg+1/LSYT3+s7Wp+2BZeUY2a1CzWTnZpPOyQuvJlNPMyigFPtPjRKMjzlDsSOCU+rdAzZyZDO4ufwgzPXNdTgq4umaPpzkdwouiRs5P2vxSTWRj6S4V7fpH3aSq1omOdBMCRh/4lutcB583nNe7T5AlE47SaUOiyQGUM0qqB/IIwL/WHMqBq7uHcqi5/JDs6819SK2VNc9J4tAEWXhBP3v0IAbfXJsYnW9U3Wp+3S6W401nswe7ob7T0BbH/32IR7tI2HA9SpBosJtdUOsL2A1xcGN/icClJ/T423+Jl04CBzNHAoEEWXhBMvASVZbR1DW320F5UVBAYPWqQ768sYw4lzfdh1oANHW3vBoBao+cr8ybiy3oNJpbb4bbPRWWIspsHEa06oSFZidWH5XI9me6qTH5xhNHAoEEWXhIGLl4BhmdHuCI1J0mAb+Wj6By3CERkf7juHv+09E+/TVlVmQ0P90D5tibJdpzc+4jWLqHBbETQlLwWp5Z7qZHPH119ZQ+/bAlGUSRgAbfPRWFSW4Q9KCEfktEe+3d4gdh3qwN6j4/dpGy4bdXoTpxoSR7wWk4ggIqPeT8s91cOT/IJLq+i9WyCKNgkTbUQkGf6QhEiayXe0Pm3XLp2OK2ZXJO3TNppM6vRyAETxYvFzswGbW+pxcIZoj5IwyYpMK5uN16dtUllJRuUjU6nTO3zEm5h4jZaAcyFfCvgXGkrCZAIYwlEF/lD6W806egPYdaAd+493I5Jhn7ZMcBwg8pR4h8uPAv6FiZIwyUBmyVdRGI609uLjA+1D+rStyLBPW6oS+6zFGlzqkXiNOtI0fgH/seX7SUJKwiRlDAzhiJp80znhFghF0XSkE7sPdWS9T9toOA4wm3hYTSLMJmFIlTQ9RrxGHmnm8wGmQjhJSEmYjIsxdeSbbm2HZH3aLp9Vgcb6yVnt0xbDcxzMJgFWi7H6rBl9pGnEAv7j+fRwBw61XMD+490odVjy+iQhJWEyKsaAUERtH59q8h2vT5vLntl+3dHEGlwauc+a0UeaRirgn4r/fG0/Dp/uhcIYGFMXhT3lJXl7kpCScJFJZV5SlhUEQhL8odRLSvoCETQd6cSnhzrQH1CLps/wONBYPxn1WezTBgwb8Ro08SbKh5Gm3gX8U/Xp4Q4cPt2rzvNzPKKyglBEgT8Yhc0i5uVJQkrCRWS8eUlFUev5BuUA+gOjH0pIlLxPWyUa50/G1EnZq9WQb4k3Ub6MNPNhH/K5Lj8YGARO/VAXeA6ywhAMSeB5Li9PElISLhJjzUtOrSxBMCQhEJIgMwbXOAcjRuvTNre2DNOrHJhcbs9KnQaOAyxmEW6nJZ54jbrDYDz5MtI0uqmVJeDAQWEMPMep0zwcsHLRFDTUT87Lk4S6JOFFixZhwYIFAIC7774by5cvx+bNm9HZ2YnZs2fjRz/6EXg++yvmxWz4vKQo8rCYeHT1BWExCVBS2DIwWp+2hvrJ6PYG8dnRLhw/402rm/Bw6oiXh9UiwizwKC+1wutVd2IYeYdBKvJhpGl0y+d68FHzeRw+3QuZKQA4zJtZjn+5ZpbeoWVMlyQ8bdo0/PrXv45//Zvf/Abz58/Hd7/7XTzyyCP4+9//jlWrVukRWsGKzUvyHFBiM4EpDD2+MDhgzAScSp+2tm4//ry7NePykaksrhl9hwHJnQdvW4SmI5042zmAaVUOLJtTpXdIE6JLEj5//jzWrVuHyZMnY+vWrdizZw82bNgAAFi9ejWamppSTsIT2ZwtCLxhNndrHYvbbcdNV/nx0efncKE/jIgk4ysLpuCy2ooRt+UFHjabCXuOdGLnvnM40zkAQF1IunrRFDTOr0ZJQmv4051+CCIf3+8bm6eLyBh1aiM21aB2GRYgjLJwF3tdTpz3QRB5mGOV0wRAkhnCMsvZ77CY3i/p0COWrzXUGiaWidIlCb/77rsoLy/Hb3/7Wzz99NPo6+uDy+UCALhcLvT19aX8WBOZ/zFSFTUtY4kO7na4dKoLDoswpLDN8LoM3oEwdn5+HvuOqRXMgJF92mRJRr9Pjt/HxAOypCDKcfFRqqwoMAsY8vjqAQoBtsGpBl5REAkpiISio8Yee13Mg88R4eUhz2ERuJz9DhN/R+PNTWs9d10s7910GSWWysqxO6ok0iUJl5eXAwBuuukmvPbaa5g6dSr6+/tRWVkJn8+H0tJSPcIqOJKkYCAkIZxQyzdZYZvEPm0HWy7Ev28x8Vh6WRVuWlE75vOMVT4ydnLNZlZrNWS6q8FIOwzGm5vO97lrkls5T8KBQAAWiwWCIODTTz9FTU0NrrjiCuzcuRN1dXXYuXMnrrrqqlyHVVBSLScZ69P2ycEOtF9QRw88x8FpN8E22Ib9cGsvFl9WOe7cbmL5yAqXBTM8LljNQkqJN9VRoxF2GIw3N01z1yRdOU/CX375JbZu3QqHwwGz2YzHHnsMZWVl2Lx5M9atW4e6ujqsXLky12EVhFTLSSbr03bZdDemVjqw/3gnSmzm+CGNVFsD8RyHS6aUqolXFJDqgDfdUaPeOwzGO/1m9NNxxHhynoTnz5+P//mf/xnx/W3btuU6lAKRWkWzsfq0NdRPRkWpFW3dfuw/3gU2OHcxXmugxF0N6STemHwcNY53+i0fTscRY6HDGnkrteQbjsjYe6wLuw62o7tPXSSrdNvQOH9kn7bY3O5nx7vABpPH8NZAAj94ci0LtRrycdQ43ty0keauSX6gJJxnGAPCkozAOMm32xvEroMd2HtsWJ+2+smomzp6n7ZVV0zFFXM9ONfhi++gEHi10aXFrG5D45I0usxEvo4ax5ubNsLcNckflITzhKwwBMMSgmFp1KI6o/Vpu3phNRrqPSn3aZtW6UCpTYTFJMI2WKtBi04XiaNGWVYgyQzL5nqylrS03CY23ty03nPXJH9QEjY4hbF4RbPRDraN16fNLI5sDZ8MxwFmkYerxAKryOWkSM7XG2vgHQij6UgnBJ5D84luuEvME97SRdvESL6gJJyGXBaPUZhaGcofkkY9VtxxIYBdB9ux73g3ohn2aeM4wCTysFsu7uN12E3wRkY/QJFNrR0+NJ/sgavEnLXFuWws+OVroSCSfygJpyg2spIUBbLMsGxuFe649tKsP4+sKAiGZQRGSb6ywnDktFo0PdanrcQq4isLqnHl3CqUptinTRA42C0mWEx8Vmv9pkuLxbmJPiaNokkuURJOQWxkFYnKGAhFwRjDB3vPAQy442vZSMQMoYiEPn8EoYiUdNohaZ+2yhI01qfepy22pSw2z4ssLbBNhBaLcxN5zLFG0flWk4DkB0rCKbjgC0NSFAyEouA4da5UVhiajnTi6glsP1IYQzii7nSwRdWFt+Em2qctNs9rs0zs2LBWtNjSNZHHTDaKlmUFe450wum0wm2jPxmSXfSOSkG50wJZZmCDhaQZY+A4dc9sJpfNUUlBKCIjEL642GZL+PfR+rQtm1uF5fM8KfVpMwkcbBYTLGYegsFrM2uxpSvTxxw+iu7zheELRvGPL86j6WgnrppfTVMTJKsoCadghseJpXOq8Ld95yAragIusZoginzKl80MDJGoEt/fO3zG4WznAE6c6cXZzgEcarmQUZ82gedgs4iwmASYRA5GmG5IlRZbujJ5zOHb5nzBKBw2E5wlZvAcZ/gTfST/UBJO0bqvXQoOiG+lEkU+pUtchTGEwjL84dE7Fv9/f/8S+050x2s+cByw+NJJaKyfjKmVYz8+x+Hifl5T9g5SAMW7QyA2it5zpBP/+OI8nCXqlUc+nOgj+YeScBru+NqluPryKSld4kZlBaHw0CmHRLE+bTv3t6GjNwhA/SO3W0SIAoevLKges2jOxekGAQKf/RFvse8QmF7lAGMMuw915N2JPj0V6wf3RFASTlOyS9zYG29SqRWVbhsC4cEphyTJN1mfNlHgUFpiUUeyHIdwREpauUwd9QqwW0VNdzecauvPm8I6Wv7RD1/g43iO6kCModg/uDNFSXiC/vTJaTQd6YTVLMAfklBfW4aVi4Y2uByrT1vdVBf+8ukZCAKHUEQGz2NE5TKTwMFqMcGao0W2nr5gXhTWycUffeICX+1UN+2OGEU+VsQzCnpHTUBrhw/7T/QAAPoDUSgKw6dHOjF7utrgsrXDh89P9uDEWS+6vGoFszKnBQ3zPFhyWRXsVvXl/+xoF06e6wODOratm1qKqZMcsFlEWM082i8EcKbLr+klXuKIsqLUNmKfbURS0O0N4kzngCH+qHL5Rx+7+jFK6xytZXJ1kY8V8YyCknCa4rscwlGcavfhQn8QFrP6MsbeeOe6/HjrHy1o6fDFpyQqXBZ8vaEm3qctpq3bj56+ECa5bWCMwW4VwfMcIpKMqjJbTkZ7w5/juoaaIZfhsWmT9z87i/f2nDXEZSb90Wsj0/dbvlbEMwJKwkjtk19WWHxvb2yXg9Nmir/xOA4IRWQMBKN4c+eX8VGt3SrCbhGhMAa30zIkAQNAnz8Cm0XAJLdd3UkRkeD1hdHdF1JHd2mO9tIdxSQbUb7X1Ir/95b5WFBXgSOtvfjz7laU2EyajzjTib2Y/uhztdg1kasLqqOcuaJPwmN98sdq9wbHWGibVuXAsTO9iEosXmLSaTdBlhW4Si4m3WSLbSaBQ81kB8yigIFgFJKsDEkmPf2htEZ7mYxiko4oZfUQyqJZk9DTH1KLt2s84kw39mL5o8/lYtdEry6ojnJmijoJJ9sFsPtwO+bXVaDMYUEoMnrt3j/tUhfkwtGLzTRrPA5cs3gaSqwifv2XowhHJZgEHjzPxxfbYseI7Va1eE5FqQ1LLqvCRwfOx7tZxJIJYyzl0V6mo5jkI0qW03Y9mdZrKPQ/+lzXscjG75rqKKevqJNwbBeA2cTDYhYgCjz6fGF82daHOTPKRtw+1qftrY9b4gttHAfYzSJMJh7/91UzUV1Rgg/3nUM0qmAgKoMxwGoWsGbpNMya6obVIsA07OTb1xtrsGLRVLSc8w5JJjM8Tiysq0jpgEimo5hkI8rrr6xJu11P4iUzYyyty+dksUtSavUaCvmPfqzfqRaK5erCaIo6CVeU2lDqUKcMAiEJobCMiCSjtMSMtm4/+vwRuB1mlDutI/q0xYgC4HZa4tMNXd4g/nGgHXabCKfdBJPIw2oWsXyuB067adRYaqtdI5LN27tOo/lkD0SBgyQzLK6rwNcbkl+KTmQUM3xEueDSqiG7AMYbcSZeMscW8RyD8+WpXD4Pj93rC2MgGMXHB4q7XoMe896FfnVhREWXhBkYopKCcESB1SJg5mQnmo52QpYVyAqwoK4cx1q9aDrSCUVREIwokGRFrRkx+BixUQIARCXAH4xAEHicOOfFFycvIBiRIAomlLrMMIkC+gYX2qaNcwQ50dBLURGhiISmw52YPc2NZXOqRtx+hscZP2orChwEgcfCSyrio6bx/pgybdeTGKckKwhFJIDj4CoxQxT4lBcSF9ZVoPnLHkiSgoFgFE6bCQ67PvUajHLqS6+RaSFfXRhRUSRhjgMiUQWRqIxARIrvbujvDaKq3I5plQ582dYPkQc+P96NcFQGz3OISurtOKi1HMyigN2HOyDyainL2HRxICRh6dwqnDzbh6oyK/whCf5gFK0dA3DZzZAVNfEn++OOfc9kFhGNSPF/S7wU7R+IwBeMgDGG7e8eQ1dvcMTI8O1dp7H3WBeikoxgmMFpN+GzY1344sseTRd0EuOUoyxeWlOSGQAFkYiMo629KS0kLpxVAZtZxMcHzsNhT79eQ6bJM/F+X5zsGRpTXQXmzSxHhcuqSz3hTEemRvkgIeMr2CTMcUBEUhCNKgiGo5BkNqRy2Yf7zuGz412IRmX4AlHYLSKCESVeREdWGHgOcJaYIXBA/cwKRCUZnx7ugMIuJmAA4HlenbpwWhCOyBgIRBGKSFAUwDsQht0q4uV3jgAYepkOADs/b4M/HEUoLMNmEWC3mLDy8imYf0k5GANCEQm+YGTwZ+JgtQgjRoatHT78afdpBEJS/GfsGew1V+awwGE3aTaaTLxkFni1zCc4DqGwhEBEAmMMf9rdikhUGfIhkGzRqflkD/5p5SUQeD7tS/BMdxEk3i8iqR/U5S5rfFrkg33nsO94FwSex3UNNfjqoikTfcnSlu7IlI4P5xdjF5pNmzri9IckdHuDuNAXQn8gguiwBNzW7UfTkU7wHIeIpCbUgZAUT8CJRIGDpDD09odQ6bahbmop5MGb2SwiKt022K0ijp/x4vR5H850DSA4mIAZ1NZDVrM6nRAaHGFbzALe33sWf2k6g4gsIxRWjyvHji3vbG4Dx6l1CkJhWU1sAGxmAYoCSJIyZHHm6BnvkAScqD8QhiwrSRd01BN/3TjTOZDxKx67ZA5HZSgKg9UswiTyCIQlgDGU2i1w2NQPgcTnGW3RyWwSsKCuAv3+CAYCEQQj0riX4IkJ3WoRYTELI54vlfvFjo5LspqM/aEoAAZBUBdu32tqndBrlYqJ/k4yfS2IfvJ+JByb441GFQQj0uBl8Nh6Bxd+ItLIpCtwgKIO5gAAfQNhcDyPj5rb8PGBdlSUWuGwi7CZTYhIMnr61UMVsWiGC4RlBMIyFKY+tiQzmE1cvMgPxwGKAgCc+v8M8YT59cYaTHJbseO945AUBcGI+lgAcKjlAhbNmhR/ntioZ+TrA0RkBTzHDRlNZnO0NPyS+WhrL97+5DTsFhEmk9rpefiUwmiLTodOXcAXJ3sg8OpiZMOCqhGLkbFL7WhUgcnEo9ubWa2L4R8EJoEHY+r7ieM4qIN6DiLPDdk/rdXlfTZ+J4V0krBYplTyPgn3eEOQRtnLO1ysT9vfm8/HE3DiIhsG/99qEWAx8fAHJURlBl5R4GcMLocZff4ISqwmdHkTk+9F6u4zLmF/MQMH9Q9aVhgkWYHPH0E0qiZGDgwKLk5/BMPq3uJIVMb+E92orijBsjlV+GDfOfWxOA4OqwmfHe1ClduGy2aU4bLpbphNAkIReUQ8jAGKzLB6ydT4G/nTwx14d88ZWC0CrGYxK6fgEi+ZGWN4b89ZCINb8ZJNKSRbdFp4SQWaT/aoffB4Na7PjnRi+WVV8ceOJarEKRyTKCASlWE2CWlNYQz/IBAFHjaLOPh7VX9/DqsJJpMwYv90tmWrFkahnCQspikVQyXh119/Hb///e9hMpnw+OOPY/r06ePeRx6lHXyi4X3aEk8OJ96bgzoSDYZlBMMyOCA+L2wWBfhDUQRC4aTPEXuzqOFcfFRFAXhe/ZoxdY6YMXUeyGLmBz8M1H9XGOALRMEBeOl/D6udkEUBc2vK4HaYIQg8RJ5DMCyjbyCMt3edxp92t2JuTRmuWlCNf3xxHsGERGwx8ZhZ7cI1i6dh2ZwqtHb48JemM2g+2YNAOIqBoDo1UlpiBgfgSGvvuNXCUhmdpLqqP3wE3dMfwhdf9iQ9vTe9yhFPVBwHhCNy/Ki4w2ZSpw+CUZhEPuVdBMnivLGhBgsGd5UcarmA5pM9CIWlEfunsy1bI9hC2Ourd0W2XI/ADZOEvV4v3njjDezYsQOHDh3Ck08+iW3btmX8eKP1aVs+z4P3Pzub9D6JCZnngBKbCVaLiIFAFH2DHY5HE0vCwwfHidMENouA4OB0ggJ1qsIkcPE/Fn5wOoJhcJFIUsBBwt5jXbBZRNhN6jaw/kAYHM9BVhQEQjI+OdSO0hIL7FYRoagMDD6GrDD0+sL43d9O4u+ft+Fctx/9/sjFk3hg6v7oiASB5/DO7laYRR4czyXdm5vO6CTVVf3hI+ixTu/FElXsNeY5Lr5I6rCZsGbJNExy29L64xktzulVDiyaNQlXLxwYdf90NmVzBJvve331nFLRYwRumCTc3NyM5cuXQxRFLFy4EKdOncrocQaCUXx6uAOfHu5Ev19NnIl92h791adj3p/nORU8Os0AAA+ISURBVDhsascKfzCKrsGuF+NhI6eXAWBwblHdYxwKj5wuiA7OYcfedMqwOW0GIByVMWeGG209AUSiMgAOdrOIYEQCD4BxnDrNEYhC4NXdGlFZgSQz9Y9a4HD4dC+cdhM4To0pce6cKYCkMFhMPCxmMene3ExGJ+mu6o93ei+WqGJXMsrgp1vsw+uyGWUZ/ZGOFWeu9sxmewSbz3t99ZpS0WsEbpgk3NfXh9LS0vjXLIVpBgBwOtRfzOn2fny47xz2Hu2EJDOIAocr6ydj1RVTMcPjjN8+OjIPAgBEgYfTboIgcBgIROMJPFUmEw9JUiAIvLqHeHC4ZrWIcNhM6OwNJF04A9RpAzk2BE4Q+2PkACydOxnzLqnAwS978D87T6rTEhEJHM+BKYj3luM4fsjDRBUGq6jOzcYKwsfiiM1fO+wmDASiAKfOi4JTbxuWWXxv7InzPggiD/PgQhsEdZEx8TbZcMeNc7Fi0VR0e4OY5Lahbpob8uB2FLfbjusafHivqRU2s4hAWFJrMnMcrl8+AwsuHXmIJZsEgdd0r/Dwn7222qVbLOnIdiyJv2dO5sDAcP2VNSn9ficSS67e48MZJgm7XC4cPXo0/jWfYgeJnfvOYteB9vgWnNISM66c58HSOVVw2NRjwv2+i1uzTMLQRGwWeThLzGAMGAhEku6YGA/HAZdNd+NQSy8YU+ecY49itwgwizysJmHIfG385+SARbMm4WBLLwKh6JB/iyVTi1nA9MoSuG0ivlLvQV9/CO/vPasu9skKXHYLYi8XYwp47uJrZ4rt3QUgiuoo3xeIPQ8Hp90Ei0nAAKIAUxcOeU6d6rAIXPzy28wDsqQgwsvxUcLw22SL2ybCbVM/OGVZGfL4X100BbOnuNDTH0JUUmAa7Hg9vcqhecH1XBR1T/zZx3ouIxWY1yKWxN9zOr/ficSSzfd4ZaVz/BsNMkwSvvzyy/Hcc89BlmUcOXIENTWpzcO8/tcTAIDaaica6ydjXm35mI0vH/lOAx7++SewmAQ47CYoCkPfQDilrW3JCDxwzeJpuOPaS/Gfr+3H4dO9UHdEQC0ByXEIR2XctKIW3oEwPth7Lr5zguOAeTPL8f1b5sMblLDn4HkcarmAo61ehAc/KSwmATc11g65HIrN+f3501YcOd0bX4zylNnUOsRM3foW21rFGDC3pgyd3iAsJgF8ycXRb+y+sX8PhaWkvdSMtOCTz5faJHW5/j3r9R7nWKrX/TmwY8cO/OEPf4AoivjJT36SUiL+j1/sQkO9Z8zOxIk4DrBbTPjJy0244AtDlhmqK2zwhyRYzSIsJh5RmaF2shM3XFmDo6296PWFB+dbOXjK7fAFIuj0BlHltqGhfvKQX1LTkU6c7RzAtCoHJpfbRyyOnOkcwCcH2xEIRTFvZkW8DkTiJ/iZzgEcbe0FMP4855nOgSHPMdbzD7/taF+PtTti+H20VugjvkxRLMllI5ZsvMfTGQkbKgln4sCxjlHnWhPFkq/dKsZHyoX25skWiiU5iiU5imWkvJyO0ArPcWqLIau66k8IIUZSsEmY5ziUWEXYKPkSQgys4JKwwHOwW02wWQRKvoQQwyuYJCzwHEqsJlgp+RJC8kjeJ2FB4FBiMcFqFkG5lxCSb/I+CVe4rPHTYoQQkm/yvqg7JWBCSD7L+yRMCCH5jJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToiJIwIYToKO97zBFCSD6jkTAhhOiIkjAhhOiIkjAhhOiIkjAhhOiIkjAhhOiIkjAhhOiIkjAhhOiIkjAhhOioaJPw66+/jrVr12L9+vU4c+ZMzp9/0aJFWL9+PdavX4+dO3ciFArh/vvvxx133IEf/ehHUBRFs+eORqNYu3Ytli5dinfeeQcAcOHCBXz3u9/F7bffjmeffTZ+2w8++AC33XYb1q5di+bm5pzE8uyzz+Kmm27C+vXr8cADD+Qsln379uG2227DnXfeie9973vo7+/X7XVJFoter0t3dzfWrl2LO++8E7fffjuOHTs26vu1ubkZa9euxW233YYPPvggJ7H8/ve/x7XXXhv/e4rROpasYUWot7eX/fM//zOLRqPs888/Z/fdd1/OY7jpppuGfP3KK6+wX/ziF4wxxn784x+zv/3tb5o9t6IorKOjgz3zzDPsT3/6E2OMsZ/+9Kfs7bffZowxdvfdd7Pjx48zSZLYLbfcwnw+H2tvb2dr167NSSyJ/x+Ti1ja29tZIBBgjDG2fft29txzz+n2uiSLRa/XRZIkJssyY4yxTz75hP3gBz8Y9f26du1a1t7ezgYGBtgtt9zCJEnSPJbf/e537MUXXxxxW61jyZaiHAk3Nzdj+fLlEEURCxcuxKlTp3Iew/nz57Fu3Tr84Ac/QG9vL/bs2YNrrrkGALB69Wo0NTVp9twcx6GqqmrI9/bu3Tvi+VtaWlBbWwuHwwGPxwNJkhAOhzWPBQCef/553HHHHXjrrbcAICexeDwe2Gw2AIDJZIIgCLq9LsliAfR5XQRBAM+rqcLn82HOnDlJ36/hcBiyLMPj8aCkpAS1tbVoaWnRPBYAeOONN3D77bfjV7/6FQDkJJZsEfUOQA99fX0oLS2Nf810KJ/x7rvvory8HL/97W/x9NNPo6+vDy6XCwDgcrnQ19eX03gCgQCsVmv8+c+ePTskptj3vV4vPB6PprHceeed2LhxI3w+H771rW9hyZIlOY2lt7cX27dvx4svvog//vGPur4uibFwHKfb63LixAls3boV58+fx7PPPouPP/54xPvV6/XC6XQOiUWL9/HwWGpra3HLLbdAlmXcc889WLRoEaqrq3MSSzYU5UjY5XKhv78//nXskzWXysvLAQA33XQTDh8+PCQmn8835EMiF2w2W3wEFXv+0tJS+Hy++G18Ph/cbrfmsZSVlQEAnE4nGhoacOzYsZzFEgwGsWnTJmzduhXl5eW6vi7DY9HzdZk1axZeffVV/OxnP8Ojjz6a9P2aLBYt3sfJYhEEAWazGWvWrMGhQ4dyFks2FGUSvvzyy9HU1ARZlnHw4EHU1NTk9PkDgQBkWQYAfPrpp6ipqcGyZcuwc+dOAMDOnTuxdOnSnMa0ZMkSfPjhh0Oev6amBi0tLQgEAujq6oIgCLBYLJrHEvvjkSQJ+/fvx4wZM3ISiyRJeOCBB7B+/XosXrwYgH6vS7JY9HpdIpFI/P+dTiesVmvS96vVaoUgCOjs7EQgEMDp06ez/reVLJbY68IYw549e1BbW5uTWLKlKKcj3G43br31Vqxbtw6iKOInP/lJTp//yy+/xNatW+FwOGA2m/HYY4+hrKwMmzdvxrp161BXV4eVK1dqGsOmTZtw4MAB2O12NDc34+6778ZDDz2EX/7yl2hoaMDs2bMBABs2bMBdd90FjuOwZcuWnMTS19eHkydPQpZl3HzzzZg5c2ZOYnnrrbewZ88e+P1+vPzyy1i1apVur0uyWE6dOqXL63Lw4EE89dRT4DgOALB582ZccsklSd+vmzdvxn333QfGGO69916IYnZTTLJYXnrpJfzjH/8Ax/3/7d1fSFN9HMfx947OLLcuBG+aFNZIR5CU/4tCatXsoigoCdaVorYaXiXBhBS1LKObTC+LjNY/6UJMtFmQku5KoYuUhkGtGBkJmmbbbM9FeEjUB5+n5/Es+76uDodzzu+3c/Hhx+Gcz3RkZmayY8eOZZnLf0X6hIUQQkN/5OMIIYSIFhLCQgihIQlhIYTQkISwEEJoSEJYCCE0JCEsooLf7+f48eNaT+Nfcblc+P1+rachflPR+eKcEL9oZmZG7Vv4v6+33O+Zi5VFQlhEjWAwiNPpxOfzkZmZSXV1Nc+fP6epqYlv376Rnp5OVVUViqLw8OFD3G43oVCI/fv3c+bMGbxeL83NzcTExBAMBmlpaZk3Rn9/P3V1dSiKwurVq7l79y7hcJj6+noGBgYIh8OUl5ezZ88erl27xocPHxgZGSE9PZ3u7m7a29uJj49nfHycwsJCOjo6OHnyJFVVVWzatImdO3dSUFBAX18f69evp7GxkZiYGDweDw0NDRiNRsxmMyaTCafTyaVLl3j27BlxcXEcOnSI4uJiDe680JKEsIgaw8PDXLx4kbS0NJxOJ11dXdy7d4+WlhZWrVpFdXU1XV1dmM1menp6uH//PjqdDofDwcuXL4EfX1Q9fvyYpKSkBce4efMmLpeL3Nxc9XPXBw8ekJycTGVlJV++fKGwsJBdu3YB8PbtW27fvo1er2d6epre3l6sVivd3d1qi9jPPn36xIEDB6isrKSsrIz+/n4yMjK4cOECbrebxMRE7HY7JpOJsbExOjs78Xg8KIoyp+tA/DkkhEXUSElJwWKxAHDw4EGuXLnC1NSU+qx4enqadevWMTo6yuDgIEePHgVQuwGSkpLIyMhYNIABtm3bRkNDA0eOHKGgoACAFy9e4PP5ePToEQCTk5N8/PgRAKvVil6vB8Bms6kF4p2dnTgcjnnXX7t2LVlZWQBYLBbev39PYmIiZrNZbTbbt28fX79+xWg0kpCQgMvlwmq1kp+f/6u3UPyGJIRF1JjtA5jdNhgM5OXlUVNTM+e4W7duceLECcrKyubs93q9agfvYkpLS9m9ezdPnz7l2LFjtLa2EolEqKurU4tyfjZbYwmQnZ3N+fPn+fz5M2/evGHr1q3zjo+Li1O3FUVhZmZm0arU2NhYWltb6enpoa2tjSdPnlBfX/+38xcrj7wdIaLGyMgIQ0NDRCIROjo6KCkpoa+vj0AgAPzo1g0EAuTm5tLe3q5WKQYCAcbGxpY0xrt377BYLJw+fRqTyUQgECAvLw+3263+Rc+rV68WPDc2NpacnBxqa2v/0ap148aN+Hw+RkdHCYVCeDwe4MeKe2Jigr1793L27NlFxxUrm6yERdRITU3l+vXrvH79mqysLGw2GwaDAYfDQSgUQq/XU1NTw5YtWygqKsJutxOJREhISODq1atLGuPGjRt4vV4URWH79u2kpaWxefNm/H4/hw8f5vv376SkpNDY2Ljg+TabjaKiItxu95J/V3x8POfOncNut2M0GtmwYQMGg4HJyUlOnTpFKBRCp9NRXl6+5GuKlUNa1IRYBlNTU6xZs4ZwOExpaSklJSXk5ORoPS0RBWQlLMQyuHPnDm1tbQSDQfLz8yWAhUpWwmJFGh4epqKiYs6+1NRULl++rNGMhFiYhLAQQmhI3o4QQggNSQgLIYSGJISFEEJDEsJCCKEhCWEhhNDQX6MWDRqnFU20AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"sns.set_context(\"paper\")\n",
"plt.figure(figsize=(15, 8))\n",
"sns.lmplot(x='beer_servings', y='wine_servings', data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7582447457a970b35af417e7bfe7fb9e13045e9d"
},
"source": [
"#### 2.Plotting Preset - talk"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"_uuid": "9ba82357ef06175b55b23b252f861026631acbde"
},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"sns.set_context(\"talk\")\n",
"plt.figure(figsize=(8, 6))\n",
"sns.lmplot(x='beer_servings', y='wine_servings', data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "cb41c074e1dea6e8a1deb766776bfed9cc2f89e8"
},
"source": [
"#### 3.Plotting Preset - poster"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"_uuid": "b7c7e9d5f617de2c464e7617dea90c3f519bc702"
},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"sns.set_context(\"poster\")\n",
"plt.figure(figsize=(8, 6))\n",
"sns.lmplot(x='beer_servings', y='wine_servings', data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "7e8b1c2c4d44cde925e2c9baf4e0d54c29adbca5"
},
"source": [
"# 23.Plotting categorical data <a id=\"231\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Scatterplots\n",
"2. Swarmplot\n",
"3. Boxplot\n",
"4. Violinplot\n",
"5. Barplot\n",
"6. Countplot\n",
"7. Wide form plots"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8705d5125d49dd7a92a7dfc10d49656e21f51bda"
},
"source": [
"#### 1.Scatterplots"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"_uuid": "0706495b152a080a101347bd95ef2da581dd72b2"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"_uuid": "9d0aaf8edb925c4c6de5184a8c22a0effe7b50b4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>title</th>\n",
" <th>original_air_date</th>\n",
" <th>production_code</th>\n",
" <th>season</th>\n",
" <th>number_in_season</th>\n",
" <th>number_in_series</th>\n",
" <th>us_viewers_in_millions</th>\n",
" <th>views</th>\n",
" <th>imdb_rating</th>\n",
" <th>imdb_votes</th>\n",
" <th>image_url</th>\n",
" <th>video_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>Homer's Night Out</td>\n",
" <td>1990-03-25</td>\n",
" <td>7G10</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>30.3</td>\n",
" <td>50816.0</td>\n",
" <td>7.4</td>\n",
" <td>1511.0</td>\n",
" <td>http://static-media.fxx.com/img/FX_Networks_-_...</td>\n",
" <td>http://www.simpsonsworld.com/video/275197507879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12</td>\n",
" <td>Krusty Gets Busted</td>\n",
" <td>1990-04-29</td>\n",
" <td>7G12</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>30.4</td>\n",
" <td>62561.0</td>\n",
" <td>8.3</td>\n",
" <td>1716.0</td>\n",
" <td>http://static-media.fxx.com/img/FX_Networks_-_...</td>\n",
" <td>http://www.simpsonsworld.com/video/288019523914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>14</td>\n",
" <td>Bart Gets an \"F\"</td>\n",
" <td>1990-10-11</td>\n",
" <td>7F03</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>33.6</td>\n",
" <td>59575.0</td>\n",
" <td>8.2</td>\n",
" <td>1638.0</td>\n",
" <td>http://static-media.fxx.com/img/FX_Networks_-_...</td>\n",
" <td>http://www.simpsonsworld.com/video/260539459671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17</td>\n",
" <td>Two Cars in Every Garage and Three Eyes on Eve...</td>\n",
" <td>1990-11-01</td>\n",
" <td>7F01</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>26.1</td>\n",
" <td>64959.0</td>\n",
" <td>8.1</td>\n",
" <td>1457.0</td>\n",
" <td>http://static-media.fxx.com/img/FX_Networks_-_...</td>\n",
" <td>http://www.simpsonsworld.com/video/260537411822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19</td>\n",
" <td>Dead Putting Society</td>\n",
" <td>1990-11-15</td>\n",
" <td>7F08</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>25.4</td>\n",
" <td>50691.0</td>\n",
" <td>8.0</td>\n",
" <td>1366.0</td>\n",
" <td>http://static-media.fxx.com/img/FX_Networks_-_...</td>\n",
" <td>http://www.simpsonsworld.com/video/260539459670</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id ... video_url\n",
"0 10 ... http://www.simpsonsworld.com/video/275197507879\n",
"1 12 ... http://www.simpsonsworld.com/video/288019523914\n",
"2 14 ... http://www.simpsonsworld.com/video/260539459671\n",
"3 17 ... http://www.simpsonsworld.com/video/260537411822\n",
"4 19 ... http://www.simpsonsworld.com/video/260539459670\n",
"\n",
"[5 rows x 13 columns]"
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/data_simpsons_episodes.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"_uuid": "f05550ce139766da3c76323d65c09356ae0d8613"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.stripplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "c2b6ebb786dbd98dd546362a2346b8f0e4dca1fa"
},
"source": [
"#### 2.Swarmplot"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"_uuid": "111880843157b6edc22a5ebbf9d06e5686254eeb"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.swarmplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "4d909c671038041d58a4e4b3dca3d0d5bc3bf883"
},
"source": [
"#### 3.Boxplot"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"_uuid": "2922ba2d47c810f362c7a1b8e6bb4dc0181e18d2"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.boxplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);\n",
"# sns.boxenplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "0cb7f658bc504b36ad23d51daae2ec472a6bdff4"
},
"source": [
"#### 4.Violinplot"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"_uuid": "7972ea45bc24a3470acf6c430820456bc39f617a"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.violinplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "939f3aa8ba0318087eb295ad06aada3fd7b94980"
},
"source": [
"#### 5.Barplot"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"_uuid": "79725418a2049974d70b2e0b595bedf63023c5de"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.barplot(x=\"season\", y=\"us_viewers_in_millions\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "4f09caf12ffa7c248bdbf444a66706a151bef27b"
},
"source": [
"#### 6.Count Plot"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"_uuid": "8dffc91ab3a4682d8f7a755f2495727d9ad22deb"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.countplot(x=\"season\", data=df);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "62b5f769410b48e96880ffb0c86564dc4934c2dc"
},
"source": [
"#### 7.Wide form plot"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"_uuid": "73fa21a03b7b91d7fc20813b242119d3c27b4701"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>beer_servings</th>\n",
" <th>spirit_servings</th>\n",
" <th>wine_servings</th>\n",
" <th>total_litres_of_pure_alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>89</td>\n",
" <td>132</td>\n",
" <td>54</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andorra</td>\n",
" <td>245</td>\n",
" <td>138</td>\n",
" <td>312</td>\n",
" <td>12.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Angola</td>\n",
" <td>217</td>\n",
" <td>57</td>\n",
" <td>45</td>\n",
" <td>5.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country ... total_litres_of_pure_alcohol\n",
"0 Afghanistan ... 0.0\n",
"1 Albania ... 4.9\n",
"2 Algeria ... 0.7\n",
"3 Andorra ... 12.4\n",
"4 Angola ... 5.9\n",
"\n",
"[5 rows x 5 columns]"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/data-alcohol.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"_uuid": "7d173cd7675a262df02185c117878517cc0bd767"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.boxplot(data=df, orient=\"h\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "8ffd9bea4f44ff1da5cd3690ebec622a3bb29937"
},
"source": [
"# 24.Plotting with data aware grids <a id=\"241\"></a>\n",
"---\n",
"[**Go To TOP**](#00)\n",
"\n",
"\n",
"\n",
"### In this section, you can learn\n",
"\n",
"1. Plotting with FacetGrid()\n",
"2. Plotting with PairGrid()\n",
"\t1. MLB Players Height, Weight, Age and Positions dataset\n",
"3. Plot it with PairGrid()\n",
"4. Plotting with PairPlot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "09b33f3006fc87ea5a3627dd614ef9d01e23e87f"
},
"source": [
"### 1. Plotting with FacetGrid()"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"_uuid": "85228153c49345cdc87a166505302b38e804efad"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/datasetsdifferent-format/data-titanic.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"_uuid": "dd03387f1052a8ed71d0a55ca929ebed8ade473a"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 539.35x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(df, col=\"Sex\", hue='Survived')\n",
"g.map(plt.hist, \"Age\");\n",
"g.add_legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "17a570fc4e7f187e4f75baaa8e01c403937c3581"
},
"source": [
"### 2.Plotting with PairGrid()\n",
"#### MLB Players Height, Weight, Age and Positions dataset"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"_uuid": "e8e6f1e37a916c9bda843de68fa734b2fe972352"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Position</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Catcher</td>\n",
" <td>74</td>\n",
" <td>180.0</td>\n",
" <td>22.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Catcher</td>\n",
" <td>74</td>\n",
" <td>215.0</td>\n",
" <td>34.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Catcher</td>\n",
" <td>72</td>\n",
" <td>210.0</td>\n",
" <td>30.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>First_Baseman</td>\n",
" <td>72</td>\n",
" <td>210.0</td>\n",
" <td>35.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>First_Baseman</td>\n",
" <td>73</td>\n",
" <td>188.0</td>\n",
" <td>35.71</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Position Height Weight Age\n",
"0 Catcher 74 180.0 22.99\n",
"1 Catcher 74 215.0 34.69\n",
"2 Catcher 72 210.0 30.78\n",
"3 First_Baseman 72 210.0 35.43\n",
"4 First_Baseman 73 188.0 35.71"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mlb = pd.read_csv('../input/datasetsdifferent-format/data-mlb-players.csv')\n",
"mlb.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "6e4311aa954b72a2f842847d9db7747ef15be449"
},
"source": [
"### 3.Plot it with PairGrid()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"_uuid": "bdb984348110b4e98ee7d5cd1209f0345e7a9389"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 638.45x360 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.PairGrid(mlb, vars=[\"Height\", \"Weight\"], hue=\"Position\")\n",
"g.map(plt.scatter);\n",
"g.add_legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "e0eb43eabe15ae3a3d8396209892c86338f5001a"
},
"source": [
"### 4.Plot it with PairGrid()"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"_uuid": "7805ab03e6e7961c2512338039270392d077dd6c"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": 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4fAJ4qXAtARDM/o0tUmw0+3fV/aL395/KGUG62lvkCAC4q6oBZ4Fx0lRw1TTgbdoCgKumHldtM5mIG0u9g/5vvkZVWwMN97bhqvIgWRskE7MgLISwUBQNFA2Xy41tpRDCBhRsoSIEqIqNS7H44hc+ixCQTqWwbJv+c4O88WY3J06c4PTp0+zZs4f9+/dTXR1YlkpQKpXk8R/8lH17O2nbcxgFQXWoBrfHj+byLV1GllQqRSaTyXU+3W43Xu/VcSJWG/F4nEzGJmX6qG/9AI1b7iedTiEEoHqK2s/lqaap9S5qGnahqBoIgbBNIuOnGRv8OaHqKj738Gfo6ztf1h7mCPk8zP7gzwh03IawLTLT4yiqhuLxoV2nAQmJRCJZLVxzZ0DX9RZgy7xkj67rd7HYfL6jCPRLgAd4eYF85b7vDuCfcByIjxWceyfw58Djuq7/sWEY/77SMq8F6bTJ9PRCUUnXhrnRqfHx2VzaYL9BbpleMEisIGQIwO2ryr23M6C27GN6OsnsbIR0KknAdwe2ezMoKiIDUWOCxGSM4AfaUa7BLq/lruF6caOM7q2E7ZQjEBBgzjLS/yzTE8WjwY2tdzE91s2lgReL0i4Xpe2iYfMdJKYM6n2TPPiJ93HaGOT4q69z7Ngxjh07xqOP/NqyVIIECrFYjKMvH+foy8dRVZXPf/4LzMYyOGrBi+MoHCU4duxY0Uj0tm3bOHToEG63n9ra5dvgjWA7V9tuPB5IpRK88sor9Pf35+5VW1sbt99+O6ZlIoSSa7+m1sPUrb8Zy0pxsfdJIuNvF9nMtn2/im1nGHzz69Q3dJa1B3DUgz7/2YdYd+TXmH7tSYb+6rfAtkDVCHTcRvjOB8l4a0maK78u5Gr8Xt0ItiORSFY312Nm4NeB/31eWi3wQgXnzvUmv1LJF+m6HgZ+AFQBd8zbXOyfdV3vBt4C/q2u69/JKgfNjfpXsTBzswfXvwd6jYmOXcy9d4dqSo77C5wBLS1Qm9rp6zlF9+vH2R64mRbfrhKXzppIkO69jLdj3TWrt2RlCQQEsctnGDr9GEVRfFmFn8jYKTbv+ChNrYe5NPDiAmmniIx1s3nHRwEYePNbbGv/OHAzx199HYBoLEm4oZPI2NIL1GsaOunrHyxKyyvNLB1pOKdwVLiwGRwd+56eHnp6erj//geoqmrH41nbM18eDwwOnufpp58uSrdtm3A4zPDwMNHZCDd1thBu2IXXv45QvU400l+6mLjAZlp3PUhTy11cGniR6bFTJfYAsH3bNtTENCP/8O+Ly7Et4mdeIX7mGOs+/Nv4WvbfEA6BRCKR3Ohcj1/KCE7M/dwLwJ6XNv91HqfT/g/AfYZh/LDC7/oQ0AAcK7fLsGEYvUAXjhN0Tzb5fPZv6yLlbp6Xd1WQNFPYU5Hc50CotiRPSORnCnxJwXTcJH3G5nDt/+A4AnPYoyjWW7mP6TMTclHfKsXl0sCcLXUEihAMnfkhoXodl6e6wrQgIz0/YKfeQlVV1glV3DRvvY+ld5dVCK+/kzfePFWUWqnSTDmFo3I89dSTTE9PL13gKkbTVFKpRIkjAI7605YtW+jq6mKn3sL5k/9AY4sTFgQsqSo08PZjhBp2ZO1DlNoDcOjgrVz+/p8vWs7kj76OOzWFeg1mJyUSiWS1cc1nBgzD+CoFm3Xpum4D44ZhXLXFuAW0ZP8u9rSe6/3OrTj7RfZvp67r/gUUhW6dl3dVcDE2QjCeD7/QgtVY8/L4zancdEiIEOIXbjb7dhTliYoLhDLfB1SEazcIFWsqiT2dQgtXHqcteXfg9ZhcPPsslSjEjA+9Qv3G2xg992zFaZGRo+y9qROf31H9OT80wfr2jy8iL6qwvv3jnDYGicfjudQHHngAt9uPaS7tlGqa4OjRY0vmAzh27Bjvf//7K8q7GnG5bF566ZWyx/bs2cMbb7yRU4Ey0zNYZgIrkyQyfoqKbGbw5QL7EDl7ePmV4zzwvvvQRs5gxaaWLGf66ONU3fcbJGw5OyCRSCSLsRK/kn+CE7t/LZiLeTlQKCs6RzbtQPbjOQDDMIaA13HWJny6zDmHcRYhjwLln4DvUi7MXqQ6XtD9D5YuvPOmJ7KPb4WGbR/EbefDI2bMCd6afZ63k0dRMFFIo7gmcsfN4VUXVSXBWQQ8PdFdUd7IeDehde3LSpse72bvTbuYmZnB6/Xy9E+fo28oReveR6hp3JPfPVhRCTfexLabv8y5C2mOv/o6qqrSsX07n3v4YTZsaKlYf15RRFmFo3L09vaSySy9/mD1IujvL5l4BaC1tZX+/n7atrYwPfE2AJrbjydQ66wRqID59jE93s0OfRu/8uDH2LK+gchPvlFRObGzXbgrWCcikUgka53rriZkGMafXMPifwLEcWYI/h9d1/+1YRgpyCkTfQUn5GcKeKrgvP8TZ8OxP9N1/eVsOBG6rjcCf5XN858Mw6hQ1uTdwdDsRToLnAE74C86rqbjuNNRMh6F6lA77mAjAJYweSv6AmNpR41VMzVsP6gKKOZZBE6+zPAM3s6G63Q1kuuFENayFH6WnSZs4vEYJ06cYM+ePdi2zfFXX6f7baNIJUigMHRhhMn+MVq2bKNlyzZCoRCpXzxJyK0ws4yNqGzbxrYru6Z83rUZgrLYvRJCYNs2CqJgp+HsbMCV2oyw8fu8BFQ3CpazWLiiii4jr0Qikaxhbmhp0eViGMaYrutfBv4G+C3gE7quz608OwCsB1LA5w3DmC447zFd178BPAqc1HX9GRzpkfuAEM6i5K9fvyu5PlycvsCtSedBLRQF/MXSiZ6oIykqPOBfvyeXfj5xikBjFZ5LftKZBJZtEcNNNRlIvg3e9wBgXoohTBvFJafpVxOOfGjlCj+Lpbk81TRsOkj1unbc3hp23/W/gFBAUfjtL38eIezsX0gkk7g0DSGEI11pO7arqQKv20ZRwOu2MV0KqMuzOVVVUVW1IodgLu/ytj9ZPWiaxnvuPMTWLZucTr+qkUykCFT5Cfi9fPk3fw1FUVnf9j5qGnbh8gQBwa7b/w2K5sI2U9hWmulJg4kLXZjpeTOI821GUVEUlZTmwYVN6L5fR9u4A1s4AxCZ/tdJvPEUVjRSfJ6qOS/pD0gkEsmirJgzoOv6A8CDwG4cdaGSsJ4ChGEY2yop1zCM/6rr+kng94C7gPdlDw3jOAn/t2EYJfPVhmF8Wdf1n+M4EYdxNjs7A/wt8I3VNitg2RYz43klISUYAKV4pNMTc5wB1evBHdqYSx9KnWF/wz2MxOKkhQZAnxJlb6YHhRiK10akVLAF1mQcV1MQyerBFio19Z1Mjy+t8BNu6GRmsqdsWlPrYUL1OuNDLzNy7rmc/GRNYydjAy8RKdi8qmXnJwmENjLa/9Oi9LyM6cm8ZGn9LvxKCq/HTSpd2ei9EArbtm2jp6dnybzbt2/H7XaTSKQrKns14fUIVBFlnbePoZM/LGiHXdQE72DygrOsqrZ5L8G6bSiKwszls3h9tYwPvVwiJ7p1zy8xM2E4bZcl3LgbK5MXLgg3dDIb6cfta+D8SISazfvp7e3lxIkTjuxr21YOfvyPoOcYsa7v586r6jhIZtHHikQikUhgBZyBbNz+P+LsAQCVzbUvawjOMIzXgc8ts2oYhvEPOApGq55L8XH80XxnRq2uZs7bSSVinD/zGmZsimbVR0N4k7NBEDBtTlBTV8/wbJQZLd/JP4cfTaTZYw6guqNYqRDgyIxKZ2B1kUq7WL/tCNNLLghVaNpymL43/mtRWsPm24lOncftD9Lz2rdzZTS1HsbjD3P21W8UldvUchfCznDm2NeKv28hGdPxU0TGu2nt/AzeYEdFDoFlKRw6dKgiZ+DQoUPZDcjWljPg9QjS0bMMdH+X0nZwZGJbdn4Sb2Ad6WQEy0wwEx3D4w8XtXP+nNK2m7OPdHwym/YzGjbfzrmT38FMR9m041Oc7TtLKBTmlltu4cSJE/T09tHT28f733s3jQc/kXUIFGru/BRRc7kb2EskEsnaYyXiN/4Q+Hj2/b8Av4GzU/B7F3nde/2rubq5EC1ePDxBgK+/BH/5M8GpN14lPjtF2oZB00Oibns+X/oCjY3b6Y1MFpQmqPJmGAk2MqqGwc7POFgTi23sLHk3Yts2qubJ7g+w8L6Bm3d8DFV1l6TNTp0jWLulSGbS5akmVK+XSE8ulF5MecnSge7vohGtSF7StgVut5/7739g0XwPPPAANTWl+3GsdlRVQSNa6ggUIRg8/U+oqgePv5bhnqeW2XYhWnZ9ipnxMwy8/Tihep3WXQ8yM2FkQ4kEF848zq4drXR1dbFly5YiydGnn/8ZtB9CC9ZS/5HfJuOtlfLGEolEUgErESb0WZwnwx8ZhvGfV+D7Jcw5A/nIp564j2hAoVaZQJ2nzBp2Nefej6UmSF3Om41fjbJra5zqgFNWPFGPOXAWBUd+1ByPI1ldeNw2F3t+jNdfR/uBLzI+9EpJ6E7D5tuZmTAYudxH/cbbSMbGcrsSK5qb8aGXKewg1m86WJK2WHop5SVLR889S2Pbx0imlnYITBM2bmzh4Ycfpquri97e3tyuutu3b+fgwYO43f41ueGYx20z2v8MlbQDwGj/c9Rvum1ZbddxyyNMDh/PhQyND73CuvUHikKIQDB18efsvamTN998k927d9PV1ZU7evzNt7nvc/8RC6/ccEwikUgqZCWcgS04m479xQp8tyTL8OwIG2L5mYEZlzPC1qiO5tI82LgVjSrNGQkVQnAhsw9Ppg9FAbfLYl/bDF5v/qEb8NtMb7Gp6TNRLBd2LIOdNFF9q2qt+prGpdm5zv/kyOvUbzpIR8udueMzkz3ZsI5ZUFQ6Oz7I1OhbRCPnqWnYidtXQ/fR/6uozNC6dkbPPVfyXQullyMy3k1Hy50FzgBExk+xof3DVPpTZ5qgaQHuuuse7r77cM4ZEELFsqhoz4LVSK7NK8nrDTpt0fqeZbVdY+tdRR3/yHg3jQV2Ncf0eDfb9hzmWNcJ9u/fX+QM9Pb1cffhe0hXuFZEIpFIJCvjDEQA7wKbe0muA0IIhqLD7IzmnYFpd5Bav02zGGdu8cBNviT1rnWIrLrHrGWDbza7zljQsT5a5AjMofpU0uvP4r3g7DpqRZKozXLdwLsFVVXQNIGiiILOsIJlKdi2AIqlRRVwFp8L4fwtXIguHAnOmoadJGPjuL01gMLuO/8QFAVb2KiKViRXOqcwFKrfgctdtSxJSrcvzLb9X2C458ckoyPZc4vPV1UFr6biVlSELVBUhYywSVk2ti2yr7kr0+YKv4I7uYqoQE52rt1A4HJX4XIF6LjlkZxdzEz2lFcPAqftvCF23v4/Imwb20qBouDx1rLrjt9HUTVHhcjOYFspvIEwjz7yqygo/Najv04mkyadNonHk3jdNh7NAkXDtFQypoZHVZz2FgLFbSEUEyEsFEXFFhoZU8U0ydq3RCKRrC1Wwhl4Efi0ruubsxt+Sa4zkdQ0sUycmtm8MzDlrmZ/cwr38EwurVYzQW3M58lYuP2OfN+66hQNYcefE0Jw+mQUV90GOjY5D/pkuB/PxXYU2409lQTpDLwrcLkgk4lz9Ogx+vr6cs7Atm3bOHToEFV+H6qigKLS1HJXiRrQnLJMTiVm8CVsK42wTaYuvcG5k/+9KJyoue1eorMXqQptLiozlbiMbaWxliljmklOMzncxZbdnyGdmKL/rb8HFFwuZ9Tf59JwpW2mfj5ArO8yc/qUVdvqqL19M6ZbI2lKLcoSlmiHQmUo28qwdc8vcbH/aSJj3eXtoij0h1zbjQ2+RHPbfZiZOLHIgKMsNfhScTmNnTRveS+KojHS/9MShaJ08Pac7YUbO1m/5QjpIZv4VIJAp59LPc8VhbXV1O+ifvNh3O5qLNtT8UZ1EolEslpYCWfg/wA+AvwZ8Msr8P1rnuHoCKolcmsGBDDrDtLoHmcqm8enCNwKxGlhbveBMSuOGkgDgi2N0Vx5ly4mmZ5MEcQmVu+iymeiqBbpukG8E9uwp+Qk0LsBlwuGhwd56qkni9Jt26anp4facDX61ioyqSladn4SYWcWUIlxlGU27/go7QceIT49xMDbj5XJ56jJtOz8JECuzJkJI6eNIntWAAAgAElEQVRA09x2H+GGXUTGTi1Zf0ey9Gzu+1t3fYr2A4/wxpvdVIfqaN28BXtolgtPzlMMsgWxnkliPZM0PtCOb2O1dAjmYVpORzsyVionO6cC1fPat2lqvXuR9s7bRV49yGF+23Xc8gi+qsYSZSmETeTSSSKXHBUir39dweZm8xSKWpywo8ilU7TvfwS1JoJxotRep8dPMT3ezfr2j6N4N+Pzh6VDIJFI1hTXfYWVYRincNSEHtB1/Se6rt+j63rVUudJrh4XoiOEYhZq9pk46wqwvk5BZPId/BrVeRom2ZBLi1vORE5dME11wDluWXDhnLNIOBYzuTARyOVP1zo7FJtTec1wyY2JqipkMokSR2COqqoqduotDJ1+jMhYN4HQxiVVYkb6n8fl8pZ2DOflGzz9T1iZOFU1mxnpf75IgWbiQhcNm+9gaQViR5JyYvh4rtyBtx/H5fIydGGEp556kmQqxuRL5xctZezJHlwZuyIForVEOqPSvPUI89uhUO3J5QkSatixZHuXKj8Vt53LE0QI+wrKKX/M5QmCajNw+nuLljfS8wPcWhrLSsn2l0gka4prOjOg6/pSw2vvz77QdX2xfMIwDLkC9SoxHL1ITcF6gYirmk0hCzOVlwGtVm1SthePK9+5t9TLgI/WglmBi8MBMpkJACbiNtVTaawNoKkg/LNY3hmYUnKx2ZIbE00THD16bMHj+/Z2Ern4c0AQbuxktP85loqjr990GyMVKtCMDR6lpqGzRIHGTM8yM2GwecdHF3E+HMnSvARlvtyR/mc5dPAOhoaGOHasi1v3dDJ77GKZMvJEjg1RfXcrCVvODsxh2wKLIK2dnymSFy1Ue6rfdJDxweUqPz3nyIkWtN07U5AqPYaiVFxeZOQolu9mmpq3UMFm1BKJRLIquNYzA8pVekmNuKvIcHSkyBmYclfTUGVhpvPhPFWqzUC6A7/m3PqUMIkFPQR9GWqqMoCza2v/ufyaAoHF5vQ4l2e9uTQzdAksgT27tjZoerehKIK+vr4Fj7dtbWF6wtm4O7SuvSJlGSdfyWbfZYmMd+P115Y959LAi6QTEdoPfJFw4x4ndh2cGPGmm9hx6HdJJ6ZK49Cz5TbU1wLQ29eLp3X+KHIp0d7LuBXpuM4nlVYI1rYXtUNhe1VqF+C0S7hxN+0HvkhVTUtR2y3XbkLr2hc9tpzypse7qQ1XoyjSE5BIJGuHaz3avvUaly9ZJikzzVh8Ar1g8fCMt5rtXpvJdH5PAL9q05/uYFv286g5jvCorK/Lj7xaVh0ZswpPtm9WRYKgnWJkxkNDTcr5vuoRvOPt2JEEWk3eSZDcWNi2jZ0dCu3o6ODee+7A5VIRwkZRnF1cA7c+ihA2mubLxWn7guvZ1P5BvFUNjuKMopGMjTPc82NAKVpwOqc2U72uvazCjNtbDQj0W7+MmUngcvkclaFsviHjCcKNnUUypi53FZaZLOsIAM73Z+tg2zaiElUgW1QsYLTWELbJuZPfoX7TQXZseS8ubzWdd/5Btu0rXOjtFISiqpw7+R3abvrsvGOiYuWi6nXtuD1BOm59tFStKGu7msu3LFWj6mAARbVQFY10RpUKQxKJZNVzTZ0BwzAGrmX5kuUzNH0RgaBuOr9Czq6pQVUomhlQhA+hrMt9nuQymmrTFM7H/yeiIdaFIZkVIPIqcUbT1bimE7DZSRNVUwg1gxVJ4m69ttcmuXJUVUVVVX7j87+CpqQZ6fsRXv+6nEJMoWLLjtt+BxSVtpt+BY+/ltH+Z+cpuuxi6+6H0Nz+XAexUG1mIeWhTGqW6fG3CdXrTA53lZS5Wf8IMxMGZ09806m0otJx4BEWDf9QVBRVy12j5nUvnDd3M5Tc5INkHoqGmYkRqN4AqsIF459z7dR55x8sS/lJVT2sW39zmWNKxcpFC6pYDbxIU+thFM21bFUjMz3D2df+2lG72noEiyApuW+BRCJZxcg4/DXG+cgFANZN52cGlLoQtpVB2I6DoAKXMpuoduV7RJe1BA01SVya0/GybB+xqEp9XZrBaTeqkkFVLAbTdexJXWQmvo5QwERRwKwew47UX7+LlCwbIRS+9BufIzHdx8Dbj9HUendOIaZEzWW8m/YDXyIdn+DMm39XejynCPMlwg2deP11C5eVzduy8xOoqnvJfIVKNOGGTlKJKVLx8QWvK9zQSTo5zW233sxUZJZYMkbwlmaiJ0YXPCe4vY6MkKPB5bCFQvuBR0jHx4vavm3v58iko8tSfpq8eAJvYB2mWSwwMDPZU5Fy0WI20rb3c5jpKGeOfW3RfOVVjXqKlIlaOz+DN9ghHQKJRLJqkeNfa4zzkSE8aZtgwhl1M1Hx1Pgwi0KEBBcyrVRrzohqkjRxzSqaFZhNhUCA22WjqP5cesSqpdmeYmrWk0tLVl/EiqSu9aVJ3gGKoqGIJANvP+aowhQo+swnOnUel8vHwNuPlz3uIOh/6zus33Zk0bLm8g6e/j6oCiP9zy+aL68SE6Jh8+0EQusXiVN3VGrOvfV37NRbuPnmm/nhvzyBsjWAVrXwDEH40GaSpowTmo+qKmRS6axCVL7tfcH1ePy19L/598tWfho8/X00l7dIESgy1k1z270l5RQqFy2lYuXx1zo29Q5UjebyDXR/F42oVBiSSCSrlus+M6Dr+t8u85QUzq7Fp4FnDcMYvvq1WjsMRIaLZgUue2qo8QusghAhv2IxbDazK7t4eIIZPC6LcJWzCFgIwWhMJZzN73Z7MOfWBwsVFzb2dAaanCQzOIE1nERYNoom/c8bEa/HZNhwlH+WUnNp2nK4IpUgMz2DZaYYH3plybwgGO1/jvqNt5ZRhinONz70Cm03/TJC2MSmLxBu6GQ0On+kv1hhaHrkKFrN7cTjcV5947UFVYUaH2jHdKsIuc9ACZpqoWGVtP3G9g8y2v8sZnqmIuWn+epB44MvFykChRs7ic9cLCmnUpWh+k23Mdr/7JL55qsalVekcvKNnnuWxraPkUxJh0Aikaw+VqJn9mvZ168WvH5t3qvw2CPAHwD/BTiv6/rf67ped11rvEqwhc1gZLhovcCEp4aQ1y5aL4AIYKMR0JwH3wQzNIaTzAmsxMdTRDKZXHa/Pz/K6ibFrFVNTXQay3JO0DxpbFcce0bODtyoKFjzVGEWVl/xVTVUrM4CYlkKMwspw8zP5/HVMjNhMHj6ccKNu4sVhhr30H7gi0UKQ5HxbgJ+Z7aqt78X3/YwzI30qgrBjnVs+tw+VLnh2IIoWHh83pK2L7SHRZWfsu0yXz0o1+7ZPOs2HGDw9OMl5VSqCrRcNaI5VaOFFKmcfKdwadIuJBLJ6mQl1gz8CeAFfhMIA/3Az4G5Ybr1wF1AGzAFfBMIAAeA9wC/BOzQdf1OwzBk73IZjM6OkTCTNEzlnYFoVZh1KkUzAwnb8bUCBTMDO2vyx1N9MRJteWfA486HBKlKjL5UG9tdBpfiYeqqnSmDdNU4diSJVpsPKZLcOAhhFezkuoSaS2HepQtelsJMpfnSqelcx01RVDoOfAkAj7+WiQvHOXfyO8UjvMJGyY4U27YNPo2WL9w8t6aUjBDELRtbOgILImwLYSul7TTPHi4NvMjkyOvUbzpYpPw0M9mzgHqQjdsbovPO32dy+ASZ1CwIu6QctydYmY0s0+YURS21lzL5pMSURCJZrayEM/CfgOcBDXjIMIzvlcuk6/qngL/FcQCOGIaR0XX9duAJYD/OjMHXrk+VVwc9k+cBaLyc78gnw45iUOGagWmrGZ+qoCkKcVIIbyy347CwBPTGSGzLOxQuty/3XlNiDGY62Sfe5FxUoS4bjpsIjmJFUlSg5SK5Dqiqgsdt49JsEBaKotHcdoSJC12OlOhiqjAFx8vJhUanzqMoClXhVtzeWrbv/0JZ6dFkdGTe+eD2hdiw/QO588vKQSoqbm+I7fu/gOb2o2ouLMvJs27DLeXDjBQVFJU77zhI29YWvG4TWwGEAoqNZlsE3BqmpUo5yQVQNZcz0D+v7RXVXdZeFHCUgVBwuXzUrd9PTf1OXN4qmtuOEJ06T/OWw45tIFAUjdrmfWguP+vb3kf1uu25c53203ISopGxbmobO8vK1C6lRlRcSRXV5aFt768sLjmatR+JRCJZjayEM/BHwEHgsws5AgCGYTyu67ob+AecMKH/YBjGK7qu/0/A/wd8GukMLIueyXMotqA+UiArWlcLFK8ZiFqNhN35WYGmcP6YfT6Ob9YkKfIOhcud3z9AVWJMm3WkbQ/u2SSsd0IxRNUUVqRYNUSyMng9Ao1ZRvufcUJ4CiQXt+37VVyeqkVVYZKxccINu4qkR4slHjtpbL0LVdGwzTgTZWRCt+z+DNg2lpVa8PzpsW5n9H+eHGQqcZnJ4VdJxi7RsPkOJi6d5NLgS4QbdmFlEiUKMQA1DZ34fB7qfX0MnXyCppa7ysqmSjnJ8rhcYAkwLYraPpW4jG1liuxlMenPhs13MDF0klDDTmobdzPS/0zR/W/Z+UmqQptIxEYZOfdsyblz7b9l92eIzwxz9sS3SiRDzUxieapGwycYPf/8opKj4YbdmJZ2Te6tRCKRrDQrMdTxEJAGFnQECvgezgLiXy5IexywgV1Xv2qrm57L56ibsXBnIyFmXAH81S6EbWNl8h11m0AuRGiSmdwGYgBWTxRVgJXJp7lcBc4ACUBwyWwmGJ3Byg7OubwpMrOXr93FSSrC6xGko2c50/UVR7oxFxrkSC4ax/+SmQmD9W3vYyFVmOGeH7Oh/YM5icfI2Kl55Zzk7KvfIBEd5fLIL8ocP8WZY18jER3FMpMLnu/xh2lqPZw7p+e1b+Pxh1nfdoSJ4eNFaU0tdznldn0tf14OheYt99D/i79mevwUTS13LVr3M11fIR09i9cjZwfAcQRisWnOnz/PGaOHjdm2n5kwEHaGsye+lVMRKpT+LNfuPa99G19VIwpw+thXi/I0tdyFsDNO+qVS25xra6+/jjPHvoawMzS13FWSJ5OMsKH9g1SiatS89R5nJmzed5TYz9b7SKXlzIBEIlmdrMSvWyuQNAxjyeDcbJ4ksKUgLYajLlR1rSq4GklbaQYiw0UhQqPedYS8FlamYPEwXkCjSlMRCGY9k1T5siFCpsAeyIYTpfLOgKqqaJoTAKQoApUEo5n11FszzMTz6wlS9ghCSjauGKqqoBFloPu7LC65+M/YdobWXZ+iXIfKTEexzeSSEo8Dbz82T7qx9LjHX4svuH6BesyXfnTSbDuzSL75nxVaOz9N5NJbmOnZiuUppZykg6oqWFYKy7Lo6upia2sTlplipP/53H2cUxFq3fWpyu7t248hhI3LE8ylVtou+bYNlrGPuTz/jG0mF7RfB0dtamr0LYK1W0psrNh+HsIiiJB7T0gkklXKSjgDs0BI1/WdS2XUdX0XUAPECtLUbJocZl4Gg7PD2MKm8XI+RGjUu45qT7GSkGk7D+iA6qwXqK6J5o9dMsF0HohqKk0h80OFRjIb8JEhEcs/jONVl7CmZajQSuFx24yeW1oSFASXzr+Iyx0sqwqzqf1DjJ5fbD+AfDlz0o0LHR/tf46N2z+wjPOdus1PK87nfN7Y/iHab/ltRHYxKlQuTzknJ+lxr23nVdMEY2NjvPHGG+zb24mdmmD03HPUb7qt6D5eGngRlydYsYzs/HZdTrvkz13IvgSj555f0H7DjXvouPVR3L4wlwZeLGtj40OvsLHjQ+w4+Ht4gu0yZEwikaxqVsIZeAFnuOZvdF0PLZRJ1/VqYG6byecLDm3BWXx84dpVcfVxfmYQKF48fLmqDrdWvHjYFM4IWbVLMMEM9aF8590cy3eMXMkMdsGD2120iDjKjB0mZgfQZvMzCHbgMrZ0BlYMl2YvS+ZTc/s4d/I7+IJNdBz4Eh23/CYdB75EILSByNjVkQuNjHfjCzYs6/xK0iLj3VTXteHxhRg8/U+59OXJTko5SUURhEIh+vv7nYXXgdrcvZ5/HzW3/4plZJcrBzp37kL2tZj9+qoa6X/rvzsLkxcoIzLeTXW4jaQZko6ARCJZ9azEAuJ/B3wEZxGxoev6t4CjwEj2+HocBaEvAs04YUJ/UnD+Q9m/5QWhJWU5O9WHYosiWdF4qI75i4dt4cwM1GgW/a4pWgOO8yAEpCYEc11+f9ImpZj4hRMeVOwMZDcTyjRRNTuDECEUBVz+OGYkige5TcSKUKEkaLB2O5v1j6C5/ei3fRlF0RACbNtEVbPjB1dLLnQpycZyxypJEza2ZZceW67U6RqXk7RtGyEEtp2VZp27f+Xu4zuRkb3Scxc6J5tupp2F8qP9z1Rel2yaEBaaqgKaVJeSSCSrmuvuDBiGcVrX9Y8C38HZo/bfLpBVwdln4JcMwygcMpoA/kP2fEkFWLZFb6S/ePGwFsAT9ALJopkBSzhLMXyaCxEaz200ljGrMAtitQNJm0ShM+DJ7x+gzjkDZiM7rYtMJesI+k0UBVIzQwRouYZXK1mQpSRDgR23/Q6KqjHS/9N5Ki+foiq0keG+Z2lsec+ypBuXPL5YnnLHKknLlTtPZnKZspNrXU5SVVUURUFVVQRK/v6Vu4/LvbdFn6/w3IXapxK7WyyvojIbmeLEC09zy3s/itsfImOubVuQSCSrlxX5dTMM4xlgB/CnQDdOKJCSfYls2p8CumEYT88799uGYfzbeQ6CZBEGZodIWemiEKFLvjpCXufBWzQzQBAVMFWNUCi/XiCdDGB787sEBJI2CTU/y+D2lJkZMBupEilm4/nzYpmhq3dhkmVhWo505kLsuO13SERHF1B5SedUXmYmewg3VCbmFW7oZGayZ9Hjyej4ss6vJC3c6EhBzr/m5dVdykkKoTAzM0NbWxv95wYBhXBjZ9n7uFy7KFQwu1KbWsi+KrG7xcqoaejk3JmT9J06wT9+/Y8Z6T+F27W2Z4kkEsnqZcWGOgzDmDQM448Nw7gJZ4fh9dlXwDCMm7LHJlaqfquJ05edB938xcNBr0AIUTIz0OSeZUqNUBvMx/vHZlzY3rwyUDBhkVTyzkVhmJBKHDCZsuowhQsrkY+5TXvGEJm1HYe9UqQzKs1bj1BOYSVYux1F1Rh4+zEKF3GWU3mZuNCVk5JcHIWGzbczMXx8wePNbfcy3PvjZZxfSVpeCnL+NS+n7lJOEixLobGxiX379nG25xya5nP2CrhwvOQ+TlzocmaNKrQLze3PqfhcmU0tZF9L293iZSg0b7mXvlO/cD4KwbOP/w2ZxMyaV5eSSCSrkxviSWcYRtowjEvZV3rpMyTL4eSEM4lSTknISsedeF3AFj7AzUZPhHjwInPh4amMl1TCxvIVOANxJ0xoDkVRczsRK4qziFigMmnW447lm1QNzGBOFUqZSq4Xti2wCNLa+Rnmd7o26R9mpL9UaaicyouZnmVmwmDzjo+WlJNHobXzQWYmjPI7umaPpxNTJKOjZY9v3vGxeedXkqbQsvOTaK5gNta9+Jorr7uUkwTHZjxuN6qqcuS+w4z0/5SZ8TNs2Pa+kvtopmdRNR8tOz/BYvd2rr3GBn+eU/GptF3ybR0tYwtOntZdi9vd4mUorG//OIMXJtmy60D+NCE48cITaKocyJBIJKuPG8IZkFw7ppIRhmaHSxYPj3rrqPbYZJL5UCBLOOJODe44Wk1+UiaZchYV2768fGh1zCJBod47eMotIjYbCccvk8o4pqZqNump4at1eZJlkkorBMLttO59hJoCyUWXO1BWzWUhlZdLAy+STkQWlG7ceehf4a9qpm79/rLHdxz6Xapr23F7Q6XHm26i49ZHSSem8jsQl0tr3EP7gS86aYMv5T6n4hNAvtOWSit4gh3sOPh7hBtv4tLgS4vU/SYpJzkPYZsMnuqirjZEZNzZFToVnyDctAdF9RTdR83tIxWfXNAucu018CKR8W7CTbtzeS4NvoSiedl56F8tem4qcZkdh34XRXVzafClkjya20+oXl+8jORUiT3VNO6hde8j9A2leOqnz7G5fU/RfTjX/ToKJhKJRLLauKYLiHVdvzv7Nm4Yxol5acvCMIyfXbWKrSHmZgVqZy3cljPKOav5SXn8+FxxZpP50TNThFGwCbgsrOrc1g6k417AQrg0LE1Fs2zcFpgFuxBDdhFxPAKAypwz0ESH+TbnY400hp0Y4eTsAFXo1+yaJYuTTts89v2fsm9vJ/vu/AizM9OIhZSGFlF5uTTwIpMjr1O/6SAdLXeiKBpuXxhhZztMLkDAJv1jbNI/ghCWs9srCqCRSV0GIdiw/X7Wb3sflpnE4wsDCsn4OPWbD1G/6aBzjqphWxnqNx+ibv3N2HYGMxNHVd3UNOykpmEnM5M9nDv5Hcz0LOs23V5U11RaQVVDNLZ9jA3tH3auSdXY0P5xNrR/1FFaUlRMSyOZUaV6TAHCtvjFz/6FHfv352xhru0bW95DaF0HGzs+lGvj+XYxx1z7AKxvO0L1unY0l4/OO38fYZlYZhKXJ8jA248TrN1CZ8cHyaRm0Vw+FBQsM5lr5wvGv9C85TCdd/4+QFbxymb2ch+2nSFU1cQm/cNs7PgQytxCchxFrHUbb0PV3AjbpqHlTuo330k8kWB2NkHa8rNl63a2trXj93oIVNcQn53Onmsh7PL/C6qq4FVN3IoJtgWqRkashFifRCKRLJ9r/Wv1Ak58gQHsmpe2HAQrI4P6ruetXIhQweJh7zpCXoGigFk0M1BDg2uMaEjB7XKaKJXRSMxmR1kVhYzfjRZ1nAA1kYT8ZEDZRcQTZgMeYRJPOspFALH0EOuu+pVKKkVVVRKJBKm0TSqj8vff+T5f/s1fK6/msoTKy5x0o7AyhOp1Lpz9obMHQVaFKNzQSWPrXUyPdedH9Mum7crGjDs7HPe+/v8WfWfHrY9y9sS3KlabURQVlwvMgoFc2xYkUwqlPyVa9jWHdAQKUVQNVdUcqdYCWzDTs1iZOMI2yaRnUVU3Lk/AcaoWkPRsaj1MqF5nfOhlRs49V2AnTvvbdoboVC/RSD+huu2cPfHNkvo0tR5mfdu9jA+9XKR4FW7oZH3bfaBoXOz9Scmx5q3vRVHdjPQ97eyHUHCsaet9RKNRnv7pD7FtG1VV2bZtG0d++XcZfPsEb7z0E1RVQ1FVmGeCPpeNOzVF5OhjxM8ezzkDgY7bcN/1adw1jdesbSQSieRqcK3DhAazr4tl0pbzkhI0V0DCTHJ2qg+Yv16gjpDP6fBkCmYGLFFDs/si6XB+xD8ery7qGxWuG9AS82YG3KXyomnhJWpXQyp/nuVaWD1Gch1QFI4cOUIoFCKZTNLW1oYtKKvmUonKS1PrYTz+MD2vfZvIpZP5DruwiYyd5Oyr38DjD9PUeniRtFP0vPZtZi/3orn970ippqahk0tjlxkeHsQlhxDeMQKNrbv2E4/Fi5SZ5jr2M5NnOPvqNwBHkGAhxaoiOylQqyps/1hkgKbWwwuqAS1exklOH/sq0alevP51JcfOdP0Fsch5vP66kmNG11eprYpxy4F9gLO/Qk9PD997/PtUNbWx764PsLXzZsQ8R9LnsrEHf8HFv/k3xM+84jgCALZF/MwrDH/7XxMzuvBJJSKJRHIDc02dAcMwthiGsdUwjCNl0pb1upb1XK2cvnwWSzgPp43T+fjnUZ8zM2Cb6Zy8nxAKlqhmvesirlBBiFCyuqhM4c2vG/Akitd6F+414MwMOA/Ay+Y6/OkMluXUQXOnycSmrsIVSq4EIVw0NTXR1dWFZVkcOnSIU929rG8rVRpaSuWlnNpQmW9k6MwPCdXrOfWYhdIGur+HbSZp2nJP0XcuVwXo+Rde4qmnniSTSUgFmHeIZWvcdt/H0fxhwhsctSCXp5qarHM2dOaH+ILNKKpGJjVDw+bbmd9OldrJ4Ol/IlSvs37b+0rUgK7c1uaV37Cj7LGRnh+wU2+hqqqq6MhzL7xIy65buO3ejxVJzaqqgjs1xeSPvr5ofcaf+Dru1JS0Q4lEcsMiFxCvYubWCyAEdZF8mNCYp5Zqr00qG98PYIla3JiEg9N4vM4sgmkppOLzTKRgEbE/bmIVzJlrmgvN5cwAqIqNihOCNGnVU2tPM5PI7zeQHO+/OhcpWTaaYnLs2DH27NnDq6++yvT0NJ07t4Gi0rrrQQo7ckupvJRTGyqPYHzolZx6zGJpI/3P4vKGSpRqKlGbaen8NGMTUSYnJwHo6upCW9tbBbxjbFvgDlTz+i/e5O0zA2za8SnqNx0knbica/uN7R8kMXORsYGflW2n5dqJqmhs3H7/Oyqj2K4Kjg2+vOCxyMhR9t5UOrPx5slu3IFQkbqUVzWJHC2W4l2oPtNHH8erSSUiiURyYyKdgVWKLWy6J88AzgZhrqTjDKQUFzOuKqq9gnTB6HxG1NPkHiVdG8qlTUW9iHmLhG1/fl1AKGoVyYsCeApmB1yqs/DusrmO2vQEM7G8MxCfOf8Or1BypdhC0NfXR2trK/39/TzxxBNomsLpY1/BH2wuUXO5NPgSiupx0ptuKlZoaegsqzZUjsh4N6F17RWlKZBX/Ml+55yCUcetjy6oFFNd2873f/CjXFm9vb0oigzReKcIAX19fRx/9XXOno+zbsMteAJ1ubb3VTXkPpdTmlpIlaockfFuUDWCddvRb/utKy5jvl1Vcmx6vJttbaU7pPf29pbIzLoV01kjUAGxs12456mvSSQSyY3CikbU6rp+M/A+YDPgNwzjCwXHPEAzIAzDkGsGlsn5mUFiGWczsY0FnfBJTxgUhaDbIj6Tj93P2I2s954jFQ7mxuJis0FUtfgBaAXyzkB41mJazRC08usBPJ4AibjjBGjKDACXrXW4EjOkUkHACUFKpi9ctWuVLA/btrFtO6vD73SUhe2oCZ05/hcEa7ezSf8wm/QPI4RNJjnNzGQPl0d+QdOWw2zq+BDphONIKossLi6hrGotRpsAACAASURBVFpR+bRCVZqOA1+iMbu4GCA6dZ5AaBMbOz5IJunY2pxSTfuBR8tea/ECYclyyd9HOP7q6+zcsQ2vq0BpSlhFylPzFYXcnuCy7MS20kxPnKE6vJVN+kdytviObK3CY0qZkf6ydmRb+TUCS7GcvBKJRHKdWRFnQNf1BuC/Ae/PJik4c61fKMimAseARl3XbzEM443rW8t3NycnTufet6eqAWdjpwlPDQoCtzmFlXE2/xLChSkaaPK9jB2sy51nx2pRtZmics1AfuS/dtZilDSQj7H1eAO596ridNTSwkvMDqKZVQjhCNSgTmNbKVQtH3YkubZomopbsxCKxkMPPUQwGKS6upqb9uxCUTVQVFzuKmqbdqO5/MyFaLh9YarCW4hGBohPD+APNnH2tb8GYdNx66OLqg0VoZSZiFwgzZEgBTMTQ9FczFzqZeJCV35zKEUlGG4tVptR1JLyVFXFpSq4XRYp2yUlQ68QVVVRVTXvPKIUK00pWonyVKGi0HLtxDKTCCuNwEbYFmYmittbg8tbg5marqiM+bg81TRsOkj1unbcniAdtz7KzGRPiV2pmvNYrKqqYs+ePbS2tgLO/4+iOLsy27YA1ZG8raiTP5dX+gMSieQG5Lo7A7quB4BngD3ACPAT4CEgUJjPMIykruvfBP4d8GlAOgPL4FSBM9AczT8YJzxhqj2C2MS5XFrK3oRfS+GvzRDPTgvMxN14hR+LYmdAeFxk3CrujI3HFGTSCdBqc8c9nlJnAJxQoZALYkkXQb+JokAqdgF/aNtVu2bJ/8/ee8e3cZz5/+/ZRSNBgGBv6pS46sWqlmzLJXYcl8S2HCdxip1zkosTx8nlcve9JN+0X365u9zlSu5yySW5y8Wpl+YUJ+5FclOxJEtWXRWKohrFXtCB3f3+sSAAkgAJqlLyvF8vvoDdmZ0dAA+BeWae5zP58TgtIrEgL7+0icPNzZimyV13vZ177rqJzmMbiAQrmTLnLnxljRhGlBMHHhsizThlzl1M1m7jVPNzdJ7YQqBqLr3tu9MqP73tu8fsQy6FmHznLNOgZupaYpFuuk5sJRo6zfQF76G/U+f00Q15rptPeFhS+8wZM4jseBqz8yiBNXeTcJcRTcroyPFiWYLGxpkcPHiAFcuvwON2gpm0w8TadxENdWDEg3ltYbx2ojo8RIJtQ+VHq+cxY+F7M5K0Y7SRbR+jSZpm21Vp1Txc7mLuvfde4vE4O3bsYMuWLUPkRletWoXTWUTCclDctMJWERoDb9NKEjjHrCeRSCQXg4vxq/gQtiPwGjBP1/UPAcE8dR9NPZ7RRmVvVkKJMCdD9kqAIhRKusPpsk5XgGmOQ8QG7B2GLQtiZiM1rjaSZZmBfNeAG4+SI7RCCMIlmbAgJRgaUmzvNZCaURZhSMXJdhmVBOihL5y5NtZ39Kxep6QwPE6Lo60t/PgnP+HgoUOYpsmK5VfgFp0c3v5t+jp2Ewm24S2dQrDnMPs3/dsQ2caaKVdjmQn2bfomve27hij7jEflp2rylcMUYvKf01/7Nq6iAHUz3kLniS1p6clBOdJc15XWreZUWyfLli1Ln12xeB6R158kvH8jJ//7M5itr0uZxzPAMASrVq1kxfIraJzspr/jDeLR3rRy0ImDj1PkbxihAjXI+NSgrsstHXp6mCTtKG1k20chkqaDbQbq1vCTn/6Czs5OWlpaOJT6f4GM3OiPf/xjTpxoJSkcBNYMTbjP15/SNeuIJmWomkQimZhcDGfgHuyQoId1Xe8do+5e7NGk3K52HBzJGmTXFlchTnemj9XifuqSmSS8mDkNwwpQ72glUZrlDPS7cSm5B01xbya0xxGMDCkTQsHhzOQVOAbzBpIVlMa76A9lnIFwX8s4X5lkvKiqQiQW4alnnkmf83q9zNGmcOrAbwHLlmwsa8SyDI7u/Q3Z6ii55ByzlX2S8WBBKj+TZ7+D/k49E45RwLlj+/+AaWYnXdqykWW1Cwn2tAy5rm7WHezTW3n66aeZNm0aXq+Xt153DRzchBHqTV/f9Ucp83imuBwqixbMpOPoc/jKptOy6+cM9Bxh6ry7ScZDCMWB6izKaQuFqkFNnfdOetp2Zn22wxlNOtRuI9uGxiNHGqhZyKEjbYTD4SF2lIunnnqSeDxM0lNOxW0Pjfqaqt7+CRLushEJyBKJRDJRuBjOQBP2AH9MGQZd102gHwic705dTmQ7A1NFOYTtAXufx8k014F0WcKsJGwsQgiLusBpLIc9cxVLKFiREpJiaMjFIElvJm+gqD86otydlTegCnsg1m1U4Ah1YUUzakWJ+Ck7KVBy3nCqBps2bRpybvGiefSefJnBwVHlpJVYlklb83MMHzDlk3PMVoyJRbpHqMcAaZWf2Ss/QTzSk9ltuGYhTcsfHHoupQaUPgeAxemWDSOkR9uOrMeyDBAKpdULmLrozzl8LMaW17YDsHPnTt617k6qunVCm3877B2RMo9ngqoYdBw/QMfRF6ictCLLJuyNxmYt/RDh3lZOHnwyry3EIt2UBGYMUQgaLAtUL0Bb8XEsyxwzBGhQOrRh1q0j2piz6lOUlDUSi3SDUMYlR9rWsh7LzHwf7dy5k/nz5+e9YvPmzSRRUKYsof6Bb+CdvdrOCwBQVLyzV9Pw4X/C27RChqZJJJIJzcVIIFaBhK7rY06TaJomgIwEjaQgmrOcgUlZSkInq4pRhP22q64A3cHVgANvcQzKMjP2XQNuSvGSYOis/yCGLzNbVtY5so7b7SUUtHXeFWGrzsQtN+GIRVGJj2hcweMyEcIgETmNq7juzF+sZAwsDjcP3dNhxvQpHNuVkd/0V8zC4S7JKdvor5hF25Hnc7acrRjjL5+JEA4mabdT13gjRjIKlkl/10GE6qK0ag6lVXMAgauonO6217POZdSAhs8I93bsoWnKGtqOPDfk3NxZt+L0zeFwcyvP/vYZQqHMV8Thw4e5+op5ORwBm9CBzQRuuI8IMnm9UAQGgcoqWvf8jqapV6Vtwl8xiwNbv4u27EFcxWW2LKhlDlESGqS/6yAHt/8XYDuZ84apQQG07nuUQujt2ENd4400Lf3IkPaF4qC/6wD1jTfR0HQrQijsfvnvC2qzr2MPjQvW8upGe57q8OHDLFmyhM2bN+esf+jQIa65Zi3RhELcWYn3+gcI3HCfnVCsqCRw4g6UpmrnW+mQSCSSi8/FcAaOAbM0TavTdf3UGHVXA25g7KwzCQCGadDS35o+ruizZ0ANBaIlGf8rJuZDKqGt1NNHvDQzwO/qd1ONlzh5orhKS7GwF8bLe+P0J5PgyJiSy5NpSxWZvQy6jUr8Lov+sAuPy15RiIWOSWfgPJItCen1elm8aD4+X4k9iBKCYE8LDmcJkCUTmY2V53yKbMUYgKZlHx2q8MPggPE/02ouFQ3LKSmdAkKMVHMZcf/c0qPBgQF+/NPf5H3N1mhx3FLmcdxYpmFLblrmUJtIPbdSO53nUhLKRVvzs/jLZw6xFX/FrHFJhxrJKAPdh/BXaCiqi4qG5QgE3tLJdJ/eSefxzcxY9P5xtZktK2qao1+XLTdqmhYRUyUyKD2aejtKCruzRCKRXFQuhjPwDDAL+CjwpXyVNE1Tgb/FXt99/MJ07dLnROgU8VSctd/lw91px+wPeB3psFZnkZ/egar0NRVFpzGL7ZUB04SeoItG4aHTyj1gcjpcdJWqVPYZKBbQ0wNVmfbcbm/aWXCKfiAJOOhOVjBZGaA57KQ6kHIGgq34qnLtBio5FwxKQi5bupg52hR6T77M3lcfy1JTmUdJ2XRcntLc0o/D5CJHZRTp0Gw1l90vfz2vmkuhbY422FcUBcVbhnflnblXB6TM47gRipqSE1VQncVZkqIp+xBisOJZ2Mr4bM3p8hEJnh6hDlQ1eTUuT4BZV3wIxeEeV5vZdqUoo4f2DP5vSSQSyaXOxfgm+wYQA/5G07QPaZo2og+api3Hlh+9GugD/v3CdvHSJTtEqKGkFtrtjcUGvBm/T3VVYJr22+50mgR8mdn73pAL01IoVvPL4AkEHZWZEAvR1TWkXFFUTMVeHRACHIN5A8kKfPHThEKZBOPIwFGZWHdeEbz1xutpnOzm6M7v0tcxXE3FVmiJR3oJVM8bcfWgJGQh5JMJNRLRgtVcCmmztGoeh5tbR9QdpLGxkd179tBeruFdeeeIcinzOH4sVHo7O5gyZx1GIpS2iUH76O86SDzcc1a2Ml5b6zq5ld72XTntCcsimQjRdXJrwW0Ot6vGxkaOHs2veDZz5kwsSzoDEonk0ueCf5Ppun4UeF/q8LtAB1AOoGnadk3TOrA3G1uL7TS8R9f1zlxtSUaSnTxcV1wNbbYzECrKyNpF4xXp5yW+OJ6SzBRpT9BNkeVCqKPPpJ2uzSQJe1vbIJEcUq64MgvkDmE7C11GBUqkC0c8QNKwZ+AsI4SRKGATIckZkTQdTJ1cyamDv2M0NZXmN35K3fQbGK6KctbSoVNWozqLClJzGakQk7vNQN0aduzMHzm4aNEidu3axdMvvAizVqGWBIZcL2Uex49hqlTWz8Drb6D5jZ+PkJbtPL4FV3H5WdjK2dpaNlY696C3fU/BbQ63q0E7ysfKlStJJvMWSyQSySXDRZnW0HX9UeAqYCNQhh28LoDFQEXq+Sbgal3Xn7oYfbxUGSIravkhHMEUEPFkPur+UHX6eZEnhCNr/NUTdFFKMQklNup9eqpLCBbZbTqiCUqfWk/JS1twnLadD7cn06hDsc/FLQ/RYIxSl5f+cGZmNhY8dgavVFIIDjVJW8tIlaDhJOP9hPpPMGXOXWQPnAqVhMwnE2okY7S3vjTm/QcVYjLKQXnanHM3+/RWwuFwzlauv/56Wlpa0uVbduyhaFFmo/PK2x+SMo9ngGlaeIo9nGp+jmS8f4S0bN2M6+hr3wMoBciH3j3sc7UZtLWpc9eNev2UOXflvH4otj0FquYWZL+D0rSDdnPTTTcNsaPh3HzzzTidRdKOJBLJZcHFyBkAQNf114CrNE2bgZ0oXIftnJwGNuq6ro+3TU3TrgVeKLD6VF3Xh8QaaJp2L/AgsBBb9Wg/8D/Ad1IypxOa3lgfXVE75MehOCjvs3MHou5MTK+heEnGbWlQVTUpKupDOO1BfSIpCEYd1OMlKUZ3Bjw4eXlxCTdvtHMShGnh6OnDu3kHoVVX4Pf56EzlH9srAyag0JMoo8yl0Bl2Uu6zpUtjoWN4yxecy7dCksKhmvS27ymobuu+3zBn1aeYtfTDdBzbmFaGOd36ElPmrGP2qodpa34+fX4w56B66tWZXWFT56omX0l/p06Rry6nSlEubOWgq4iGO6iectWINqunXY/i9KO5o/T0DnD48OEhO8MuWrSIlpYWtm7dmm7zUHMzV627DW/XcUrXrJM7EJ8FijDtzx5bSapm6tq0rQD4KzVikS585bNoWv4g7UdfGmErNdPW4nB5cXnKiEW6R5T7KzWMZHSEDQ6W1864np5TOwqQH80oUR3Y+p9D+jqizek3sH3nAbZu20FTUxMrV64kTIziMi/9/f3pjccURWHmzJmsXLkSp7NIrgpIJJLLhovmDAyi63oz0DxmxcJoAx4ZpXwFMAc4jK1qlEbTtP8APgZEgeew90K4AfgWcIOmaXdPdIfgSF/Gt6n31qCe7gYg6s6ERFgio/Nf4kvg82YkGXtDLkAQwEtMdI86mVtkquyc7sFhWqzZFaUobDseAijarRNfu4rjlgePiKKIJKrow7DK6E5WMFMNcTjkYlAxNhrMH/8tOUssY3wKLYkwR3b9PKcsZHvrywSq59E0ZQ1Ot49EbIBg71GCvS1pmVCnJ0A01I6iOO1jV8m47u/0lDK56Q4sU1A1+SoqJ10JQiGZMPns99/gc/cvp8jlpqKigiVLlqQvPXr0KE888cQQiVGwFV8UfyXeGz5EyFAxk3Im90yxzKG2NFJaVsVfMQvDiONyB1LynreAZWKaBn0d+zi84xFmLHxvXhsblJd1uHzp8kFb6+86iGUkC3IEUh3O2dfh91SdRSxcdAULFl3ByWAb/7j9exztO0HA4+fr13+Wa65Zm3YGLEvBMCAp7UgikVxGXHRn4Fyi6/p+4P585ZqmDU5R/iB7nwNN09ZhOwJtwDW6rh9Mna/BXmm4E/gE8M3z0/NzQ3PWjr71JbXwRjsAUVdmJtQwMuE7RUURfN7MPgE9QVtRqJRiTlknR71XkWmbzp7GIoKTq7nudBn+ja8jkgZqMISrq4egqMDDCQCcosN2BowKXInjKOFqLKsHISAZbcc0oiiqZ7RbSs4EoY5b4WU0Wci24CnajjxP09KPjJAQRSg0Lf0Ih1//QfpU0/IHx3V/IRSOfucNMC1QBMUzyii/chIJTI6dHkBVBEIItmzZMmIztVwoigJCJRIXjB2qJBkNoYy0pRHSsssf5MDW79K07M85sPW7uT93IUgmQqNKj6bbHWZr/sqmM1YsymnXQqG8bgnhRCcbTuzm0f2ZnboHYkGSmBBXYVAyVNqQRCK5DDnvzoCmabl3LBoflq7rN5xlP67EXhUwgB8OK/5s6vH/DDoCALqun9Y07UFgPbb60b9P5NWBIfkCxTXQZifDRd2ZH8VE0l4ZUBQL1TGA15vZQbg35MZlOfDgIiaio/7ulZgZ0wkqCUxvMbGGWjxHU4P/k23ES6eBYR87lA4wm+hKVqAMbCUgZhGMOvAV2WvtsdBxivwzz+4NkIwgadihEL3t+RMhB8ml8DKeernO2wozhd/fDJu2IwBgWoQPdRM+1E35jY18+p7FKAIsS9DY2MjBg2P3NaP4IgdxZ4tpqgSq5tqKUHnIVhfKV3e0suEMt6nx2tNY9hyomkcyHsToOsDq0mqYfWPaIVjesBjFdDBhv/AlEonkHHEhgmevxVYGujbH39pRyob/nS1/lnp8Utf19LS3pmmTgKVAHPjV8It0Xd8AnABqgVXnoB/nhYSRoHXgRPq4pqgCTtkrA7HslQHLdgZ8/jjFniiqag+SYgmFcEyllGIMxcCwEqPer9jMJACHFLtuvDaz14CzoxvLkQlJcopOwCRmFRGLxAi4vfSFMrsex0PHx/uSJQUQTyjUTn8Lhaip1M64fhSFlky93EouuZV/ivx1VE9ZU9D962bcQPfLp3OWdj9zmGVTyhEmGIZg1arC/hWl4su5IxJTqZsxui1lqwvlU/E5c9Ug257qZoxUvRr72vx1mt/4Kf5KjZ6WF1jbMJ+Ax49AcNfct2ElZH6JRCK5/LkQ33Q/Sv09kuNvMEs1V1n234/OpgOaphUD70od/vew4sHA4z26rkfIzWvD6k44WgdOYKQ2CSv3lOEJJSEaw2LoyoBh2ZKfXm+IkhEhQoJSvCTU0ZOHwc4ZEKnJ1qhikMTEKC3BUu3ldCUSpdxKErHsZGUhkjhSuxF3Jyspdwn6s5yBcN/hM37tkvyYpoVBCVPn3cNYakDxSA91M64fs95IJZf8akKhvuMkk9GCFGYShgO1In+oWN/mY7hVgWlaOJ1FvPWtN4/yyqXiy7nGMExQfUydezf5PstkPAhAfeONeVV8zkyhKmNPlmmOee2UuesAJd2fsdq3layWEzy+kZtnXMXHVnwAn+KXtiORSN4UnPcwIV3X789XpmnazUC1rusfPM/deCfgA9qBPw4rm556zL+7DAxmuE4fpc64cLkcVFX5xq5YIBu7TqWfTytrwDvQR4jUqkBKSci0igAnLpeJxx3DX5IZ9Pem8wVsZ0BRRp95UxB4TQdB1Z52DTuSBEw3yfJSnB124nJ9pJPdVNGg2m+fQ7STtCroTpZTp/QiBsoBW3IoETlJeZkT1VFY3sC5fO8uNc7EdmKumXnVVAbVgI7t/x01U9fmVoKpnk/t9OsI95/kdOtLdqMFqAmdbn2JpqUfIRTpzXv/uhk3EI7CD374M+67+322qHAOgoe6qbpuBiUBe48Lr3cWNTXVbNq0aYTiy6pVqygtLcXlcuVu7BxwqdngufrOUWli9sqHaWt5IbOJ3LDP3e2tpqxuMeH+kzk/91i0h/L6ZWgrPs7plg0jbCLbfgLVC4bZ058jFBdzVn2KU83P5r3WWzolr82l20slIw8qD7W1vMDNa/4a4fRS5Dx3OUyXmq1IJJI3F5dVAvEoDIYI/UjX9eHxL4O7Y4XIz+D00oT9Rtc7M4JMk0vrMXa2AcOShy27+6WBOAiD4qKslYGQvaNwKcXElN6C7uk1nWlnIKQkbGegIpB2BsoGOukqmUVDypdyKu1EzTl0G5WI+HEqjSYGIq2pvAGLge5mAtWF7RYqGR+Wkcyp4DJEDah6HqrDgxAqk7TbaWi6BTMZxzBiOBxFNO/6GYHqecxb81ckon0jr6+aAwxVhRkkW81lvnYblmUihEIyESGWUHjkxz+z+zlabL9pDZmpdblcVFVVcdNNN3HdddelnQGn04nb7c7fjuSsMPujHPj7f2bu332B6smrQSgpu1GwwA4BEgLLMvGVN2KZBvUz38ok7XYs00AIAULFNOI4XCVDbM00EyQT4bQaVWnVnBH25HD5OLT/BE1NJp6SmryKRKVVc8ZULUqTtYuxKhTc59ARkEgkkonOZe8MaJo2E7gmdfiD0epeSOLxJH19+aKSxodlWexvz4TZlCplRI/sBCDmHukMuN39OB0hFMUeWIVjKrGEimopePHQRwTTHHt5vNhw2NvFAQMijmlaJEp9FKXKiwb66CrO5BHY+w0k6U5WQOg1asQKWoLudBLx6eO7SYjJo95zcIato2O0DYfODxNldu9MbMdfrI6p4JImpQqUrRbUtPxBoqHTtDWfwl8xiwPbvgeWicPlo2rSSnv1ybLsRyGGtDVIMj5A25Hn8ZfPzLQtFCYv+BhgK/+I0WLBFYFpWaN89rZiUCQSx04BOj+ciQ1OBNs5V985PodK5NhxrGQybQdjMsymBlWHsMwhzwtpZyAY5tWNW2icMYW2I88PsedBe/RVzMLp8jFj0fvo7zpI8xs/HX2TskE7FQqmee6+X87F99VEsB2JRHJ5c9k7A2RWBTbqur4vR/ngrL93lDYGVw8u/Ai0ADoj3Qwk7JfhVt0EXKXp5OHoMGfA708CUUqyVYSyJEUFghiFDRi8QxSF7AG94fNiYQ/LnMEgVpXKgOnHp/QjhIVTdBCx6ohF4xT5HEQGvFBlL8oEew5QPvltZ/o2SEbBNBUClXPp7Ri/ggsMVYAZfO4uqsBfqdFx7FVOHXk+KwxjLtMXvIf+Tp1YpHtIW8PbLq2ax+Fme+Vo5oyZxI/m/xcrmVlOQsZwX3RM1UHZiuWEW46fsU3lsqdC1IWy7aX5SCuVlXPpS92/ZuraMe0x3x4Fg/0LVM0naag560gkFxNN09ZjC65M13W9pcBrpgFHgA26rl97vvomufS5rKUSNE1TgQ+kDocnDg/SknqcOkpTg9PVLaPUuWgc6c+kOzSU1CJME9o7AQi7M6o/huWjNGAPvIfsL5BK5A1QgoVFhHBB9y0xMm0PqKmZWIcDsziVNGxZTDa66bSq0/Wciu2kdCWrwBnGF6rHHJwQNPpIxvsLurdkfEQSjoJVhXKpsGQrwHQe30zdjBtxFQU4uO37mbhxAMukt303B7d9H1dRgLoZbxmiBlM1ZfWQ40DdGnbstAdzyxcvJfxGR96eBVZNJpqUQo8Xm5jLyeR71nH0Bz+hdtpoCeeDjLSp4fZUqLpQ7fTr0/ayY+ceAvVXAYKaqWsLsseaqWtH7V/t9BuIxS/rn0XJONA0zRr2Z2qa1qtp2mZN0z6jadpFjUfUNG1aql/rL2Y/JJc+l/u33luBBuzZ/1/kqfN66nGepmlFeeosH1Z3QtGctb9AfUkttLWDYaSUhDKz966iEhyOMIpiUlyUnTw8mC/gJeGIYWEUdF9flrxov5oJyzD8Jennk5NddJvZoUK2M9CZrEJYnUyyalI7H9sMdOVavJGcLYZhIpQSps5ex+gqLHfnUAvKKMDYKi0C00xwbP8fyK/fb3Fs/x8wzcEUHVvhpb9jf1odpm7WHezTWwmHw9x801uxjoQxwrklbatvnkXSqUh1l4uMogj6Er0ctnqpu/VtRA6fZIp2F+O1qWxFoWQ8WJC6UN3Mt9Nxsp01q68EIBQKsU9vpWH2O/FXagXZo79Sw+HKDrvJqArVNlyHiKtDotwkkhRfSf19DfgDMBf4R+A5TdMuVITFB7D3SjoxVsUsTqSu+cBYFSVvbi73MKEHUo+/1HU9p8acruvHNE3bDlyBrTo0RMZU07S1wCTs3Yk3nse+njHZOw/XeWvgDXsbhZ7iCkjtAWBaDiqrEoCFtzia/sELRVQShu0TluElpkYpFH/WykC/EsfCQiBIlpbgarNneGtj3bxc1IRpKSjCxKH0I4jQkaxGiR+jhEZOBH3g6wKgu303ZXUrz/StkIxCKKbgDcxmzspPcvLIc/RlKayUVs2jrGYVA/0D+PxNlFZ3jyj3BObQH0wwY8mHOX3kKcbeyMvidMsGGppuxVNcSe/pXZxufYnS6gUE6tagHzhGb1+Qd9/zTmL9vXimejH7ygkf7knvQFwys5zAqskknQrRZGFOquQ84jD4ze7H2Xz8dT6/7EPMoAwzGmP28k/QdvSFIao9pVXzCNStwTAclNVNIhoZalPRSDelNUuZuewhOlrXA+RU/ymtnEvNtOuwcLPz8V/jL6/hPe9+F9u2v87WbTuYMvlWQsc2Uog92hKiK2hreSGtKhQLd+PzzqR7/RZ6Tm6h4c8+RPSynyeTjAdd17+cfaxp2hRgG7AGeDfwkwvQh9axa424JgHsPw/dkVxmXLbOgKZplcDtqcN8IUKD/B32hmNf1zTtVV3XD6XaqAa+narz9xNx9+FQIszJoK0cJBBUF1XC8a1YwJHKJhT2AGAIHw5HGMtiyP4C3SkVIYel4sVDp9pTedPR4wAAIABJREFU8L1dlorbVIkpBoawCCkJSkwXhi+TflEe68HAQa9VTrmwQ5ecSjtdyQasyGaEG0oGGqDOdgaInyKRSOB0OnPdUnKWhGIKHlcRvT1+ps59IKXsohAJhkgkVbz+KpzuYuobb2NS021YlsXAQJDDza08+9tnCIVCfOD999Dbsbeg+/V27KGh6TZM06K8fgUVk1aTNCyMpKCpxiTcuRdj63b8czWEz4FnVR3FqyYTiSZJWhYlZUWEkwamdAQmBKZqsPWELU7wta3/xdTSBr6w/CMIU6Fhxm1MmnWbrQglFBIJA9NScZhJTNx0RWcwfcFaRGra4HBzK8/+7+8BWLxoHjNqp5AwFKqn35xSHjJBCEwDotEYiViIOUuvxuXxIJIR1qxawVVrVlPkUdhzaE9B/e/t2MP8WbdSUbsUM57ACIUIbzrCkcefIdHTi1BVptx/HwipRiXJj67rrZqm/Rr4KPZmpGlnIBWn/3nsyIRaoA94Ffi6ruuvZrejaZoA3ptqZxYQADqBQ8CvdV3/96y668nKGdA07cvAl1LFazVNy/aGv6Lr+pdHyxnQNK0E+Az2JOgMIAHsAr6r6/qIvZ1S7R/FXmn4IvAeoA57ovSnwJdyqDVKLhHOuzOgadrzoxSXF1AHwNJ1/YZx3vr92Fo3+4f/Aw5H1/Vfa5r2HeBBYJemac9i/2PcAPiB3wHfGuf9LwiHeo+k5RjrSmpwKE5oPUmndwoRjyOdFW25fekQC19xZva/JziYL+BFIIiI8NgTbFn4DScdij1Q61fjKWcgEyZUEu4Hy6LTrKJcSTkDop04U+lNqgQ8JjXRKfQld+N0mDgdJof1Tcyef/WZviWSPCiKwOU0cTpgmjYPy4Ij+3eza9MGwgN9Oa+546Nf4Be//u3Qk6ZRmPILgGUS7O3iDz/8D+YtX8ukmXPBsijxlxPcto2Op+xBGMDkr/8zH//+jiGXf++zN+CQoUETBsM0MSyTgMfP2xqvYmntHBQ3dEZ72da6j1eP72Rl3UKub1jBz/7ps+nr7vjoF3j5lc28/MrmnO2+8uoWXnk1k1PwwQ9+kG99bSMf/Ngifv9fXxy1T+/+xOfGZY+xzk52Pfy53MWGYdu3zCGWjM1gQFn6C0rTtMXA80AZ8DT2IHkS9oD7Vk3TPqjr+o+z2vgqtuPQAjwK9AA1wEJs8ZN/Jz/rsZ2HT2IP0n84rCwvmqb5gReBRcBu7PFNMbAOeETTtKt0Xf9IjkudwFOp1/QUkATuAD4LVAMfGu2+konLhVgZuBbSAjOj1RmNMxkNDG5kVpCcqK7rH9M07WXg49jet4q9vPYD4DsTcVUA4FBv1v4CvgaIRDFPd3Jo8h2oIiM36nDZ6RCqYlBUZMf3W5ZFXyiz2RhAeJyCST7DSYfTdi761Tj1CbBcTkyXEyWeQDUMAskg3Uo1YOcDOJV2MCw6klWUqkEcST9msAwC9upAx8kdTJ+1HLdban2fK9wuC5UB2o48S297JgSjrHIO7/jggxx4Ywfb1j854jqBLflpmhnzt7BXEwqVgnR5irnxno+wa9PzbFv/J0zTQFFUps1exJK/+TThzds4/fs/Yg4L1lYVgaoIMKQzMFFQFYW7Z9/INQ3zGTj2Kh2vf5+OlC0tqJrHW1bdz4nmI0QH+lAUFdO0JwoE1gg7yoeiKKiqypXXTgGhDGknF5bFuOyRUWSThaqCIj0ByeikwoTWpQ43pc4J4MfYjsBDuq7/R1b9fwVeAb6nadrzuq4Pxv0/CJwE5uu6PmSvo1R0Q150XV+vaVoLtjPQMjyUaQz+DtsR+AVw7+D4RtO0L2CHQ39Y07QndV1/dNh19cBOYJ6u65HUNV8BDgD3a5r2f3VdbxtHPyQThAvhDPyIMxvMnxW6ri88g2t+BvzsPHTnvHGwJzPgbyipgyOtnPA3EXaV4hMZZZ6i1CZMxcUZpaBwWMUwM/kCcUeUpJVJLC6E7LyB3sEkYiEwfF6ULnvGtzrWzQHnFBKWE6dIoIgoCgN0JKqZZXYAfnwDk4innAF/UYjt2zdz5ZW5lD8k48XtsogHD3B0zy8Z8q9omfR17KGvYy9TZ90O3DzCIRjobqdxxgwOHjqUPjdc0nE0SqvmERoI8vsffCM1arMxTYPmvdtp3vc6173tPUz9+J/z8qGhm91duaAORVyELw9JXjxC5aqyOk5s/y9G2FL7Lvrad1M383YS8QjT5iymec82AI4d3MWMGTM4lGVH+WhsbKSnrZWqih4ikQDT5iyhec/WvPVb9N2UVc6x8xHGoLRiLj2v7chbXr5qJYbilEYnGUIqJAfsScKpwJ3YkuMvkREnuRKYjx1q8+3s63Vd36Zp2veBh7CjFv4+qziOPcPOsGs6z90ryKBpmhO4L3Xfv8ye6NR1vVvTtM8BvwY+gr1aMZyHBx2B1DXtmqb9HjtJeRnwx/PRb8n55bw7A7qu33++7/FmJZyIcDx4CrDzBWqLq0ke2MCR8iWAhZrlDLhSMfglWc5AZzAz816Oj7Bz/NsoBIxMbG13VvKx4fPiTDkDk5LdHGAqXVYVtcJObnYpbXQkJyFiOjgbcQ3UEseORQ544+zZt49p02ZSV9cw7j5JMiiKQGVgpCMwBItThx6jaeED7Nu2MRMyJAS+QDlLymuGOAM7du7h7jtvTA2+Rhs1CWqnXc8vvz3UERh6a4sXnvg593zsS7z0v5lVLiHgnhua5KrABEJRBIoR4tT+3zKWLU1f9GEWrbmJ5r3bwbLYu2U9b7n34YKcgZUrVvD8/36LrvaT3PHhL7Lsutto3rstrw3t2rSBd3zwQfo69o7SLwBBVfVK9G/+Y55iQcM71xFDHaMdyZuQwdh8C3u/ob3AL4Fv6bo+OJBfmnp8Xtf1XAb0DLYzsDTr3I+xZ/b3a5r2K+zQnY26rned4/5nMxt7X6WdWSsUw/vJsH4O0jeYUzmMY6nHsnPQP8lFQEomXMIc6m1O5wvUeqtxKk5ajjlJqB4EURRh5/IYOFIrANYQZ6A75QyUWB48uAgp49f4L0tmnIEeNYaZ6k92EnFtwk5K7jQz+w24lBNErGIihn1PJelBidjfI0JAhS/Gq69uIB4/fzvJvhlwOU3ajjxLIUorPW2bmL8ytVm3EFxz+3tp2fc6R/e8xvXXZlZpBiUd62bdwWhSkJNn38Xx5iN58xEyt7bYtuFPrJ5XOXhrPvmuJfiKHFJKdALhcpqp3X7HtqXuk6+SjIe55rZ7QQjCA3207t06xI5ycdONNxLsOknX6RNgWex69XGccYPrbnkP+TQ/w8F+IjGT+jmjS5xOnb2O7vWvkejtzVEsmPnwQ1i+MmlzkhHoui5Sf4qu66W6rq/Udf2fdF3PXkovTT3mC5M5mXoMZJ37S+ATQDd2Mu9jQLumac9rmrac88Oo/dR1vR8IDevnIDn+eYDMyoaMsbtEkc7AJcyeroxi2BT/JKLHuznmsPdOc4jM/6zh8APgciZwOu3/WdOw6A/bqwUV+DAx6ReFKwkN4rFUig17gckQFn2pUKHsJOKKqN2XdrPejjcHHEo3CkFOxDxY2KuUzt7azDX+KKFQkG3bJqSa6yWDQzVtmcYC6Ovcy/TZC2icv4x7Pv5FVIeTna88w46XniDc3sK73/VOmpqaUBSFLa9t58jxOI1XfIxA9UI7FhvsHV+rF9C45KO4iibz/G8eKejeR/ZuZ7lWxlWL6vnmX1zLosYKLGNCpum8aRmvLRWXFDPQ28U7/uwzNM5byhuvPE3odDPvXHcns2bNQlFsm1EUhVmzZvG+976XYjXJEz/J5Ewe2bsdFMHkWfNZ9/EvMH3eUpRUTL+iqEyft5RbPvJXbOjay4buk9Rf8SH81fOH2mPNQmav/BTFgdlUrrmaijWr7dwA7ByBijWrWfgv36Bo4RJilvxJlJwxg7MetXnK64fVQ9d1Q9f1b+m6vhSoAt4OPIKdt/iMpmnnY2l81H6mkou92f2UXP5cttKilzuWZbE7yxmY5p/C4d93YCr2jLwnPQkBitt2Bkq8mfyk4ICaHpiX4yfsGcAwz2wWvsxwEVZtJ6PHEaXMcGN4i7EECAuKY0GcZoKE4iKiVlJs2HsQuJRjtCVqaVR6wSzH0V9LrM5OMi4viSOExcGDOpMnT2fSpCln1Lc3Pdb4lH+8/lLKqup47tc/YPrcxbzjgc9QUlqOUBwIxcW1a69l7TXXYJomiqIgVIWOQBP1k1ahIDCx2NVxGKv9IKtrFo6a+JmNaRpUlbp54Pa5CNPClI7AxGOctiSE4PWXnkTfsZG5y69h4eq3AOAPlHLl8iu45uqrMC0LRQh620/w/C++TVfbsSHNmKaBWuLFaSmUKirX3voejFvfTVe4BwOT1zv28/c7f0hv1F5hfL5ls61ytORDaXsMFFUSjToxTQvFX0nDAw/Y8qGmAYqKoTiJCxVzlMRiiaQAtqUer9U0TeQIFbphWL0hpEKDHgMe0zQNbBGUGxi299EwBr9gxzMjvx8IA7M1TavTdf3UePopuTyRzsAlyongKXpjtuPuUd0UBUtp688azItMyKHHbc/Se7NChE4HM2E8lfjochyHMxx/lSXdnHDZbXeoUWZQCqqC6S1GDYYRQFW8l5OeKk6Zk2jEdgbcaittybWIxAlQy1FiPoRZjKWEcagWZd443UE3Gze+yO23343HI9WFxo1Qx6W0EurvZesLjwHQ1XaM7esf532f+hpdf3iMk7/7A0JVafiX/4+/2vRNVKGwrGERN828hg3H3+Ax/dkhzc0umz6mEswgiqIiFAUracpo7YnKOG1pMNwmPNDH1ucfY+vzj6EoKvd++u/41be+XLBdGMEgu//ib7AMA6GqlK1cQekdN/PiwH4ePfzckPq90X5+vudxfr7ncQBUofBvb/tqWkHINC2iqPZrGRw+WeTPaZFICmcjdi7BImyJze8PFqQkR/8ciJEa3Gua5gaW67r+cnYjKVWimtRhhNHpwbbgyYV2Utf1hKZpj2ArGf2Dpmn3ZakJlQF/m6r6vULblFz6SGfgEuX19jfSz2eUTuXQho70cUXoKMlAZuDvcXsAE29R5nulK2jH+nstD4rDpNc883ylqmRmkH7ambmv4fOiBu3jqlgPJz1VtMTqmeneiWWZqCKIRZBgIkiJaidBq+FakiV2Iml1WYLuoJtIJMxrr73C1VePd6sJSdJQCFTNo7d915h1SyvncmT/UIWg6XMW0/Xs87irqqi/4+1E2tvZmlqRMiyTzcdfZ8vxHdx/xTu5XXvLEIdgR8d+ps5ZzJE9Y08wTZ93BZb8OprQnK0tAUybs4TO01GmzV5sJwWPwfQ5i+l9ZbOt/4+9D0D3qxvp3riJVQ8+AI03jHAIslnesBjFdJzpPIdEUjC6rluapr0feA5bQvROYAeZfQacwANZSbtFwEuapjUDW4FWbBf1Guzk3TewVwpGu2dQ07SNwGpN0x4DtmPvkfSirusvjnLpZ4GrgPcBCzRNe5rMPgO1wA90Xf/NeN8DyaWLDJC8BLEsi63tO9PH9eFGervt58IyKXeeQBX2j2cCD6Yp8HvDKIo9+xWPeIgm7IFXrQhw0tPCGS8LAJUJD6mm6VVjRIQdMpTMSiKelLSdjSROnN5MIrFbaeZ4zJU+dnbWpZ/XBBKI1DzxkSOHaW1tOeM+vlmJJxRqp7+F0bf5ABCU1a5i96YNWacEi5ddR8eTz3Lkv35A2RVLKF93O08eG5rHYWHxw+2/YnHdPAIef/r80y2vMOeqG/ImfmbfZ9m1t5M0ZO7ZROasbAlACOatvJlnH29m3qq3FWQXi5ddR8dTz44ssyxOfOe/ucY3e4jNDe2F4K65b8NKyJ85yYVB1/Xt2AP5HwALsJOCbwGeBa7Vdf2HWdVDwF9jh+2swlYauh97XPY3wNW6rkcZm/djb4x6JfB/sTcyu36MfvYBa1J13cDDqXsfAT6o6/oDBdxXchkhp+IuQVr6j9EZsQfXbuGmf1vmR7Whbz+nal24UmN701WOsGCyL7MM3taXGXw7fFFCybPLE3KgUJH0pDcfO+0MMy3uH5JEPKgoBBByTsaZEjJwKcc5GJ2OVhpF4MHRX4793RRDEKNxqp9DR23J0y1bXmHBAg2XK9N/yeiYpoVBCVPn3TOKvKigbubbOfDGDsLBlKKUEFx3y3sIb96WVl9pe/IpEu+8kb7oSNUpC4unD73I9TPW8OjeJwA7ZGNj115Wv+NeXv39z3KHYgjBDesewFnkJ5GUoRoTmTO2JQAhuPq2+ziwP0T7qQEO7A9x1W338fIfH8lrF8PtbwSWRe/vnuCtN17JLw4+NawXgo+t+AA+xY8p7Upyhui6Ppbnm+uaZmDMwbSu6wngH1N/hbR77Sj3uzNPWQt5vHdd1weAL6b+Crl/3vciteHZlwtpRzIxkVMmlyAvn9iUfq71LSQcsv9HVSPOVG8PCTPz4+ksrqTRqkXxZwbjbf32YNqhCBLJfEph46MmUZR+ftxpJypny4uWRXoRqVjjU/FyPB4fAEJYJOkimTxtH6OgJjMCCpOrE+nBfzgc4pVXXjkn/X0zEYsLXCVNzF75KQI1w5V/FtK45KO0HjrFtvVPoigqjfOWsu4Df4nn8CnaHv19up3uza/hV/PnbWw9sZMVDYtRU+2rQqE92s0kbSHv+sRXaFywfIgSTOOC5bzroa9QN2M+iaT8KroUGGJLw1WkahYye+Un6e+D11+0pcoHP+c7P/IFOrrL2Li+FUUR9PXEmTx9Hvd85HM0DlMIalywnHX3jbS/XPRufo2rahYNsblVk67g6zd9jvnlczHj4x7LSSQSyZsOITWVLzjrgbXxeJK+vrFyg0YSSoT5/Cv/PwkziTcoaNpzAwlhx/83dm/HeUUD7SftfCTTEjTUXMscn4vIFDs+Nx518OpBW8+9rMhJGedGx79bjfFU4DgATkvhnp6ZqJagdP1mlJh9j/+efDsd7jJKXUnurN/PqZP23iWGVcSiohU0eGfafazrJVqVCjEQKt3Wdbyxy44/FkJw7733oqpeLjRVVb6LPbJYz1nYjqIIXE4Th2oCJkIoGIZKMhTBMmIkw2EwDAa276Tj6WdJ9Ix0FKf869f4yy3fzHuPf7/lqygomJaJ0+GgyOkh3G+HjamKgSCJZZp2sjAODPPSU3GpqrId2Y6Owjfpu8i2s56zsJtcZGwppTAkFJKGSiKpoogkgiSKAKEoGKaKZTnAslcXFEWAAg4ziWIlCCcjOBxOoskYYSOGXy2m+f98Maf95WLJ975NqNiJaZkoQkExHZBUJoRdnYmt5GjjYn/vSCSSyxwZJnSJ8cKxl0iYSSp7ElzxeiMnS2xHwJMIUjOngl29bThTdSNqDTONeuIVr6WvP9mX2STM7yDHJuhnRpnhosRwEFSTJITJSWeIyQkfyYAf12l7V/Up0dN0uMvoizvAU4VQjmKZCVQR4WA0QkNqfO/sKCVa6wejHyyDKv8AFRVVdHV1YFkWzzzzDDfd9Pa0TrmkMEzTIhoT2Dlq9kxsCTH2fOyhdILmaAhVxRzlLVeFgoICUScKUO63B0JBcyB1fwVIhXilU1Qu/oBNMn4ytjT8J8TEwP6chw6EM/ZlGPZhAgFFgoef+zuMLIWib6z6FMn+wgbPQk0pA6Vszu4BSLuSSCSSwpGjqUuIcCLMC8deYfrxGLc/b9BWPCdd1lgZJlRbjRJuSZ/TfFfg9sQwvHZ2sWVZnOwuBqDYpaIkEuesbwLB5HgmR+Cg285DSJZlkvsaU6FAACdDHsrLM3uehK09JE1beUgkBaoxNV1mDuxkXo3XnlEE2tvb2bdvpFKJZPwkFQdlK1cUVDewcnlaSSgXg8otEkmhKIbKsoZFQ85t7dpP6YplBV1fvmolhuIcu6JEIpFI8iKdgUuI51pfpLa1j1te7uNw+UpMxR54lRYZuBsr6Ty5BzU11S+UOuaotcQrm9PXd/Z5iCftGeGAW0WIczt71hj1pSfkjruC9CtxkoGMM1Afak8nCx7ud1NWXgvC/iFXRYhTsUxeg6urAZGahrasKCXHHmN2rCVd/vrrW+joaD+n/X8zEsfBpHvuLkjZJXDH20YoCaWLpXKL5ExIqqybewsiK8fxyWOvUnbnLQXZZMM71xEb135LEolEIhmO/OW+RDgdamfva09z68t9dBVNo9s7KV02tcnNQPcRIn1H0+fml1yD5Q6RKMvs6Hmsy47DcakKbuPcrQoM4jNd1CeK08dvFHVi+EswHbbT4olHqI7bA/6j/W5MHATK6tP12+L70s+dxw3cxzvTx5H6MmbSRsAMAmCaJhs2PMPAwEhlG0nhmKaF5Stj5sMP5R98CcH0TzzIiwN6TiWhbOUWmYMkGQ+maeFT/Dy4/P1ph6A32s+LA/tpePCBUW1y5sMPYfnKpM1JJBLJWSKdgUsA0zJ5/MUfcev6HpIUs7/6ynRZwyQn0YFDdJ3ckT7nd66kzuklVr8LUrP/vUEn/WF7Fr7G60wr+5xr5kQC6efN7n46nTGSVWXpcwuitsOStASH+jxUVdWAsJWIepKtRI2UrLIoxt1VjkjYKx2Wy0FkaiUrEjpOyz4XDod4+uk/0tWVcRok4ydmKRQtXMLCf/kGFWtW23HY2PHYFWtWs/BfvoFv6VJWNa5k1aQrpHKL5JxixgULKubx9Zs+l7avRw8/x+aKME3/+DUq1lyZ0yaLFi4hZsmfMIlEIjlbpJrQhWc941T2eHbzL6l+5AmcCYXX62+mr8jetMvtEUxtOE7nsYzUaJIG3lJ2E1QdIdZgx9VbFmw/XM5AxIXf46CC5DkPEcrmRd8pTrjs+P+A4ebOfcX439ABCBb5+Fb9HSAE1UUJ3jWrm57efjra9gAwq3gZM4oGY4hPEZm2h4Q/lG7b0yMYaO5ko3MOZmpQKoRg1qw5NDbOory8ElU9P2EDE0DVYz3nWBUmG0URuEiiGkkwDVBUDMVJXKgZFRiHgakYBSm3nAsllYmIVBMqjPG+T7nsS7WcOBMmqpHIaZMTHakmJJFILgVktt8EZ8feDVT8+EmcCYXdtdemHQGAyfWn6Ty+OX0cMgMsLb0OUXWUaH0mwfZEVzEDERduh0KNC4zE+f0RXRKqpM15DENY9KoxXp7m4G17FIRhUhIZYEb0FM1F9bRHnBzuczOzzE93dwNG/ATHovuZ5pmPIlSgDhQniH1g2UnQ0TKLkqZqVh3cx2bHbAyhYlkWBw7s5cCBvaiqSkVFFZWVVVRX11FfPwmHQ5p5IZimRZSUOsugP2WRzvMwTQviCtiaQfa5dCWJ5OzIZV+28JACwp3TJiUSiURy9sg11gnM5q2Pw388gjC8bG+4mU7vlHTZ5PpTdB9/Of2jmFBLWTL1CirmbCba8EZ6z8H+sJPmNh9OVaHBq2IZ50hLdBR8ppMloYr08cGSEC1TfenjG3u3p8OUNpzwMRBXmDa1AZNSomaQ47ED6bru1jKU5HQQmetjPgP3wgrWVjUzubgHVcmEPBmGQXt7G3v37mL9+qf55S9/xIsvPseJE8cwzfMTGiWRSCQSiURyqSKnTCcYcSPBgZbtHH3mCfz7EpwqvZI2XyOWSMXMEqOm4gBW9DDVdW58Pjcl/iKKSpII8QbZw92BsIM3WspwOx3UuQVK8twnDedjZsxPryPOIY+dcLphnsrUI6BYUBbq5vaOV3mqYjkh3Pz6UDnXNAxQP3kWJ1t3cTi8nRrXNNxKEUpSxb2/lmidE8r3YmG3ZznAqvPRSIwZ1mmScUEo7iQYcxKNqximwLRsjyjWt4t9r+/i8B4nlRUVlAZqKC2fhLu4AtXpQ5HShBKJRCKRSN6kSGfgIhNJRvnjoacwf3uaRLwcQzhIKm7UkivxX3+MSUVRpirbcTgiqE6B22XidACUZbUydLbfNO3QoNbTJVR6PRSbCUiaoFy40FOBYGmoEgs47Omnz+dg00Ivq3fa8f9z+5uZHmnhl8sbaetfwJ9aAqiilEmOEprMjewKrucK31tRhILTcOA8XkPiWDktPp3yya04nRnHRgiB0w0Bd4KAbyyHpwuj/wDdWaI4hqliWG7cRT5criKE4kBRi/FXr8BVXJ+/KYlEIpFIJJJLHJlAfOE5DjSYpkUyafD8kVd4+rFnCJxaNaTS4gX7aKgfn45+f9hJ14Cb3m6Vem8x9bPno46l1X2esSyL10MtPNu3i7iR4C2bB5h7JJouf3mxly21M4gfWpI+52eAlZ7XqFZ9LCy5DqeS2TW5LdbO+h4H1VXdLFsWxKH0koz3wXl4mR5vLbOWfzJ97HI5NgA7gE+d+7sVxBDbmei4XPZcQzx+/kPTLiRn8rousu1cFLu5XD//8XAu3oMJ8L0jkUguc6QzcOHpBUqHnzRNk2QySTIex0gmMQ0LhJKW2VZUe5Mw00xgGAZYJkIoWIaJOfgZXkox8UIgFJFzDG+/5uElCkIBRVUAK1Wax3aFQAgl85ivC4qCIgQoCoriQFFUu10hAIHI7UhtAK4d7aWdR3LajuSS4WLZjrSbS5+L+b0jkUguc2SY0IXnCDAdCAKHBk8qioLL5cLlcl20jkkKYsfYVc4bOW1HcslwsWxH2s2lz8X83plwaJp2C/Ae4EqgFnACHcBO4DHgp7quTyhNY03T7gf+B3hE1/X7L25vJJKhSGfgwrNk7CoSSU6k7UjOBGk3kssCTdOqgV8Ca1On9gFPA3FgEvAW4Bbgq5qmLdN1/ehZ3MsC0HVd7vMgueyRzoBEIpFIJJIJjaZpAeAVYCawEfiorutvDKvjAx4EPo+tsnHGzoBE8mZCOgMSiUQikUgmOt/CdgS2ANfruh4dXiEVGvQPmqY9CoSGl0skktxIZ0AikUgkkjcXdT390U8nDfOOpGE5HKpIqqry23K/51+AUxe7c8PRNK0RO0cA7BWBEY5ANrqup3NjNE2rAu4F3gZoQB23fkbTAAAgAElEQVQQww4x+jHwn7quG1n1vwx8Ket4iFLF8LAhTdNWAg8DV2HnLwwALcCfgH/Tdb0rx+vxAV8E7gbqgU7gD8DndV3vzvMezAE+A1yfeg0RYFvqHn/IUb8FmIqdL7QY+GTqMQAs0XVd5qFI0khnQCKRSCSSNweipz/6pe6B6IO/fOZAxeY9baphWqiKYOW82k+/60btvjKf+ztlfs9XyCvXdlG4DVCAXbquvz7Oa98K/Cu2xO5BYBP2oP1KYCVwo6Zpd+q6Pvh6dwCPAPeljh/J17CmaZ8FvoYtf7cHO3zJBzRhD/ZfANYPu6wUO9ypAXgR2I3tSHwUWKFp2ipd14dsmKNp2rtT/XCl7vNHoAq4GrhB07Sv6rr+xTzd/EvgIewVlSeAycAlJD0ouRBIZ0AikUgkkjcBPf3RL7227/SnvvWrHaXZquKGafHqrlPqxt2nqh965+JPLZ9TQ5nf8+WL1tGRLE09vnYG124DVum6vjn7pKZpdcDjwDuAe4BfAOi6/jvgd5qm3Zc6vj9Xo5qm3Qn8LbZK1726rj82rHw5uVdZ7kjdd7Wu68FU3XpsJ+WKVF9+mtXOQmxHIA7coev6E1ll87AH+F/QNO0FXddfyHG/jwK36br+p1yvQyIB29OWSCQSiURyeVPXMxB7cLgjkI1lwbd+taO0ZyD2IHYoykShKvU4vp04AV3X9w13BFLnTwF/nTq8+wz6NBhK9FfDHYFU+6/pun48x3VB4IFBRyBV9yR2TgTADcPqfx57ReCvsx2B1HV7gE+nDh/K08//kY6AZCzkyoBEIpFIJJc53f3Rv/jFM3rFWPuMWhb84lm94qN3Lvxkmd/zNxemd+cXTdMc2LH2g/sSeLBDe3ypKk3jbK8WWAQkGCWMKA/bdF1vy3F+f+qxPus+CnAzdsjWr/O0tyH1eGWe8kfH2T/JmxDpDEgkEolEcpljGOadm/e0qYXU3by7TX3g7fPXARPFGehIPVaP90JN05qA3wFzRqnmH2ezU1OPrbquR8Z5bWue8/2pR0/WuQoyfWvXNG20dqvynJfyqpIxkc6ARCKRSCSXOUnDchhmYTnBhmlhGNZEGh9sA94PLD+Da3+N7Qj8AfgHbBWhPl3XjZSjoGOvEoyHs0muHk/y7qDzZgA/OcP7jddZkbwJmUj/7BKJRCKRSM4DDlUkVUVQiEOgKgJVFckL0K1C+RPwz8ACTdOWFKoopGnabGABdq7BXdkSoilmnmF/Bmf3J2uaVnQGqwOF0ok9mC8C/h97Zx4fVXX3//e9M5OF7JCEnUCAHCAgyJZURCxiBetGRfu01trWWqtPF/V5nm7P73lau7e2j9rlcam2tnZTcWn72EIVXJFVBCHAISEQIBASIJM9mczM/f1x7kxmJpPJJCQkJOf9euU1mXvPuffcmXvPnO853+/n+4XQOAONpi/RAcQajUaj0QxxHA7zxaLCMZGD4agUzR7jcznM5/u7TfFi5w14xn77iBAiMVZ5IcRUWy1opL3pRBRDAOCWGIdpt4/VadLU9vl/HxXY+8lumt9rpJRe4FX7bW+CnDWauNDGgEaj0Wg0Q5yR6UkPfvRKccboxiHGMOCjK8SZrPSkh85Py+LmC0A5KjfARiHEnMgCQogUIcR9KLei0ai8An5gthDisoiyn6YjkVk0Ku3XrmIN7rdfHxBCXB2lLQuFEBNiHD9evo0yTB4WQvyLECIy6ZkhhFgshPhQH5xLM0zRbkIajUaj0Qx9TmalJT7yhZvmdcozEMAw4Is3zavLSkt8BIimeDNgSCnPCiEuBZ5FJel6XwixD6XC40El8VoMJAKngLNSyhohxP+iDInXhBBvoK5rDjAb+AHw9S5O+SJwL7BBCLERJQmKlPKz9usLQohvooyCl4UQe1AJwdJQmY6nAR9EJTs7l+veIYT4JPBr4E/AD+3rPosKGp6HCqz+EfDPczmXZviiVwY0Go1GoxkGZKUn3b9o5uiHHrr38upLLhrrc5hqktlhGlxy0VjfQ/deXr1w5uiH7AzEgw4p5Ukp5VLgWuCPKF/6lcBHgHyUS83ngKlSyoBf/5ftbbtRxsIqlLGwCng8xun+ExWn0Ggf/3b7L7Q930ZlAX4OyAZuRK1c1ALfQrkSnTNSyj+jDJifAc3AMmA1yuDYhbrGn/XFuTTDE8PqTnRYo9FoNBrNUGLs2frWe70+/2qfz3I6HIbX5TCfz0pPepjoWXM1Gs0QRhsDGo1Go9FoNBrNMEW7CWk0Go1Go9FoNMMUbQxoNBqNRqPRaDTDFG0MaDQajUaj0Wg0wxRtDGg0Go1Go9FoNMMUbQxoNBqNRqPRaDTDFG0MaDQajUaj0Wg0wxRtDGg0Go1Go9FoNMMUbQxoNBqNRqPRaDTDFG0MaDQajUaj0Wg0wxRtDGg0Go1Go9FoNMMUbQxoNBqNRqPRaDTDFG0MaDQajUaj0Wg0wxRtDGg0Go1Go9FoNMMU50A3oCuEEF8ElgJzgFwgHXADu4GngD9IKa0o9UzgLuDTwAzAB7wP/K+U8k/dnPPjdt2LAAdwAPgN8IiU0t8nF6bRaDQajaZHCCGOAHndFFstpXxJCLECeAXYIKVc0d9t60uEEMeB8RGbLaAeNSb5M2o84znfbdMMXQatMQB8FWUE7AXeAZpQHcFy4ApgjRDiI6GDdCGEA3gBuA714PwTSLTL/1EIUSyl/HK0kwkhfgncDbQCG4B2u94vgCuEEGu0QaDRaDQazYCyHqjqYt/R89kQACHEZ4FfAU9KKT/bh4f+B1Bt/+8EJgJLgCLU+OcKKWVbH55PM4wZzMbAvwDvSSmbQjcKIQpRg/XrgdtQM/cB7kEZAvuA5VLKU3ad6cBbwJeEEBullH+JOOaNKEOgCrhMSllqbx8NvAasBr4IPNwH1/UQMA/YZbdXo4kXfe9oeoO+bzRDiR9KKV/vpsw7wEzUJOKFyvellG+HbhBCzAA2oYyCzwK/HIiGaYYeg9YYiHwIQraX2LP43wauxDYG7FWBr9jF7goYAnadUiHEV1HuRf8JhBkDwNft168GDAG73ikhxF3A68DXhBA/74PVgXnAMo/Hu6yuriXqKkV/kZOTBkBNTcP5PG2fMpDXkJOTZpz3k4YzYPdObxgK91s0enNdA3zvDMh9M1S//57QF5/BIOh3LjiklM0ol5ohhZTygBDiVyjPicvRxoCmjxi0xkA3eO3X0CWyD6Dcio5LKd+MUuc51FLeIiHEeCllJYAQYgKwAPDYZcKQUr4hhKhE+fAVo2YcNBqNRqO5UBnrbay9D5/3Bsvvcxqmw4vD+aIzNetB4ORAN+5c6SpmQAgxDSgFDqFWDu4FPgFMBVqklNl2uRnAN4BlwFjUWOM08B7wtJTyRbtcqH//7UKI20Oa0dduQwECLlKuyB1CiGJgDfBBlFtRpt3uTcADUsptUeo4gM8Dt6I+k2SgFjiO8oz4oZTydESdVOAL9rkK7LYcAp4FfhrFo+O7qInY/wJ+B3wX+BAqFnQ/8D0p5Qt22aV22UVAErAF+IqU8t0obf8QcANqpWQCkGp/PoF2dzIIhRC/B26xr3cbcD/KJTwDKAeeBP5nuLmFX3DGgBBiCurGBfhryK6L7dft0epJKZuFECWoWbJ5QGVEvRIpZUsXp92OeuAvRhsDGo1Go7kwMbyNtd/0Nbrvcr+9dlRT6XYHfh+YDlKmL7ov89I1tzlSMx9xpmbdjwpaHaqYwIvACuBNoAR7UC+EmAe8DaSgBqp/Q30W44FVQIJdF9Tgtwi4BGVkhI4PNvVT2xfbr/uj7PshcCkq1nIryoiZgRq03yCE+Ghg0B3Cb1GD42bUdZ8BsoFpwL+hApaDxoAQYhIqbmMGKqbhHfs8i1ED69VCiMullHVR2pcPvAvUoTwuJqI+u7VCiJsBA/gjsBN4FZiLihN9XQgxT0p5KOJ4jwNjUN/fG6jvdTbKhXyNEOJKKeXmKO0ANQn8CMp42AiMtj+7B1Df9b1d1BuSDHpjQAjxaZR17kJZfpegvvDvB6xzmyn2a0WMwx1FGQJTQrbFWy+07DmTkOAMLiGfbwbqvH3JULiG3jKQ905vuJDa2hMutOsaqPvmQvuc+oPB8hl4G2u/2Vz27j2nX340I2ys7/fRJLc4muTW3OwPf/6eEdMW4EzN+taANbT/mYIaeM6SUpZH7LsPZQh8RUr5QOgOIUQaUBh4L6W8zw4gvgR4s59WAhBCOFHjn08DH0PN3D8apeiPgI+Guknb9W9AeT48KoT4u5Sy1d6ejzIEjgCLpZQ1EfUuBo6FvDeAtShD4GHg64FJVCHECOAJu30/RcU0RPJpe99XAjPvtnLkz4D/AdKAm0NWXhzAM8CNKDfwOyOOdw/wWqjhYbfxbpT4y2Modcho3INaqfheQJlSCLEcZYR8UQjxgJTyRBd1hxwXQp6BJSgr7+PAZfa2/wK+E1Eu1X6NFTDUaL+G9sy9rafRDFv215Ty5Ze/yb+v+y7H6y54rwKNZjgw1tfovquTIRCGxemXH83wNbrvQrnHDEZeE0JYUf6e6uFxvhrFEAA1QwxKzScMKWWDlHJLTxvcS94KXBtK3fAw8N/Ay0CRlLLTBKaU8h+RhoC9/SWU0mIOanI1QOBa3400BOx670W4CF2Dct/ZBNwb6k1hx2l8DrWK8EkhRHqUaypHGRChLjiPoGTjJwJ/C53klVL6UAYOKNenTtcVuQIhpbSklL9EuQDNEUKIKO0A2CKl/G6oRL2UciPKGHCgYjKGDYN+ZcC2tD8rhEhGWfOfBr4F3CyEuPpCtdw8Hi91dV15JfUPF0pAn2kaOBwWhmHh9/sxTRPLMvD5DEaNUrbbAAUQn/dzRmMg7p1QLMvil1t+R3WL+o14ZMvvuWf+5zuVu1Dut57SywDi/mpO3Jzv+yZwzWfONHb5PPv9Q9kTpc8CiPukLd7G2nvdb68d1b33j4V709pRo6767JedqVlf65OT9y1dSYtGFR3pAj/wUhf7tqH82R8XQvw3asZ/IDT9Q6VFDdRAfi5wNeAQQnwy2gBeCJGDGrQXomIGAuO8mfZrAeozBKW82ARcL4T4GvBHKWUsedar7de10fI8SSkbhRA7UZ/fQpT7TSgbpJTtEXW8QogKu63ropwzIOoyLlqDbLelqwGBikFw2Lty7NcCQEap+nK046ECz6/s6nxDlUFvDASwLdB9wH8IIaqAn6CWgT5iFwnM3qfEOExgFSC0Z+5tPU0/4HRCe3szmzZt4dChQ8HBw9SpUykuLsbjSSAhIWGgmzmsqW45HTQEAErd5TR4GklLSI1RSzMc8Xg8+HxdP88uVzJeb/fH0fQBPu/qptLtju4LQtPB7Y6RKz51IzAYjYF4pEW7oyrGAD/gd385Kgi5TQjxHson/fdSyr3neO54iSYt6gJ+gPLlXyeEWBSRa+lu1NgoOcZxgzP2Uso6O+j5Cfu4P7CDojcD/wc8E5HLIN9+fVAI8WA37c+Jsu14F2UbY+wP7EuK3GEHJn+NDgMgGtFWKKDrnBT1XZ1vKHMhuAlF4yn79Vr74QDl8waxMxROjCh7LvU0fYzTCZWVR3n66acpLS3F71d9nN/vp7S0NLjd49GJFweSI3Wd+9Aj9ec9149mkOPxeMKe22jPc2XlUZwXzJTUhY3l9znx++Ir7PeB3zeUv5kul8iklE1Syg+i4gDuR+UomoOS83xfCPGf56eJUdvWbrfDDcxHzWADQSWhX6IGxveh/PpTAFNKaaACY0GtMoQe8xlgEsrr4jeoQOKbUIHF+4UQodmQA4Pu1+39sf6i/Sh0p9ATt4KPEOKjKNWhRuB2lKGSLKU07OsNqEN2Jc07rNSCuuNCfdhrUfKiTmAkcAoVfQ7Kn60TdnDLbPvteyG7Av8XCiGSu1AUWhRRVtPHmKZBe3sz69dHWyXsYN26deTm5mKayUPexWCwUtFwrNO2U83VzGHWALRGMxgxTYO6ujrWrYv9PK9fv45bb70Vh2OEfp77GcN0eDEdxGUQmA4wHcN6zcZWodkMIIRIQElRPgZ8WwjxjJSybIDa5RNCHEGJocykw+Vnjf36oJQy2qz9tBjHrEVNsj4FQQnWJ1DxBT8APmkXDXT+f5ZSPtbba+gjbrJfvyal/HWU/V1er6YzF+rKwGUoQ8BNh+TVZqAGmCCEuCxKnZtQikTbAzkGAKSUx1CGRAIdN1cQIcQyVBR/lX0OTT/gcFhs2RJfXNaWLVtwxLXYrekPTjR2dtc92dQpZk0zjOnJ87x161b9PJ8PHM4XU6YvimtpIKVgkc9wOJ/v7yZdKEgpPVLKJ1Ey4yZqpSBAYKn6vEyu2go7k+23jSG7RtqvnWZrhBCjUVr6cWEbOj+w384N2RUIqu40VhoAYl3vbLpWEdJEYVAaA0KIS4UQ19hyWpH7lqCSQoBK6uGDYNT5j+3tjwghckPqTEf5AQJ8L8opAzf9j2yLOFAvF/hf++0Ph1sSivOJYVgcOhQpIRydsrIyDEN/FQPFmdbaTtu0MaAJxTAsysrimzjVz/P5wZma9WDmpWvOdO01EcAgc8maM87UrIfOS8MGGUKIfxVCFETZPo2OINxQJZ/A5OJM+hnbLfrHqGBbDx2rAtCRcfk2IURKSJ10lPtPJ995IcQCIcRNQoho/vHX2K+h1/o8sAu4QgjxSyFEVpRjjrXlVvubwPXeEeIuHjB8niJ2HIEmgsHqJjQNdfO67cj0KpSs51QI+iK8jJIYDeVB1KrBtUCpEGIDajVgBSoY5OdSyr9EnkxKuVYI8QhwF7BHCPEqSsrrCtQD9BIqWFnTT/j9/qBPcfxl9bN+vvH5fdS2ujttr24+HaW0Zriin+dByUlHauYj2R/+fOc8A0EMsj98V50jNTOQjGk4chfwCyHEIVTyriZUYqtLUR4Ev5dS7gwpvwml+rNYCLEdlQDLC7wlpfztObTjG0KI6pD3OSjXoHEof/cv2J4NAZ4EvoRyay4XQmxCWX7LUDESTwGfijjHFFTitCZ7rHUcSEQlWJ2CCqb9ZqCw7aJ0PfB3lJb/rUKI3ajZ+WSUcs9M4ATKzag/eRCVI+F6oEwIsRUYgQr8PoxKSntdP7dhyDAoVwZQUfvfQVmg01GKQR9CBcM8D6yWUl4T6d9vrw7cAHwRKAOuQj0I7wK3SCm/1NUJpZR3o26snXadq+xjfAG4MbACoekfTNPENOO7HXtSVtO31La5sexBRKorBYehBnAt3lZava0D2TTNIEI/z4MTZ2rW/SOmLXho/O0PVKfMKPZh2gaY6SBlRrFv/O0PVI+YNv8hOwPxcOUbqMy2jag8R2tQE5Sv2f/fFlrYTuC1EuVCk4+KLbgdWHqO7Vhlnyvwt5yOQf0iKeWvItpxBpVV9wlUEPDV9vvn7NdKOrMJdb1vo4RSbrDP04gKOJ4tpQyLlbSlRxehDI9dKAnTNahMzM0oNaM19DNSylJUEPWzqLHsdaig6UdQ35tWf+wBhmXpoK3zzOvAMp1nIByXC9588zVKS0u7LVtQUMDSpR+kvf383rs5OWndra/3N68zQPdOgANnS/n5LvUbNCF1LA2eJuo8Sont/xX9G2NTRgfLDub77VzoZZ6Bgbx3Xuc83zcuF7z11uscPHiw27ID9TyfD/ooz0B/3DtjvY2191o+72r8Piemw2s4nM87U7MeBnQWQY1mmDFY3YQ0FximaZDg8uN0+MHygeHA6zPxtJtxqYT4fAbFxcVxGQPFxcVam3yACHURSk9MB4ygMeBuc4cZA5rhRVgfgI8Vyy8lJzuTXbtLaGrqOsF7UVGRfp7PPyedqVlfAb4y0A3RaDQDjzYGNOdMYoKFgwaqyl/FXVMClh8Mk8ycQsZMWYGPVNo8sSe3/H4LlyuZq65aGVNedNWqVWRkZFBX19ZlGU3/0eDpEK9IcY0I8wuvba2LVkUzDOiqD8jJKWTN6ivZL4+ybfvOTvVWrlxpJx4beqsCGo1Gc6GgjQHNOZGYYOFpPEhFybOEBaRZftzVe3BX7yWv8GYSUwto95okOkxchonltzBMg3bLT5vPj99v4fXC+PGTuPXWW9m6dStlZWXBjKXTpk2jqKiIzMxMOwOxNgYGgnpPh7tDijM57CuvbescWKwZ+sTTB0wXH8E0FrBl27thz/P5yEBsmkbMfkej6Q9sNcIfd1uwg+/ZfvAazXlHGwOaXmOaBg4aOg8CwrCoKHmWGcX34PJnUPt2BU2HzoLfAtMgZepIsj4wEa/LQavXh9cLDscIli69nMsuWxY0BizLxOfDNgQ0A0WoMZDsGoFhdCjAuNv0ysBwI94+oFK+wLzieyicczGGYQSf5/5eEUhyOnB6/N32OxpNP5BORLBxNzwBaGNAMyBoY0DTaxJcfqrKX6XrQUAAi6ryDaQ3X0JT6ZmOzX6LptIzNJWeIXfldJLGp9Hq9eH3WyjvE4MOuUE9gzcYCDUGRjiTcRkdXcjZKJKjmqFNT/uA3PzraW0z4ih/7iQ5HfgrGzi+LmJ81UW/o9H0JXbiroEWndBo4kLruWl6jdPhV/7BceCu2cuI/NQu91evK8XZ7sc0dd85mKkPiRkY4UwmNaHjO42Wf0AztOlpH+B0nJ9Bt2kaONv9VEcaAhHofkej0Wi0MaA5FyyfChSMq6wflSela9xbjpHo0LfkYKYh1E3IkUSaK5jokrq2+oFokmYg6WEfYBh+khKtfh98JzpMajcf674gut/RaDQa3QNqeo/hACPOW8gw6e52ayw7i8vQM3SDlXa/l2av0qk3MEh0JpLoSAwmHmv1tenEY8ONHvYB7a11VJe/RJKznsSE/nMVchmmihGIA93vaDSa4U6PjQEhxMeFEKt7UP46IcTHe3oezeDH6zPJzC2Mq2xmTiHN5Y2xC/mtuCcZNeef0FWBFFcypmFiGAapoasDnqGVYEwTG69PSQjHQ2ZOIfVnDuKu3sOBrQ/haTzYbwaB5bdUsHA86H5Ho9EMc3qzMvB74Bc9KP8w8LtenEczyGn3Ohg75Qq6j5EyGDPlCpoPdqM2YxpxTzJqzj/hOQZSQv4fEfy/TisKDSs87SZjpqwgnj4gZ+IHOF25zX6vVMYcNPaLy5BhGhDvcXW/o9Fohjm97QJ72nvrNdghiMvpo6m+kokzrqPrr9hg4ozraW6oZERBRszjpU4bSbulVYMGK2E5BkIMgDBjwKPjBoYTfr+Fj1TyCm+muz6g/rTEG7ZyZFF1eAMJrr6flm+3/KRMHRlXWd3vaDSa4c75mA/JRGeIGpI4HX6O7n8eT4ub6QvuIDN3Tof/sGGSmTuH6QvuwNNSy9H9z8dUEwLILJ5Iq1ev1w9WwmRFw4yB0CBi7SY03GjzGCSkFjCj6B4ycy/qsg84VfFGp7r9pTDU5vOT9YGJcZXV/Y5Goxnu9GueASHEdUAGcKA/z6MZIGwlkVMVb3Dm5E6yJxRRMGlJcHf9mVIO7/lTyGxg1z+4uSun43WZWFrve9BS3xbqJpQc/D9VKwoNe9o8BqaZTm7+9UwouAZPqwre7dwHRGD541cj6gF+v4XX5SB35fSY8qK639FoNJo4jAEhxBeBL0ZszhZCHIxRzUAZAaNQ2WVe6nULNecN0zRINL24DC/4fWA6aLectPmd+KMF4wWURCw/Xk8DVeWv2gmIomCYONKTGHV1Pu63juNr8IBpkDptJJnFE/G6zB4n/jFNgwSXH6fDrwwTw4HXZ+JpN8PaG285TWwa2kMTjkV3E3J7dMzAcCKsz7AsMJ1gwMF3H485yHcmpJEzoYi0UdMxTEgd4evzZ7LV6yNpfBoTPjkP95ZjNJZ1ZCA+l35Ho9FohhrxrAyMBKaFvLfsetOiFw/DCzwLfLvnTdOcT5Kcflxttbg3raX54LagMTCiYDGZS9bQnphFqzfcqyygJOKu3tPt8TNyCtm1u4SamlqKVxeTkjgCBybtlkWzz4+/hz/IiQkWDpQB4q4pUQMPQ7VnzJQV+EilzWPEXU7TPfVt4dmHA+iVgeFJaJ/hHDWGxJkLqT6yhaS0sWTmzMJdvTdqvdF5y0jPFtQce4eThzf26zPZ6vVhOgzSLstj5LLJgVP1ut/RaDSaoUg8xsDvgLft/w3gn8BZ4KMx6viBeuCglFI7EQ9ykpx+/Eff48T//QJl69n4fTQf2EzzgS2MuuYLJE26OMwgCCiJqB/9WLN5Bpljl/DKC/+kubmZg6UHueqqlYwfPwmvt+ft9Xnb8DQepKLk2fDzWn7c1XtwV+8lr/BmUjILaHZ3Xy4xtUAbBHEQGjOQ3FUAsTYGhgWhfUb6JddhTZrEwd2/BiycdUeZMudjuKtLiOwXRuctIyE5k9J3f8X5eib9fosWv4+WPjmaZiARQswA7gE+CExEjUlqgOPAZmCdlPKVgWvhwCOEOALkAVOklEfO8RihWEADyu37GeCXUkodDzpE6NYYkFIeBg4H3gshTgBVUsoN/dkwzfnBNA1cbbWdDYEwLM783y8Yd/tP8Liyg8v4oUoinQbcQQzGTr+B/fIozc3Nwa3r16/j1ltvxeEY0WO3AE/L2RjnU+2tLP0H0+eP7bZcRcmzzCi6B9NM1y5D3RAqLTrCkRT8PzSA2N1Wj2VZGDqJ05AltM9wpGaQOGtx0BAA8HoaqD8tmTjjOo4d+GtwuzMhjfRs0dkQCEM/k5roCCE+ipqcTAAqgdeBWiAHmA98AFgGDGtjoI9ZD1TZ/ztRBtglwGLgJiHEB6WUOtPkEKDHAcRSygn90RDNwJBoenFvWkvsmX0Ai7pNz5NyxWdp8XesDrR5DBJtJZGqwxtw1+wNLvuPHLuQ7IlLsSyYkdzKjBnTaG724EpIxufzYVkWLhe0txtx/+h721s5Uf5Kt+3NnrCYk+WvxnVdVYc3kJt/Pa1tegAbizA1oRA3oQSHiwQzAY/fg9fvpcnbHOY6pBlahPYZqfM/RPWJLUQ+Z6cq3oai4l8AACAASURBVGB03jKmL7iDmmObcdeUkD2hiJpj73Qq2xmLqiMbyJ2in0mNQggxBvg1yhC4F/i5lNIXst8ELrX/NH3HD6WUr4duEEIUAJuAYuBOVC4pzQVOv6oJaQY/LsOrYgTioOngVjKvuI0WEsO2hyqJjC+4hoaGehITk7D8bZwqX0/d6X1BAyEju5CUcUvYX3aUHe/uYurUqRQXF+NyJcflMuT3tdnuB7FJHzWdqsMb47oud81exk2/Bv04dI3H56HVp1aETcMkwZEQtj/FNQJPmwdQrkLaGBi6hPYZSdPm4d7766jlIlXGEpKz2Pv2j+I6h7t6L+OmXQs4+qrZmguba4ARwGYp5UORO6WUfuBN+0/Tj0gpDwohHgP+E7gcbQwMCXo9+hFCGEARMBvIAlyxykspv9/bc2n6Eb9P/Z1jWb/forXNwOc3OCDLmToxkZOlLxHpF1xXs4e6mr1MnX4DMI9t23dSWloadwyBFa8UoWXFL1nYT/KGQ4n6sOzDIzq5AaW4RlDb5gaUMTA+dex5bZ/mPBLZD8R4dgIqY5avnZHjFvTsmcSHNgb6jbG1LXX3+fy+G7yWz+k0HF6H6XgxKznjQeDkQDcuCrn2a3VPKwohUoB/BW4CBGqsUg48B/xEStnYRb0i4Euo1YYxKH/5I8DLwM+klGciyn8Y+AKwCEgHTgEbUbPr+6Mc/wi2bz8wHfgasNBu3/vA96WUf+2ibXnAd4Cr7HMdBp4Cftrd59FHBFyHOo377M9tDR1xHVnAaeAd1Oe9JUodB3AH8EmgEEhGuYBVAq+hPsOaiDo9+l6FEN8CvgncDzwBfBf1+QXk778vpVxrl12CMnaK7LZsBb4ipdwepe0rgNWo+2QCkGp/Pq/T9Xf/FHAb8GlUTO63gStQebkOA78BHrCN3PNCr4wBIcS1wC9QF94dBmpEqI2BQYRpGjgcFh4jiYxbf4RpQHv5Tlp2rcfX6A6Wc6RmkjzvKlz58/Fb4HEk4TLB54vu2uMwTWaKSVTsfoxId4BQOUEsi8Wjs8jPz2PDxre7jSEItNfCCMqZxsSIsxyocsb5yL934dJV9uEAoSsB9ToL8dDGdKi/gEEQ4zlzJqQxOm8paaOm4/O29OiZNA2T1BG+MDngdq8D0/RjGBaWZZHgMjANP5alDIfeypMOI/lhw91S983a1vq7Xtj3j1E7Knc7fJYfh2GycPzc+z4ya9VtWUnpj2QmZ9xP9/5c55Oj9usVQojZUsroUlURCCEmoPzeZ6ECjTcDragB+zeB1UKIy6WUtRH1vg58DzV+KbHrpQEFwH+jBqivh5T/AWow70cN7iqBi1CD25uFEGuklC930czbUQPP7cDfUQPbIuAlIcTNgQFqyLlmAW8A2cAx4C+oAfd37Hrng8X2a6eBLupzuxz1uW1DJZ0VwI3ADUKIj0kpn4uo8yRqcNyC+vxOo65vKnAfaoAfNAZ6+73aTAbeBRpRn+MEYAnwrBDi43Z7nwF2oeJP5trX85oQYr6UMlJW/1H7GCV0rEzNRn33a4QQV0kp3yY681ArK6dR91QusBT4oX3MSFn/fqPHxoAQYjnwIip7cTvqQ61EfRGaCwCnE9rbm9m0aQuHDh3C7/djmiZT86dQdMPXoXQLTVtfJKVoNUwvZsuuvRxa+7eOcjFcexwOC/eJTURTEelKTvDGG67AMJPwtLWSlOyl3Rv+Ixza3tG5IxmVPYu6mti/BfVnSmPKG4aSmTMbr0/PQMaioRtjICzXgFYUGtK0W05GFCym+cBmfE1NXcoLB5759tY6qso3kJQ6Jv5nMnc2De5yJQAQ6CtyCxkz+Qoqjp+h9uxZRMFETh9+O8wNsTfypMNJftjdUvfNnSf33vPY9j9kWCF9tM/ys/X4e45tx3fl3rnolnvmj51NZnLGtwaupZ34C3ACGAe8J4T4J2ogtxPYLqXslODE9l54FjVg/AVqZrfF3pcMPA58AngQ+FRIvdWoyctG4ONSyr9FHHcRIasnQoirUYZAE3C1lPLNkH3/AfwY+IMQokBKGW1l4yt2vXUh9f4fanD/A2BtRPmnUQPlp4HPSik9dp1C1IAyJ8o5zhkhhBM1QP0k6nNzA/8bpehPgFuklKci6l8LPA88KoR4WUrZbG/PQxkCx4BFUerNQ333gfe9+l5DuA01AP+3QNyJEOIu+1oeAFLs9j9n7zOBP6IUNL+KMt5C+XfgdSllcBbVbuPnUIbC40KIQillNOP6y6iVim8HVgGEEJehvse7hRA/llIei1Kvz+nNdOg37HpvAflSykuklDdJKW+N9de3zdb0Fo/HQ2XlUZ5++mlKS0vx+9Usnd/vp7TsEL9f+xeqRwqybvw61SMFv1/7F0rLDoWXKy3l6aefprLyKM4Ic9I0/NSdDvfpD5UTdFfv7ZgZtOUED2x5mGZ3GZb3LCWbfkB1+UskOetJTLBwOglr73u79pI57lLUhE3XnD6+jTFTrui2HBiMmXIFbR69MhCLurb4Vwbq9MrAkKbN7yRzyRpSilZT2WySMXYJkc9Z6DOfMGIk7pp9nD6+lZyJl3Qq2xmDnAnFVB58ObyvOLWHA1sfZlzuCKblJVOx+zE1KRDZn2x9CE/jQRITup/YTkyw8DQe5MDWh5RBcw7HugAYW9taf1ekIRCKhcVj2/+QUdtafxcwaHz9bInyFcAO1CTm1cCPUDO3Z4UQm2y1oVBWohSGtgBfDgwY7eO1AJ9HuR3dIoTICqn3Tfv1PyINAbvudinl8ZBN/2a/PhxqCNhlH7DPn4Fyg4nGz0MNAZsfA3XANCHEpMBGIcRSlHJSHfDFgCFgn6sEZUD0Ja8JISwhhIWa/D2MGryuB4pstckwpJTrIgf09va/oWb4R6JciAIEXMB2dlFvV4QR1dvvNcARlAER6vP8OHAGZeysC125sAfpgWCn0HYH9r8UagjY2ywp5WMo16iZKMMlGtuB+0Pdgex7aD1qnN3pfP1Fb0ZAC1HTvrdJKSv7uD2afqauro716yP7nXA2bXuX9qwJ/PO12LFY69evo729BdM0ME2DpEQLw4CCBZ+jYNFdjMlfQVLqWNKzRZjEYGcsKvY9T0JyFkkpo8N+hB2mJ6y9TU1N7JdHGTv9BroeVBjk5F3BqdON3ZbLK/woPlKxrCHxY99vhLsJdQ4OTtGJx4YNfr+FN2kkRuFy/v7KBvU8Fqwm8Jw5E9LIyJmF19NEwaLP40pIpWDhnWRPKKKx9ggTZ1xH7GfyJupPS7yezilqnAmpmKbF8QPP0508qYNGTLNrw8M0DRw0xiU/3N2xLgRqW+rufWHfP0Z1ZQgEsLB4Yd8/RtW21H35PDUtLqSU+6WUi1AuHd8HNqD8yk2U3OWfbV/sAFfbr89H872WUjbRYVwsgqBq0VzUwPe33bXJni1fYr99qotiv7FfL+9i//9FaZsH5f8OajUkwLJAnWirIajVgr5kPepzCPz9HeUJshL4uRBidLRKQohsIcSnhBA/EUI8IYR4yv5uZttFCkKKH0DFY3xYCPENe6UgFj3+XiN4LdSIsuv4UEYCQLQBUqn9Oi7KPoQQE4QQdwohHhRCPBlyvWPsIgXR6gF/72LF4ECs8/UHvYkZcAANvU1moRk42tra2LKlU+xOkJSUFObMmUNBQQE+n4+PfvSjVFRUsGfPHpqamoLlcnJyuHzZErJHZQIWDocPX3sTJw+/qpR+gsvss8ibdSMtjVXEJSdYvpHx01ZxaJfSLK8oeZap8+8mJSUl7Pzbtu8E5jNz7p24T26iLmRpPyOnkMyxS9gvj7Jt+8ssXhS9XGbObMZMuWJIuQD0J2HGQIisaHCbdhMaVngxeO/9PRQXF5OXl0dqWjIzir7MycMbGDVuARgGLY1VES6Bs8iZeAk+b2uY5GiHG9Bsxkz5IE5XCm0tZ3EmpHUyCHoqTzpu6mqa26LHNyW4/FQNI/lhn9+3ekfl7rj8IXdU7nZ8ct6aG1HuL4MKKeU7qBnXgAtHMWo2/0PAbbYLynNAvl3lASHEA90cNuBaExiIHg2dcY7BKCARFStQ0UWZwKB+fBf7j3axPdCRJoVsC8RpdpqRB5BSuoUQdaiViL4gmrSoCxV8+xVgvRBiQYTM653A/6DUn7oiPaTNDUKIz6CkY78HfE8IUYmKA3gZ+HNELoPefK+hHI+yDZRbWNT9UspGIQQQIaUICCHuR3nMxBpPp3exvSfffb/SG2NgPzBXCJGkk01cWLS3t1NWVhZ138KFC5k8eTK7du1i27ZtwfiA/Px8Vq1axZEjR9ixYwerb7iG3OxUqso3sP9gCaMnLSUhObPzzL/lx129F3d1CRNnXMfovGWcqngjZvvcNSWML7g6ZIvF6eNvMveiQt7ZHC5/um37Tkr2SebNLeSiD3yYpqZGLAsOlR/l1RdfCRoPoeXy5yzDMCAtLZ12r4PWoRcc2G+EBgWP6M5NqC3ahJVmKGGaMGPGjLD+Ii0tjeuvXYWv/RTlu35LrP6gueEkSamjmTl1BabpxOf14K7eS9nOX+NtbyIzZxZT5nyM+tMyrN/okWRw9V7GTljJCF8yXpeDVm+4EprT4VfGSDzHGgLyw17L5/TFqebks/z4LN+gv1h7Zvgd229/G8qF5gaUO0rA8HmDjlnfrggM5M/lB6G3dS8oKTspZbsdYP0Z1CrKStSgPRBP8QjgBf4D+BtqcN0spbSEEN8Hvk7E0qCUcq0Q4lXgeuAy1GrLGvvvW0KIpSG+8735XkPp7vOO+/sQQtyICihvQAU6bwROhsQw/BH4GF0vhQ6a7743D/sjqMjvW+xXzQWC3+8P+v6HsnDhQtLT01m7dm2n8mVlZZSVlbF8+XJu/cTHwHMcufVJwIo7o+ixA39l+oI7OHNyZ9Sl/46inSU+62pKmDpnWSdjAJTL0KZ3tjF+wmSefTYyxqpzuU3vqGN85jO34/UGRK408VDfFpJ9OMrKQKiBUNfWgF9LtQ5ZnE44duwo69aFr6b7/X4cpoeKPbHdbgL9QWPtERpry6O6EHY5kdBDyWCr3Uvl73eRu3I6SePTwg0Cyzes5IedhsPrMEziMQgchonDcMSR+WVwIKX0CSE2ooyBwGxwYPD4nJTyl3EeKjBTO1EIkRzH6sAZlPpMIkqlpjRKmcBMdl+4VQeOMTnaTiFEJn23KtAlUkq/LY2ajfKJDygl3Yga+P5MSvmTKFWnxTimmw53JIQQU4FfofzmfwR83C7am++1v7jJfv2GlPKJKPu7vN7BRo9jBqSUv0F9WQ8LIdb0fZM0/YVpmphm+FeekpLC5MmT2bgx+mxbSkoKSy4pYuL4HFKT4dj+jmzFPVmyrzm2mezxizvtcSakMTZ/BQWL7qJg4V0Yposx+StwJqTZVf0YWMF23HrLGj55y43cessallyymJSUFAzD6HRdPfkMNN0Tnn2488qA03SQ7FQrmhYWDZ6o0t2aCxzTNGhvb2HdunWdnslbPvYR3Cc7K4lB5HN+Jw5nMiPHzedk+WtRyyuU4ZCeLTr6g4BkcDwYJmaSi3GfmI4z209CgofkJDp8/w1Hj451ocsPO0zHiwvHz40rqczC8XN9TtPxfH+3KV5sdZbuCATaBtw8/mG/3hSlbFSklFUojf8ElGpOd+W9qGy8xCj/Kfv19XjbEYPAMtk1Qohorie39ME5usV2z5psvw3t7Efar50UcIQQOcCV8Z5DSnkI5TYEagUiQI+/134k1vXOBC4+v83pPTFXBoQQj3exqx3wAM8IIcpRwRoxpnyxpJR39q6Jmr7C5XIxbdo0Dh7skMmdM2cOu3btilp+8aL5zBSTcJ94m7b6JupOhPv+9yzLbwkFk5ZQdXhDcFvXcqMhLgJH3yIxKZk1q6/EfeJtju35W7BcdvYs1qy+kobmFvLz87t0gQpl2rRpWJaJXhWIH8uywoyBZFd0N8YU5whavMpzUAURd+Uiq7lQcTgsNm3aEtY3BJ7JgkV3qbicCLp8znMLmTLnXzq5AoXTMZFQdXhDzySDc2fT1FRBxb5IeVIlF+r1mV3KonY61hCQH85KznjwI7NW3bbt+K7cWEHEBgYfmbXqTFZyRqdMvwPI3UKIhcAjUsqwZWI7iPfTKJcSUBrxAC+hpM+XCSEeRc3eno2oOwa4Vkr5q5DN96MkMB8QQhyTUv49os5CoCpEUeh/gOXAPUKIdVLKTSFl70Mp39ShEl2dK2+h9O/noSZkPyelbLfPNRP4rz44R0zsz/t7qFWBdsIDbgOBr58UQjwZSPwlhEhDxQRkRjnexagA279GWYm51n4Ndffp7ffaHxxAGTh3CCH+ESLzmouaNB/0rnYBumvoZ1GjpkirPHTbVPsvFhagjYEBJjExkeLi4jBjIC8vj23bOrvgLF40n6kTE4PJw0ZPXtp54B+yZB+ZUAzDoP5MKaePb1WuQRFL06HSg7F8i6cvuJOW+iOd1UMsP3U1e6mrKWFswWo+ePmlcRkDRUVF3WY51oTT5G3GZ6kJxURHAk4jereR4krhdKvqk+s8Om5gKGIYFqOy0pkyISHYNwSefVdiOgULPhf27I8aO7/r5/zUHtyn9nYbUxQ6kXD6+FamzPmYEiqIadAredLDe/7USZ7UfWoveYU3k5gmGDNlhW1YxD7WmClX0OK54CcRTmYlpT9y56JbOuUZCGBgcOeiW+qyktIfoSPL7GDAhZph/5QQogo1ID6Lmpm9iA7VlR9LKddD0JXlBpQCzp3Ax4UQu1GzuEmoAegslAxlcNAopXxBCBHIVPuyEGIPKqFUGip51jSU68pxu/zLQogfoTTo3xRCvIXSxZ+DUs9pBT4RTTazp9h+97eiVgg+BSwXQmxGDbI/iFImWkBHIPS58jUhxKdC3mejDJHxKH/3L0eIyfwGuAflrlUuhHgbNVa8DDWB/GtUrEEoecCfgWYhxE7U95OAmlXPR000/3egcG+/137iIdSK0IeBMiHEVlTG4mV2e15CxbAMerozBr7XzX7NBUZGRgZXXbUyKNdpWVanOIKUlJTOWYSj+eraS/ajJy2Na4Y/QE9iDWYWf5mq8n/GLHfy4IvMKL6HD3/4Gl5+uZNKW5CVK1faidIu6B/08059W2xZ0Y59oYpCsRYKNRcqlmUhCiZ2TBKEzPqXbHog7NnPv+gTGKYDue2XnFNMUUi/4/U0YpgOJs5cE+ayGI7BxBnXdylPGlAqm1F0D34jjbzCm2PIiw4t+eHM5Iz754+dzQ8/9PVoGYh9H5m16kxIBuLBxJOoYNEVqOy3c1D69O2oQflvgSciM71KKY8LIRajEkXdbNcrQvn6VwI/RSVRJaLet+0YhC8Bl6J84etQKj7fQrkShZb/mj3w/QJKzvIS1GD0aZQiz75z/QBCzrXXXp34NnAVarB5BGW8/BjoflYsfq6KeN+G+tx+h4oLeDeibbV2276DmjH/MOpzeAE1oI82KbwFFVS8DJiBMmY8qMH0T1F5GMICgXv7vfY1Uspye2Xj+6j75Fr7/I+jvp+H+7sNfYUxFDq4C4zXgWUej5e6uniUy/qOnBzld1tb20B7ewtbt27l4osv5rnnngszCJZcspjspPKwLL8Fi+7i4I7Hwn6Yx+SvICEpE8vfHiOPgMHEGddhOBJJHzmd9tY6HK5kThxah/tUHMvzuXNISskNcy+KRkbuHMZNX01jk7qusrKyoCLStGnTKCoqipoxuSfk5KQNtK7g6wzAvXPgbCk/f+9xLiptYX65l4zMXPjQMpg6Oazc5hPb2XZqJwBXT1nBpxbfCEBNzdAyDALPUU+ua4Dvndfpo/smZYTFiYMvUlezlzH5V5KQmM7R/S8Q7dkfk7+C1sZT8bnhxHrODZPCJf+Bz9uK6UjA62nBayWQ4PBSdeT1jkSGdtbg3Lyl1FWXRF1pCF3BNE0XzoRU/JYDy9fMyUOv4K4JPda5yw/35l6Jcoz+uHfG1rbU3ev1+1b7LJ/TYTi8TtPxfFZyxsOEZNfVaDTDgwvGn0nTd3i94HCMYOnSyzFNmDp1KqWlHSII+VMmcWxP+Ax7NF9dd3UJk2ffzIEtPyOeGX5vWyNtLadJSZxsL/N3T7RYg2jU1ZQwoeDa4HVddtmyoDFgWSY+H3pFoJfUexqYe7CFy9+148TOHoVf/RHuvg0mdcQFpCR0rBroXANDE5VhfB9j8z9ERu7MmM/+ucYUBcjMKeRM5Q6qjrxGZk4hORM/wIgR6Zw89ApJKbnKNQkD05GAMyGFA9t+iTeKvG1XsQsZ2bPInnQ5Y6dfy/iCa7D8arvXN6Tlh09mJWd8BaUVr9FohjnaGBim+P0Wfr9S1iguLg4zBgw6xwKkZ88gK3d2mK9uZm4hVeUb6d6P1qKp4QTJKbm4q/eSkJTVM0m/OMtZlj94XcpNMRDwNyR/zM8bDQ1nKX6/KXyjzwd//gvc9zmlNQmkhsmLhg/Gmps8eNq8ZGQlYxgDvcCi6S2W5WP0pKWkjZxCVfkGYj1bDkdiHzznBjkTPxD0/XdX78FdvZe8wjU4XSOoKn/VThymKFj4+S4Nga5iFwKxRxNm3AgJE0hITA9ZQdR9h0ajGfr02BgQQlzSwyptgBs4HC11tGZg8fstXK7k8DgCOscC1FRuZ/rFn2HijOuCLkEZo0RcM3+j85Zh+dqCvsO5eUuVTF88A4UeSP8ZholpGjgcFoZhhawMGPh80bOQarrHuf19ktrVZ2cZBoYB+C2oOQM73ofi+UB4PEFgZcCyLDa/dohdW5Xwxvi8TK5aXUhikp6HuCAxnKRnCzBM3DVdu0GPzluGYTrP8TnvyvffoqJkbfQ4g4D0aMg5441ROn7geWYUfQnL9GGaTsCv+xDNBY0QIhuIpvffFT+UUh7ovphmqNGbX+S36d10SasQ4jXgp1LK13pRX9NPeL0wfvwkbrvtNs6cPkVSchKziu8Fw+Bs1S4a3RVkTyjiZPkrJCaPYvqCO6g5thnTkRBTTaix9gimM5GMbEF7ax0Fiz5P/ZlSGmuPxC8PmFNI/ZloeVwiy83GbzkwzXa2bt3Gvn37gj/kU6dOpbi4+JxjBoYraXsOB/8/fVkhOW0JsEXFBhgb38ZaNBccjrAsxLWtbgB2bjkaNAQAKivcbHq1jOXXzDhPrdf0KZZBzbF3yJ10aZeD/MDg+2zVrh495411RxmbvyLoz+9wJdPWfAafz0PBorvCVMoaa49gOhKYUfQFLL8XDAetTTU0N5wkM2cWje6KYH/kdI7gREwRguDFUXV4I7l5l1F5op71/9yg+xDNhU4qcFsPyj9FhzyoZhjRG2PgBKpXHYmSUALwAbX2/1l0+Ge02NvTUTfl1cAqIcS3pJTf6W2jNX1PgtMPZhvO1ncp3VbSSRHImZDKftvP9szJnWRPKMLhSu5GTUgF850+tkUF89nHy520lLSRU+OSBxyTv5yynb/upvUGGWMv4enf/4kxY8Ywb948kpKS2LFjB36/n9LSUkpLS7nqqpWMHz9J/5j3gPazZ0i1gx99JninTARHBuzeBy2tWLV18P4BuLiQEc5kTMPEb/lp9rZQ39zM6+sPdjqm3HuK+ZdMInNk5+RlmsGNYfhx1+yLuboXSEbY6K6IWwZ0TP5yDMPByfJXgn3I6LxlZOQW0lx3jBNl64L9yvQFnyMrdzYny19VqxMhfdWY/CvIzJ5FW8vpYH9UsPDOHsUo5U5aQm52CsnJyTQ1Nek+RHPBYst+ar9MTbf0JgPxBOCHqAH/euAKIFVKmSulzEUN+pejElGYwLellOkoyajfoG7MbwkhlvbNJWi6wzQNkl0OvI1ttNe3kp7gItnlCGbiHJHkp8l9gANbH1bKH6G63NV77eV1gtu9ngaqyl/F721j0swbg764QVWPYN09HNz+CAnJmYzOWxY83sEdj+L3eZg08yN03U8Z5M1aA34/46etillu7PQb2C+P0tjYSFlZGWvXriU9PZ2FCxeGlVy/fh3t7S0dGUg13dK0uyMh3fHcBBUk7HJBoQhuN97cApaFYRhhqwOb3pA0NbQBMCIlgezRqcF9f335AJU1OkvxBYflA8sfFBSIRvqo6bhr9uH1NFB/WjJxxnXEen4nF96Mr72F/ZsfVApjtiGQkJzJwe2PhPVJ+Rd9Ak/zafZveThKf7OXA1t+RsPZUnze1o790WSRu7w+Vc59chNzLyrstFv3IRqNZijSY2NACLEK+BnwBynlKinla1LKtsB+KWWblPJ1KeXVwB+A/xVCrJBSHpRS3o5KAmEA/9pH16CJQZLTwQgv1L9RweHHd3Dk8R0cffJd6t+oYIQPkhOd4G/k2P6IpF5hWPi8LZ38ei3LT0r6+BiyoqrusQN/JT1b4ExIC24rffdXpGRMYvrCz5GZO6fj2IZJZu4cpi+4g9amany+NpLS85k6/24ycy8KK5eRO4e8uXdy6Fgb27bvDDvrxo0bmTx5Mikp4br4W7duxXFhJxI9rzTt7/ALPzw+gRGGnX24sIDAB2kdPwGHlAx00BiwYN+O6mDdGXPH0J6WEHx/prKeb/1mO9sPdJTRDG4cDlMFfxsmp49vJWfiJUQd5IcMvk9VvIGnxc30BXd0+ZynZk0N8+cPuBlF9itJqWNJSM6iYl/svqpi31oSkrNISh1rn8voUewRKHWyqfmTohbRfYhGoxlq9NgYAP4N1RN/LY6yX0f9WvxHyLYf2q89DUTW9JAkpwN/ZQPHn95FU+kZFfQJ4LdoKj3D8d/twmpstuX8YvvTRpsJtLA42Y2iSKBkzbHNZI9fHLbtZPmrNLmPkZQ6moIFn6Ng4V3MXvJVklJyObznT5yqeIOaY5txJbhoaHGQnHMZsy75KvkL7mHinLupaZ7C2hdf6WQIBNi9ezezZ88O21ZWVoZh6Dj2eLAsi+ayDjefs2NScAaUgJKTQHQkHjfe2gpAWoKa/U9uzKLJ3Q6AK8FBZVs7G8pq8Nn3cwJbwAAAIABJREFUShIGTr/Fky/v4/R5zreh6TlOJ7icbTTUHiYzZ1bsWf+Iwfepijc4vOdPIc/55ylY8Dn1nO99Bq+3idA+JOBmFNmvjJ9+dbcKRgqLqvKN9opi9L6rK4IxSpZfqapFQfchGo1mqNEbY+BioE5KWdNdQbuMG5VRLrDtMNAI5PTi3Jo4MU0DZ7uf6nWxg29N04u7pnt/2mgzgQ5nUlx1Qfnipo+a3mlbauYkqspf5eCOR6k++jZ+n4eqwxuCCiHumhKwfDz77LP86c9rOVPbzFO/e5an/7CWdzZvo6mpKdrpADh06BB5eeFZ2f1+f6eMy5rotJ+uwapX30Oby8CblR5e4KKZwX+tfQfhVA2pLmUMZJ7pyD+QNDKZ/3v3OBYqr3yANAw87X5e3hyWXFIzyHA6obLyKC3NDVSW/SPYD3Q16x85+A7KEweef8Og/mwZpyu3kZqZh/tUeIBxwM0okqSUnJgKRqG4a0pISlU/MTFXMcJQMqanK7eBYSpVtSjoPkSj0Qw1emMMJALpQoi07graZdKBpCi7W3txbk2cJDpMajcfi6OkPy5/2sBMYF7hGoI/qrb/cFxEKxe2Tf0Q+7ytncpYIeUsy4r7hzhaOdM0Mc3e3PbDj9ayDkPyZLaLNGdEwG9mOkyeGHxrvLmVtIQUsCC9Nje4fXtViO57Uod/RcCB6529VbSePYXvdAWW39en16A5N0zToL29hfXr12Fg4W2rC1sRiDbrnzFKMCb/CsBgdN4ypsz5GC2NVRzc8RgHdzzKwR2P0dpYxZQ5H2PctJVq8B1KVz7+Pe1vQmKc4oldCJUxzcgp5FD50S4+E92HaDSaoUVv1IRKgIUod6FvdVP2PlSgcXD6WAgxEhVk3FlmRNNnuAyTpkNn4yhpBlVBupIHNQyDlMw8sCAhOQux+F85deQNMBwx69afKeX08a1qlj+az25wW8cPcUbOzE5lDMMkJSWFOXPmkJaWhmmacRkE0X6wp02bhmWZ6GRC3dN6tGMwVDXKSZoRxaafOwuOKKPTevd9Mj4wlcSWVFztSmjMixVcDchOS2TulFFUvH8KgEzTQYW/nZWu7XjW/pZ2LMyscSSv+nfM1JH9em2a+HA4LDZt2gJ05B85VfEGo/OWBSWG3TUlKvnX4Y1k5hQyNn8FbS1n7LifU1ETfbmr9+KuLiGv8CZGjZ2v1MYChOQKCOtXDJOCRXeF9ytdYZidXJUi2xyqejYmfzm1J3fZ7TDIHLuEV174Z9RD6z5Eo9EMNXpjDDwKPAn8lxBiFPCAlDJsCkUIMRH4d+ALqB7z0ZDdy+zX93pxbk03mKaBZRhYfqsjRiAGLeUNZGYXkjhiZEx50LrqkqA86KixCxg79UoMw0lmziwSk0d1UVfJktaflrS1nO2ULyAzp5D2tkamL7ij6zK5s8FwsGrVKnbt2kVJSQn5+fmUlZV1e21Tp06loiLcBaWoqEjLAsZJ27GOx7pmpItJZnLnQmNyIDcbqk+Dz0fOe4dJdXR4ANbbr7kZSVx10ThcIdmHE/wWxQmHWJHc4Wrmrz1B68ZHSL72GzpT8SDAMCwOHToEQPnho2Rnz6KuZi+nKt4ISgwXTFqCw5mE6Uik0X2EujOStqbTZE9Y3K24QEXJcxQsuisseVjAzSiefiXMiAghM6eQ1sZwT9bQNs+YshzDMPF5W2hrcVN/ujRoCATUyZqbm6MeW/chGo1mqNEbadHfoBJTGMDdwGEhRIUQYov9VwEcQRkCBvC0XSfA9cAp4K/n2HZNBIbDpK7VxxN/20dNXQvEIX9Xv/sM46at6pE86JkT29m/+UF83hbGTb86Rl0lS5qQnMnY/BUR7gAGuXlLcbiS7WDhNzv8dUPKjJl8OZZlsXbtWsrKytizZw/z5s2L6/OYO3cue/bsCb5fuXIlLlcylqVn9LrDsizaQlYGajKdpJlRVgYMQ60O2IzYtpd0d8AYaCfBLGNxmuTKqZDkcuBwmiSndqgKFbuOE4mvqhTf0d19di2a3hPqH79rdwmZ4y4l4GoTkBiuq9lPo/swJZt+TEXJM1g+L6OnXM6pI68TT7Bv9dG3w8QFTh/fytj8K+PqV0bnLYtyTJW3oLLs7532qDZvwOdtoXTnExzc8RgJSelUH3ubzNyLmL7oi1HVyQLoPkSj0QxFerMygJTyM0KIzcD/AyaG/IVyHPiulPLxiLqf6s05NbExHCa7D53h4Wfew7KgcFImC6aOVCpCMXCMcGFZni5n8ALL9Mlp40gfNYOcSUvw+31Ulv4Dv68Nw2/GJS0qFocqySq3oLrqEjJyZuL1NIb564aWMQwHBhZLLlnMrt0lNDU1ceTIEZYvX87GjRu7vK7ly5dz5MgRWltbKSgooKioSGcP7QHes2fxN6vg7DaXQUOKSQqJ0QtPmQgZaVDXgL/NS0rDSAzaSHO+wUizAdrg9N79ZE1dQdrYixmRnkhLoweAFr8KPTrjSyFl/DSSqpQR0L7/NZx58Rl9mv4j4B/v9/tpampivzzK1ILVnDz4ImAFZUAP7/kz46evIiN3DqbpBKz4g32r9zLrA/dRdXgjgX7E72+Pq1+ZvuCOsFUFMMgrXIOnpZbWxqqwGoG+LGvMxRiGiVh8NwYmPstg/MxPU1p2mExfEpPzJlNbW8+h8vKODMT5+RQXF5OQmEJ7uzYENBrN0KJXxgCAlPJXQogngEUohaFse9dplAvQdiml7jXPA6ZpUN/iDRoCAD7LS+r87G6NgZGXj+Vk+T+J9qM7Om9Zl8v04/JX4HClcFzG+sEOYHHqyBtkj19Ma1M1ORM/EFzizxozl4JFd4W5IWXmFAbLnDx7iPEF15CdVM6a1VeyXx5l2/YdLFy4kDVr1rB7924OHToU/NGeNm0aRUVFJCUl4/P5mDfvYizLxOcDr1ffjvES5iKU5QTDiB4zAGCasHAubHib2uQxgIMU5zacZrhPt7t8I0kZk0hK6VgZqPNlAvBaayEFSTO4CGUMeI+9j9XaiJGUimbgsCyDqVOnUlqq3Pe2bd/JpInXMqPoi1Qdfo2k1NG0tZxl+vzbwTDxehrwNJ8hKXV0j4J9Lb836M+flDo67lWFgGRx1ZHX7HiFKzCdSXhazpKZOycYGxDIZlx99C1Obv5pWF+Wm3cZiQlJeL1eNm16h3GZCeSmWMxffZ1a+bIsTNMgMTEZjzYENBrNEKTXxgCAPdjfZv9pBgjLMHjm1YOErlwXTk5j3963mfrBi3G/1tkVI0BCThLug51n8AIZQGMF/xVe8u89khYtLLiaM/+fvfeOj+sq8//f905XHXXJRbJlS8e2XJLYjp04jp0CKZAQcMJSYkILJLBLWxbYwsLul98LCCwsu7AJnaWn0WETUk3ixE6c2Ikt28dFtmzZ6tKoTNHM3Ht/f5w7RdJoVNzDvF8vvWbunXPOPVczc+Y85zzP5zmxgyO7f5EMKtZ1F4N9hyiuWEz5nEvRdBc9bdtHlZkjbmKge49KBNRwC3AJL7y4g3379rFs2TIuvvhiNE2jqKgI01QT/5ERCxW7DpNPKnKMZawxoKPh01wTlrcWzCOwdQ+BohqWLnqFyoowpuGnqyNCx4kRsCwsy2ToxDa8ZVcn6wWMEoKmm+0jCzAGfKzw12AF2sGyiJ9oxrVgzRm9zxzZMQyNtWvXJo0BAH9JMZYVYdbC69AdbkZCPcRjQSLBTo7v/x2Nq+/CNGPJIOBJ0XSMeIQju39Jw8r3ozvcNG+9Z0r9C3Q3s1S8kbLZq4gMd3NkzwNU1V3BSLhfKRzVrsPhyiM0eIIDL97LRGPZ3EU3s2zxXJr3t1FTO5df3/cFXn7qD8xvuoRVG2/C5SsiGsvFsJxrhBCLgI8BV6G8ETSgG+WJ8DzwiJTysbTyFoCUMvfm5ciRhZw+2msAE41te9pHndMsix1/+R2HO1+ifNMC8hpKUzEEukZeQynlmxZgZZDrmygD6GgsYtGhaUr9KZWhBP6KJvo6dnHy4P8RHu6gp+0FLMsYlWdArRom5CYt2g/+hsWilvz8fILBINu2beOBBx7g/vvvxzBMYjELcwqB0zmyM9YYKNR9WQN6Tw5p/KVsObXXhKirC+DLc5Bf6GR+QwHL1sxPvu3DXftxeVK+WgOGn10jdURxcbg7iqOiPvma0bZ77GVynGVM08Ll8nHdddcDkJ+fj9OhY5lxjFgYIx5G05TRnRwvLGt6ib4qlzLYe5Ca+qvpO/kSsUhgWuNKNNxP87Nf5vCuHxAZPklr84MUltbT07adY/t/C2h48kppXPVBGlffTXX9tWnZ0CHhcqTrFosWzaOguIzbP/UfbP70V7n8xtvRPSXE4rmfynONEOJvgFeAD6KUiZ8GfgXsBxpR6oVfPFf9mwpCiI1CCEsI8fRpam+e3d7R09Fejr9eTmlnIMf5gWGaGGMmwJamoesOdjzzB44cfIV1N7yNyiubMC0TXdMJDwdw5FtouoPGVXeDRlKyb6IMoONIkwCcFE0nNjKU1Bcf7JEUlQv62ncm1YQ6jz1DRe26cfUYNQm1CLRvZcXyJp57PrUhldP+Pr1E21PGZW+xk6JMSkJp7GyD+osNivzj04cU+AaZL8pp2d8DlokxeACP5mPE8hLHxasxlcm4c9AgXjoP2ApA/PhuLMtEyyRLm+OsEY/D7Nm1bN68mZ6eHmIxA4+7EN3hZrj/MFiWvUOoYgic7gKKyxfhr2wi0KXOT4xGzfxrcLjy1G5QLIiuO6Y1rozHovvYc9Qu2YTLXUD74UdV/EJWNSLlclRc0YTlLOCHP/oJCxYsYO3atbhcPnI5xs4tQohq4AeAG/g48N9SSiPtdR24wv7LkSPHNMlqDAghErkADkkpbxxzbjpYUkoxg3o5JkHXNXwOk69/eKXybXXoBEMGJXke5i+5mMKSCmqXrGLnK7tp+eOjmKbJHe96G0XFLtpbHsv4I+l0F7DvyMTBuQkSq3+Brj2TlvVXNDHYeyClL77kVty+EqrqruTAS98hHh1SWUwz1IsMd406N9DdzIJlG0YZAznt79OHFY8T7epMHvcXOWjMYgwYJnQNhXnL0pQBcfxIkIK4RUmD8vmvqoH+Hjf9PVFC7TvxOxbTGa8BIOysBHuz4KRVziyXD2JhrNAAZn87jtLZ466Z4+wSj4PDkce8ujmYsUFOHn6SWfWvw+0rASDQvTcZY3Ty0KMEupupql3P3EU3Z9lhVCIBfe0v03nsGfwVS6iuvwYLDX9FE4GuyXeG1LgyPsu6x1eKEQvSsut/R197jGtQVd2GpEEQ6G6msvYKXB6NVSsv4oUXX+bgwYNcd931zJ5dmxMfOLe8EcgDnpdS/ufYF6WUJvAX+y9HjhzTZLKdgYX2YzzDuemQm6GdAVxOk1h4kBee+h1H9u7ENA103cH8xRdRufZ1rH3dJo4eb+PBh3+drHPH5rfByAn27XyIiX4kF1/28SmtyvW0bWf+srdPafWvYu5lHNn9i8TFaN37EIvXfhRNdyXL1NRfQzwaHFWvpv5aLNOguv7aVKIhy0Qbc72c9vfpI9bTDYZadBvK04m5dAqzGAOtPXGurD1EYmNmoD9KhzRoaBuks7qIqkL1WapvLGBXfz/hUD8leiedKGOgxOWkNaLevJMDJnNK52B2qgme2X0oZwycJ7icJqHAAVqbHwAsKmvXg2XhcHqpql0/KsbI6S5Ed7goKFlA07pPYsSjBLqb1Xc4FlT5S2qvwIiH8RXWqGSDmkagqxlfQQ019dfaiwzTGVcUCTfHcfFOo8igRmSZgEUsEmDBXA8qNullHn30ETZv3ozDkfdackGsifb3f8KKx28x44ZTdzrimsP5a3dpydeB9klrn30SKc27spbKgu1m9DFgGeqD8QLwOSnlsxOUrwM+DVwPzAZCwC7gu1LKn2co/3ngc8C/AT9EJWV9HVANfBO4iFSepQ2JeAabLVLKjXY7fuBTKBn2+Sh37h7gEPColPKLdrkfAXfY9evGtNcqpZyX1jcXyr1qM7AYcKEk4H+LyhU1SmlECDEPOAK0ouZ8n7SvNR8YAB4BPjs2x1SOC5fJjIHX2Y/BDOdynENcTpP2lj088fD3SY8cNk2Dw80v0X78MG94z6d58ulUUp7a2lryvLBv1xhDYBQWRjw8pW36eHSIwR5JXdNttDY/OEGbWgbZUHWd9pYnKK5sYv6yt6uSuhMzFh5VL33VMLm1f+wZlQ3VJqH9nVMLOj2kuwj1Fyl/8KIMOQbCwUFOHtnLUKCbVev8JEKQjh2J4dH8vLJ6IY7j4G/oweMycXt0Zs/L49jhIJqe+k3PS8uHcaI/jl46K2kMGJ2HcYlMWvI5zia6ruHQhpKGgMJC053oTu+oyfeEKmSVTTRc8n7QNEbCfViY9J7cMW53srCkHt3pZe6iWzi+/zdMb1xh6m6O6WpER56wXY40krFJKz5I815JMBhk+/btrF9/1WvBXUiL9vd/LtofuLvtgYfK+l540WEZBprDQemlqz8x56233uEu8d/rLin5N86vRbzEpPMaIcRSKeXk29FpCCH+Hfhn4Fngj8By4GrgCiHERinl82PKrwX+D/CjJsW/BkqBjcBGIcT1wB0TKCY2oBQVIyifRycQQE2gI8B1qFxLj6TV2W9fN8+uswRl+DyOmn/V2OfWkoqLeBYoADbZZR5Ka68n7V689r1sRBk0T9mP61HGztuEEFdLKVsm+Pfdj9qZeRoVs3E58C7geiHElVJKOUG9HBcQWY0BKeUTUzmX4+yi6xqx8OA4QyCdJas3sG37dkAF/V20YikXrVjKiQO/ZbIxPt39J6HNXVjWkAwATsQWxKNDePIrcXtLEas/DBo43QVgGaA5iI8MAxDo2p0xU6jall/HgR3fpnbxWxgJ9+NyF+KvXDZKflS1kdrar128icNHjufyB5whou0nk8/7itQQMVZWNBwc5MCuZzCNOGWVbpxOZQiEQwbdcYE+S70hcQMOtxeypHYAgJq5PjpPRohEUusLDiPVblt/DH1BaifA6Dx8em8ux4xwu0w6jjxO+tgx2HuQ4rJFxEeGkpNvpUJWwpHdv6RizqVU1q0fNW4cfPn7zFrwOjx5ZRx59RcZyxzZ/UtmLbiWwrKGpNxoQiJ0rPRwpnGlqKzBzlkwOYHuZhpr19Fx5An8FU2MhPsZCXUzNjbp0KFDXHnlBuDCFqWJ9vd/rn/HSx879K37itN/OyzDoPf5bY7ebdsrF374ro+VrFqJu6Tk8+eup+P4LXASmAXsFEL8GdgCvIySMR+YpP6HgUullC9BMsbgPuBO4N9JW+S0J88PoAyB/wQ+mYhPEEIsBZ5ArbBvBb6d4VrvQCVm/aCUMpr+ghBiG8oY2D9BzqVbUZP+PwK3SCnjaXUdpHYWkFJ+TwjxOMoY6MmSw+nfUYbAfuBaKeUJuz0f8BO7/s+AyzLUrQN8wMVSyr12PTfwfeB2u/6lGerluMDIReZdgDh0gx1P/W5CQwBgbsMyWlpauHT1Jdz65tdR7j2Mpk0tEVBP23Yq5l5OVd0G5i97O+HhDg7s+DYHdtzHgR3fTgYBV9e/jryiWRjxEJrDQefRp2neeg/NW79C89Z76Gx9Gs3hIN9fl/lCyZ0Hi2P7foXHV4K3oApvfqWdlXjsD73a2s8vmk3T0otYv/4qHI68nCFwmol2jN8ZKNRSbkKWZdEqX8Y01D++pCyVN+BETyW6L/WG5A8OEQzlEQgqdzBdgzkNpYQsE40QAPGR1Of4ZCCOXlyVDBo3+09iRUOn+xZzTBOnw7TdAVP0tG3H5S0ETcUMON2F+KuWEQ33M3/Z2yYYN97GSKgHpyuf+csnGlvexkioF8uI0d7yJOWzL6Vp3T/QtO5TLF336Szjg41lTU/lDEi4HHl8Jcks6APdzSyorwVGZ2K+gKmJ9gfuHmsIjMKyOPSt+4qj/YG7wfbjOw+QUg4B1wI7UIuYNwJfBh4D+oQQW203oIn4XMIQsNszgc/ah+ttN5oEt6FkS48Cn0oPVLZ3JD5nH35ygmv1Ah8ZawhMkSr78fF0Q8C+tiGlnJqVa2NP+O+2Dz+SMATs9sLAXcAwsFYIsS5DEwD/L2EI2PWiwN8Bg8DqLPVyXECcspqQEKIM9cXJk1I+d+pdSvq3XYn6wm9AyYZ5UXrCzwPflFI+naX+O1BfgOUosfn9KB++e+1BYKJ616PkyVbZ12sBfgF8VUo5cso3dprQMDiyd2fWMhYqCG7BXA+tr3wbsJgj3jClH8l4dAgjHsGbX5k1z0Bd061omgMjFmL/Kz+ZuNySTdSveBctr/x4zI2k26Jqy37WgterLfssd9Z+5Ekq57+JWE73+4yQbgwkdgbytVT24YHedsLD9kKcplNQmg+o38v+tG9J5ckO5h9qofuihXQVVeHPV+1WllmcKMrD23+SsLGQeMzCp0PYhOERk2DcibOoEmugE7Awulpwzll6Ru85x2SMlyAGsBLSv5ZJ3ZJNhIc7J81PUrv4LcRjIQ6+9J0Jy8xddDPBoRPMXXQz7Yf/nHQlWnzZJ3B5CohHhyfop6Z2J6epRjR30ZsAGOjeO0rWOBGb9FpQK4v29X+87YGHyrItIgFgWbQ9+FBZ/Qfe/1F3Sclnzk7vJkdKuQ81+bwceAOwBrgEKEG5rlwuhLhhghXyP2Ror1MI0W/XLwMSKasTq+8/l1LGMrT1I+B/gIVCiNnpE2ybx23jZSa8aD9+WgjRA/xBShmYYVsAK1GuRCfT8y8kkFL2CCF+D7wdtXuwNUMbP81QL2DXe2eWejkuIGY8ugkhNgkhXkb5tb3EmCh+IYRfCPF/QohH7ICY6bAB5Sv3CVTgzl9QPnt9qC2tp2wfwEz9+hZqy2sV8Axq5aARFcDzkL09mKnep1B+dVejth7/iApa+gLwtO3Ld15gmQamaWQt43S6WCxqaT+Y8rnVNMcEUnxj6roLcTi9tO7NHlvQ2boVLJPWvQ9nLde692HcvhK8BaMXmsYqgQS6m9EcTpzuQmrqr6Vx9d00rrprnDZ4oGsPLucFv0p3XmJZ1qiYgb5iBwW6F2fa56bn5NHkc1dFLT63+iyOxHSCEXt9wQwx/1ALGuDt7sPpLCYwlNpBmLugCLczdZ0KT6r9rqE4enHqs2L2tJ6u28sxA3RdQ0MbNXYkdg3VeQ2npxhvQTUeX8mk+UmO7fsVKsh4ouzSagfQ4/VjGjF8BdUqR8Cqu9AdblzeEhpWfUCpjyX6pOn4K5fRsPJORkI9U89xUNGE011AgX/+eLcjTU/GJqXUyi5cLCP+5r4XXnRMXhL6tr/oMGPxTWe6TzNBSvmclPKfpZTXAuXAOuDP9st3CCFuy1BtokDXQfsx3Q8y4ad4ZILrR1AuS+ll05nxgGUvct6Dmnv8BLXrsVcI8R0hxHUzaDLrvdgkYgUy3UsgizFy1H6cM4N+5TjPmNHoJoT4Asqn7iKU0pDFGGdK+wPUj/LFe+s0L2ECDwNXSilrpJRvlFL+jZRyGfA21DLkZ4UQV43p1ybgQygLf7ld782ogJ59wJtR21tj72cV8CVUUM06KeW1UsrbgHqUIbIW+P+meQ9nDE13KC3uLLhdGoGTW0n/UY7HQlP6kZxqAN7shhtob3l80nJg0dHyJLMX3pB2Tm3LJ7bkVTG1ypjNNamqboOdwCy7MZRjZhgDA5hhFcQ94tIIefVROQZi0RGGAt3JY70wZSP3DXkADV3TGMoLJAeEvO5BZRR0BJOLkuX+OAVlBhpqK8HvTg1FnUMGur861aeeo6f1HnNMD4duMBgI4K9oAkZnJw8Hu4mG+pmz8Hosy5x24O5kZVyeglFjQfPWe+g7uQMNnbJZK2lc+QG1YLDyA0n3oda9D1Mx93Im9+9XCmaDvYc4+PL3xrkdFVc0cbhFzSFfC2plZtxwWsbUxk3LMMA0zvs8RFJK0/ZIuBG1iAdwS6ZyM2h+pgHU4cmLTIyU8tMoBZ+Po5KqlaBiGx4RQjwqhJjJ+3I+BYPnOA+ZtjEghLgW+CeUn9ntqC2o7gmK/wg1Il8/nWtIKZ+UUt4qpXwmw2v32+1iXz+df7QfPy2lPJhWp5OU39xnMuwOfMbu55ellNvT6g0D70EZJx+awQ7HGcHCwfwlF2ct48vzMtCjfHwTK+1Ylnqc5EeyqKxhSrEF3vyKKZUDtervLaiwjyZQArFX+Q6+9F0lK5jY5rfdBw6+9F3cPr8yCHKJqM4Io12EHKBpFOupCf9gb0fyeX5RGTX+VCBw75ByJSotdBHNdxLyqM+ZMxrHEwhQGehmJG2NqW6eG5euFtjy0xLLdQ/G0YtTxoDZnW1RK8eZRsPAsBz4Z12B0100Kjv5iYN/wlc0i/ySeWiaPq3xoKisYdIylv3dHzsWHNhxH9FIPwPd++wFg/uSmcvTVc4mHusSamU7aZO/GadIBBr+mnXsemVPUq3Mmsy95jxHyYdOaWMAzeEA3XHBmD+2X3/Cn74iW9kpkHD7qc/0oh1gPGtM2dOKlPKIlPI/7XlQDUr5pw14PfDeaTSV6N/8LGUS95npXvxCiOIJ6s3LUi/HBcZMZlR/h7IyPyOlnMinLkFimeiimXQuCwmH+eT2lBBiDso/Lgo8OLaClHIL6kNbjVrpT9RzA4kl659lqNeCilNwo1YfzjmG6WDVVTePycw7GssywTJHBQHvf+G/6WvfydxFN5PtR9Lh9E3N39bK7EecuawJlpXcyo+G+8etxPkrl9LXvpPJtMGLK5swran9qOWYHulKQv0JJaE0WdGhQFKxDi2/jKpCZQxYFvQPu3E6dFyM4MXJyQoVk+cumU/98EJw30VJ51XJ3YGSwhjL6hxc5s9jhcPDxnwvK7xuBgYMtMJysHe/zKEezMhMXXBznCqWaWAYBnv3tzJ/+e2jVv/j0WGVIdoyMeMjMwjczV7GjE8Ug6nGgqJykXQfTKHh9pXg9pWyaO1HMrr3Xib+AAAgAElEQVQTLVrzkYxjUKJ+7ZLb6AuMsGnTJmbNem0kHNMczl+XXrp6SlsDpWtWG7rL+fCZ7tNUEUJMJUCs1n5sO8XLJT4Ub59gFf4O1A/ooQzxApOR+EBPa3XfzoXwI/twxTTaewm1cDtbCHHN2BftmM+b7MOnJ2jjnRnqFaPkRrPVy3EBMRNjYI39+OOspUiurA+iJuCnk8SSUnpylMRSebMdJZ+JF8eUBRCozIZ9UsqJdAwz1TtnmKaFy1fENZveO6FBoGn6qO38xOpaZ+sWouEADSvvnNDnNnEMZPffn2IMQqI9TXdkUQLRqKy9YrTbUEYsulqfzWYH5TgFMucYUG5ClmUxPJAyBkyvl0SKgOGIE8PUKS1wgGnhNZ2crHCTV7sW/9JbcDvLQNNwRYsY6S9PtuGqameOz0Gj28PNRfncUVLIm6N5RHaZUJBKWm52Hz2Dd50jG7pDRwN2vLQLp7swqRxUU38tjas+SGfrXwAN04xOOSappv51uLzFGWOCkmg6hpFNt2GMu1HaGBYN93Nwx30cevmHVNZezuK1Hx3lTuRw5VM2+1IWrflo2nimU1y5jLoVH6RnwEN1Te1rSq3MXVry9TlvvbV30sFT05hz26297pKScZl+zyEfEkL8UAgxzrdMCOEUQtyJkuUEpYt/KjwIHEetpn8x3ZNACLEElVQM4KszaDthPCzMZGgIId4shLhyrPeCrQp0rX2YHpPQjTIIqoQQJWPbs+dC99mH3xBCJIOx7B2Oe1HeHduklBMFAf+rEGJxWj0X8A2gGHhpoqRtOS4sZuJ7VgIMSimDk5ZUnNZpmxCiGni3fZi+cpHYBssWvJMIIkrfMps/5rWp1jsl3G4nFRVjV7SmTjgWwTmnmhs/8A/I557iSPPLozIQG4ZGcWUTB168l7Er7Z2tW+htf5nyOWtorFuPy11AbGTQ1vj+BeVz1uCvWILHV5Y5eZCdAMyIRZL5CCbDX9FEeKhzAqUgjbqm2xjoas6wXT+eQPce5oibqKg4L7y2zjqn+tnJRldfyuOvr1gNDxXeIvK9XiKhYWLRCAAOhxOXJ/W5Ggi60XHgJg4OnXzNhaNkCQUFadLVlgkMUtSziGip+v0oK45wzHOU+pEFo/ph9FrA69B0Dd1sxhtup6Ti8jNyzzPlTL0HZ4qZfm6CgwEG+7qor68HLKpq1yfHBb9lUlp9EaHBk1hWHH9FE4Gu3RO2lZ6QrHnrV8aNKelBvP6KJgZ7suczCnQ3s1S8MelylBjDEuNIPDrIgR3foWHlnaPOF5U1cmDHvcm8BQsvfh+G5aR57wEe//VjhMNh7ryznrKyiYKcp8d58llpd5f471344bvG5RlIomks/PDdA+4S/72k1HXOB1yo3/13CyE6UJmA+1CJwJaTctu5R0r56KlcSEoZEUK8FSUo8kngzUKIF+1rXWX35SfAd2bQdqsQYidqYfFVIcRLwIh6SX4FJZ7yUaDbLteNmnRfbl9/P2m5DaSUMSHEH1HxkDuFEFtRMQs9UsqEEtRnUYIqG4GDQogn7TLrUfKxx8iw+m9zDLW7sMuuN2D3ZS4qsdm7pvs/yHF+MhNjoA+oFEL4sqzAAyCEmAUUkYo6PyVsS/qnqC/HE1LK36e9nBi1sxkpCT269JF5pvXOKd3BXv7l6a9R7C3kC6//CMvXXp187fihvbQdasblPMpELjfx6BAdLY8DEBnuGDWh72nbTsMl72c40JJVInDuoluY3XCjrT+ezZ9WBeqhO/BXLhudQKhyKRVz1uJ0F9jZTaeAZU5yvRwzJXQ8tcPeZ+8MFDuUm9BAmqFQ4C+jyJeSdxwIuSjKc5OInSuO53NxPLWRNtJ/FI/7EUKFxZwsWI5n0ElxkVpyjZUfZuf+aqIOB2UOB/VuJ7qmATqW6/WYMZ2R9lzysXOFaRoU+Uu5uKQSXXcmdxur6q4kNHic1uaHWHjJ+3A4fVTMvcweS8Z/P9N3KrNJilbVbaCz9S9UzL2MI7t/kb1zlokRi3Bgx33ZCmXINGylXXs3ga491DTcQjweJxgM2vf92lMsc5eU/FvJqpWs+NpX7m578KGyvu1pGYjXrDbUjkAyA/H5xPdR84hrUUmulqEUd2Iot6D/Bb53ulappZTbhBAXoeIJrwfeghrcnkcZAT+fIPvwVHgLKkfCBpSkpwPlmvQVlCtQBLgCWIpSSwoAh1Ay59/PIFt6J2pedh1KrMWJWhT9jH0vESHE61E5BTaTMmiOooyae6SUvRP01bLb/Ixdtw7l7fFT4LNSyqMz/B/kOM+YiTHwIkrj9zrgN5OU/ZD9eLq2ke4DrkFt4Y0NHr6giEbjDAzMTHRAd5s82PxHLCwCkUEGo0H+/P2vjpIbfcfH/pUjr/5x0rYmytZpmrFJJQKP7/8Ni9d+lHlL38bRPb+coKxG3ZJbGQn34XIX4i2oorFW5ShxeYqxLJMDO+6jfsXt09IGN03o7j67fuTnyereKX12smFGwkR71W+CocNggTIG9IhOcCRCX1dqodDlK6K6MBUNHBj2UJEXwTDU+zc/PA+3pYaXeLCHgb2/p7QxTk/1MkzDIjxQRHFRHwBVJWG2ONpoG6nicMSg0ungE7P9uG1vWMt5FaHjj57193siEp+D6fTnfPjszPRz43ZqHN23i3lLLsaMhzm+/3c43QUUlQsOvvRdnO4CPHll9BzfRlG5YO6im8eNHU53YbL8ZDFBDSvvxFc0h6H+I5PvFE7qSqQYm2k4XdI4ce32g79h8YoP0rxXEg6HsaxTH2Nm8lmZqI3ThOUuKfm8u6Tk2/V3vv/j897z7jdjGk50R1x3OR92l5R8g9Hut+cF9gT4N0w+5xhbL6tngpRyXpbXWkkJj0zlWp8HPj+FckeBjAnSpJS7ULseU8aeyL9/kjIx4L/tv2lhJz/7gv2X4zXKTIyB76ECR74ohNgmpcy4lSiEeA/wadTInyll97QQQnwDeB9q6/KaDNdNLFPmZ2kmsQuQPjLPtN45w3QY7DjxSvJ4V/d+6hZfxJHml8grLGbZ2o3kFxbTuPIDoGkM9x9F0zSVCdiy1LmA8qZyuQuTE3Cnu5CKOWsoqb6Ik4cfZSoSge0tT1A9/yoWX/YxwkPtuH0lyWtEQ/34imoYCfXS17GLWQtUxveWV3/O7IbrcXqKsMw4VXXrcTp9LLr0bzHNWLJvBWn9Hew9SE/bduLRIfyVS4kbuQDi0020I/WVChQ4MHWNPM2NS1P/61Ai0RhgeT04HerzER5xoFsFWJb6KnkNHxXxymTZocNPgRknNOIj4lTCFJZVzNBwH4UFoOtQV9vGYItKvtkVN2gujHFJ2IUVAjQHRnwdxmA/jqJxbrE5zjAWDgb7u4lHg7QfeR6wRskPV9atxzLjdLZuQXO4Ka1egbj0w3Qe3ZLcBZyqXDFYdLdtY9aC15NXUEVR6YJx3/90xroSJcawwrIGsCyVFDEewenKx+Hy0XTFZ9DQiEeDoGlj2rQItG9lxfImenoDdl6B1+wOZLu7tORTwKfOdUdy5Mhx7pm2MSCl/J0Q4n6UZfuSEOKXgA9ACPEhVET/DagtLg34TpbAlCkhhPgP4CMo/7lr0mVD0zhqP9ZlaWrumLLpz2uZmEz1zhmGaWKkraD/+ehWPnPFuymtqKRx+Qr6O55nz9Yvp/njNlFZt56BrmY6W7dQVbeB4somulqfURNuTR/lB1wYC9quP5MT6G6mZsG1GLEwga7m0S5AFU24fX6CgVYq5qzlwEvfpaC4loZL3otlGux97qv4K5uorF1P78mXxvXt5KFHMvoUl81aSXDkNf1DfU7IpCRU5FCyoqZpEgkOJl+30tbbBkIuivPjybejOlajklEB7cZxHAPK9Sg0kpIo1TSdgV4XhQVKjGxWeYiW44MkbPKOsIG73svInhDgAa2YyMvHyN+YMwbONgn1Mo9X5+BLalxI7ChW1W2gqKwB04hTVbeBwpL5xGNhHE4P/oolVNZeAVi4vH6at94zpesFuvZQPe8q9r/w31ljChK5ShKuROnxCGNjnCrmXk5/5yt0Hnsmeez2+se1OdDdzIJlG1iwUEwrcFjXNTx6HJcWB9MA3UHMOu9l+nPkyJEDmNnOAKigkR6UG9DHUJN+i9QWVOL4G6gAnBkjhLgHlYm4F7hWSjmRkHVCbrQpSzzD6jFlQQXkhIFSIcSCCRSFEgoGOzO8dtZx6DoOTU8aBIHIIAOOCAuW1HF09/cZ74+rfGLnLrqZ+hXvIjYykAws9hZUUbv4LVhmLLmFX1l7xbQkAjV0Duz49oTXrVuyCSMeIT4ymPQNrluyifrlt9Pyyo8JdGbu2+i2EvVuRXcWYEVyhsDpZmzmYYAiTSkJRUJDSZ11tzePYl8oWTYw7EW3lKuGbjkojaXUgp71vcoG+7k1FBl9QauSWOwELhd43SblZW0QaAQ0uoNxNI+GI+8oRkipCsWOWZjDUfQCNznOHqZp4ckvRiecpvevAondPj/7t/0X9RfdgSevDCMewYj1jctK3rjqrmmNKaaRJlM6QUxBXdNbGQn3EY8OTT0eoXY9na1bkseDPTKZu6SzdQtYJnl5XmKGj3h8amOM12niGuknsPUhQgdeSBoDeY2X4lp/G67iyskbyZEjR45zyIwyN0kpY1LKvwOaUKmz/4IKcElo8v8HsEJK+XE7GciMEEJ8CfgH7EzGUspXs/TpOCoDoRsYl45cCLEBlZegw+5jol4UpRoAmfV064HLUPJdkzvhnwV0w8Gq2SmpYb+3iGpfPsf3jf4BHo3yx3X7SmhveSpZLtDVTF7R7FE+vto0JUPjsWDW67bufRiH05smHajOuX0leAtqJuxb5rYeIh4dxOnMuQmdbkbSdgb6EjsDtqxoeDgVH+DxFVJanAoeDseK0Oz3rCxWjgP13gzofewp7ce0dxH0oSDEU8OB0+Glryf1Xs+qGKTEo4yMrqAqp5daYNpGiqUR2dN1Wu41x9TRdY3ISIRQOJIcFzTdmUw+5nQX4HT56Dq+DbevZJwhoCpo0xpTMpPKM9K46i7yiubg8ZWOS4SWrW4qL0HquL3lqdR5Tcfp9Ex5V8DrNDGP7eTk9z9JaP/zyhAAMA1C+5/nxHf/nqDcjtf52gtGzvHaRUp5VEqpZYupyPHa4pTSuEop90sp/1FKuVFKKaSUDVLKK6SU/yClnFxvMgtCiC+gYg4CKENgKqvyX7QfvyyEWJjWViXwP/bhlzKkJv8S6lfk0+k6xkKIAuAHqP/T/0gpA5wPxB1sWnJj0hXjhgVXMDxFf9yOlicpn706ecZf2URHy5Oj6sbjSjJ0KmQOxht/3VF64Gl9mb3whlHH6X2bqK32I0/gcc/YxswxAdGTacZA8egcA+G0eAFTL8BtT25icQ2PlnovymOp5J8dzn3EXRr9haotDXAMjvb5HgkVpeoWjtDoV3kMukNxTMtCL6xGjz+X6uPBPszgRImocpwJHA6Lbdu20XLkGMXlalyIx8LJGIC6JZvoOvYssxtuoKPlCTKNQ4O9B0/TmGLR1foMsegQuu7A6S6gfvk7px6PMGocShyvTp4vrmgiFjfxeLM2BCgjyTXST+8fvpnl2hbdv/8mrpF+dP20qmznyJEjx2ljUmNACPGqEOK/hBC32ZPqM44Q4mbgn+3DQ8DfCSF+lOHvM+n1pJQPoZJoVAO7hRC/F0L8CjgILEEpEXxz7PWklC+ipLPygOeEEH8WQjwAHEbJf21P6885xzQtCvUi7l69GQ2NldWLGezZN6W6ge7mpCY3KN/fQPfo+ACny0fF3MuZPEWE8tmdPFHY+OsmznkLKrKWycRAdzO69hrJBHSeYMZixLrVqrvF+OzD6cHDLl/Ku7A/6MWB8vt3G24KTHv3xzIY5gAAPSWp8o6h1I4CgMtVzuCAvQugQ2NVgEJXhJgBAxETfKWgtYNpGyqmxcjebnKcPTTN4vDhw+x6pRn/rCsADY/XT6B7L1V1G/DklRPoasabX0GgO7MXZ0/b9tM2pgS61W5mbGSIfc9/HYfLN+F1M9VNH2MSx4lHf806fvqz+zl69MikBoFHjxPY+hBTMUIGtj6Mx5FbwMiRI8f5yVR2BpYCHwZ+CbQLIfYJIb4thHiHEGL2GepXadrzVaj035n+rh9bUUr5IZS7z8uoifx1KIPib4FNE7ktSSnvQQU+P4WKLbgJFRfxL8AGKWUoU71zhRnVWFbWxJdf/0/4nO5p+eOOPrbGnbPMOIM9krmLbmbiH2+VKGywR04pUVjG/lnm6PPTuAdrqmVzTIlYVyfYuurBAhdxp3rfCzUvlmURDqaMAX9JSsqxN2gLbVkW9eG04cQ6ZucKgB5/mjEwZmdA03S6OnzJ46qSMLUFSnK0K2igaTp6fiW6sT1ZJnqoHyuWm1idLUzTxDRNgsEg++Qx5i17J5qu43TlU1QuiEeH7e+yMeF3OB4dmtKYMnfRmyYfUywTMz7CQPde5i66GSMWOoXxLxWX4HDls08eIxQK8difHyMSDmd1R3RpcRUjMAWCB7bjso3mHDly5DjfmEoA8bdQmeqWoowHATRi69oKIY6iEmZsAf4ipTxyqp2SUv4IlXxjpvV/Dvx8BvUeAR6Z6XXPNLqugdPAdBgYpgm6TpFWRJ7LnJZGP6Qk+Fxef7Ju+rnOY89QVbuehpV30n38+XEqQVXzNuDyFNG696GpdT6TH7Cmjz4/DZ9ibaplc0yJdCWh3qLU/7ZA8zISCWIaavJt4qGkKBWbHxzJp9AKUhs6RqFxZXJ5Iap3MOyZB5ygO31nYGD0zgDAcHA2htGCw6FR4I0zzz9Ay2A5PaE4AjdaQTUMvQRmP+glWFGDaEs/HlE+rq0cpx9d19F1HdM0eeHFl1m16v0Y8aGkXGhl3Xr7u+wYNQ6NlflE04jHwjSs+gDdx57LmHxwtFrQBGg6RjxC57FnqF28CXfaGDYpY8eNxLGmE4/DCy++nHxp27ZtXHX11RPHD5hGKkZgMqZTNkeOHDnOMpMaA3agMEKIYpRRsB64ErgElcVuPjAPtVKPEOIEtmEAbJFSHjgTHf9rQ3dbDJkDPLznT+w48QqGZeLQdFbNXsHmZTdRXNHEQNfuSdvxVzRhxCLMX/Z2uo8/R+/JHfgrluDxlSVl+RLnOlu30Nv+MuVz1iQThTlcPkCjq/UZXN5i/BVLRmUvznbdsX7A/oomIsPdWctkoriiCTMn23daSY8X6LGNAa/mwqM56U9zEXLml+G1s4HFDQ1zxKDa7KAmPIjpmQWAhcmRfDc+e3I2bmfAnhgm8Hrz6O2yqKxR56pLw8zpC9AVVDKiWkEVGqCZr2DpG1V/9/fibixD03J+2Gcay9JYsGABBw+q76ZmGQS6mimtvpi9z/8H3oJq9V0OdifHg2wyn5W166msW8/shhuIjSi5WsOI0te+k96TL07aH3/lUty+UhpXfpDB3gMEuqP4K5oITHH8Sx9jEsfFFU007xs99hw+fJiNGzcy4U6G7lB/U5nkJ8rm7IEcOXKch0x5RiWlHAD+YP8hhPABl5MyDtag8g3MQbnpvMMu14naMXjbae35XxG622J3bzP3vvgTrDT/VMMy2d62kwM9LXztqo8w0LWH7P6rGjX11xAabKP1FaX44Qy00nDJ+xkOtCRl+ZyBVuYvezuBrmbi0SE6Wh6no+XxpHxfQrXD6S5Mlpvsuul64Ilz1fVXc3TP/aOOD738g0n+Gxo1868hMuIg98t6+hiVY6B4rJJQyhgoKvOghLWga8hHjd5L7dAxLH1xskzEFSZmxfBaDrAg6NMJezR8IxZa3EAPhTHzUzkHvO44bScrqaxRwcNVxRFq8gbpDkaAArT8agA0oxnLuQ5wYQQixDuGcdWc+8y+r3UMQ2Pt2rUcPHiQS1dfgmlGURNk5ebX07ad+cvfwfH9v2fe0tvw+Momlfmsa7qN4cBRTh78E0ByLOk9uYPJxpLqeRsxjTgHdtw7qm5gCuPf6HEodTx78Tt57Fd/HlU64R4FmV2FYpaTvMZLlYrQJOQ3riGGa9JyOXLkyHEumLGvhZQyLKV8Qkr5eSnl1YAfuAL4R5RU5xDqF6OaDFKfOaaGrmsMmYPjDIF0ZhdVY5geahpuYTJ/XE13jpP+M83YKFm+TP69TnfhOPm+mfsBa9QtuZVouJ/IcEeyTDTcT0391dnbWnwrDncxhpEzBE4nI2k5BnqTsqJqwh62k41ZgL8k5TPROVRE/fARdCzQU7n+hl2qvI6mDAJNG7M7MNpVyOOO09GzgJERtZPgdpmUF0Uw4so4wFcKDhcaUTQjFewePdB7qredYwqYpoXX4+UNb3gDi0UtpjFiJxcLKfea6BCD3fupnHs5sZEhSqpXTCrz2dr8IPlFc5Jyw/HoEEOBo9Q13cZkY0l/xyuY5ghOT3Gy7vTHodRxRd21yViBdBLuUW63hdNp4HZbuFwkVYFGTCf+dbdmuWbq2sXrNhGJ5+SQc+TIcX5y2hyvpZQxlOrOk/bfs+SWbk8dp8HDe/80oSEA8N5lt/HUlmc5fHyEuhUfpLhy2ShfWH/lMhpX343L61dZfdPaKp+zhs6jTzP2h7uzdQvRcICGlXfir1yW9A+erNyo61Ytp3H13UTD/coP2O7LorUfweHy0fLqT5N9i4b7aXnlx0TDARpX342/cvmotoorl7FozUcpKl+C11dwav/THKOwDINYR4aEY7aSUGJnwHJ4KSlIBQ8XBocoMIJYaFhpxsCQ1p98nmcqI6A7axAxuN0OOttTK6fV/ghFzn6iRhxN09Dyq1RZI5VqJNY6gBnOBWWeDWJxndo5NQy0b7VlRZ8fJRfa2boFp6cAj6+UjqPZcoUkGC837PWWYllW5rGkchkNK+9MjiVKljilH5F1HEqve+yZ5LGmu/D6F3P4+MioWIEECxcu5Pjx4/zgB99P/v3lL09hGCGcTmUkxTwllL3xb8lmhFTc/HfEPCXJpH05cuTIcb5xSo7XQggPyj3oSvtvLZBvv6wBMeBF4JlTuc5fM6bDYMeJV7KWKXQV0NLSwiHTpHmv5KIVTSxYvpGCfC/x6BDDgVaGA0cpqVo2ToKvqKyBjiNPZmw3PWagbNZKmrd+ZdJyKrZAw+0rIdDVjGnGKZ+7lvI5l4LmwIhHwDTx5pWzeO1HGejez3DgKMUViymfsxYLJ6blZFbjLcxufCOWZaJpOqblZCTqoMSXl7EPOWZOtLMDy46SjOZ7iLrVRKpYzyMWHSEWVZmDXYXFeO38DjFDY1HwkGpAqwQ7U3FcjxMyB5NzI5+pDItR8qKD44OIvZ44x0/WUjtP6Q+UF0XwOePs7+xi+axZaPnVWINtaFYvmieENZIHFkQP9eFdVnWa/yM5xmKaFrpuEehuZtaC1xPobsYZODrKTdDp8mHEw/bx5AS6m2msXUfHkScAjfySOpq3fgWnK39UnBKoPAVHdv8iubsY6G5mdsONpJLdZxqHAE3H7fUTixkUV6+ibM5laJrO8HCQQ8c6eOXVxwgGgxn7t3z5cv70pz/ZrkLKbejgwYMcPHiQ6667ntmza4nEdby1FzPrfV9lYOvDBA9sT2Ygzm9cg3/9rbiKK+kdzEkh58iR4/xlWsaAnYRrHanJ/ypUxt/EssgQ8Bhq8v8ssF1KGTltvf0rxDBNjElUMqykbysEg0G2PvcCW597gUtXX8KCuR7a7d2AguLaKUmLppOIGSgqXTilch0tjwPQtO7TtMnfZum1RsPKO+k69izx6DB1TX+D5ahgJJr6cR//8cxtNJ0JRo4fSz4PlHqSz4t1H+HBVLxASbkHUK4U/QNuKk2lKmTo85IDQMg1NGqR1GcHemeTFwXwumN09s5hePAgBUVOdB0qiiMc6W1naU2NUhSy0fVDGCwHIHqgD09TJVouodMZxzQNnK58NF0p96S75xzf/zuwLKws8qLjSJZTLjtmfCTZbvpYMnFdk9rFb+HYvl+R7rqYqqtR03ALvqJ8fvCjnySr3nTTTUQiEZ57fmJZ0KuvvpqjR4+Ocx1K8Oijj7B582YcjjwicZ2oq5z8q9+H/5o7ksZADBcef7FdYwryyzly5MhxjpjUGBBC3EJq8r8C5VqU+OVtR036n0UZAK9myO6b4xRw6DoOTc9qEKRL/6Wjtr4vYclFd9F/8lnlj5FBRnRKsnxpdSdF09WEISvKTWB24xvwFdRgWAW2IZDjbDNy/Hjyeac/9b4Va3mEh08AYOluyvyp1c28QMpIiDkX4rafBx2p85ByE+ordmJqoFugB8MQi4MrNfx4PXFAp6Pdw8IiZfRVl4Rp749wMtDL7DRjwArtAPdFEDMxh6PETw7hmlNEjjOLpjson7MGIx5NjgWdrVuoqttAw8o70R0eFVw8jXFCd3pYvPajOFx5gDWtuqYZx5NXxqK1H6W95QkGxkiVVs+7GsNy4nI5uWLdGnbu2kM4HGb/vn2sv/JKbr/9drZv387hw4cxTRNd11m4cCGXXHIJhw4dYseOHVm7sH37dtavvwrTVDsnYdNBOBFsbK9b5BwaTx0hxEz8q/5XSvluIcS7gR8mjqdxzY2onENbpJQbZ3D9ydq3AKSUM/7Rm+D/YgEBYDfwY+CHuTlZjqkwlZ2BxLKLBkhSE/9npZQtZ7BvOQDdcLBq9gq2t+2csIym6aOk/8YXUJJ+Tmc+/somPN7ScTKik8mDJvyDpyojOhX/2EB3M1X1NxCOajgcOUPgXJG+M9BWlHrfCjQfgWEVDIyvEH9+NPna7KFOAAw8uEjp/Q+QihcA8NnGgOHQGC50UzSo2nAMDWOU+lPl3MrQONFeTX1DG7quUZwfI88TR3afZNbCpeBwgxFFiw7gnOMifkLFL0QP9OaMgbNA3HAoCc/u5lFjQcI9p67prRix4NTHicqlxCJDtO59kHgsyLwlt+GvbCLQOTWJUNAYCffT2tJLPD6f+mUb8IICUf0AACAASURBVHrcuN1uhgNHObTrh8RHBkDTKa9o4va3v5FYzMH+ndv59b3/RuOKNaxdfSVXrl+PaVnomoama9x//wMMDU2+kn/o0CGuvHIDkwcQ5zhF/jfDuWpUQtEgkCnZzbNntEfnFw8DCd9LN0ruPaHy+AYhxCYpZS5gJUdWpuMm1AFsxd4JyBkCZ4m4g01LbuSFtl0Zg4j93iJMw8HaNWvGGQMJN6GjO/+HhBRoJhnRBRfdMak8aE/bCzRc8t4pyYjW1F+DEZ1CwmbLJBwK8uOfPZz0wZ0wwU+OM8ZIW2pnoKtErWwW6z6cmpbMPFxc7kPXlcdfMKhTElVLnyGXIM/WIRhxhokzkt50MmYAoN/vShkDg0OjjQFvFLAIjcwi0NtCaYVyV5pVGuJQu5Pe0DAl+dVYg8pw0fO6AOWCEWsbxAxG0fPd5DhzxGI6eR6vkhIdIykcjw7R2vwADSvvxJNXPqVxoqruSg7v+t9kHEDbwT+pdjsnlwitnn81R3b/gqoFb+LFHX8mFAoRi8WVW+TB34yub5kEunYT6NpDzcKbwBwhNDxInVjBH35wD6GEwatpvPPv75mSIQCTS4/mOD1kWtG3V+6vA3qms+I/DV4AFpPwizy/+aSU8mj6CSHE5SghlzcDNwPZfHZz5JiSmtC3gX1AFfBe4AfAQSHECSHEL4UQHxJCLDuTnfxrxjQtCvUi7l69GW3MCtRbFlzD5xdtRtfidB8/xNUbNyRfy8/PZ7GoHffDOFZGFEDXXZPK8tXUX4PD6ZuSfJ/ucBMJdU9QJr24jtOlJnCPPvoIsVg4KduX4+wQHxjAGLDVglxOBgrUxMav52MaBpHQEJbmoKwkVcfTnwoAjjvrk8+DtqRoOnlpyeGyKQrpOuR5Y5jkceJ4aliqLgnj0E0OdJ1EK56TqjC0H71EqR1hQfRg3zTuOsdMMAwTdCfxWHBCKU9Nd+JwTXGc0Efr7k9HIlTTdKrnX83hIx2EQqEJx7vRWLQf+j2Nyy/i6re8h2MHdo8yBK668e0QjaFP6uKoSLhn5njtIaUMSSn3SymPTV76/ENK+RypHZON57ArOS4QppKB+G4AIUQpqQzEG4CLgLdi5xAQQgygdg6eQWUf3iGlzK3zngbMqMaysia+et2/cKL7GLM8JeTrHrw46dnyDNYN1Wz59Q9Zvu713Lbpzex6ZTdVVWUETj7LZDKi5XPW0N7yGB5fGQ0r76T7+PME0n1vK5qomHsZhhGl7cDvJy032CM5cfD/mN1446T35a9owpemDpTug5vj7JDuIhQtK8SyjbFiRx6h4QAAmq+IiuKUDkDJgJp4xzUHHivlyz+kB8bNwxJuQgDtpamJk2NgvKJQvi9KKOKmu6+CcLAHX74Tp8OiuiTMid5ehmpnJaXKzK79OJfeRLRf9St6sA/P8qpcIPEZxjA0/BVNo2IFEmNB+Zw1hAKtBLr34vGVTjpOtPcdpnz2pbaakCLRbuPqu+lqfSZr3cq69ZjmELquc9XGKxho38pUJE0DndvJL1zOkw8/gq47mL/4Ii5adRWh7S8RsfKyu1ymsXDhQixLn8I1z0tqhgcjnzAM6xbDMJ0Ohx7XHdqvC4u8X0fFAr7mEEIUAv8K3ArMAnqA3wH/LKXsG1N2IxliBoQQ84AjQCuwEPgYsNl+HpNS+tPKLgP+HTVf8qDcrP9HSvm9M3KD4+mwH8dluxNCXIvaNbgClSi2wC7/NPAlKeW+DHW8qPt9K9Bot9sHHAWeAL4wVjBGCFEGfBx4E8p9KeFu/hPgm7YkfXr5HwF3AO9BKVH+O8rdyQfsAj4rpXzKLvtG4B9Qc1HN7vvfSynHfXmFEJuAN6DUL2cDXqANeNS+3+MZ6jyNeu+uQikAfA4loJNv38N/SSm/P7behcp0MhD3obaafgsghMhndAbiS1H/7DegRseIEGI7yjh4RkqZRRoix2S4Yhb+oSjDv3qWtu0vYBkGmsNByepV+I04pmmw65n/48Cu51hy6UZWLG+ieevvR7WRSUY0ec4yx8vykZL0q19xu5IlnaRcPDoEms4ccdMkd6Syf2ppc7ecD+7ZJ9JyOPm8v8wHKPu9VC8gOKT8/0urfDh05d4TDDrwh9TzIdcciiwlKWpqBsPW+J0Bj6XjsDQMzaKjJM0YGBxWSlZpH4B8X5TufoiZ1XScaGN+owq/nFse5GRvHgdDBhfpLjBjWOE+9IIwmtuBFTUwQzHiJwZxzS0e14ccpw8jblI9/xoCXXvGSXm6vMXERgaTE/ipjBMpadEUna1b6O3YxaJLP8zsxhsx41EMY4TBHjmqbmXtFaxYvoSFDYsozHfSvPUnY7ubkUB3M0vW3MDfvPtTaJbF0MuvcPxLXyMWGGDFf32dtaJhSsbAmjVrLkS3Rm14MPK5oaGRu5957GDZgeZOh5KN1WhsqvrEla9ruKOg0HNvQZH337hArZwJKEYtVs5GLVbuQU2E7wIuFUKsHTsxnQQN5at/vd3eXqA28aIQYgMq+aoPNXHcCdQA3xZCLDnlu5kaiSQe4yb2wH0oI6AZ1X+ApcC7gFuFENdJKZNxF0IIHfgjcDUwAGyxH6sAAfwz8E1SBkjCGHoEZXi1oSbrOmpC/jVUPMONUspUMFqKVcC3gBaUodGAmog/KoS4BmUA/CfqPX3UvtebgNVCiKVSyrEZKe8HIqj36XGUcXYR8CHgrUKIdVLKAxn6Aeo9/gTqffwz6n2+HPieEMIvpfyPCepdUMw4z4CUMoiSEX0MQAjhQr3JV6CMg8tRVtUG1KBySjkN/prxaCbhV3dy6L++qSZQNpZh0LdtOxVvuQldd2CaBqGhAXY88VtWrL1iajKiaeeySvpNtRyAZSqJwTQN8NGksn+Wz035n+R8cM8+4TRjoK0sNVkv0/MJDbZjaTpV5Wmfue5w0lSLOhaA/XEKu4JYGaRfNTQKDBcDzighr07c7cQZjaMZBnowjFmQ2hnK96nfhLhVTufJKHPmmbjcOl63SZU/TGtvF4uL5uEJ2BO1nn04Zy0ldlTtYEQP9OWMgTOMQ7cw4iNJOdH0saBx1V2q0DTGiYmIjwwQDfdxYMd9WepaBIeH+PHPHuZd79w0LUnTWH8fBz71L6lzmsbCj/wtZl4RLofOddddz6OPPjJhE9dffz0ul494/MKaLw8PRj53cF/Xx37/4KvF6UOzaVrs393h2L+no/Km25Z/rGFxJQVF3s+fs46efm4B/gRcLqUcBhBCzAK2AZegVrt/No32EhP/JinlofQXhBA+uy0f8EXUzoNlv7bB7scZQQjhBuYBH0Ut1B5HrcKP5ZPA01LKQFpdDfgAylD4jhCiKS3w+AqUIfAycKU9/0uvdzkwmHbOh1o4ngX8I/DVhKeI7WVyP3At8E/A5zP078OoVf6vpbX5ZeBTwPdQAeQbpZTP2K95URP19agJ/v8b0947gD9IKUNp7TlRq/3/AnwDuCFDPwA+DbxPSvmDtLq3o/6v/yqEuDe93QuV052B+DmUFfcEKtA4oUKUW+qdIbquoQ31jzMEErhK/FjhMPOXXAxAXmExq6++GU13pLJw2mja+HNJydDJmGo5UNKimmNK2UA1LTXxz/ngnl0s0yTSktIBOOBPLYyVUsDwYD/e4kL8Beq8acHsfuVBYKLhYnay/LBz/K5AgkLD3qXWNELF3uT5sXEDHncclzMOOIgaZbS3hZOv1VYGMS2TFues5DmjYzfOuSkVoVjbIOZwpkWmHKcDFc8T4eCO+1LZfqvSM4Vr0x4nZvRa8nUNTXfQsHAh+QWF07quI085nGkOB2XrLmf517+Kb/nFjFg68TjMnl3L5s2baWxsTI5Juq7T2NjI5s2bmTXrghQ7qBkeGrl7rCEwCgt+/+CrxcNDI3ejVrJfKwyjJnRJ/0Qp5UnUajbANTNo8x/HGgI2t6J2IA6j3FqS/20p5RbUZPt0ckQIYdlSoyOoFewPoQyStVLKcYOzlPI36YaAfc6SUn4bNY9bDKTvYCQyOz6Tbgik1ds6ZkL8bpRb0ANSyi+lu4zbXiZ3oJLSftg2JsbyfLohYPMl+7ER+FbCELDbjABftw+vynC/D4ydsEsp41LKzwIngdfbbmSZeDjdELDr/hS141KE2sW44DnVDMRuRmcgvoxUBmJIGQG56L4Z4iZO2wMPZTQEqt/yJvLWrOTVPdtYuuYqikrKqW1cxu5tTxIOhpQMYFdKpi8ej4yT/ZuqZOi0pUVNgyO7f5F0E9AdbjTdSX/HK8mtfn/lMuLx1GreBe6De8ER6+rEDKlxXcvz0Z1vAho+zY0jGiMejVC7qJiEaHp3n4eyuHo+5PJTYKZ2dYa0vgnftkIz5bI6UOymyI4tdwwOEZuVyh6saVBcEKEnUEDMrKKjrYdZc304XTp5HoPK4v+fvTMPj6M4E/6vu2dGx+iybMmWb8uyyxc+uGwgYGOucJ8BsgmBhCSEhN1AkmWzyX4LfDl3k03ItWST7OZg8yUbwpIACYGAsQGDDTbYGIPL8i3flnXPaK7u/v6oHs1ImpFGtk6rfs+jp2e6q6rfninV1Fv1HhF2tFpU4yOPBE59LYY/gTm2AOe4UhyitccpWHIqzWGGDwG/w+HdzwNup3CiE6ZfiGNH8fkKCbcc6NM40XI8szlOT9eS16PtjeQVT6ei0EVuWt9tvMtG6bh5OP4AS376Y8DANv3EDAvHSXXgRAIsq5Dzz1/BBRcs78hD4Lomts2I2xEAaG2J3PfSX2vH5uBWwcvP1469/PoFny0qyf/ioAg38GyUUh7OcH6bd5yY4VpvPJHlfDKSx2+llJkyZT6KMjvpL9JDixqoVfPTgVsBUwjxyXQlKIkQYjLKrHsOalKbXJlLOoLNRpkRgdoRsIE7hRDbURPkIz3IlHQafCzTRSnlQSFELUrhmAV0NdHpti0npWwUQhwHxma6DiQHjIzfpRBiNsrkpwblI5FcPfB5r2tQ5lxdeTpTe6i+Mzfb/UYafc1AXEjnDMRnoWyvoPPq/xGUr8Aa4CUpZe8jtCYjPidB4/rumTIn3HAtkeoqnvnVv4HrMn3OIsoqJvDH//o2uC7nXH4TYye9z/tRVqO/z1/A+OkrOoX9yxQmMBN9DS26f/vTaWYCLzDrjE+k7H3Tyv11VSoc9Ai1wR2xtO9MmQglJowDQ4UFHWcVE2ppIFDgo7I89VtmH0gtrLT7qinywobGrCgRN/suaYmdUgaOjfExxXtttXR3Ii4piiplwK3Ett/h0P4IU2YoU6LplW0ca85ne8FsTmt/F1wb5+hW/JPnEPWUgVhtA/mLJmhH4gHAZzne/78iGU50xmkfpHbjT/EFiqhe+GEqppyb0zhRMeUcdm/5TR+vpa7HHT+tza1sfPFJLr3lLibMWNxpvMtWd8yEZby+6k+cceENxBOmKp5hscVxXC+YgUFqnjTylIAkju1ev33rkZxsMOU7R6xLrpl3I3CqKAPZogIlV83zs1zPxlEpZXuWa8mwZ7uzXN/Tx3v1RqbQooWoSJAfBopR9vTp1x9Cmej0NAfs2HaVUu4UQtwHfBtly/8jIcQu1C7CH4Enuig+yTBzjwkhepO/gu7KwP4sZdtQykCm68kflE7fpWcO9O/Ax+nZSiVbspr+7jvDkl73VYUQVwshvuU5AzehNLIvoWyz8lEf7gHg/wF3AXOllFVSypullD/SisDJ4do2rt15ccE/pozCpWfw4jO/AdelsLiUQH4hLz7xy44fNROTbW9tVHG1k/3fdbuFEc01nN+kmsuw/IVMm3dTj+Wmzb8Jyx9k/PQLmFB9Mb5ASYd/QLoiMG3+TdhugB071C5r0gY3l2Rlmv6hfUdq/G2sSNnuj7OKaWk4StW0kg7/3vqWPGZH0gIuGNM6XoYDPcdlL05TBg52ciLuXq8kGAFcbLcMxw1weH97x+5RYb7NhDHt7HLLCHs5j50DG7Eqgxh5ao7jtieI1zV3a1fTD7h2N7v89PEjEWuj+di7ADmFB+08JqSuTZt3U5Zr3vX5N+Ni8e57uzEtH7fc8wDjp88Bo7DzeJehblXNNWx/exNy41oMRtfKg207vvTdj55wHBfHdk8lP7/+jlGXTREYFngmMfegtNerhBDzk9e8yDr/jHqGTwAzgUIppeFlRE5q4UaXNn8ATAPuRpkgWShl4zFggxAifTKdVDr/hEoa19NfV2df6P376sv3+VnUcx5C7ZZMBfLTnvc1r1y2gWNUxDfM5Z/9j6Rs/5Pswlv1R4Xe2tP/omlA2bQaltVJIai47BLeemNVx8R/3lkXsGXdqk6rW45js+HFP+O670cs/iROohl/fin75ZPdwoN2Cue37xW1upYWzm9C9Upi7Y1Ew/UUFE9k7rLPcmjXC93C/lVVX4Tr2Lzz8tc6sh7XnP5RYu2N1G37Q6dythvgZ//138yePZulS5d6zniD/vGOWlzXJfxuapV3dyqJMJVmCeG23cyalzKh3Lm/iFmGGhPDVgFFTqpCppCi6RTbqWRgdWUurmFguC5mOALxOPhTyoJluZQVt9PUWkjcqcRM7OfA3namzVTWh9PHt3GkKZ/3fFM4I7ET+8hWfPE2fJOKie9KORIHppWh6WeSPkddFIKuYUZb6iXlVadnDQ86fvpyTCuPQw3PpdpLD2OciFBaOZ9oe0O3uhOqVxJpO0pjS4INGzexYNYc8gMFtMVMXDPC3tqDzF54J42H19Fc/25H3dJx8xgzYRnb397ExtXKwsAdZTGMLctMmKZBLgqBaRqYlqFH5BPjgHecnuV6tvP9ipSy2TOrGYcyZ0kO+B/wjl/KEua0poc2D6N8Hn4MIIRYhDJ7WozaRfqSV7QOFWXoESnln07yUU6W5PPeJaXMZPKT9XlHE7lq/pLOk/+DAyeSJp2E6WPM0rNpePW1jnPFpy9iz69S0aym1MznzdWdAxQ4to1ppra2m45tJVgyscfwoOGWA4ybvJRJsy4H18UwLdpbj7DnnceYMO18CosngWEQj7tMmn0Nk8XVuK6DYZgk4mHq5NO0NXr+VK5D09F3aDq6lWnzb2bB+d4YYVgkbB+JWIyPfezOEW2DO5KJHz1K4rhakDECAd4uTZn5jAmDWeXH8hbxW8M+ihsb8RbjafZPpNJVk3MHh1Z6XonPc03yHIuoaRPzucSLCgi0qvtZLW3Y6RnNgHFjQkoZcCvJYz+H97czYXKQvDzI8ztMGRdm77FKauxDlLph7P0b8E1+X4cykDjYit3QjlVecNKfkyZFwjaz2uWnhxktKa8BXAL55UwWnccJx45hx9tpPvYehSWTqcwSdtSXV8rs0z/e6TpA89H3OLT7eaac9mlqqquJ7NiJb67KeWmYFm+99Ffe2/gapy1bzvR5d2IYBq7rsnvbO7z0zCOEW1VfNU0LwzRHyZqfwrSMJ2bPH/+5bVsO92oqJBaMt32W+fhgyHUKsgaVoPVWIcSDGfwGPjQYQgghSlEmNZAyoQEo946ZYuvPBZbkeg8p5WYhxPdQEX4WpV16BhUt6AOo3YGhpKfnvQRlpjTqyUUZqJRS1g+4JJqMxPAx+eabaHhtXcfKv4ta+e/AdTu/B3Zv28zKG2+npAx2b/4p4DJ+2vm5h/0DZp95N8cPvsEUcRUt9ZI9W/+HqQs/w69+rX4jgsEgN11/CXs3/wc9Zf3cu/V3zFl6L5FEibcqZXMq2OCOZMLvpXYFrKmTafN+K/IMP3ZDAxOnpcwgaw+NYalvU8d7x5qZTEdAJBDCcXsOz21gMDaRx8GAUgBaS/MY24MyUBKMkhdIEI8p52LHgX2725g1x8s7UBHiYEMhm/0zOD+2FbtuHb6ZF2JVBrGPKofoyNtHCK6Y3tePRdMDsbhJlZdjINP/bVcfodo3/xMwqFlyO9vW/5DZZ96VPVRo17aizSTiIbZv/EmnZGNH9q4BwMDljPnzKQ7kEcECXFwsZsxbws53NrD+r0+y/q9PZm1/xvzTcUdZtOvikvzvXnDJrNu3vXO4she3Cs6/eNbxopL8hwdNuFOL36Mi39QADwoh/jkttOj7UGY2A4rnM/AjlEVHI8qHM8k24BLgE0KIZ5Jx/oUQlSiznW7/GEKIlSiz8OfSIwMJISxSzsJ706r8BJWg7HYhxB7gX7tG8xFCzADO8yLzDCTbUE7KdwshPi2ldLz7z6T/IzuNWHLJQKwVgSHEcVzc4jHU/N09HeFFVQhOlVegsLiUYGkJt/ztlzFwcV3YI9+hrnYrl9z8N8j13yf1w+12bMv7AsVUTF5K8dhZHcmfWo7XUr9/fUdSH39+CfnByk6JfvILU8GiFi+a3y3LcWZcDu9+gcrqa4lEtWPncCDdRKh5UhnJhaMqqxS/eQS/T8UFCEctdh8q5qI85VwcM/wUOKloPT2FFE2nPE0ZqC/zMdZz/8rkN2AYMKmymV37x2K7RVhGG/WHI1TXjMHyxfFZLtPHt1F7sJQD5lgmtxzAadyLf+aEDmUgvrdZ7w70M47jYhoFTJ5xFYcPrKFi8tkZxo/Xqape2WHzP6H6Yg7vfhE19hjdzIyyjkMH3sCfX8rsMz7ZOVEZqHGooBBfcxNOYUmHn5HtWJx54TXs3Loxo0NwB4bBmSuuJmErJWIUcaioOO+Rqz+wsFuegQ4MuPoDC5uLivMeIS2BlCZ3pJRhLw79n1Ax7G8SQiSTjl2Aiml/Xz/e8ttCiPRoQuOBM1DmQVHgji7hQB9GJRe7Etjh+YMWoKIg1QF/QOVlSGchKnRnsxDiTZT9fSEqmmQVqq/8S7KwlLJNCHElKhLPA8DfCiHeRoXxLEaZLdUA64GBVga+gYoidBdwofddlKOe9zVP9nMHWIZhz+haGhmhRF2TgoVLWPjdb3PgscexQxGmz13CmIpxzF64mPq652lOs60dM24u4oabaW89SPqPXTI8aF7BWErGCY7VvcohL/tw0sZ/xmkfpKVeEm1v4PiBDZ2yg5ZWzCcSSfDhD3+Y9evXUz1jGnVbskXd6kzTsXeYOOsqdJcbepx4rJMysKsi5dQ7KeSjcmLKxn/f0SKm2oc63jcHKihzUvb4zUZ9TvOpsYm8jtf7ypUxKWRWBgDKitspLowQj47HstrAhV27LWbNUrsQE8vDHGooYIs7nQnRRqxdq/Cf8VGsikLsY0rpaN9wkOAl1RiGVkD7i7htkG9WUrPkoxze/UK38aOTjxCds553DU88ftryrONQ9cIP0d52hF2bftFNhrKKBfjw4Y4dT9RN9V3HcfEXlHDRjXfywuP/mVkhMAwuuvFO/AUlxEehaWJRSf5Ds+ZW8sn7zr/75edrx8p3UhmIxYLx9vkXzzqeloFYc4JIKVcJIZYB/xelAFyHipjzGSnlj73IPP3FjV3et6NW6R8DHu6aWVdKuUsIsQT4OiqZ2NUoP4efePJ+L8M9ngLKvGepQU2e21CRdn6M8g041uU+W4QQC1E5D65FhTs9FziGUjp+g9pFGVCklK8JIc4CvobKCXAtKtLT11AKzLMDLcNIwNDRWwad1cDyWCxBc3PfAhKYpkGABK4ZIBFvJtK2g0M7niJ7lt9riLU3dWyt+wLFzDr947Q17aJu25M91isqq6b2zZ91igA0Z+lnaWqDI0eOUF5ezviKYt5d+80MbWRm3rlfpK090HvBHqioUE6tx471HMFmIKioKB7qWeVqTrDvpNO26S0O/lCN977ych69spyjtlrhv7UtyLQp6jGjcZO/bqzhUl5lvKWuHyxcznj7dAAi/nZ2+DdnvY/phfd0HJcEDo+X78YxoLDd4RNPqA1H17JovuJCyDBhjydMtu40KbXWAmC7BZx9XhWBPCVLc8jPW7vKmZU4yAK7jsBFD4BdRPurqahzwQun45/av1mJT6QPDnHfWU0/9BuAgnyING9j77uPkcu4M/vMT3WYBvkCxR1hSMdPu4BAQVkv49C1KjmhN34lz89ddi+RRCm2ndng3+9ziLe3sGH1U+ze+iaOo/ynZsw/nTNXXO0pAoOT3LA/xqsB6jtVrS2R+2zbud6xXZ9pGQmfZT5eVJL/PdSqr0ajGUXoZdoRhOO4RLBUhgwz0YMiAOBSt+1JZp3xCY4ferNjUu848R5+gFP1xNmfSTtnUDXrOiJxH0899QShkNpx/OTHb8sYXSQjhpl7hlDNgNL25oaO1/7ZNRy19wBQ2g7jK1KBw+rqgzSHA4wvVpNvG5M8d2pH3VZ/pwSWPeLDZFwin6P+COECk3ieD380gWHbmKEwTlGwWx2/z6F4XACnwY9pxLGMdl7fMIHzzm3FMFxKg3HGl0WobZzIJLuecbtexL/gJnxTSkjUKZnD6w9QPD6ImaeHupPFNA0Mt6UHRQC6jjvppkHJMKTT5t1IoKCc2o0/7aWdP3YZvwymzb+FhFuUVREApUSaeWM45/IPce7lt+A6DoZp4uLDdqxRuSOQgUPFJfn3A/cPtSAajWbo0bOzEUggYFNf59nh9ojLsbrXGDfpbADGTV7KkT2rc6p3ZM8axk06m9LK05i26C521kVZ++p6TjvtNGbPns2tt97K8YZmSsfN66UtRVnFAs9GVzOUuIkEbZtSSRYPp4XgvCDuIz9fDQnxhMG+AxXMtFI+Yc2B8ZQ45R3vW4xM4aGzUxVL5TJoKEsLJ9qU3e9g2pgI9W4qS3Gs/Ti790zqeF9T1YLf7/CWv4b4nldx2xsJ1JRjBLy8A+E47a/t1/kr+oGA3/ECDuQ+7iRNg5Ic2bsGX6CIY3Wv9amd0srTmLPsXgJFs4jGel8odxyXeMIklggQd/LVMWHmFFZTo9FoRht6uWwEYlk2zfVbey8INB3byuyp53F49wud7HdzqTdv1pVs2vwuzz/xV0KhEKZpsmLFCkzDAAxctxhz7CXKX6GX8BQTZlxEe8zspZxmoGnbvAknrGzqfaVlyKJ2aIfyFpsZlanJel19kOMtYzm34I2Oc3FrHqajbAWaqgAAIABJREFUlIWIP0y72z2DcE9MigfZTAMAeypNxnvJ7P31jcQnV2Ws4zPBLBgLUWX6EzDr2L7zYiZWHSM/P4rf5yImtfDO3jJqjQrmyT/jX/whAvMriL6l/B/je5uJbD5CweIJfZJX0xmf5ai4/zmQHHd2vf3rbhnOLX9Bn9qZN+tKNr/9HpUUE42dqPQazfBECDGHvmV6/oIO7KLpb/TOwAjEzZAJNHvhtHKu26d6ba2tvPra6x1mQY7j4No27RFoj7hEoga2W8S0+TfTc1biW7Ap0quzw4Dml1P210ULF7IrfgTDcTkvZlFQkNoV2HtgHKGETZVPxWW3MSlwZ6TaCTT0+d6ldoDShNoR2DshtTPgqz/eY/SX6eMKibmqvGWEMZxGNm1JpbgfVxJlQlk723xTaKrbhNNyEF9lEN+UVELM6OYjRDYf1n3wZDiBcSdlGvQB0jOh93UcWvvqemy7a7h2jeaUYAJwex/+ioZGTM2pjN4ZGJFkzgSakXQ7/Qyh/Xqq53aZ4JumqRL1pBGNGeQVzWbO0ns5vPsFmo6lZy9ewIQZF2FTlNPWvmZgiR8/TnjrOx3vbTGTI+FtzKi3mTk95WRbVx+kvbmSOXnvdZxrC8ykyFXOkC4OjcbRE9rkmRorYouvkSNj/cR9Bv6EykRshttxgoUZ61QUONRZVQScfQDkmXs53nAWe/ZNZPpUlf9w1sRWWtv9bHRrWLHpN+Sdfx+BOeNww3Hs48ppNrLpCPbxdgrOmYxZ4M94L00PZMlAnLlscpwwMHwVNLWOZ+ppn6H58JoTGodUOGW9dqU59ZBSrib7appGMyjo0XUEkswEmgtllQvwB0opqzytm/1uT5RWzGfnrn2dztVUz8R0u3eZaMwgkiihsvpa5p37xY6/yupriSRKtCIwTGha9XzHCnzBzJnIQBN5MZdl+QHy8lIRhPbsq+K47WNZXm2qsplKStmW30zCOTF7jalRtajlmAYHKtN3B3reaZhSkfJtCJh1GLSzbXs14bBKjmZZLgumNdHqD/JOm429cxWGaZC3eALW2FSugXhdCy3/u432Nw/hRBLd7qPJTp/GnYr5RNubmLvsPorK57DpjWP8+qe1HKo/A5eSnNtJjkM1NTW4GcYejUaj0Zw8enQdgcTiJhNmXEzviwkGE6ZfRHu8kIkzr2XcxKVUVV+SU72yqvPYtPmdTmeXLltK1M7cZRxHmQ21hX20tQdoC/uIRA3tsDdMsEMhmla/2PG+aMkZyMgBljQ6TJ6SWpHfdaiESEsF+eZhxljKtyBulBN0J3aUqfefeC6iEidAWUKFl903PhVm1n+kZxPYiuICEpbavTAMl4C1A9u22PDWfBzPj6Egz2belCZ2+CZyUL6E07gHw2eSd3oVvqkpkyESDtEtR2n5/buE1+3Hbome8POMJmJxkwnVOY47M1ZSVDCNwpIqEo7FRVfN4Y57zmX+kum4FObcTnIcWrp0KQmtu2k0Gs2AoJWBEYjjuNjkbqufSNi0RUzyCsvIKxzba72qWdfxntxHOJzKHv7+Sy6hIK8Ax8nR1lczrGha9TxuNAKAv7ISa9o0jh/by1lTi0laX7SE/ezbO5WQ5bK8IKUIxgIrMLz+Eg60EbKbT0qWqTG1O7BrckoZ8B2px4jHe6w3saKy43W+uRODdlrbitj0dsp/oLw4xrypzWz0zaD59f/EDTeoHYK5FeSdUYVZnJbnwnaJyeO0PrGN0KrdJI60aZ+CHnAcNycfockzriZxNISvINhRL2E7xG0b23WIJeyc2kmOQxdccAF+f4H+bjQajWaA0MrACCUaMwh4tvpllQtTNrqGSVnlQuYszRyGz/Ll9Vhv5umfZvf+GK+/8SamaTK7Zha3ffjDTJ06nUhcm/uMRBJNjTT85c8d70uXncuu8D6uLMmjsFB9/wnbYMuOKkw7SMg+jvBW/21jIvlM66hbHzh40vJMjxaBC83FPo6UK7clw3XxHzrSY73C4Bj8AWXyYxk2fkspLIeOVLKvLpX/oKI0SvXUdtYxmfDaH+CE1K6Db1wh+edMJm/ReMySvE5tx+taaPvLTtr+soPE4b5FSRpN9DTulI5bwOxFd1FcNhtjzAR8BQW5tTO+SzteOOPmcBFiznwmTpyqdwU0Go1mANEOxCOYaMzANJWt/sRZV3U47iZsi0g8e0ztbPVsxwcunLZwDAsWLMIwTUzXJOaYROJ6R2Ckcuyx3+FGlSlMYPx4zBnT4PifGFOW+vffXldGuKGKw/4oVwfeBMAlgB24gmR2iNb8ZlqcvkcR6krQ8TM+XsCRQDtyWj7jG9TkO29XHbEpkzJmIwYwDIOx46Zx+OA2AIqsfbQ4VSTcyWx5dzolxTHKypQSU1kWIeDz88aecZz1yncoOOvjmOXVGIaBb0IR1vggTkM78b3N2MdSO2D20TBtz+7EN7GI/CVV+MZldmoezSTHj/Ezr2PSrKvAtTEME9c2SMQt2sktnn/HODTjWibWXA2odmxH/VVNMrBtSOgkYRqNRjOgaGVghJO01e/+Vfb8A5q9HqgNo/RNI60IjFRaXnuV1vWvdbyPnHsOh1ufobw09Z3uOlREw7EZ5Fc0MeFYAzXBo7iYOL7LsVARhBzD5rBvX791hepoMUcC7bw7M59z3g7ht12sljZ8h4+RqKrMWq+gsJTConLCbUopCfo20pYoxHbLeXn9LC6+wKGg4CgAZUVxCucYvHugitlrf0DhrIvxzboUw/JjGAbW2EKssYU4bTHie5tJHGjp+LdJHGyj7WAt/qml5C+ZgFWW3z8PforgOC7tEVDjR9/Gnq7tqHHI8v5OrB2NRqPRnDjaTEijOUVpr93OkUd/AUCkpJiWa8+goOptCgtSNhe7Dwdpb6+ieOxxDh+NcUPhG7gU4PivAau6o9zhojqiTqjfZJscC5LnWEQDJu/MTE20C7dKjFjPvgPjKmbg8ynbf8tIUOx7mTxzJyYuz74saG2b3JG2IOBzmTwtTsNp0zja+ibh175B4sAbuGlhLc2iAHnzKyg4fyq+ycWd7hXf10zrHyWhl/dpR2ONRqPRnJLonQGN5hTDicVoXvMi9f/7GOGqMhILKymfEsUyGzvK2LbBtv1FHPGZ+MIJAo0NfKxoL6Z1Bo51Ohgp85jGwmM02CceQSgTPkzmtpexKXic108LMndPhPyYyjkQXP8WoTMX4hZkXo23LB+VVbM5fHAbjp3ANBIEfZsocLdiu2W8vi5IxcRi5swKE/CrRFX5eQ5MDtICuM5LWHtW47NKsQon4suvwucrwfKV4p9bgm9qKe0762k/0kwCGxMT364jRHYdJ1BZjG9+JQWTS3FxMUztR6PRaDSakY1WBjSaEYrjOOzds5Pwkc04LQexXZuYA6aZIL8wQeGd0xhjuUCkU72G1gDb9/p4t/gIf3NwChX+sRglM8FY2c0wo6HwKAfZMyDyz4qUUJvfTCgvwQtnF3PlKy0A+BqbKXn+FRJjy3CChcQnjidRMbZT3by8IFUT53L0cC3xuHo+04hjGsfwc4zQEdhUbzBhRhkTJ/rwWaknM0wDJ98iRhvEt6u/NBK2QaTEIlJgkbAN4raJbRsqCZ9jYG3x0ba2GOKF5PkC5Pnz8Pv8mJaFgUHJhHKqz1ygk2RpNBqNZkRg6HBtg85+YJLjuCQS9qDeOBBQul8sNnJDcwzlMwQCvjXAJuDeQb+5olPfefXVtRzZs4aaia05VW4J+9hzMI+jDU28NznMHfUxJvHxjGUdv0NkWohIQfuAmm4fTDTxWGgjCRwWyTDLN7ZlDDYZ+PD1GGUl3c47ts2h/Ts5emA30Ug4Q00w/H7KJo6jvNyiuDBOQeDk/+9s2+DVbRXYTuYJ/+mnnc55K85PyT+0fWdIxpxTYbw5WfrjMxgG445GoznF0crA4NMElA61EJoTZg2wYojunbHvuK6r8j84No7jkIgncOIxEnacRCKKY9s4LriOjYuLk2YvjwumaakJv+sOsc+m0T2QkIGKLnSC45RpeG167RqGibLsMVS0LMMAw1S3wHt8FzAsDNPCMJQzveE1YiTrmRY4DqZlYliWOno3MSwD08qoJAxV39FjzshnKMedYYUQwgRuBW4BzgTGASFgF/AM8AMp5dEhkOta4H7gNCDpfLQE9f+3G9grpZzeD/e5A/g58Esp5R19qLcCeBFYI6VccbJyaE4ttJnQ4LMbmAG0ATuGWBZN39k0hPfO2HcMw8CyLLAsLMCfl626ZogZqr6jx5yRz1COO8MGIcRk4A/AGajYZq8DL6Em3+cA/wTcK4S4U0r5u36654PAA8BDUsoHs5RZAvzee7sKOOS9bkAHatGMALQyMPgsGWoBNCMW3Xc0J4LuN5oRjxCiHHgZmA6sBj4mpdyddt0PfB74GvBbIYQtpXx8kMS7DjWf+rqU8std5PYDc4Gew6RpNEOIVgY0Go1Go9EMd36EUgTeAC6XUnaKjCCljAPfFEK0Aw8D/yWEWCOlrB8E2aZ4x9quFzy5tg2CDBrNCaOVAY1Go9FoRhdVodamzzm2fZ1j2z7TshKmZT0RLC77LikTl2GDEGImcLP39tNdFYEufB+4E2W7fw/woNfGamA5cKGUcnWGe/wCuB34qJTyF965dGelB4QQD6S9fyh5Pu3cz4UQP/de/1JKeYcQYjo9+AwIIYLAZ4APAALwo/wfHgO+LaVs6+FZuyGEuA74e2ARajdiA/DVHOpNAb4AvB+Y6tXdAvzUexa3S/nVeJ8nymTrH4ClQDlwg5TyD32RWzO0aGVAo9FoNJrRgRFqbX4g3Np094ZVT43d/d4my3FsTNNixtzFnztz5dW3FxaXPRIsLn2I4ZUC+iqU7f1WKeWGngpKKV0hxK+AbwHX4CkDJ8gvgcWoifVmOvtubEor8z5gJrCWlF/OK7017vlAPAvMA44Br6FiQZ+FUjKuF0KskFI2Zm+lU3v3A//ivX0V2ItSilYBP+ih3oXAE6hAAzuAvwBFwDKUs/JK4CNZqn8A+BTwLvBXlEO3NokaYWhlQKPRaDSaUUCotfmBvds237vqiV+UpkfochybnVs3WjvffbNy5fV33DttziKCxaUPDp2k3TjDO76eY/k3vOMiIYRPSnlCsV29lf0HUcrAH7I4EP/B21WYCfwsuavQG0IIA/gdShH4IXC/lLLdu1YA/AT4MPBd4I4c2lsCfB1IoFbmn0q79vfAv2apVwU8jpr83wH8KrkL4O0WPAncJoRYleXZPg3cJaX8Sa8PrRm2aC93jUaj0WhOfarCrU13d1UEOuG6rHriF6Xh1qa7gapBla5nKrzjkRzLJ8uZKLOV4cj7URGQ1gGfTSoCAN7rTwFHgQ8JIcbk0N49gAX8Ol0R8Nr7FrAxS717gTHAv0kpO5kDSSnrgE94b/82S/2/akVg5KOVAY1Go9FoTnFCrU33bVj11Nhec3a4LhtefHpsqLX5s4Mj2YCQKXfhcOMK7/i4lNLpelFKGULZ+/tQZkO9sdw7/neW69nOJ+V4LMv1jaiwxIuFEPkZrv9vDrJphjnaTEij0Wg0mlMcx7av3/3eJiuXsrvffct63xW33gh8cYDFypVkRKDxOZav9I4OKtb/cKTaO35LCPGtXspW9HIdYLJ33J3l+p5e5HhDCNHbPcYCB7qc29urZJphj1YGNBqNRqM5xXFs2+c4dm5lHRvHsYfT/GAjyn5+WY7lz/aOm/vgLzDYlhJJxWwN2SfqSQZywp2U439Qzss9Ec1wrj3DOc0IYzj9s2s0Go1GoxkATMtKmKZFLgqBaVqYpnVCTrcDxNPAvwFzhRBnSSnfyFbQc8xNRr5Jt52PeceiLFWnnbSUfaPOOz4mpfxRP7R3ALXKPx3YmeH69B7kqAG+IqXc2g9yaEYg2mdAo9FoNJpTHNOynpgxd3FOWwMz5i2xTZ9vsLL39oqUcgfwe+/tj7LYrif5O2AB0IpKVJYkad4yp2sFIcR44PQs7SWViP5ePH3GO36gn9pb4x0/lOV6tvP9LYdmBKKVAY1Go9FoTnGCxWXfPXPl1ccxevGtNQzOvPCq48Hi0ocHR7Kc+QxqFfss4M9eMq8OhBB+IcQ/AN9B5Uj4uJTyaFqRF5LteOE0k/XKUbkCsu0YJJWIuSf9BJ35A8r8abkQ4seeHJ0QQkwQQnyie9WM/AjlI3GbEOKK9AtCiPuAM7PU+xbQAnxJCPEZIUQ3pUcIMV8IcUOOcmhGINpMSKPRaDSaU59DhcVlj6y8/o5ueQY6MAxW3nBHc2Fx2SPA4UGXsAeklPVCiPcBf0Rlvd0hhFiPsqcvBs5FhRENAZ+QUv6uSxO/Az4HLAG2CiHWAgGUcnEQNTm/LsOtnwXCwA1CiJdQJjg28KSU8smTeB7Hyxb8Z+Au4G+EEJtRCk8+MBuVg+AoKgtwb+1tFEL8EyrXwNNCiPSkY/NRmZn/LkO9Ok+O36PyHXxZCLHVu2+ZV38KyqdARw46RdE7AxqNRqPRjAKCxaUPTZuz6OFb7nng6MwFZ9qmqXxHTdNi5oIz7VvueeDoNLHoYS8D8bBDSrkPtcJ9G8q8ZQZwEyoD8B7ga0CNlPI3GerGgIuBR1BOr5ehTIZ+iVIkmrPc8zAqA/JqYCFwO3An2c2K+vI8+1HOzvcAb6Em7Teh8g9EUH4SOa/ISym/AdyIyl2wxJP7GHAJKsNwtnovevf+OkoJWOa1Mx/YBfwj8OU+PZxmRGG4vcUc1mg0Go1GcypRFWptus9J2Nc7ju0zTSth+nyPB4tLvwccGmrhNBrN4KKVAY1Go9FoNBqNZpSizYQ0Go1Go9FoNJpRilYGNBqNRqPRaDSaUYpWBjQajUaj0Wg0mlGKVgY0Go1Go9FoNJpRilYGNBqNRqPRaDSaUYpWBjQajUaj0Wg0mlGKVgY0Go1Go9FoNJpRilYGNBqNRqPRaDSaUYpWBjQajUaj0Wg0mlGKVgY0Go1Go9FoNJpRilYGNBqNRqPRaDSaUYpWBjQajUaj0Wg0mlGKVgY0Go1Go9FoNJpRim+oBdBoNBqNRqPpCSHEHmBa2ikXCAFNgATeAH4jpXx70IXzEEK4AFJKY6hkGGkIIVYALwJrpJQrTrKNvVLK6T2Umw7shu7fkRBiNbAcuFBKufpE5BjJaGVAo9FoNBrNSOFZ4LD3uhCoAM4ELgK+KIR4CviklPJwlvqaDPTHpPxURAjxIPAA8JCU8sEsZfagFNUZUso9gyRav6KVAY1Go9FoNCOFb3ZduRVCmMDVwHe84xohxLlSyuODLNvcQb6fpv/4CEq53DfUggwFWhkYfB4GFgObgHuHWBbNyEL3Hc2JoPuN5pRGSukAfxRCvAS8DswG/g24Y5Dl2DaY99P0H1LKUakEJDFc1x1qGUYbq4HlsViC5ub2Qb1xRUUxAMeOtQ7qffuToXyGioriobYDXc0Q9Z0T4VTob5k4keca4r6zmiHoN6fq998X+uMzGKC+U5Voi33OdZzrXMf1GaaRMEzzCV9R4LvAoQG430mTZorRo023EOJK4GnABianmwsJIcYC9wHXAjMAA+Vv8CjwQyllvEtb+SgF+maUguEHGoA9wAvAV6WUkbTyWX0GhBAzgK8AlwLFwC7g58B3gZ1kMDNJNz8BZgFfRJlE+YG3ga9LKZ/McK95wK3AxcB0YBzQjPKr+L6U8i9dyq9G2ctnopPZkBDCAG4BPgac7j3LEZT51teymckIIa4D/h5YBMSBDcBXUd/BsPQZSH6fWXgI1Q9+3kOZrt/nXOALwEqgCmgHNqK+k0zf4x5S3/9i4LPesQxYIqXc1MO9+4TeGdBoNBqNZnRgJEKxBxJtsbsb19WNbdvZYOG4YBoUzSz/3JhzptzuCwYe8QUDD6EcdEcif0ZN2MuBC4HfAAghTgP+AkwE9qOUZBNYijIvulIIcYWUMuaVN4E/oSZuzcAa7zgeEMCXgR+S8l/IihBigVe/HGWGsgo1ofsqcHYOz3Snd783vOcTntx/EELcLKX8fZfyn/PqvAdsBlqAauBy4HIhxOellN9JK/8XIAJchprYpysLHbsdQgg/8FvgBtREdoNXfgHwceBGIcSlUsoNXZ7/fuBfvLevAnuB07zP4Qc5PP9Q8UvU5HsR6nNMn3xvAuq9MjcBQeBxoC2tTMdrIcStXtkAsBWlsFYA5wMXCSG+IqX85yxyfB64B7Xr9QwwBXBO8tk6oZUBjUaj0WhGAYlQ7IHQzoZ7jz63o7TTBcelrfa41VZ7vLLy0pp7gzPL8QUDDw6NlCeHlNIVQryJWhWfDyCEKAD+iFIE/hH4tpQy4V0rB/7HK/8l4EGvqfehFIE3gQuklKHkPbzV8XNRk+we8co+ilIE/gv4VHIHQgghUCvaVb00cz9wRfqKvhDin1A7Dd8AuioDj6J2LfZ0kWUp8BzwTSHE76SU+wGklN8UQqxDKQPbpJR3ZJHjKyhF4CXgQ8n6Xtv3oCb2vxVCzEn7fJcAXwcSwA1SyqfS6vw98K+9PPuQIaW8w3MgXgT8IYsD8SvezkQQ+EKmnREhxEKUIhADrpNSPpN2bT5qgv9/hBAvSilfzHCPTwFXSSn/dHJPlB2dZ0Cj0Wg0mlOfqkQodnc3RaALR5/bUZoIxe6m9wnqcKbeO471jnegTC1+J6X8ZnKiCiClbABuR5mufMabvIPaAQB4OV0R8Oq4Usq1UspwDrKcj1pdbgTuTTdFklJK1AS7N37Q1bQHNYluBmqEEFO7yLcm06RUSrketZvhR5lK5YynNP0darX7A+mKgNf2D1E7KTNROxBJ7gEs4NfpioBX51soM5n+YpoQws32h2ciNAR8GbUjcH+6IgAgpdyK2skB9Vll4ucDqQiA3hnQaDQazQCSiLXQfGg1rhOjePy55BVOHGqRRiWJtth9ja/Vje29JDSuqxtbcdHMz/qCgS8OtFwDRHKhM2lKcYV3fCxTYSnlQSFELTAPZZu/HbUjYAN3CiG2A49LKY+cgCxJW/ynpZSZnEf+H/DvvbTxdAaZY0KIXcAS1I5HJwdYIUQxcCVKESlHTUZBPR8oH4i+cCFQAPxJSnk0S5k13j3PAZIT/+Tz/3eWOv8NnNFHWbIRovsuSTpFwI39dK+c8MzN3o8yu8sm2xrveE6W6//b33J1RSsDmh4xTYM8M4HfSIBjg2kRd31EHR+OM1JNSjX9ge4bmt5wXYf63Y8RCx8AINK2h6o5d2H5i4dYstGH6zjXt+1ssHIp27ajwRq3YsaNKIfVkcg479jgHau942PKMqdHKoDtUsqdQoj7gG8DPwJ+5E2+X0WZHD0hpbRzkGWSd9yb6aKUslkI0Qz0tGOTLdJN0kwpP/2kEOJalElSeQ9tlvRwLRPJz/DKXhxrQX2GSSZ7x2yr8nv6KEdP1Pdg4pR0IB5UZQC1O5X8rI/20v8qspzP2Hf6E60MaLKS73PwRxtpWvt7wttf75jwFc4+m7LzbiKeN4ZIQluajUZy6RsaTUu97FAEAJxEmNZj6ymbePEQSjU6cR3XR65KuuOq8iMQz8xnifd2i3dMKkF/ImVClI2O3ARSyh8IIR4DrkP5ELwP+LD3t0kIsVxK2avfgEdPH35vzqA5O4sKISajnKYLUP4Ev0FNuENSSkcI8UngP1BRfPpC8jOUwLpeyq7vY9unMsnPzSb77khvDHgYuBH5z64ZePJ9Ds6+tzj49A/pNIY5NuFtrxHeto6xV91D/tQlWiEYZeTaN5zCpZh5BUMmp2boaTy6pdu50PHNlFZdhGEMdaTe0YVhGglMg5wUAtNQ5UcmVwJjUD4Aq71zdagIPI/01fbaC036Y+8PIcQilIPuYtTOyZd6aeKgd5yW6aIQosSTt7+4CqUIPC6lzCRbzQm2W+cdt/S0+p6BA6hdhemoEKpdmX6C8owU6lGT+QLgHillWy/lhwQ9i9N0wzQN/NFGjned7HXC5fjTP8QfbcQ09Y/6aKEvfSPenM2sVDNaaKmX3c7ZiRDx9l6jMWr6GcM0nyiaWZ6LWQtFNeW2YZmPD7RM/Y0QYgwqbj/Ar9Js25NOmx842XtIKTcD3/PeLsqhykve8SohRFGG6x88WZm6kDQNqut6QQiRR3YzmZh3zLZI/DxKwbpYCFHWB3mS9vAfynI92/nhQm+fS49lPGf15723N/WjXP2KVgY03cgzEzSt/T29h5l2aV77OHlWTr8vmlOAvvSNpld+jx0d/snRNANDLNJMPKosKAzTR15RamE00jZUQT1GL76iwHfHnDPleO8lYcyyKcd9wcDDAy1TfyGEMIUQ16Di8NegYuP/fVqRn6Amx7cLIR4UQhRmaGOGEOLDae9XCiGuEEL4upSzSDkk52LLvQZlrlQOfCe9PSHELCBbbPkTJZkX4EYhRDIiEkKIACr0Z3XGWmoFH1R0okyT2iMo34ky4EkhxJyuZYQQQSHE36Tf16vjALcJIa7oUv4+VBK14Uzyc5l7EmX+L0qR+p4Q4ta0iFWAMm0TQpwthLj05EQ9cbSZkKYbfiOh7MBzILR9PWUX3U47eQMslWY40Ke+Idcz9pKPoqLYaUYbbU2peVI4lk9+2XiSc6do6x6oPHdoBBu9HPIFA49UXlrTPc9AGpWX1TT7goFHyCGZ1hDxRSHEHd7rfJTT5emoSSrAH4C7pJSNyQpSyra0zMQPAH8rhHgbZcJTjJrE1aBs3ZN23QtRuwzNXt6CQ0AhKtlXFerzSSbSyoqX9+A2lMnSJ4BLhRCvefJe6Ml0FjCV1ArzyfAk8BbKb6LWy6wbAc5DOSl/HxUitKuce4UQyXpvCyE2AlF1SX7LK3Y/KnLRzcA7QohNqEzKLsrcZxGQh/o8j3jtbvRyInwdeFoIkZ50bH42eYYRzwJh4AYhxEsoUycbeDIta/ATwArg10KI54Am7/w/SCmPSyk3CCE+gnLq/g0qz8O7KAf3CpTJWSWqPz03OI/Q5pp0AAAgAElEQVTVGb0zoOmOY6u//i6rGfn0sW+4um+MWnamJSGtb3TY/F7KbzPStlf3jSHAFww8FJxZ/vCUjyw+WjR7rE3SxNM0KJo91p7ykcVHg9XlD3sZiIcrl6HyAnwE5R8wGxWr/hvAaVLK6zOFvpRSbkFN8L8E1KIUiJu8Yz0q3v8n06o8BTyECjFagzKvOR+lBDwALJRS5hTlxTMtOhMVRjQIXI+aPD+IMpOZgFo9b8jcQu54ZinLUXkIDgGXenK/hArh+VYP1W8AfofaxfggKovxlWltx6WUtwDXoJSYiSjn6ou95/qN92ydfAOklN9AfX7rUMrGVcAx4BLURHrY4vmMXIVS5hai+t6dqH6T5IfA/0HtEFzlXb8TpWgm2/ktSgH6Pkq5WI76rGpQ2Yw/610bEgzX1SEAB5nVwPJYLEFz8+CaUFRUqH557FimUMcpSvxRDjzy6dwmfabFpLv/nZb44OwM5PoMA3TvoXaOWM0Q9Z0kfe0bU+/5MY2RU2tn4ET64BD3ndUMcr9JJBK8vfZhxpWonEzb6ko43FTI8oXNGK6SYfysj5JXNGVQ5Bkq+mO8GqC+U5Voi93nOs71ruP6DNNIGJb5uC8Y+B5qAqkZJIQQyYn6O1LK04ZaHs3oRO8MaLoRd30Uzj47p7LB2UuJazOQUUOf+oZYihHQ0YRGI3V1eykIpCweQlFlkXq0PhWgJhbe362eZtA45CsK3O8vyZ8VKCuY4S/Jn+UlGNOKwAAghCgSQnSzJ/fO/cR7+4tBFUqjSUP7DGi6EXV8lJ13E+Ft6+jZUdSg9LwbaUtYvZTTnCr0pW+Uve8mrLwCYPB3cTRDy4EDe5lckJr4hyMq1HZzpIDxxAGIhvajU49pRgkTgHe9LMe1QBvKTOgMVBz6VQyhiYhGo5UBTTccxyWeN4axV93TQwhJg3FX30M8bwxuQisCo4W+9A1/aeVgi6cZJjQe38/Uqeq17Vg40TAEimkOp3YRI211uK6r8w1oRgNHge8AK1EOyKVACOWw/Fvgx1LK+NCJN3wQQlyH8kPIhXop5RcGUp7RglYGNBmJJEzypy5h4p3fpnnt44S2r+/IMhucvZTS827UGYhHKbn2DZ1wbHQSDodw4qmkrLGogxVtJhEoJhTxkbDBZ4GTaMOONePL60vIco1m5OFlKf78UMsxQliMctLNhb2AVgb6Aa0MaLISSZjE/OMIrryTsotu75jwxfETsi0cvSMwasmlb2gTkNFJQ8Nx8gMpB/NwWxQz1gauA4ZJSzhAebHyJ4iG9mtlQKPRdCClfBAVZUkziGhlQNMjjuPS7li0o2x+6fiN14rAaEf3DU0mGhsbyPenlIFIewLDdfDZ7SR8QVrC/jRlYB/B8gVDJapGo9Fo0NGENBqNRtOPNDU1kJe2MxCNOAAU5aufm+ZwoONauGnX4Aqn0Wg0mm5oZUCj0Wg0/UZTU2OnnYFoRL0uK1Y+JK1pTsR2vAHH7o+kqxqNRqM5UbQyoOnANA38fggEXHw+m0DAxe9X5zWjE90nNH3BdV1aW5s7+QwkdwZKSksozPeTcExCEWWhahgQCx8cElk1Go1Go9A+AxoAfD6Ix8OsXbuOnTt34jgOpmkyc+ZMli1bht9fQCLRezuaUwfdJzR9pb29nUQi3mVnQCkDecEiykogHInTHPYTzFedp71lN/nF04dCXI1Go9GglQENatJ34MA+nn32L53OO45DbW0ttbW1XHbZ+5k0aaqe/I0SdJ/QnAitrc0EfA6mt+ccjzs4tos/Lw/TZ1FWks/Boy20hP1MLG8HoK2hljGTLhxCqTUajWZ0o82ERjmmaRCPt3eb9HXl2Wf/QjzePuzNQ7RZy8kzHPqE/h5HJq2tLeT5nY73seSuQGERAE4sHwOT5lDKidiOHcN1dQQqjUajGSr0zsAox7Jc1q5dl1PZ9evXc/75F+I4vZcdCrRZS/8w1H1Cf48jl9bWFgK+lIlQPJZUBgo5fNDi3c0BAmVFuDQTTxj4fS6maZOINuDPHztUYms0Gs2oRu8MjHIMw2Xnzp05ld2xYweGMTw1gaRZy6OPPkptbS2ONztNmrU8+uijHDiwD59Wf3tlKPuE/h5HNuFwiIAvbWfAUwYsfxD5TgAwsCNBwKAlLcRoqLF2kCXVaDQaTRKtDIxyHMfpmHDlWtY0Dfw+h4Avjt+MqKPPGTITjuFg1nIqcSJ9oj/Q3+PIJxQK4U9TBpI7A21tRdi2+r7saBDXhea0EKMt9dsGV1CNRqPRdKDX10Y5pmlimmZOEzrTNLEsE8OJ8Oaqp9j25locx8Y0LWbMW8KZF16Dv6CEeML0yhsE/A4+ywHXBsMiYZvE4iaO0382wkNt1nKq0dc+YZq5rSmYpoFluRiG22H647oGtm3gOC5+v9Pn7xEGp49pciMcbqMo2F0ZqK8v7jg3e1aIg42Bzn4D0UODJ6RmxCKEmAPcC1wITAEM4BiwH3gN+IuU8q9DJ2HfEEK4AFLKEbOyIYTYA0zrcjoKHAJeBr4jpdzUpc4vgNuBj0opfzHA8q0GlgMXSilXD+S9TiW0MjDKcV2DmTNnUlvb+zb9zJkz2bJlC/X19Sw5YzmBgiCbXn4Gx7HZ+c4Gdm7dyEU33klV9QI18aOVw7uep+nYVnAdMEzKKuYzYcbF2BQRjfXP+NdXs5YLLliO+g3RZKIvfaKmpgbXNYGeJ969+QHk5+WTiEf79D2uXLkcv9E2KH1M0zuu6yozodLuZkLRmFIGAn6bhU3PYtpT2d9eiu0YWKaLZcVJRBvx5Y0ZEtk1wx8hxC3Ar4AAcABYDTQCFcDpwDmoSeBf0+rsQU1cZ0gp9wyyvL9gkCbAQ8SzwGHvdTlwFnAb8EEhxG1Syt/21oAQ4g7g58AvpZR3DJCcmhzQZkKjHNs2WLZsWU5lFy1axNtvv82OHTt47PEnCI6vZvH5l6cKuC4vPP6fmESJtW1n2/qHaTq6RU3SAFyHpqNb2Lb+YWJt28kL9M/K7VCZtZyq9KVPLF26tFdn3lz8APbV7SMRT+T83Zx5xmLamwavj2l6Jx6PkUgkCPjTHYhdMAO4KJOg6fm7yE+0MZvduK5BUyhlKtS8f/2gy6wZGQghJgD/hVIE7gOmSSmvkFJ+SEp5KVCJUgT+dwjFPBHmen8jkW9KKe/w/q4BqoFfoxaZ/0MIUZ5W9h9Rz/nEEMipyQGtDIxylHlGAZdd9v4ey61cuZI9e/YQDoc7zq1avYap886ksLi041xhUQlOoo29W39H9tVil71bf4dFW7/YfffNVCX3sqOVXPvE+9//fvz+gl7DQubmB/Asgbz8nL6bYDDIXDF1UPuYpndCoRBAJwfieMzBdoId76ezHYAiIlgONLXldVxr2bsWNxoaJGk1I4yrgELgNSnlw1JKO/2ilNKRUr4kpfz60Ih3Ykgpt0kpTwmHGSllO3A3EAJKgMvSrh3ynrV5qOTT9Iw2ExqlmKYBPhvHsok6DpOmT+a2225j/fr17Nixo5MZx6JFi9izZw8bNmzo1s6mzVuYe9YKNq76IwCnLVtO/f6X6M1sBFwO736ByupriURPbrI2EGYto51EAiZNmpqxT9TU1LB06VL8hXlEnSiW38S0LUhY3ez0o9Eo69bl5gdw8OBBZlZXU7tjR4/lFi+aT9PBtQxmH9P0TjicTRkoAMBvxijx1wPQXDaVQidAY1s81UDQJPLarylY8cnBE3r0UhUKhT5n2/Z1juP4TNNMmKb5RFFR0XdRtt/DjUrveDSXwmnmJ0l2CyHSi8yQUu4RQviBW4ErUKZGEwEL2AM8BfyLlLIhQ/t78MyPgMXAZ71jGbAEeCut+M+FEOmydJgNZfMZ6NL+LOCLwJmAH3gb+LqU8skszz4D+ApwKVAM7PI+i+8COxlAsykpZasQYjvqM+jwK8hkMtXF9+B2IcTtaU11MhvyvqePAh8EFgFB4Ajqs/iNlPLXmeQRQpwBPACc59WRwPellP+ZpbwB3AJ8DNUfir37PAt8retnJoRYAbwIrEH1oS8BN3nPJaWUizPdZziilYFRiBlwaXWaefydP7PhwGZs18EyTG6YezmXrVjO8uXLsW0b0zSJRqOsWbOGffv2ZWxr565dnH79NR3KwHSxgH3v/iwnOZqOvcPEWVdxst0wadaSizKQi1mLRpFIgGUVcv75K7jgguVKGbBMEkaCtmgYv+3H75iYrkFjtJG8vHzyKcRJs9OPx+MdfgDBYJDFixZQPWMKBi4uBrt272PT5q2EQiFeWbuWa6+9tldloHrGVOq2PJ3TM/RXH9P0TjgcwjRcfJZS0hzHJZFwcVylDBwvO8aDNeOYFImzxKqgNAb7G30d+QasgEV0+xv495+Lb/KCoXyUUxkjFAo9EAqF7l63bt3YnTt3WmkLP59btmzZ7cFg8JFgMPgQw2vFJPkDdJEQYoGU8p1eyu8AfomamAWBx4G2tOvJ1+NRfgiNwDZgE2pV+0zgfuAmIcRSKWV9lvt8HrgHeB14BuXUXOLd+33ATGCtJ0+6bLlyJ/Bl4A3gz4AAlgJ/EELcLKX8fXphIcQC1MS0HPWZrUIpKF8Fzu7DfU+GEu8Y7aXc74FlqIn6TuCVtGsdr4UQY4A/oXxCoqjP8yhKcTsPWIAyT+rK+4HPoRSA54CpwLnAz4QQZVLKf0sv7CkcvwVuANqBDShFYAHwceBGIcSlUsruq6KQj/JhmQu8BGxGmbSNGPQv5CijPR5hy/GtPPLGo7hpY/3nl97F5PzxvLjqRXbt2tWxClxdXc0FF1xAc3MzTz31VLf2HMehsKSMcy6/GcuE4rIxzD7jk2AYtByvpX7/ehKx1szCuE7K1vskSDdr6ckcJWnWkkgMp9+44Y3juF7kJQMzYBKx2zFisGndxm79ZNmyZcQC7QQCBR0KQdJH4+yzTmeumErTwVeo2/JUh7PvuHHzuOn6S3hP7uP1N97EMmDliuWsf2NDVsWhsDA/937TT31M0zuhUFvGhGNJZaC19DgAB/L9HHT3cb4xBdw8GkMBKkvVvCFcXIj/lV9ifeAbGJb+eepvQqHQA7t27br3ueeeK00/7/nvWLW1tZWXXnrpvdXV1QSDwQeHSMxM/BE4iJoAviWEeA416X0TeKOr+YmU8hXgFW/lNgh8IctKeDNwDSoKUcc2lRCiAPgRajX6Kyjzl0x8CrhKSvmnLudf8lbDZwI/OwkH4vuBK6SUHT9sQoh/8mT6BmpCnTxvAP+fvTcPj+K68v4/VdWb1FrRhgBJIJCuQMZgdhvb2HiJ7cTGC3bixGRznMTZl0kmmXd+k2R+78xkJjPZJonHWSaT2EkmCY5jO4lXbOOFzYAhIOAiEAiB9n3rver943ZL3VK31AJJBtOf59HTUtWtqlvVV93n3HvO+T6CcgT+G/h45J6EWhZ5CSg+y34khRBiKWo1A5RjlRAp5d+EV3DWAq+NkUD8c5QjsB3YKKVsjLqeC1VZKh5/C9wvpfzvqPb3oZ7RPwghHpJSDka1//9RjsArwPuklKejjvsU8J/A/wohqqSUI6cUV4fvd4GUsmWs+z5fedsHTwsh/lkIYYV//maMdu8VQrwqhOgRQvQLIXYLIT4phHhbPaO2gY5RjsCX1zyIowd+9eijQ+EgoL4gjh07xqOPPorX6+XWW28ddT5d19Ewmb9oIZkZbdRs+xZHd/8XR3c/jLe/mXmL76WobF38zmi6+pkEosNaKisrh2LPdV2nsrKSTZs2MWtWaWpV4CzRdQ1dM2lraBlznLQ1tGDoJg6HRn9/P6Zp8uEPvZ/KuenU73+YnraDMcm+PW0Hqd//MPNLnKxZtRzvYD856Tr3vedd5LvqaDjwI0799Yc0HPgR+a4T3HfvrdjtruTHzSSOsRRjM1JwbMgZQDkDAxmdYFnkdQcpa/RxrKsOw9Bi8ga8memYvW0Eal+f3s5fHBQPDAw8ONIRGMlzzz2XPTAw8CBTbDhOBCllH3A9arbWhgrJ+FdU5aBOIcTr4WpDEz6vlPKpaEcgvN2DmvEPAneNcYqfx3EEJpP/jHYEwvwbyolZIIQojdp+FSpUqQv4XPQ9SSklytidEoQQuUKI21AJ3DrKMN46CeddCmwA+oAN0Y4AgJTSK6V8OsHhj0U7AuH2jwKHGV79iVxnBvAZ1IrR3dGOQPi4H6BWJ+YDNxOfT16ojgC8zVcGhBArUZ61xRi1JIUQPwQ+AXiBLUAAuA74AWpZcqOU8oKfXhwMeNhc8+cYR6AsezYlriIe3fzomMc+//zz3HfffRQUFNDW1ja0/R03Xoe/v47TRx4jZlXZMuluPUh3aw0lVbdRVLaOlvrYz4acwkuwsKHr2qTUhI8b1qLrWJZOKERqReAc0B0W/kEfzz8/dgnv559/nk2bNvHmmzs5dOgQaWlpbLzjhtHjIwaLpto/Ur3sE/T19ON0DiB3fZ+R46mn7QA9bQcpq76H0oV3cerQ78ftd07BJQRDRtL3meLsGS04Fg4XslwEbT5uOtZIea0Pyzt8zLGKM3TZCof+1rJcWEDgzT9hr1yLpr+tv6Kmlf7+/s/v2LEjL5m2O3bsyLvuuus+63a7vzLV/UoWKeVhYKUQ4grgnajZ2GVALir84wohxM1nU6JSCHEZ6jt/LmolIWIv+IECIUSulLIrzqFTXb1oVDyklNIvhKhDxeXPYjiEKjLr9qew8zSSXwM/msS+vTQiDyPCXuDOSbKZIlUsnpRSto3ZcjSJYkmPoMJ5ZkVtuxZIA/4spUyUl7IVNe4uR+WTRNMipdw2wf6dV7xtP2mFEE5U3F4LKp7v9gTt7kI5As3A1VLK2vD2ItSy2h3Ap4HvTUO3zxld13AQxGYGsUIhNMMgZNjBrqMHvdwj1rNRrGdP82GePv4amy65i507kyvpt2vXLq688koef1xVB3O73ZTOyUPu/B5jGXoNR56kYvkD9HQcJbewmsy8CrAs7K4c+vt7QXdiszknZdY+OqxF5YExRt9SjEdkPGmWzivjJAJHcgLsRpDLV1/KZUuq8PkC9LXuIZlk3+6W3RSUXM6RHb8cam9zZFIwZ/XQmImEn+UWXYrNkUXQ3zvGOTVmzrsOjz+VMD4deDwDpNnjrAxYaWT5W5h3xDf0LmiGA81wkNfRQ3txMV6/jsthYtg0gm4nWl8bwWM7sFde+RbcydsT0zTvOH78eFKe8fHjx41rrrnmLlTi6nlF2OjaBhBeuV+DShK9EZWI+mcp5fgzBer4DFS8+W3jNM1CzbiPpD7Zfp8l8ZP1IPLB54raNjv8GrdPUsoeIUQPMObK0ASI1hnwocK4XgVeklJO1gduJMH4bCouTeTZlYdf3xlJ6h6DgjjbpnocTDlvW2cA+EeU93cbYy/zfTX8+rcRRwBAStkihHgQlRTyFSHEf57vqwNOzUTr7eL07zbTtXMXVijE7HvuIO/6y2k99XqMMNMl+QtZs/Z+stKK2HI8cZx9TNKnBpmZWay9YhX79tdw260303xiC8kYej5PF2WL7qLl5Ms0nXgxRiAqu3gtXr8DV1pOKoznPCJ6PM366P3U1dUlbBudE1C396mY97eg5HLsjvShlaFEBr5uOGiuGx5PRWXryMoXtDVsGzFmFhHKq6Ri+QMc3v5t4o8/jbLqdxMiY9zSpykmB49nkOw4gmMmaczqjHyXa7jnryO9eAmappPW34ikga5+J8UzPIDKG8ge8BE48krKGZhETNO0TVCP5by3D8LfyduEELegJv2WoSb+knIGUHH3twGHUI7PbqA9Kta+ERUulSiywHP2vU+Ks7E5xvrAm0wb5pvToPB7Lh/eE7nXiJMsgfHK38WbPZ3qcTDlnPf/7GeDEGI1Ksv/11LKp8Kz//HazQGWo5YCR314SCm3CiHOoDzuNYRnI85HnJqJ569vcuz7P1AGFjDr7g2krSql7vCvKZizisKyK2OMr9aDv8VecTMrli9l1xt7R1V7cbpcaBq0n3plRNJnNffdeyua4aLpUM24fSsqW4dl+pG7fsjoUKIDdLcepLjidgx3OqbunJSQoRTnxsjxNPOBD8cVBHO73dx803UU5mcR9PdTNPcq0jJnDiWOR97fSKgYQHbBIvze2Ek2V1oeaVmzCPlVecqisnU40nKo3fMTEoWflVXfzaK1X6Kx9hm6I7kImk5OwSXMnHddSoF4GjFNE5/PNyqB2LTsgI0snyrGkrb4Otw5i4fauDNmkWG20DXgGHIGPJmZZDd3EWo+itnbip5VSIpzJ1w+NClhv7AeywUzNSOlDAkhXkQ5A/FmbhNxd/j13SMrFAkh3MDMSeridBCJpy+Lt1MIkYUKqbqQiMzux41HmkQawq8HLlYl5LedMxDOLv8F0Imq/TsWl4Vfa8IJQ/F4A+UMXMZ56gzouobW2xXjCNhzc5hxzSr6Bo4xb/F74s6uzlv8HnrbJQvFQhwOB/PnzRxV7SWnYBEFJVcMz+xGx24vupui0qtG5QJEY3NkkpUvRht1MaiY8fnLPonN7sLvTzkDbyUjx9PMOzegGwYjDYlVK5exqKqM7sbXOFRbM2Js3UtvuwyPDRUqJlZ9EssMYlkhZdC3HYo5xu7KJitfoBtOMnLnjjtm6mt+z/zln6S44g5VPjR8rmDIwBvQU07lNOL1qo/PkQnEkUpCmd4OMsoKSMsZXTJ0hpZNc//wx689y8DSNDTLInhsO45lG6a49xcHuq4/Pn/+/C/U1taOGyo0f/78kGEYj01Hv5JBCKElEXoSSaaNTv70h18T2ToRldyGOPveyxi5hkkw3rUnm1fCr+8SQmRIKftH7L93mvoxEcZ7Rs+iVm82CCHyxyjxeq68gMoVvT5cdrR7iq5z3vJ2LLPxTygv8tNJDJxICayx4r0inum8Mdq8pTgIcvp3m4ccAYCiW27Eb/UMza52t8ZWceluPUjtnp/gSMvBYdeYO8sWt9pLdLvYqkAW9Yd+T1a+wObIxObIpLj8eipXPkjlio9TufJBZpZfT2HplbQ1bCOZUKL201ux20LjtEsx1USPJ3tuDumrl9PZcpry8vKhNqtWLmN+iZOT+/6L7tYDSYwZC09/C96BtjHHo3eghRmzLqOtYTtJjZmGrYBJ/6CNfo+D/kEbXt/YCem6rpFmN8hy2Mm02chy2EmzGyml4nPA44njDARMTNJI9/fgzgrgKLkcLWxbeZwDNKQpNeIZZOIPGgz6lI2q6xoBlyrRHTj6eirMa5LIyMj4zpo1azqSabtmzZoOt9v93anu0wT4hBDi50KIUbXyhRA2IcQDKE0BgN9G7T4Tfl2Y4LyR+LVPjDjnCpQRei6Md+3JZitwAOXgfFsIMWRgCyEqgH+Ypn5MhDGfkZTyTVSybibwuBAipsKVEMIlhEhU3SdpwlWAfojSZHhSCFE1so0Qwh2uOll0rtc7H3nLVgbCNXHzgHQpZaJEj4me8wrgc8AfpZS/Ha89kBF+HRijTcS7zjyXvo3E4bBRUDA5p/R1dtK1c1fMthlrlmOlWwlnVyNx2+lZJei6bdxqL5Ek4I6mvVG6ARZtDdspW3gXus0Rd/VhduU7aTz+bFL30dNWw5zKWykoGH8lc7Ke3YXIZI6deESPp4J33MCbb7xIT2cb69/zSY4dO4bb7WahKKV+/8PYHBlx4//bT++MGTMAzrTccWf7G448ycI18+jvSe4jYSJjBiDkCxLs9dHxej0DxzrBtEDXcC+YQd7lpdiyXBjO5D4WL7QxOJXjpr9fzbtEOwN+nxIcy/S3kz3PhaUNz6e0p53BY/XSFmwnN+AGoM9jJ92pJgM6XHkUexoxe1vJoQtHQdzIhwuG82SsNLnd7oduvPHGUToD0dx44409brf7IYaTQ88H7MAHgQ8KIZpRpSs7UYbvpQxXhvk3KWX0F87jwDXAr8LaBJEZ37+VUnagcgt/D/xzuDTp4fC5rkQJUK0lQdhNEjyBMsA/FxYDO4368Pvvqag8I6W0hBCbUHmODwA3CiG2owzca1HVdVaiVlD8ic4zzexAjbNlQojdQA1qhv51KWVEtfmDwDOo96ROCPEa0IZ6n5agyqzOnYS+fDl8znuAg0KIfSj1Zit8/iWAE+W4XLAlRBMx7c6AEOJyVNLutUA66kFHe7A5wH+Et39mhCjEWOdNA/4HlSn+ibFbv72wgiGsUOyMupGRTtOp54lneEUnZlqAt785brsRV6GtYTv5s1eFk4YV3W01zFrwDg5ti7xlkeYm/d31WKEglSs+NspQjCtEZplMbn5TirMhejxlLlvCyV/+B6YZoq+9kRtuuIHBgR66G1+jqOzqhAm+kTChyJhB0+hsepPi8uviOg7RDmbTiRfJn7UiZpwl7mzyYybkCzJwrIOWp0coVZsWA0c7GDjaQdHNFbgX5CXtEKRQDA6qj+mRKwMWaeQ7O9BcC4gsRPscHjyWKujhcbSRH6jCsHT6PHaKclTd0cG0tKHaLYPH38RReGE7A+cLbrf7G+Xl5WzatCmeAnEovCIQUSA+n/gZcBKlNbAKWAwUogzH06jQ4J+Gxcai+QGqEtD7gHehjDlQirwdUsrNQohrUUb7EmABUIuaVPwhyhg8K6SU+8IOxt+gSp9GJh9fY4pCjqWU+8OrGv+Iqq50B3AC+DrwHZR9ZKIcqbccKaVPCHETKqLjclQ4to6yCX8ebtMphLgK5eDci3r/nSiD/FVUydTJ6EsAeLcQ4lGU8vMqlKPZBzQBv0E5eMcn43rnG9P6jSeE+CTwXYYzt0chpewWQuSj/nG3otTikuGfgQrgw1LKpiSPicz6u8doE/kHTiCje3b4/UF6eiYnAT1D09AMI8Yh0B0OFZM9gpGJmZVlV9F84sWkrtPdVkNl6dpYI80yCfr7GelMRByOxrrn6G4dK548Ck0HDNraEj/qyAzbWG2mivNkdm9Sx048oseTBZimGlevPPEL7vjY/8HlmkPH6fDkx4cAACAASURBVJ5xE3xLqlS1vqy8Cgx7OkF//5iOQ2Q8dLcepHL5x5JzBpIYM6BCT9JDjHYERtDydC1z3u9m0CBhqNHZjMHzYexM5bhpbe3E0E0MI6wtELIIBS1My0VxzkksY+1Q2z57J6Hwsx3Q+ukx+sgOuen3DPfNkRnCDIJug165h8CC66ak31PNZHxeTfLYsdxu99fdbvfD69ev//w111xzh2maNl3Xg4ZhPOZ2u7+HMnzOK8J18/8Y/pnIcSbK8P+/Y7R5GTWbHo+5CY6Juz1Ou81EqQTH2R83NnG880sprxlj33GU8xND2KB2AAellN5RByZJsvc+4pgPomb44+3bj7L3xjreh3LsfpDEta45276E9z/FaB2BRG1f5tzySs4bpi1nIBzr9z2UV/oV1FJVoqWWn6Me8C0TuMQd4XN/QAjxcvQPw8IVD4a3/TT898nw61jTTiUj2p53BHUbuatjQylDXu9wTHaYSDJvw5EnGTLgLGtUu4TEaxdH3TXa4ehuSSaeXJFTcAlYqRnZ6UbXNew2E4ctgF33ErRpzPvcp7Dn5qABum5w2VU3ceuHv8SphtN4vJ7R42gUKuQnK19gs2cw2Nswbu7K0HiwTHTDnlTfcwqqwQridIy9suU0dLq2x8sRHE33jgacxtsxnWrq8Ho9MYJj/iiNgTzHAJZWMrSv14gKW9eg39ZODm76PcPveXqGRkNXFgDBpqNYgbO2XVLEpykjI+PLWVlZFTk5OfOysrIqwgJj550jkCJ5hBAZQohR8ffhbT8O//k/09qpFBcE02l5fQFl4H9dSvlvAAnU62BYxnrZBK+hM6zCF4/y8E9O+O83w6/VQoi0BBWFVo5oe97hx8acezbSuX3HUBKxOehRhnqUAZ8/Z/XoZF5NG9UuIXEM/5yCano7aodyELLyq9B0gyM7RijIxhAvB0GjqPRatJCBUoBPMR3YbSYBTy+7XnqSE4fexDRD6LrBvEWXsfSrX8DsH2Dd7ZvQ0PB4fbyw5UU++pFNtJ5ILim8s2kfhaVXUF+zeYz2I8ZDYACbPR31cTHWNTQKSi5HvvEjZlfcgjOjMmEpUbumM3A8uZXx/mOdzFg398IvHD2NeDyeUSFCAHYthGHMwQp/dngdg/gsX8yxA/ZuMn1zCJo6Hp9BmjOErms0ufMooxfMIMEzh7HPvYwUKVKMyUzgkBCiFhXu1I9a3ViOish4Efj+W9a7FOct0+kMXBV+HVcOW0rZJYToA+Yke/Kxlq6EEP8DfAD4kpTy36OOaRBC7EU5HXcDvxxx3LpwH5qB7cn2ZboxTQsrM5cFn/nUUDnIzh17yamsVpVewmTlVYwKCertqCWnYJGasR2HnMJLsDnczCy/Phzn3c/M8vX0dtQyb/G955CD8CKlVXczeNBD1uJxDksxadhtJk11B9ny2M9iKlG53BnkF8/GluEgrSiX+XYXbU2N1BxR1V8sy4obghb3Gq4smpIUpouMB+9gG35fLyVVt42x+qBRUrWB3nZJ0N9Hfc3vqFr9OXQ9K254j2VaKlk4GUwr6cWyFAqvd4Qz4FO/59o8oA9/jA/aRytGhwjhszS1SuC1kRZOItbyLAIdYE+H+n07WZByBlKkGI9W4NvAemA1Sm14ACWU9b/Af0UJquUD/57gPPH4ppTybJSAU1wATKczkA/0Sil7kmwfQsW3TTX/gqom8K9CiG1SymMAQohChh2Xb57v6sM+Syft0stY8v3vMHi6nrTSWdhzMyksu3IoSTNeSFD76Z3MW3yviusfbxZ2zhqO7vkJGdllzLv0vWBZhIJ+bLa0s85BqJq3nqzsajxHvPTsbiV7SdL+X4pzQNc1Ap7eUY7A8mtuovLSpXQ1b+fUoZ/GKAmvuGwt7nQnfr9vaBwlUhOOJAXHc0ATEclJyZwxn7r9j5BXvIyK5Q/Q1rA9Rj07omocm3di0XxiC4XlG/D6Rq8OaLoGupacQ6Br8RbBUoyBx+MhPWZlwMKydGYYvVjagqHtA3qsM2BasK01H5fPyYxcnQGfjQLUykF6ho+m2ixK03sJNh2lo8dLXrZrem4oRYoLECllL0pwNRkyUJOkyfI/DJdiTfE2YzqdgR5ghhDCIaUcs6xV2GPNZrgG7ZQRribwEPAgcEAIERGfuA5VheCPJJG0cl5g19ByDPo662g4HCscNm/xvRj29FEhQUF/H73tMvlZWF/vkKps2aKNpGfNofbwH4aPm2AOgqG7aHnsOKGBABmVeQRSNcWnBUMPseulJ0c5AmUVszh54GckUoqeX3E7DocDNJ2i0qvGrSaExYTGg82eTkfjHoL+Plrqt9LRtJf8OaupLB1OQDXs6Zw48Bu8/bHhzd1tB5X4WJyPtYBl4p4/g4Ha8cusZyyYkRqHE8Tr9ZCTNUJ9GBf5dh/oquxrEJN9PSYzXHay7EG6/HZ2tc+gcTAdpxbgFtIZ9A5XeU5369QaxZRYvRTrnfzh5cN8aENqdSBFislASnmSt0nya4pzZzqdgf2opaurgPHKhHwQNUh3TnGfAJBSfiJcu/aTqJwDA+UB/zfw0Pm+KgDgdFj4+49SX/M7ElV3KV14J6UL7+TUodjiBi31WykqWzeBWVhQomObqVr9aWyOjOHSkBPNQTCGC0vlrClhIHjeP+q3BRohThwaToNJz8ym8tKlox2BGJRStFjzRUoX3oVl+setJmRzZk5oPFhYMeMs6O+jue4FmuteGNqWU7iYnIJFNI9wBrBMtAR994VMci8vScoZSI3DiWGaJj6fN1ZjwG9iWm5y7elD2+p8QV7qLIh7Dp8Fhulk0Df8lZTmttGfbuEb1HG5TdqPHaSpo5LivLGKv6VIkSJFiokync7AL1Gz7f8ihFgfRyobACHEjagauRbKGD9nxislFW7zayapXu10o+saBn2jHYEYLE4d/gML13wWmyOLoD92uX7kLKzdmUXA309vu+TEgd/E1wXAovnESxSWXknjsaeBCeYgFFTT31VHwZ3F6CE3QbuOFUwpEE8HlhkaKhkKsHjNOrqak1P97WzcRf6sZRze8b0x2kcExD5LXvEKOhp3JWg3TE5BNV3Nfx23XdwSt6CcjhC4bAbeEePINC2CdoPCmypofSZxedHCmypS43CC+HxeLMuKzRnwm1g4cBozhrYd98cvDKBhcWluF267n06fLRJthitNx25rpa63hEXueubZWvnTtnoeuHXRlN9TihQpUlxMTGdk7KOoFYEVwE4hxOcJC4AIIW4VQnxKCPFn4C+AC6Ui/PQ09u+CxWE3aT7xAskYck11Wyi/9L3EWx1Us7Bb8Hu6aD+9k6Nv/IjmE1sSOAKK7rYasguGK5m1n95JQckVcc8fi6oEc6b2zxzd/wM81gksPVVFaLrQdANdH16VmSsuoaf9cFLH2p0ZSScFN9VtoWju1SQ7HtrPjO80JFplyCmopu9gF7aAia6Pvp43GEKfncmc9y8lozJP5RAA6BoZlXnMef9S9NmZoxyJFGPj9aq6Sw57rDNg1zTQZw5ts6d1U+buJ90IomHh1EOUZ/Rx6+zTLJ/RgWUbxLQ0vH41LjVNIz1doz7sUJTbWtl1uIWuvthqRClSpEiR4tyYtpWBsFT2HSgRsQ3EZrFHhEQi3+B/AN4/XX270LEZpgrtSYLuthqK51+fOCSo9AqcrhmcrPltche3zBijcsI5CGFHY7xqMCkmFwtVPvT4wd2AmolNJpSnqGwduUWLaTz2TFLXiYy38cbD3Op7YsbDmMTN7tUoLL6GtseaCLb7yby6DI852qj3BkPohkbm1WXMWDc3MvQJWBaDIRMz5QhMGE9YLMw+YmUgQ7NAyx/aNju7mZlZidPFDD2EjkoijlQUSnMbdPcO0OPLoMzRDmaQl988wx1Xl0/R3aRIkSLFxce01syQUvZLKe8AbkCF5JwAvIAfaAB+C9wspdwopRyczr5dqKgZ0NCEkjRDAQ8nDvwGV0YRlcs/SvXaL1G54uNkFyzCMFzDcf/JoOmj2rbUb8Xv6aZi+QPkFC4e3q/p5BQupmL5A/g9XaNyEJpPbImZXUwxdYRMgxXX3hb2AsJ5xOO85xExuYC3Z8Ljbbzx4M6Zi88zfjw/DGtbDKNRVnU3HukjNBig/1inmpVOgGlaeAIhev1B+oJBev1BPIFQygk9S9TKgIXDNuxI+f0mBZGCBYDf8BGyxqwbgaZBGraYvIF0tw273oIcmI9DCzHH6OTlfWcIpJy2FClSpJg03hK5VynlFsZPIk4xDrrDos/sIRSwzko4TAPQNEJBHzabi15PB2dqn6Z8ySZyCmI1ChKRU1CNFRod3jM6ByGTYMBDKDCIbjjILlhIduGimDKUY1WDSTG5mKaFPS2L6+66ny2P/YyT8iC5+QvpSbDCFFGvrt3zEypXfnzC462lfis9HUcpW3QXcyrfhWWF0DQDz0ALDUf/TGnVBgpKrkiuxG3J5Zw48BvQdPKKV1BYdiW6bsfMNHFXZuGp6yNVDGj68Hg8GLpFRLQ5FLIwQ5DnGk709dqSm9ux6yEGvFHOQLodTRvkjObGtDTm2tqoHyxgR00LVy2ZNan3kSJFihQXKymr6wJFd1gc6KjhoTce4T3VN3NJ/kJ6kwgVyimoJhTwDomEjS4J+R56O44ys3x9OAl4bMOssOwqQkEv8dRiIzkIdkcGwcAghs1F+5ldSrAqThnKlvqt4bAjDacexK4FwQyBbhCwbPjM1HCdTAJBneLyS3j3p77BgR1bKF94BT1th4j3nkerV080Sby3o5aisnVk5QtaTr486v0vrdpAMDDIQNeJccOJyqrvxubIoPzS92HY0zFDfpqOPxd7zvxq0vUbcDrcCRWJU0weXu/gqORhgCxb9nAbY2DUcfGwGSEGfcNhhxkZTgB0o5XTgVLm2trY6oPnd5/mykuL0cZYAUqRIkWKFMmRsq4uQHRdo8/s4aE3HsHC4unjr7Fm7f30JjDkhtEomrsOb38z9fsfi207oiRkKOindOGdNB5/noI5q+KISu2iuHw9huGku7WGyhUfx+/twpGWO9Qu4O0jPXsOWCamGSQU9ODKmImtu17Fho+4ZlHZOjRdxx1op/v1zQwe3TXkDKRXriJn7UZMn4HuTJviJ3zxEAjq6M5cll17B3abSVn13dTX/J6R4yhaPGxCQnUll9PfdRJ7WsZQGdJRQmVY2Gzp6IYTv6cbsfKToIHNkQFWCDSDoL8f3ebEMJzhJGMNuyN9tONgmXS3HaC77SBl1ffgzKhMOQRTjMfjiesM2PXhfIEBrX/8fHPAqUNXVJiQ3WWqsaC1Ij3LWJmpEsxPt/VzpL6LhXNnJDpVihQpUqRIkmlzBoQQ/zDBQ3xAN3AY2CmlTJWQiGAL8djBv2CFv127vb1sPXOQKypvo+3o2Em7um6n/tBjCdpApCRkxfIHyMqvIj17Ds11W0atICxY9iE0zcA32I4Z8mFh0t1aE5uQXFiN3ZVFT2uNmvWPtxIQdc3KlQ9i9nbS/LO/je2fGWLwyHYGj+yg4NZP4RarJ+1RplAhQ6apEwjqODMEVas/R/OJLXS3HRx6L3XdPhQWNJEk8b6uE2TmzhtyBCIrBPFWpYrLr8eyLDQNmuq2jFpBmFl+HYMDbQS8PRSUXsGRHd9PcG0AK5WUPk14BgdGaQzolg1NywHAwsJD3ErSo3BoEDJ1fAEdp91E0yHNZcfjCdBtmRiWTrY2QI/l5oW9p1POQIoUKVJMAtO5MvB1kpobikuvEOKHwD+Op158MWAaIXaf2R+z7Q9HnoeqG1i37CP0n96uVglGCIeFQn6a6p4nmZKQPk8Xfm/PuCJmmTMWEPD1Urv7x6PbtRygu+Xg0Kx/S/3WUSsB0Q5B66nXmBHMG6N/Fm1P/QDHzHnoRl7KwJsCfH4NXc+isHxDOH/DpK+vn5DliMkTiAjVVa35DM11LyYUqtMM+1B4USQBOZFQ2YziZYQCHuoPbY67v7u1hrJFd1FQcjnNdcmVNm0+sYXC8g14fanVganC09eH2xWrPpxlyx/KFwkYfkwrubLBmgYOzWLQZ8NpVx/1hVkF1HsacegNHPMJ5tna2Bdws6+2nfZuD/k5qZXCFClSpDgXplt0zEKVFc0BBoA9QGN4fzGwHMgAuoCngGxgGVACfBVYIYS45UJQBJ5KQqZJKE7y5h+OPM+LJ3dy8/wrWb/mCzh1g4Cvh96OWk4c+A3lS+5Ts63jYHNk4kzLHW20xWDRePx5FmTPoeHIE2O2i6w0dDTtDZeOjLcNulsPMvuKvyXQ349n37OE+rvjnq/7tc24138EjzmtxbAuGkzTwuvTsNlsQJBHfrWZu+68bVRSeUv9VnKKFpOeXcLsilsI+HoAhsZb0N9H5coHaT7xYkwCcryx4sooxpGWy5H9j8Tdr7CoP/QYC9d8Fq+nK6l7SSWlTz1e7yC5GbFhQjm23KG//TbvhM7n0GHQZ5Cbof6ekZFDfUsjdr2DOt+lXJpXz752FWH20r4z3H3Ngkm5jxTnN0KIk0DZiM0+oBXYAfxQSrmVSUAIsQCoBY5LKReM2HcamA2USClPT8K1dOArwH1AOUp/qUNKmT/mgaPP83+B/zNicwgVYbEfZYP9UkppRR1zPfA8sEVKef1Z30Ry/fsI8BPgZ1LKj0zltVJMnOm0pj4EZKGM/S8DhVLKa6SU7w3/XAsUAl8CMgGXlPIOKWUZ8D5UCdIbgE3T2OfzEkPXMcYoAxkKWWimg+MnztDX00LziRfDMfpWUhVgopNFx263KukZ2raG7eTPXjX2Nsukf6CfHd0OXLd/FffqO+KebUDuxE5g3PtIcfbY7RqNjacYGBhgxYoV9PV7yS5ey0jxsJ72Iwz2nKL99A48/U0c3f1wrFBdeMyNN6ZmV9yS9FhqqtvC7PnvSO5GLDP5MqgpJoxlWfhCoRFhQhbZtpyhv736xKpEO3Viyou60+1o4XFnah1kWMMJxq/sb8IfSJUZvch4FvhF+OfZ8La7gZfDYqYXGp8B/gk1Ifpn1H395hzOV8vw8/k9cBpYD/wPsDnsfIyJEMImhLCEECkl0IuE6XQGPgPcDnxeSvnvUkrPyAZSSq+U8j+AzwN3CyE+Ed7+G+DvUJbIRe8M6CGDFbOXjNq+YcH1fGXJB0k7cArTtHju+Rc53uCjbMnHyC5cnLR+QFZeRVIrCMm2AyU+lZVXMfY2TcfhcFB77DiPbn6C1hkivkNghtRPiinBaQe/b4BnnnmGxsZGFixYwHPPPcdheYriituJdggiitMt9a9gd2ZTtfpTI7QE1Jgbb6y43AUTGkuujILkbkbTQTPGb5firPD5fFjEERyLqiTkmagzoIEnyhnA5SXPPlvt00/T0JtPnluNrwFPgB2HWs7+BlJciHxTSvnB8M8GYD7wUGSfEGLOFF9/HbAQaJ6k890dfr1TSnlX+L4+fQ7neyXq+dwrpVwavoYJ3EmsoOs21L186Byul+JtwHSvDASBnybR9qfhttFLSb9ATRteOvldu8AIGty16Jah2TJQjsDCYDZ/+fG3OFGzB9OyME2TXW/sZfPjz9PumQd6BjmF1eOfP8kVhKTbQfx2I7blFFSjRTkrz730ClSswcjIiT1ON0A30HUNux0cDgubLYTDYWG3R4TYUpwNDodJf28b23fsANTM7549ewDY9cbeEc6lTtDfRyjoZe4l78GwuTj25i+GxOwqV3wcmy2dnILq8ceKNTHhvGSFBLILqjGtlDMwVXi9ytCPFhwL+E3S9OHE3mSThyNEwoQihJx9zHEKAGx6J62BbK6YOTxh+eLe01gpYYmLFillAPgi0Ac4gBun+HrHpZRHpJSTNWteEn6tHbPVOSCl3IwSdYVh5wMp5WD4Xhqm6topLgymM5B2AdCfTFUgKaVPCNEPVERt6xZCdKPyCC5qTNMi05bFgys38dAbj5DtyuTyvEX85cffGjKSzFAQXdcxTZOBgQFe37aLfftruPeeW+huGUc/ILKCMJ5xlmw7iL8iEbNNlaEc2a9d+2pYveRG+l//3dA2t1hNQHcQ8g/y+us7OH78OKZpous68+fPZ82aNdjtaQRTC5wTwnDCYF8nlmVRV1cHwOzZs9m2bdtQm11v7OXEyQauXXcFiyreiaaBGfTh93bR1rCdoL+X5roXaK57AVD5J/MW3zv+WNGMCY0lTTeIp20xoiE5xWsJpaKEpoz+1laAmDAhAg4Mu0rqNbUQftMzMrpsTHQNQkEd0wRdB+x+CtJm4hxIx2cN4tBPk+ktwWa4CIYsTrX0U3u6h8qSnHHPnWKI4oCv9wuWGbrdskybpulBTTcetzuzvgM0vdWdmyhSSo8Q4igq77Bo5H4hhAbci5qUXIYKRW4CngH+SUp5KtlrjZUzMJHrCCFeA9ZGHd4ghIj8vklK+WiyfUqS3eG+DeVdxMsZGJF7YAghoj9kQ1LKGLtRCHE58GngStSz7wVOokKevi+l7BzZESFEFvA14C5UeFQb8ATw91LKuAlhQohqlNO3PnzMYPieviul/HOc9kPvE7AaFZ1yKSpndbGUcnyhnIuI6VwZ8AE5QoiS8RoKIUqBXBgODA//k7lRyTAXPaZfY3FeNf9649/xgUvu5PBrW2JmSwd7Opk/f37MMQMDA/z14HFKF20k8bezBpalZnPHISI+BcroKy6/nsqVD1K54uNUrnyQmeXXY3NkAsPiU9HkFF5CKOTH5siipGoDve0ynLQ6zLG6Ouzly2L6l3ntJuobTvHII49QW1uLaSpDxDRNamtreeSRRzhz5hS2VM5o0ui6Rsj0sPelP2OhDT1TK7zCBOB2u9l4521svP0G0u09BP09WKEATSdewLCnqYpCUUT0BGyODEAfGisj2xSXXw+WRdWqT40aN/HIKagm6B+kpOo2xhrHxRW3c1ieIhRKhZRNFf2tzYAV4wy4zGGj3G/zTsgRiODUNQb9w//ApmuQEtdCAOx6E01tBovKhpOUX9x7znmcFwtawNf79cHeM/tOHf7j5w+89s0FB1/75twDr31zwanDf/zCYO+ZfQFf79c5q3ftLScyURgTNyaEsAOPA78CrgBqUIanB/go8KYQ4rJzvfhZXOcvqIiHSBzd7xmO9T9+rv2JQ1b4dbwJ2b2oZGNQsy2/GPEzhBDi/wNeRzkZ3aj734Wy374GjP7QV8b4duADwJsoZ8QNfAJ4Vggx6ptbCPG+cNsPoZyNp4ADwNXAn4QQXxvjfv4W2IxKzH463N/UFNEIptNc2gHcBPxQCHFnoiU2IYQB/CdqEG6P2jUbtQR4wc1aTBa6ruEgiM0MYoVCaH4Dh57JjLwFPHr4JzFt7Q47K5Yvo7Z29Mqj3TmDiuUP0NawPW5JyL7OOgpKLh9Xgbj99C4qln0YZ1pewtrxEU2BrHzBiQPROVEaBXPW0Nm0lwXLPoTf00Vf53GCI6bzTdPEGvpe0sh/1yfoH/TxzDPPMhbPPvsMmzZtwjDSUyVIk8EWIuDzUn94H0uvunloVUnTNHRdZ8XypSy9tAorNEAw0K9KfbYdonLFx+huraGwZO3QrL7NkUnZortwZRQRCngIBT2YoQAzy6+LESqL1hyo2fatxKrUMWjMLF/PyYO/JTtPsHDNZ2mqezFGEyG7oJqc4rUclqfYvWcfly5ZPr3P8iJisLMDm26pGXwgGDTJ0PKG9vuMUalhSeEMhwpluNTngensp8y1iHpvDZbWQbcfFhZm8tc6Nem4R7bRN+gnM91xbjf0Nifg6/1aT9uRz9Uf2pw9unzvAaO79WBh2aKNn8suqMLuzPr6W9bRCRKeNZ4H+IHnRuz+F1QVw5eA+6SUjVHHfQ74DvC/QohFUspzmTmY0HWklP8c3nc9kA58YTKqE8UjbFfdFv5z31htpZR/EEI8icotMKWUH0xwzruBf0SFZ71HSvmXEftXo5KXR3IXyphfJaUcCLedg7IRV4b3/zbqPJcBP0cVkXmXlPK5qH2XoFZdviaEeFFK+Wqc630MuFlK+cxY932xM53OwD8D7wDeCewWQnwX5aFFjPti1DLTZ4HFqE+qf4o6/vbw62vT0tvzDKdmovV2cfp3m+nauUs5A4ZB7upVzPrwfZgjEmrNYJATB7dxw/XX8fwLWwA1s7tQlHL8zYexOTLIn7OaS8S78IfLNPZ3n2Kg9zSZefMxDCeLrvginc37aD+9c7g6zBAaxeXrscwQLndBwtrxkdrwoaA36hza0EpAR+NuOhr3UFJ1G8Xl16PZHKxZs4YDBw4wMDCArutoWLjFGrKWvwN/IMCO3XuSemY7d+7kqquuxUzNAYyLQzOx2zPY8NGv4krP5P4PfwCsAJpm8dH7NzHYewrD0AiZKtynsPRKCsuuwmZLx2Z3D4UBFZVeRU7RYlpObh3haC6ieP6NlC3aSP2hzRSVXT2m5kB8LQqNsuqN6IaDoH8QZ0YRnb1BXPlXUl3xTkBdq62tk788+zptbW1UVlZiWTpjhxOlOFsGe3pwOKKThy0ybcP5Al7Dc1aP3qVHkojVJKbp7MehO5iftoQjgzux6y2E2vOZlZdOY8cgIdNit2zl2sumOnf0gqY44Ot7cJQjEINF/aHN2QvXfPZBuzPrYc7zyTchRC6wBvguKtLh09EGtRCiAPgU0APcI6Vsjz5eSvldIcRNKNvkRtTM8dn0Y1qucxb9cgICpfN0GSoX8weTdPrIbPwXRjoCAFLKnQmO6wPujzgC4banhRA/Qtl81xHlDAB/D9iBT0Y7AuHjDgoh/gZVfelTQDxn4KcpR2B8ps0ZkFK+LoT4MPBjVNzWzxI01VDe/cellNuitmeilt8mO47uvMepmXj++ibHvv+DmFAgKxSic9t2Cm5/J7puxDoEmsZfX3+OS9fCPRvvZN/+AxQWzqC78TXAIujvo7nuBfJmreDonh9TVHrV0Cxt47FnYlYLypdsGqEirFYQ+rtOJyzOuQAAIABJREFUEgr5xlU0rj/0GBXLH8DmzCYju3RIkGqkAnHV6k/T1dlDe3s7N998MydPnqS3txdHWjpW/mxan/weWXd+leN1ryT13I4dO8bVV6/jwlzxnj5cdovBfg87d+8mLzeLqqq5+Ps7cKbngqVhODNJz5oJVojWU6/FKgMXVjNv8b0EAx5KF96JZQaRu35IIgO/YvkDVK3+DKYZ4OgbDzHWuBnSomjeR0Z2KTPL1+P3dBH0D1Cx7H4GfRYZWoCO069Sc7gmZmXglneolQFRVZ3KHZlCPIMDONKH/w74TbKN4fAdnzZwVs6AQ4stLxpw9uICSl3VtPpP4Qs109JQysLqmTR2qCiL7TUtKWdgDAK+3s831W3JS7J8b17pwts/a3dmfWVaOjcxXoqKrY/gQ83+jlwyXo8KD3lmpIEexVaUkX45Z2+kT9d1kuF+IcT9cbb3Ag9IKd881wuEZ/KrUc/9kQkevktK2RZn+5Hw66yo6xioZ2YBjyU4X8SQuDzB/j9MsH8XJdMaVS2l/KUQYieqTOgGhmPYIvSiYuz+RUp5ZMSx/zI9vTy/0HUNrbdrlCMQTd/e/cytWkLdob1D2xqO1TB30VL2vfo0R/dtY9Gqa1h8SRWHtz8Vc6x3oC1sxAUSzNIeoLv1IGXVd3PJlV/F7+0aEpVKVo8ALNpO76By2UfobN43JEg1sk3zyZeZXXkrRYUzePrpp1m9ejWXVi+k/dGvEhpQqSKmxVAM+3iYphlum6omkwi7XeNUw0meee45Vq1cRnXVHHQ9QFvbwSGjv6hsHc70PE4dfpzEKtO3k5lXwaHXv8VYBn7tnp+w6PIv0nzixTHaDbdvO72DqlWfxNPXzMmDv8Pb30RO4WJc7kIcabn0D7TFCKFhmfS0HqCn9SBi4UbcaS4GvalVganCF/DjsEcZ7T4TtzGcM+BhYmVFI2gaBAPD/7dBR094u8aSzPWErK2c8FiUZ7rQVJoTx0730N7jIT87pUgcD8sM3dHdVpPUh2F3W40xR9x6F0oM63zjWVRZTw2YiYobdwG/FEKslVIei2pbHn7dMCIRNh5J1iuOy3RdJxlqUSVDQYmOdaFEx56UUvZM0jUiScgnkykKM4JEydq94VdX1LZCVD4BQEccJzCaRM+1PvmuXbxMe4qllFICHwgnBJcDEZW9dqBuhDpelpSyN85pLhocBDn9u81jllJse/Z5LvvKF6g7/OZQuxOH9nHdxg9TV7OXwb4edm95ggWLqofiul0ZxcypuEUZVc5MDu/4HmPO7tf8nsqVD1L3118NGfJZeRVho244WTQzr0L1QdPo7agdCjHqbj1IYckVNJ/YkvA+ulsPUlh6JfmuOjbecQOH5SkC/gLQwMjIIW3pO7Bl5Q3Fs4+Hruvo+nTmyF9Y6LpGMOilr7+H92+6h8yMdPq76qiv+T2RsTCecnDkfU/LnIkGiJWfoKdDJggtA7AwzcCoZONERMbN8X3/PbytrYbK0rUc3f3wKBXr6Os0HN5M1erPoetZqbyRKcD0evAD7qjkYStgRw/rOgQNP6FzEAc0o5wBzekj5B/AcLhx6C5WZ99IictD4HQflxdlsbO5lxCw/1g71y0ft0bFRYllmbaJle81z9cSDN+UUr4c+UMIUYxyEBYDvxJCrImyIyKD6AiQKGwlwq5z6NN0XScZXpkGhd9z+UCdSOBu5LkGUZEhY5Eo3+PsEpcuMt6yf/bwP+txRmTNh52Ed6AyzW9FKRZftNjMIF07x/7sCHR1M7hzD9feci8v/eU3YFnMW7SUtsZTXHv7+9m55QkWr16HOzuXyhUPYndlYQZ9NNVtwZVRhLdfVQQZG4vW+lfJn71q2KAP146PTgRNlEA8OhE03iVMDMNJT9tBetpqmF9xOx2dHcy8+VP4nZns2HeQvJpDlJeXc+zYsXFPt2DBglS8+Bi4nCYBbz/5ruM0Hn6ZeZe8O8YRgLHVqM/2fTdD/knSpxhWsY7vZFo0n9xC4bwNeH2pULHJxt/cTMBhx2HzD23Tg8Oz8j7De07nt6PjD+g47Ca6btHV+DRZJbfgMNLRNI3ZrnQYDDFXs3PbzBm8MehDHk05A4nQND04oVLQmn5BBNhJKZuEEPcAfwVWAe9jOJw4Uj9/X6JE2Eliuq5zvhCZ3Z8rhHCexepAsrSiQpEcwIPxxGpTTA7nzbSpEKJaCPFvqOzzPwPvBi769V4rFMJKojRi8x+ewHW8iXd/4h+YX72c0gXVdLc1M2vuAu558Ivk5vZwaNu36Gk7RG/7EQ7v+B7drQfOTUVY0ygqWzeUCNrdenD4iyYcJ1675yc40nIoKls3/gU0Hd0WqQZi0VT7RwoKMrDyS3l08xPUHjvOgQMHWLp0aVL9Xb16dSpePAFOh4Wn5yjH9/6InraD5M9eSVvDdkYa/YnGxzm97xpJKWGrtmPrU8RTto6mu/UgNiOVQT4V+BobCdjtOOzDz9ceGi4Je7aVhCK4RoiP+XNCnDi+mTb/aH0kh6ax1u3iDi/0nYxbpvyiR9ONx3MKqpOqlpNTUB3SdSNRjPZ5Rzis+EfhP78eVZ7yedSs8o3h2vZTxXRdZ1oIV3s0AT08QTty/2ngECpP4r4p7IcfeBH1rXHXVF0nxVvsDAgh8oQQnxZC7EZ59V9EVRUKopb9PvZW9u98QDMMNCO5mPeWJ/6Es99HlZFPRnYei1Zcgaf/BHLX9+lpO4jN7iYrX9Bw5EmGjL5zUBHu764nu7A69nyjD6LhyJNkF1bT3z22rktOQTVWKNp6t+hueh0talVxYGCAkydPsn79+jHPddNNN2G3p6WUSeOg6xoG/dTX/I7I+5ZbdGmC0B1t1PseCR1K5n3PyhejNAN6O2qTU8ImgT5F9Lbxxq5loiVcPU5xLniamzANI0ZjwBkazhfw6meXLxDBpoEnSmsgWJyFs7uLvX3P8Vr3Zmr6d1E36MNnDNsqabpO4JVTBNvP7dpvR+zOrO8Ul1/XMX5BBY3i8us67M6s705LxyaPf0JVqpkPbAIIl/f8L2AG8KQQonLkQUIItxDivnBFoLNiuq4zzTSiBktVgv3fCL9+WwjxjpE7hRCrhBCzJ6Ef30DZhP8phLh7pHMihNCEEGvCJVpTnCXTHiYU9thvRYUB3RzuQ0RK9E8o4Y2nJjHR5YImqNvIXb2Kzm3bx207Y9VKunbvofH3j1F4842Ymp+mY08RMdjihnyci4qwpUKHkg0xSs8aq9KHUiAOBWNDC3raapi14J0x23bv3s2KFSvYuHEj+/fvj1EgXrBgAatXr04pEMdB1zUMw8JhN2k69gLReQG6bsQdAzaba9T4mFDieJwwnoC3l6KydeMrYYfHxEh9iqK56zi+L6x9M94Kg6ajpfJGpoT+1mZId+CwDTtb6aFhjQGvNhDvsAkR8EflDeTaSPOZGCGLAXoYCB2g21/McXsRLreDSzwhcg0D3YLBV0+R+a4KNHuqeEAUTXZn5kNlizaO1hkYQqOsemOP3Zn5ECpJ94JBStkmhPh3lPH490KIR8Iz3JFJxruAGiHEPuBE+LC5wBJUGEoFSgn3bJmu60wXj6OUhV8WQrwE9KMUiD8GIKX8XVjf4R+AZ4QQf0WtFmSiHIj5wFXAmXPphJRypxDig8BPgd8BJ4UQh1GJ0QWo51qIcgZfOJdrXcxMmzMghFiBcgDeg/KeIw7Aq6hqAKAkuC/qhOGR+LEx556NdG7fMWYSMZrGzJvfwdFvfw8A3abRfuIVoj/woxN+I0RUhJXA2NiMnKXNyJ1L4/Gxxb8idLfVUFi6NsFejbJFG0Ez6Gk/MioZWdc11l6xin37axgYUAbG7t27OXz4MIsXL+ayyy5D0zSysrIwTZ1QCILB1IpANDYbBAKDvP76DtasWhoW/1Lkz1lNKOiP7xSGcwCix0e8cZSISLLvsDOgMaN4KZYZonThnZw6/AcSGSURLYqR+hS64aCo7CrcOWXouh1dtzGz/Pq4Scs5BdVYVipfYCoY6OyA9GKcUWFCLlOVFTUx8VmD51zV1wwMf0Xp6WEl7MEgvZl2AGx6Fx0dmSwozeah/Y18IT8bl65j9vrwvNlM+qrJmJh8+2B3Zn0ju6CKhWs++2BT3Za87rYaI6qMdEitCGQ+ZHdmfWP8s52XfBulZFuOsjd+Fg412SiE2AB8GJVXsARVvaYJlZj6BHDyXC48XdeZRr6CSsrdANyJqvUfIipiQ0r5NSHEFpTTsBblCHWjnKBfolSYzxkp5a/ClSg/C1wPXIP64mhGKRP/GaUynOIsmVJnIJzlvwn1T1nF8FfDAdQ/xm+klA1CiFRQ7xj4ctKY/eD9nHnoZ/EdAk2j/IH76dr7JmhQ/J570XSLnvbDse2iQoIiBndWfhU5hdUxyrDx0Zg571o8/a0M+XETDDGyOzPJKVwcK0ZVeAkFc9bQ2y5Jz5qNYUtj3uJ7RyWl5ucvGqowtOsNVUJ1YGCAHTt2sGPHDgA+/OH7CQZTht9IbDY4c+YUr732KkuXXEJmppvK5R8dqviUnV9Fd1tNXKcwFPBQUHJF7Pg469AyjdKFd2Kzu+lsUu9hIiXswrKr4mpb9LZLmjqfJ7tgEUd3PzxO0rJaWUi5hZOPFQrh6etD12ZiM9QTtkzQQirnJ2DzYWmT8OSDwzP7Locfb5qbdG+Q3nDkmU3rxG+WkoFGt2XxeO8g9+aomhN+2YGrugDdnVIljsKyO7O+bndmPVy68PbPzxG33oFl2tD0oK4bj9mdWd/jPBUak1LOTaJNP6rcaLx9T6CM8WSudYwErqyUckwxi4lcJ5nzTeA8f48S6JrIMS+Q+D4Hgc+Hf8Y6xyvAuOI/Usqfomb3J9yX8P5jKKcjKSbruV4sTJkzIIR4FiXEoaPe4FMolbhfSSnHn4ZOobCF+MXBJ5idN4Or//XrdP/xabp3vjGkQJy//hqK776TUMCP01rEjFtv4dTJ/khpuNhzRanERleBKSq9ipKq28aIAdcoqbodw5ZGemYxVWs+Q3PdixMOMdJ0O3mzV1JYdhVYJrrhxLCnIXf9kKC/D5+3i8LSK0eLUVlmTIUhWDbkEERIlRGNj65rBAKDdHW2svGOG+hufI1D256KMaJV/L422ugHLEx622Xs+Jjg+26zu6le+yU1BjQdMxTEcLhprX+Vjqa95M9ZTXXlLQS8vYBFf3c9/d0nyS5YSHbBQoAhbYugvy+sgLx2VNJyrGrxK5RUbcDn6UazF032Y73oCbS3EbAZMSFCoYCOFv4u99kmp+iHFjQIhjRshoXdZtI+u4D05iGBWWyaShbu7vZSkpvOGx0DrEl3Ms9hB9PCd6CVtDUpmyAOTXZn1peBL7/VHUmRIsVbz1SuDNyAsip+DTwspXxtCq/1tsU0Quw+s5+dlsmLrje46YYrWP6eW9FNcLkz8Pt9vPrCZk4e2odphtB1g7lVS6lY9L5RBltvR21cgbGW+q0Ula1LOEtbUHI5fV0nsKwQ/d11NNW9RP6cVdhs6WpVoeVAgt4Pk1OgEkYHe06p8B/AsLuwolSTu1sPMqfincwsvy5BnXpVYWjhko9Rc0gOhQyBKiOq/M7UPHA0hmHR0tzM/BIn9fsfZrSo3LARHQp6RzmFvR21+DwdwPAs/oRCywovYaDvjEpWjnJACsuuJjtP0Nm0l+a6F8iaUcHR3Q8ld1MJnRCVtFy58kFyZy6lq3kfuTOX4fGnxsVk429qGlVJSAsMz8B79UlyBjQNr89GRrrSKwiVZJFxYvgzw9C6AZP2tj7mzszkRMcAz/Z5+HieCiPy1XbiXDoT3XW+lsxPkSJFiree6fiE3AAghMgAnpdSpkp7TICQaRIKGz/d3l7+t/YZ/rf2GTYsuJ6Frdlse+LXMaFDphmi7tAerrn93lEGW3drDXMvuYcjO77PSOOopX4rPR1HmVNxC7Mrbwlv1fAOtNIgn2JG8WUEA4M0HHkSmyMDDQiFfBTPuz6pRNCZ867Fsiw8/c2xNekLq5l36XvpbTtCS/1W/N5uvP3NY9SpVxWGllxazbbtw/oLy5YtYzgNJRZd13DYTVVi0gqBZhAMqRrmb3cxKl2Hwnw39fsfZbzKP1VrPoPTXUjV6s/gHWjBkZYLaOTNWk5H4x6a6l6kpOo2zKAv6dCygjlrVAJwgln87MJFGI4MdMNx9onsI+6l48xuZpZfR/6cVYCB2xa8aN7v6cLfrJwBZ1QloWiNAY82edV8/D4bhJ0BLdfAHrJw+E38Dh1NMzG0Hvr6chEVSrj0qD/AmWCI2TYDTIvA8U6c1YWT1p8UKaYaIcTfAaMqEiVgq5Ty51PZnxRvf6bSGbgDlSvwTpQIyHtRctK/ReUKbBvr4BQKQ9cxNH3IIQDIcWVxed4i/vLjb43KIUjPzGbRqmvRDWNU2EdOYbUK7xlHQKq77VDMLG6JuBVH2gzOHP0TRWVXk12wCL+3C00z0A17EiFGG9BtLnraDsfOJlsm3S0H6G45SOnCO8M16a04IR+xDkFPWw3zF68bcgbWr1/PsWPHWLr0MkaGHDodFgZ9NNe9MGrFY+a86wmRgc//9s0zMHST7sbXSKbyT3Pdi8yYtRy73U13W40aO1HPa3bFTQz0nOLUoc0Ula1L6n2PTQCOvV7DkSepWP4A2fkCw0ib0CrTyHKjESLj+Mz/Y++9w+O4zkP9d2a2YdEBorCCDTgkwSKKokRJpiiRsiTLtlwkucVyb7ITRUl+sZObOMm9abbvdeJeY7lHbnKR7UiyRMkSVUixiBIJkocFjQW9Y7F95vfHzC52gV00gQQBnPd5+Axm5pyZMzsfd7/vnK+c+t2o8c+H932pSBQcy3EPz+24Y8P1IYMMTtu9YinpRd05EUxNxx+ME/HYRqFL6yZuFeONm3gMnUjc5JnBYDJ2IHyyG8+6MjRNvXfFrOF27IDciRADlDGgeFVcNGMgEUQjhCjFNgTeC1wJfAL4uBCiCduF6MHsV1HocYOrFm9i37mXksduWX49x5/dPcoQuGL766iq3cpLh19hYKCPcG+6r3e2LDCpBaRGupEM9jaRk1dJia+QRatvQ9MNzHiUgQsHMNy5tDbsxptTOqaLUX+n5MKpR1iw5JosT2nRfPyXrLnmz+jvOp0Mbs7JX0RBaQ1F5evp65LDrkOWia5rVFdXs2nTJhobGzlw4AAbN25C110YhoWmWbh0i2D/qbR8+onn6m0/Qm/7Uapq34Y3r2bOKoiGbtHXObGict6cEuLRAPWHv0+2zyvVQBvpWuZy51K2ZBuFZeswXF5MM0ZP5BVcnvysBkHH2RcoLKvF6y9h0erbpphu1GYsOZ4v7/tSEW1rJep3U5iyMmDE/ADE9BgxK4w+Tcq3mWIM+L1R+svKyA320FvoZBTSeggDvT0BFhfl0NAV4HAwzNtK8jBMMPvDxNsCuCrndTF7xSxCSvmamR6DYn5x0d2EpJRdwJexC0asA96HvVKwHPhb51+CZYAKLk4lZnDnutt58dxhLEfBuaJsDX84/nBasyu2v46CxTX87Be/BGDbNVfQ1ryHimXbkwrbWAWkRilQpK8W1D3/+bTVgrKl1+H1L0iuIiQCQWtS0oeODPpcXJNeLyAdi9bGP7Jg8TWs2PguOprTswkVlaW4EzXvITc3j5KSEh555BECgQC6rmMYRjJ9ZmtrK299067RhsCIezbV/Yw119yPrhfMSRcSy4pPyPVmLDlIuVpyNr+r5RBtTU/T1XKIqnV3srjm9cRjQVobnqSlYfc4WX6GsVPOvgYsEyseZfmGd9F45L+zjCH7asNExz/X3/elItLaQmRNNR5XOHlMi9luOpFpCh5OEE01BnwxzleWUXq6M3nMcIKI2zt6WLKojIauAFHgrEtjecR+x9HGXmUMKBQKRRYuafoVKeUxKeUngaXYBcd+DiR+TTTgZSHEISHE3wsh1l7KsV2umKZFvl7AvVvvSWbqMCwNMyXw1p9fyMpN1/H4E8OFneobmilcsI62pqdpOPIgvrwKPL6iUf7W2QpIpc6y9rYfHeXzfergtxnoPk3Fsu0AxCK2K87JA99I/mtt2D2stFnm2HUSsAOIPb5CTh34Fr3tR0bc8winDnwLr38By9beSSgcZ9++fckg4ptvvpmzZ5v54Q9/yKlTp9i4Ye3E3WMadqcFQs4lNM0YvzAXUysklsDjK6a/8zgn9n7JdvPJICuenCLHDWzk5czkPVvqd5NXtJzqLR+mqHzD8Lg1naKKjdRsvZdIsCejUTGZ8c/l930piAcCxAYGiHg8eFPchLSobQyEjek1BixLJxy2U4zqGpiVXnKC8eT3iaH1AzFCQRcL84aDmPcHhgsYRpr6sJTxp1AoFBmZkVyMUkpTSvmYlPId2DmBPwY8j20QXIFdQfCoEGJi/g1zHDOisaG0ls/e8r/YtuRK/J4cu2Ksw8brb+XAwfRUm4dfrqNo0WsALamoW5ZJUdm6tHYFpdX27H4KiVnW7P7gYM+y/pyCBQKXJ3/8h8hSCdblyWfhypup2XovNVs+gqYbVK7cleWatjtRbsESfD4X1193Nbm5ueTm5lJRUcGjjz6abLlyxbIJu8f0dhzFZczNuPZoTHdSh45NJjnIxmBfMyWVV1Cz9V6qt3yExIrBeCsKGWVFS2QetlcJwEwarzVbPkLNVR+jZstHqFh2A4bhRTfctqxc9TFqtt5L5cqbbXmdxPjn8vu+FERaW4gbBpah40kLIPYCEJrG4OHkPUPu5N+evCiWbuAL2/fWNHBpvQDkxiLJdi92DYLX/p60QjFiLdMXx6BQKBRziRnPt+ZUHP4W8C0hxGrs2IJ3A1WAmMmxXU6YEY18vYj3rn8bPjPG8jWbqD9mGwDL125mz4/TfagDgQDHZTOrqt9My6lfk1DUJlJAaiqzxMMVZjNjV4LN7oaU7hI0lmuJRUv9E/gLl7LA18xdb3kthqeAPXteSGulMcnCWBNtO8uIRHUql08g49MEC4kl3tmF+j/Q215H5YqdhAZbx762fYOMslJUVks42EN4qMN2FTLNpPHaWj9cWb6iagclCzcTHGzLKCuGK0e970tEpLWViNeegU9PLWqvDAS1oWnP5BoJuwF7pr/QH6W3tBR/sJ+Qz1b2XVo3MWsBXZ3NlOUV0zEYJm7BQL6H/LC9UhFr7sW9eAITFwqFQjHPuKyqNEkpT0spPy2lXAHswi5nrXAwTQszoqNFTTZv3WlPiQG6y41pjlZuXtx/iJBZwppr7rPdLiBZQCqZdSdRQCqFyc2y1lHg1A3IjsbClbsALTmTOxE3pGyuJb0ddRQuWENfx1GaXv4m8aEmSosL0tpYjH6u7MPTJ952lmGaFnHyqKp9G9mLO2q4PHnjfgZp78xxB3p1smIHA3v9JRguLzVb7wWNtBn/1Pse3/vFDO5jtqwM9Z/L7IaU8XHHft+6ruF2g8dj4XLF8Xgs3G77uMIOHo54POiaNbwyYGloMR8WFiECY19gCoSDw+4/Bf4ovRUL8IdG1huAjj6TJUXDKU7PWMNtImf7R01IKBQKheIyMwZSkVI+JaV8/0yP43JE0zSGXjzEzjveA5qGrmkZq+9evfVKfHo3p1/6Lr68CjRNJxzswu0tZM0220BIFJBKY4KzxHZbE5fbz1iKZlXtXYSD3dQ991lCg62s3Phuiio2TN21xDJT3KTsoFBRs5Tc3Nxkk0TMxEQoKltPLG6M33CWEo5oePJqWLPt/tG++OUbqN7yYcJDnaPlIIWsrmOTlJVh7GBgNB3D8BLoa+bkgW9S9+xnOXngm8laE4uqXz8hWWmq+zmF5bUTclkrKs/+vl0uiMeHeOaZp3jgge8k/z3zzFPE40O4ZnwtdeaJtLYQ9XrwjIgX0NCIusKY1vS7YMWiBvG4/R3jcZtEyvx23ICDS7eDiKOhAlb6h2MFXu4dArct71YwRrxreuMZFAqFYi5w2RoDiuzEdBe+xVXkFq/mjR/4OywzzsqVK9Pa5ObmslYso+XUr4lF+mmtf4Lu1sMsXHUr0XAfpw/ZBkJhqaDSmbVPkmG1ICuajoWVOeizfANrtt2HP38Jgd6m5Exuf/cp2hqfZirBqolrp49vuBBZgtSYiXEegMoVuwhH5vZ/hXBEw3Dl2r74V32M2uv/mpotH8GXW07DkQdpOvYQFctvJNvnldV1bJKyAhpF5Ruo2Xov+SWrwTI5vvcLWVeH8oqWO65i48tKe9Oe0bIyehAsXLELlxEjzx/H57WSM/4uF5w/PxyEnlhtM02TU6dO8cMf/pDz55uJRCJj3WDOE2ltJezx4EsxBvRk8HAoW7dXiZa2OlBYFEUzvClBxINoRNDQ8IUak+1OdwQwFviT+7Fz/RdpfAqFQjF7mdsa0BwlqrlxiQ18/9uHeehH9Xjcbq65emtamys21Y7KptPfeRKsOGdPPJw0EOT+r9LTcjjNdSjjakEWisrXo2k6gb6z+AuXOkGf97L+NZ+idNFWGo/+jON7v5A2w2+7ltRN6PqZ3JCKymqx4rG0Y30ddaxauSy5n4iZWFj9ZsZetXg7cfLmvPuArmuYZszO+LT/a3See5HgYAutDU8mMz7pujvdhSyFgtJqBvvODgd7OwG88VhkUrLiySlmibiD8FA3sUhg3FSgYDoxLuPT21FHycLRheeG0Vi29q10txzi2HP/zrHn/532+l/jc/Xj80I0GuSxxx7N0tfmsccepa+vb0LjmYtYpkm0vY2gLxdvWryA7ZoTMqY/eDhBMOBN/l1aEKKvpIScUEqdA8dVqCUQJMdZDQiEYwzkDgcfR88qY0ChUChGoha9ZyEa8MzuerBgcCCMabrwul3svHEHT/7RDrhduWIZZ4/8Lq1fxfIdtNTvZqTylSggVXPVx4iEevD6F9jVYFMDjbOMpGzJNk4e/DZ5hVWULbuOvo7jtDU9Tc1VH+PM4QeSLdOCR6fsWmLfs7xq+2ioJODNAAAgAElEQVQ/dcu0g4ZTeHH/IeBK1m76KL0tz9GXVhBtPZUrds2birS6phEKxu3ZecscVTTMl1dBS/3jeHNKk3LgySm235Wm4fYWsWLDO+hofi4tgLd04RYWrrx5wrJyYt+XyStaTuXKXU6A8DhG2CRlxTJjWQvgVSzfQW/bkeGg9BHFyDp7PWNf32Hv3r3ccsstExvTHCPa1YkVixHy5VCatjJgGwMXI3g4QXDImxBHCv1RmhaW4j/bRTAnEUTcQ8wqpydYxObCQZ7vtFcETkdjbNQAC+LdQcxABD13Yu9aoVAo5gPKGJiFaGjIo63JfQuTvY/9nPziMu6+8y0cfvmIHVs8Qony5ZaNOSNvObOwvR11VCzbnla9ONMokgWgwv0M9jaSk1dB6eKrKVm4Gd3lpXLlzcmqwb0ddYiqG9A0DbeviJqr7GDR/q5Tw5WFMz5sqkuQfU/D8NLe/Oyodjn+3FHdX9x/iLpjkis21bL+mtdhGDqgE4sbhKL6vCk8paFR93InlQtq6es4ApAsGrZgyTUsWHw1R5/9DBXLtqfJAZZJRdUOvP5Smo//ivR4AZOuC/txuf1UrbuLpmO/IJusVNXejcdXjLj6E1jxGLrLiy+vEldvU/Z3D8NuSBMxCDSdeDxCw5EHRxXAM9x+Go48SGiwJUNHO+6katNHyc3NTdauyMbp06e56aabxh/PHCTaan/vRDxevO7hz0mL2MbAEGO8y1eJGdcJh9z4cqJoGuQuBvfp4fOJ4mN6sJg1xUd4HjtpwqmuQa4oycF04gVi5/rxiAUXbZwKhUIx21DGwCzENK00JdYyYzQcewnTjHPy8POsu/pG8vMLRitRWarRJrPEHPgWCWWurelpVm56D2u23Udr/ZOjZlnLll6bTP2ZliL0hfRKxYkUoQCa4SI42Erdc5+bcIXaorJa4tEQReUbKFt6LQDdLYdGKZBFZevRdC/33HMP+/bt4/Tp05imia7rLF68mJWrBNG4j2A4tdf8MATAlpkXnz3PPR/dQV/HcJrRRBrP0oVbqFi2fZQcTKSyb0IGMspK+XrKl72GvvY6uxp0SgVrj69ozHcPwy5rve3jFyYvKqvFcPkoXXhlSmpS2xAJdRzPYggkGI47ef6FF8e8j2maTjzB3F9RGkmk1f4MYx4jzU1Ij+YQNcLErHC2rtNCYMCHLycKwOIFQS7k5JOoW+kyuiEOumXQafajY2Kic6Z9ENeVJUQcYyB6bkAZAwqFQpGCMgZmIbquOT7gFnn5XuKxaLIi8dBAHwd2/4b1V2+nqKzWTsWYIFGNNsUgyKbsVVTtIBruo/n4r1mw5GrWrnotlhnDjEfo7zpFw5EHiUUG0tJNjpw17m0/Sm97HcvWvhWvv5QTe7+Utc3SNXdQUbVjhFKYSD25gMKyWjTNoK/jWAbF0Q4CDoZ0DMPP9u03csMNO5LGgGXpxOMQi80f5X8kuq4xFIhw5KV+Nm66m6a6n5P6LjTdyCgHE6050db0NLFokNLFWylfdj2Gy4emu+hueYn6l3+YVok69Z33d8pkCtlMBkHnuX2s2PDOibkhLb2WUwe/zcKVO6mo2kE42E3Z0mtxefJsQ2Qc+jrqWLVhx7jGgK7rTvau+SdPkdZWLMByaSMCiHMYcl38TD1DAzkUlg7iMixyPHFcogTtwgUsXcMgiEYYCy9nYwsQ7haORxfT2hciXOBNmm7RlgGsaBzNPXcziCkUCsVkUAHEsxALC7G+kmtvXMadf7KSWDSYVpEYIDg4SOWK9CxB8VhoVLCnrey9QKpik5pGMhFoHI8FOfHiVzh54Bu0NuwmFhmYcKXi5uO/dK6bl7XN6DSiw25IlmUSHuqg/pUfZTQElq69C3Q7CNg0LaJRiEQ0YjGDSEQjGrXmjTtQNhIy8+zuJnLyV7N8w4coLFuf5oKVSemfTB2BrpYDGIaH+ld+TCw6xLHnP5+UlUwjSrzzlvqnslayjkUGCAd7WLb2rYwVGJx0WYsMcPbEwxQvvAJ/wVIajv6UeHSCQa0Z4k4ysXr1atxu97jt5iKRtlaiHjdo4E1LLZpzUYOHE1iWRn/vcHagRdUxUr9VEkHE4XAZVxWcTR4/MxhCy3PeWdxS1YgVCoUiBWUMzEJMy2LX7asoK+3ht9/9V+qPHWL52ivS2uiGi0jEUZQTSpRpUrb0OlKVqsJSMSqOIONs8DRVKp5Im0Tu+0iwh7bmPWiaTn7JKvKKlqelLi0s30D1VX+KL78KTYuQn5ueKlIxjGlZbL95NWgQi5kcfv5ZenoKqNn6F6y77pOAlVnpn0Kw9+TlYusY8qHhzSkmEu7PnL62YuOwrCQNRYvW+icx42Hyiqro7zqV8e4uT/6o7Ei+HH9avYpMbNu2Da/XO2abuUqk5QKDOfnomoXblahirqHFvAxpl0bBDvT6k6mA3S6LlVeVYBhOelitC4CcwRKs3BZ0bJmsbx/EVTb8XlWKUYVCoRhGuQnNUuLRAZ793ffBsji2/xle+7aPUH/sUDLvtqZpDARCNDUHWLf541jRLgy3j562l9MCg3XDM0rZKyitprXhyfQbZgjkzNguC70dddQsu97OJjRGm/U1b6Dz/ItJN6Si8g1YlpkMCl276lZiMROPx0s0FqGz+Rm6Ww+mxTNUrrh53mQJmiimaeHNcXHH2zcRN+HKG27F63Oj6xrxWBhN1zIr/ZMM4IUpykXjUxnkY3jGv63paTo9+WmBwbrhJRoeSMpKpusWlq2l89zeUfdNi3NJyY5UVFbLXW95Lcdls5ONKp3bbruNwsLCCT3bXCM+MEC8r4+BxavxeYZT+2oRe6Z+SBu4JJ5TlqVztrWAVUt70TTwFxrUbi7k+Ct9eKx2QuY6dNPgmLGA1a42TsYWcqZ9EGNlGdEGe+Ugcq4fn2Whaeo7QqFQKNTKwCzE0OMceOrhpOI/NNBH88kj3PCGd9nKG2BpOi+97MQLWHZ2mFg0QFvzHiLB3uQsq+7yji4alWE2eDoqFU+kTSTcl+JaolG5cifxyBC5Rcvx5q/m4EvHOHJUMtBzhpP7/oPulv0jilUd4cS+LxAZPInXM79dg0YSi5tUrynC6w7RfX43x/d+nrrnPseJF7+MlognGcGkak6U1dqz8FORCyslIDelMnLqjH8i2PnkgW9w8sA3saw450//T2Y3JKcydl+npGThlaSuhqXGuYwudnaEppe/SXVVDtuu3gLYMQI1NTXcc889LFq0DI9nfqalDJ8/B8CgP48cT0q8QMRPWA8SN6OXbCxWyMvJ8wXJ/dx8Fxu2FJHv7wNnNaAzWsHanAYAGjsHMfPd6dWIO1U1YoVCoQBlDMxKNOI0HHsp7dhLex5loLeLN33g/2NV7RYMl4cFJYWsWuql8fA36Gs/kiwQ1db0NA1HHsSXV4GGPlrZy1BVtvPcvlEuRpOvPjuBNkklUqNq3V3E4xFMzUvH0Ap+8avHqTsmqV27nOZj6QGw6dipIg0GlctQCl6PRbBfcmLfF+3A8hGKeCalP+N7z4gdwNt5/sWpyYWm4/YVUPuaT7F2259Tvux6Go78JEuWIXvFIDjYylJhB55nuq6FRWv94/R3ymQxtYnGuZyXv+SKTTV88IMf4gMf+CDbt9+EYfiJxbJ0mQeEL5y3tzk5+NKMgVzClyB4OBWPBh29fk6cK0jMieD1GazfUsCCEtto8Q+UopW2o2ERjVuc6wmOcBWav8XjFAqFIhVlDMxCLDOezB6Uykt7HuXxn32L4opFoGmImqW0nPo1CaXH4yukYvmNgJacZTWt6ChlL9NscCwykKZUZWuXjeSs8QTaFJVvYM22+zDcOXZNhZP1PP/CiwQCAa7YVEvXuWcY3x/BorVhNx73BGeo5zi6rmEw6GTVyfDZWVC5Mj3gHDK/99GkB/BORS6KymrpOn8ALDDjUULBblZseMfoGIGUFYPmY7/g1MFvJ7MRjbxuT+srgJ3pKLEatqT69ROOZ2it342hayoI3SFyzlayo17XqJWB0CU2BjQN8g2L1h4/RxqLiTvDcbl1tm5pYGFFO/6BYo7nG6xwtQPYrkJlw8HHURU3oFAoFIAyBmYlmm6Myh6UYGigjwNP/hbNjNJ74VnS00e60HV3umJnWYSDPWnHss0GpypVReUb6Dy/f/KzxmO0qVy5kwVLrqF00VYaj/6ceDRILDLIsqULk63W164Zs3BaKr0dR3EZo42m+YjHbdLakL3ir0WMSLCHqnV3Mt57z6acJ2bxp7KaULb0Wjov7Cca7uPM4e9TULIal6+QxdW3U7PlI3aA75aP4Mstp+HIg2nBwpkyUY2Ut8RqmL9g0YSzIyn5SSd8/hwWYLoZtTIQ0C69Yp3nfAV2D3p5qb6ESMQ2/HUdNm86zsLSPtrNUlbn2a5C9R0DGAv8SbGMd4cwByOXfNwKhUJxuaGMgVmIhcGKdZvHbKNpMfo6j404ptNS/3iaYhePhuxsLSnHYtFA1tngVBejmi0fxuMroqr27lHtUu7KsnV3AhCLZMs2Ys8s97QcJhLsoevCfpaKNxAKtGO4fBiGQXV1NXfddRfxeGxy/ugTbTvHcRnmmEaUpunUv/IjDLefNdvuG6X0h4PduDx5lFdtZ+22+1l37V+x7tq/zKCcD68mVK1LyWQ1+o7J1YSFK3fR3ynJK7Qz/8SiAcAiEujg/On/cWIEvpGW1jad1GxV6asUqcQiA8SiQ0p+poBlWUQunCfsygEjTk5KALEV9jBkXfpUnW4dfLpt3A6GPByuMwgG7HFpGlyx8QQLw4WES9sBi9Ntg2BoGCU5yWtEz6vVAYVCoVDZhGYhcdPgqpvu4EzdQfx5BWzYdiPLRS2aZsduNsqjxKKRDIqMkz7SMunrOsmS6ttx+wroaH4OT04RDUd+woIlV1PjFI3SXT7E1Utpa3w6rapsXtFyChesQdNdmLEQ/oIliKs/MapdaqXicKCD6i0fpuPsC6Mq1JYt2WZnjGneQ9my65IKpp1jvpvFNXdQUlLCI488wlvf/LrJZbeZqO/6HEcjjsudS9mSa8gvrbYFRdPo7zpF57l9mPEYReW11L/8A3x5C1lcfTtLat6AhYmmGViWSVPdL8grXk5hqUA33HRfeImCsjWEAu2j3ntRxQZ03Z35nTtyEQ72UFSxgd62I7Q1PUP1lg/TcORBispqAR2vf4FTbGx8ejvqWLNiJ/klq8asaDzp7EhKfgCIdXZiBoP0FC9D06y0lYFQLA6emTGaClwQcib3g3opR146ycYthfhyDNyuODsrgvwk5GKRq4MLwXLaB8KUlOUSd6oRx87241XViBUKxTxHGQOzENO0cOcUcPe9f4vLZdLT+gLNx/4rqWwVL1iL1+saVdU1HguDZaalVSxdfA3hoJ2be8WGd9Bx9gU7LaTTrqhiI0XltZQvew22i4lGONiDbrjBsji+9wuAnbM9kfYxtfpsatrHrpZDyTYudy66y0so0DGcRrRiI5ZlJjMiga3klS2/hX379gFQ39DMggXr6Os4Ou7nVFS2nlhcVRn1aiaaprNiwzszpNJcx4oN78Q0oyxcsYvetqOEBlsY7D5NJNhJNBIgNNhKedV2BnvrGew5TWv9E1RU7cDrX0DDKw8mDcgE/V2nOHP4B6zY8A4MVw5FZbWUV23HMLzoLo9jiOiYsQhnDn+fWGQwZTZ/kIrlNxKPBojHQpOaxTfcPhrrfkZosCVrs0Q8Q2+7kp/JEGpqBKA/txSPy8RILBrF3Azp4RkbV64OhgZxCyzdRUTPRx7tZ8OWYnQdFuSF2Kj76C4/xYUL5Zy40Mf2qhI4YfePtgyqasQKhWLeo6a9Zim6rmHofTQe+Q59iVlXsGf9O+o4se9LeP2laYGVZiw8Kq2iy51D2dLraGt6Jun+s3bb/ay55j4Ky9YS6GvG5c3H7SvA7Wxdbj8NR36CZUaTM6epaR+P7/0CXef3jyoUFosGCA222UbAUAfnTv6OUKAtmUa0bMk25P6vExpsZcWGd9pjt0wikWG/3sMv11G06DVMxB+9csWuZHGi+Yqua7isEAPdp7Ok0jzKqYPfJth/jnCwm6Vr7sDlKUhm3ElUIB4ZFNzW9DThoU5WbHwnocE2Th78lu3Kc/BbhIY6qL7yA7g8eViWSV7xSnulyXATj4bounAQ+eJXOfPKD8grWp4Sc/AMS9e8CZcnl5b63ZPOShSPjq6wPZLOcy+ycMXoQOkMF1Tyk0Ko0fa7D+Tmk+NNqTwcySXouviVh7OhaVBgDMfBxHPKCAzGOdswPKbrczyYlQOAxbEL/eg5bvQ8Jz2saRG9kKlCtkKhUMwf1MrALMTODDOQPTMMABbNx39F9ZYP09VyiFhkgIHeBgrLazm5/+vJfpYZS8YHnD3xMK31T9B5bh8rNryTUwe/Pcb1Sc4sZ5plbWt6Om0lQDc8uDx5BAdaaTzqzN5qOgtX3kyan3e4n972o/S217F0jZ02MjWLSyAQQJ48i6h92xjPr1FV+3bi5GFZ8zsDjFeLEzcHaRovFeuxh6je8mGCA61Ub/kQLWf+YLd3agYkZMJ227GvM/IdJzDcfhqOPJh8x2u3/QWDPfW01D9J1bq3UrroKorKazHjUfq7TtJw9KfkFS5DXP0JzHgEDY3ejjp8eRWTmMWvpbf9KMWVm52CZ5nlYlHVjcQ7WqhS8jMpEsZAKMfHAu+wn70WyiXAzCrTBS7oi9nVBSyXD9NTwIXmfopKCygsimFocF1xDueLmpEtLuKmhVGemwwejjb24qkqmtFnUCgUiplETXvNQsbLDDNMamAlYFm0N+1J76dpowqRjRVAnNIRC8bMGjO8WvBN4rEQ0VAfZw4/MOzGYZnoumtUNprE2M+eeJjC8lqaz6a7fXT19OMvrmHNNfdTVL5xRHabjay55n48edWqAjHgcpm0nR/xzjNiy4plxTFj4eGMO87sfLYUoyMLgYWD3XRfOJh8x3aKTzswvPrKD9DVcgh54BvJ6xeV1yKu+hjlVduxnHS5ZjySNEAmm5UoFjOp2vTR0XJRtp6aTR9Aa2yg46efIb9ghZKfCWKZJuGmRiK6F9NjkesbDh6OhnUsZjbjkqFBgStldcBfgYXGmRO9xOP2O6x0GWxd00AwGqOxcxDXwrxk++jZfqyIyhqlUCjmL2plYBYyXmaYVHo76lhf83pCgXbyipdz4cxjaecT7h+ZZnkNt3/MwGAsM21VIdssa2LWv7Bs7YhTOppmpMUVpGPR3vQssDrt6JYtW4hGDWKxAspXvolF1W9Iji0WNwhF9XmfEz6BZlj0dR2fUNvejjpqll2PZcWTrkSpPvZtTU9TUbUja1BwedV2YtGhFKPOVtIbjjzIotW3MtDbzJKaN4BlYZoRNN2FFY8RiwQIB7vx5y/EVvytUQbIWPJVVXu3vaoUDRAKR/j1w7u5+663kF95A3m5ORDoI3jqIN0/+b/EB3tBNyBuEYor+ZkI0Y52zGCQAf9idFcYv3fYGAjFLw+DqdAF/cnVAS9xXwnhYBeN9cWsqrYrI28tMDhUeZ7jF5aw6orF6PkezIEIxC2izX14VpfM7EMoFArFDKGMgdlIirI2fluTSE8vxfoG3N7CUf1S3T8Ss7yt9U8kz6cGBru9+UQj9qpBw5GfsHLTuwkHuwHGzBrT3ykJB7tHFR0rKqvFjEeyGAI2vR1HWbbhhuT+zp078fv9RKMWlmURCmuMFmOlyCWwJpMeM9EuJePOSPegbK5B/V2nwLJoPvaQc8Q2AsPBHlZseGcyw48/72PUv/JjllS/npyCRcRjIQyXj/ziFZzY9xVikQEqV948JQOksHwDZ+qbqayspO6YpMjvxeqWBPb9Ku0xc2uuIYob01TyMxFCZ84A0OMvR3fF0lYGAjO8KpDA0KDIZdEds42TuL8cI9JH+/kWysrLKCi0MDSNt6xp4PG6VbzhisUYC/MwB+zvr0hDrzIGFArFvGVOGQNCCDdwA3A7sAOoAXxAB/AC8BUp5R/H6P8u4F5gI2Bg55z4LvB1KeXlk3BcMyaVHtGKRDn+6X/hqge/ParfeLOvtoGwG7cnj76O47Q176GorJYVG97BYG8TlSt3cmLvl7IqiPas/2AybWTKwCiv2j5+ASjLRNOgurqaTZs24XK5yM3Npa9v5jKYzCr0yckKpK8GZJKP0Uajrfj3dRwjFg1QVLGRyhU3oWkGPa0vc+H0o7bBlww2H8TtK+DUwW8nDcGarfc69QWYogGiUbTweh7/5R+444478JgRTPncKEMANAqvv5PBmIFS+ifG0Al7ZamnsBS3MYjH5chSXCcYvjyMAbBXBwbiELUA3SCWtxhXfxP1spuNVxWh6xoLvRrVS/fSO7SWgsp8oidtYyDWMoAZjKLnuGf2IRQKhWIGmFPGALYB8LjzdyvwDBAA1gF3AncKIf5ZSvkPIzsKIb4KfBwIAbuBKLAL+AqwSwhx1+ViEMTiuhMweWTctoVltVi5eZRct41g84WMAZnjzb5WLN+BbniwCmOULNpCT+vLSdcen/89VK27k6ZjD41aVbDJVATKPmYYXtqbnx37ATSd3Nw8Vq9eTW5uLgUFBXg8HkAZAxMhFndNWFaKymrtugPnXxyljGeVj/L1VFTtsDMF5Q1Rungr4UAnpw89MGrFJ3H9TEXBXpUBEhlkYfWbOS6bueGGG/AH2ul/5Cu2S1AaGgve+KdEvcVYMWUITJShkycw0Qn6/ZT5hj/TeNhJE3uZoGmwwG3R4sR6mJ584r5SgoEuzjYMUbUqF4BrywK8fHYPO8Qu9GIfZk8ILIie6cG7vnwmH0GhUChmhLlmDJjAQ8AXpZR7Uk8IId4O/Bj4tBDiKSnlUynn7sQ2BFqBG6SUp5zjFcBTwFuAPwO+eEmeYhwiUZ3KFTc7Sv1YP8YaeUu28Zuze3nDh9+LFxcLXcVpGWESjJx9TXUJSuSCX3PNfWmzuQD1L/+AlZvew5pt99Fa/2RGN46+9jrbjzw13gCdbifL0VgUla8Hzc2iRcuIx3EMAcVECUe0CctKwr8/kzKeaXbe5ckjHOjkzMs/IK9wGWVLr6Xr/P4sBb/slSAsi76OY6PadJ7bx8pN90zMAElxPwsFu6na9FE6OgOINavJ8fpwB7uJLFlH4OQ+MOOgG+TWXEPh9XcS9RYTiqm8CRMl2tlBrLOTfl85hjdCvi+aPBeJXX65+XMMO9VovxPLEM9diBV1c6G5lYJCN8UL7O+PFf6X6OpbTsHiMiI9IQDCsgvPujI0/fKIg1AoFIpLhTafUucJIf4L+CDwgJTygynHDwBbgPdKKX8wos8O4I/YhsLiaVgd+COwIxKJ0dcXnPJFvB6LyODJcdIjvg1P8WqiUR1iOqWleUQjAfra62g69ous/ZauedOI7D4aC6vfTMgswad303Lq16P6JqrW5uSWY1l2kSvLMhkaOI/bk09qwTKvvxTD8DgFy8ZWUNdccz/BaEEyxWNZWT4AHR2XPp1hWVn+TGsJf2QKsjMhWVl3F6FAe5qSPlycLvtqUTw6hOHOIR4LU//yj4hF+jNfv/ZuDLef5mMPZTEANdZd/9cEepvSxpmIWSkoWQ2ajttbgKa5MK04GhpxUydu6pimRjxuF+TTdQ2vHsOtxZLGQBQ34bgxLYHBU5HBGZadPzLF75y+Z/fQ9r3vUF9yBV1rixHL2ygvspXnvt4S+jqzu9XojlJ9qYOxTQtaIhrhZDkNjWhPHoXaMa64Rsfrs42YwcEY5aXvxnU4DFG7ce7O5biXFk7bWKbj++oy+N5RKBRznLm2MjAeLznbJYkDQogl2IZABPj5yA5SyqeFEOeBxcA24PlLMM5xCUc0vHl2es3Wht30dhxNUdbWU7liF3HyCAcSvyP2D7Lbk4u/eA1rt/05LfW7R7l8lC97TdpMfmFZLUULr+e4bObF/b/n6q1XsnbTR+lteW642Jmm4/UvwOXOw0Kzf/w1i+6eQbq7Y1RWGOTm+jEMF7G4m0gkjj/Po3K9XyKSsrLtflrrM8jKyl24DD/+/MWEgz3J823Ne4hFQyyqvp0l4o1YZpx4PERv21F7tSgaSMpH/2CUxWvfNUou7OvvJBYNcfrgN8n2rpetu5vWtl7a28OsSpGvWGSA1oYnCQ62UbTwenSXi6GQzuisyMPXNU2LoGkQxJm5jo9uo5gYgVcOA9CRtxjL6KEwd7gAYDTsg8skgDgVXYMKj8X5sEbcAk23cJcM0D+0nuaXjrHqGttgzMtzceb8Q0Q8G1gULaKEPMJ1HbiWFKBpSv9WKBTzh/lmDFQ729TE9ZudbZ2UMtu02X5sY2Azl4kxALaSp+uTT48YGNIwjCIWVb/ZVvKcmXzT1Gg/34Q/T1C1XuDx+TE1Ny/uP0xdnZ3K9MDBwwwNhbl6660srn4DkUgYXTc4Wid54ie/IxAIjLrfzp076e9vpHrFMl5+9hGuuvGNhCNuPBMxZlSu92lhXFkJW+h6YcbzQyHdmXF343F7KF18DSWLriIWM+33/qvHCQQC5ObmcsWmWlZu2IHX68Hj8dLcfIEf/+T3rF1TndGILCpfT2HldY6x+QwALx0evo6GZeeMr2/myd/s5u1vf8cMf5LzBzMUInDkFUKGH3dxLpq7E6/bnkE3TQgFLl/jyqXBQo9Fa0QjZtnxBO7cPs6zGFoDVC+yZ+oXLogiQ6d5bshPhVXEhrbleFsrcC/Mn+EnUCgUikvHvDEGhBCVwPuc3YdSTq1wtk1jdG8e0fZV4/G4kkvIl5qx7lu5zEs0EsIyTTRdx+fxsWPHDq677jpM087sY8YiHHrqd/gLSmgLWBQWFbF8+XI6Ons4c+YMpmmi6zqrVq1i06ZNNDY20t/fT0FxKbve+n48vpyUOxZQVXs3S+N3JI0Sw+XFcPmm/AxznZmUnVSGhgKsWi3o7Orl9OnTBAIBXth7gI7OXrZetYXBvi56zp/h9lt2gc5B/PEAACAASURBVKYRieh48jZTVXE9WCa+3AJcnlye2fM8R44MBzgHAgGee/5Fnnv+xbT71dTU4PN5KSz0XupHzcjl8A4mw2TlpuOZl7GiUToLVlKQG0TzD68KxGI5gJV0BRqLibS5GPh0WKxDa8ggbA2vYJzv8pOfE6Wy2HZ3ql7Uz2DIRVuwl27rCNccsLj6/bumdXVgtsmKQqGYX8wLY0AI4QJ+BBQCu6WUv005nShFOXpKe5hBZzvnv9G9vhy8acq6c9ybroBdd/vbiUXDrAhG+OEPf8jx48fZsGEDmzdvTrZpamrikUceIRAI8J73vIe8/IKM93S5feAeW/lXXH74/bn4/bnccsst3HTTTUkj0O12g2USjeTgrd3M/qd+R0PdIUwzjq4brKy9kq0778DjK8Tj9bF58+Y0YyAb27ZtGyWHiotH5x4709dQ2XqGjE6WpbgImbHcmRrWpHDrsNgX50x7GTEtimbEyNcGCO7tI3ydC2+hga5D7bJeDp5eQDQOz/a8TN8vI+x6860YxuUXJK1QKBTTzbwwBoBvYKcJPQu8e4bHAsCrDSCeCtMffGvgdudw66238dhjj7J371727t07qtVtt92Gy+WblvvOcADxJb9nJmZCdiaGXT04GLSVxrKyYvIKirn2dX/Cda97e3K1ycJF3DTo648CUVwuX1KGsjGdMvRqmWIA8cUazoSZjNxEWlvp3n+Afu8CFpXlc1S7QGnBcDrfoYAb0xw7l8JMBRBnojxvkMaWZQBEibPFe4zYb4cw716M7tXxeUxql/bycmMxFhpHGo/T8oNObrjxZvLypv7upimAeMp9FQqFYiLM+Rx7QogvYmcQagV2SSlbRzRJzPqPNdWVWD2YeU3kMiMWg8WLl3HPPfdQU1ODrtsipes6NTU13HPPPSxatIxYbJwLKeYs0ZhOJOYhavrsbSw9nkXJ0OVHzx8eBctCX7mLPnc3hf5oSryAQWBg5hX8yZCXM0SubwgAE4ND1vUULogRe7wt2aYoP8K6imGDp7O7g9/97iFOnjw2ruEDYA50EG08SOT4H4k2HsLs75j+B1EoFIqLwJxeGRBCfB64D7sC8a5E/YARNDrbqjEutXREW0UKsRgYhp/t22/khht2JN1FLEsnHoeYKvCkGAclQ5cPocZG+p59hnjlZspLS6jjFUTJUPJ8NFqAdRnM9k8GTYNFC1o5dW4FoNERq0DmXElFdx2x/T24thYDUFbey1ZzEQfaTSwNIpEIe/c+i5THufrq66moqEy7rmXGiZ3eS6TuCcyOhlH3Pb9wNflXvhar8ko0Q1U3VigUlydz1hgQQnwO+EugC7hZSnksS9NEutFaIUROloxCW0e0VYzANC3syTMNEikdVSpHxSRQMjTzxAMBWh/4Np7yDeSvvoGD2im83miytgDAYJ8Pu77j7MLnjVBe3El7TxkAdaGNuEsjGE3nKCsLYCy3F4dzKy+w3VPJoQtuBi27yFpPTxePPfYwy5evYvPmreTl5hI7vZfwSw9j9bVlvWe45TTh359G8xfh2Xgr7jU3onlGx2QpFArFTDInjQEhxGeAvwZ6gNdKKV/J1lZKeVYIcQi4ErgbyFR0bAm2m9ELF23QCoVCMYOEzp2j88e/xFt6Hd6iRRzjLO16DxuX9JFICBSP5xHon32GQILy4i6C4RwGhmzPz8PBq1hSUoZ15Bjl2hBGlR8AvaSVrXk59LRVcLQnTEyzjdLGxjM0NZ6hSutlRaiRImt4xQTdwFhQhZZTgDXUR7yr2S56B1hDvYT3/pTwod/iqd2Fe/1r0XMyJ1RQKBSKS82cMwaEEP8CfAroxTYEJjKb/+/YBcc+K4R4Xkp52rlWOfA1p81npqH6sEKhUMw4VjROpLGXePcAXVLSFQzj8uRgVq4jYPQTyH0Bly/ElqIguT5HobWgr7sQmL3BG5oGSyvO09SyhEDIXgk4F63inKeKYtnOGquBkuX2KojlCVK0tJHXLHTTP5BDR8giFNWJx3V6rRyeNzZAVMdPFG+xxvKFghyfH13XKMnPx2dGcHeeIHD8BcygE24WGSLy0m+JHP4detlKjJKl6IUV4PWj6QZ62QqM4sUz9fEoFIp5ijaXqrsKIe4AfuPsHgDqsjQ9IaX8zIi+XwPuBULAE0AUOwNRAfBr4C4p5XSU2zwHLDZNi1js0lbv9Hhs2y8Smb0/5jP5DB6P62ngMHD/Jb+5zYzJzlSYC/KWiak81wzLzii5afvlMULn+okR50leJqTZ7jA+T4yrVnfhMkb/LphWFf29OXPCc8s0oaE5n9YO/6hzCyvbWb/uFB732O/XsqCuuYjO/tFpkT1uF3ftvI6iglyseJTB04cJnnieeH/nuGMre8c/4l26bvhaM/+9o1Ao5jhzzRh4H/DdCTR9Wkp5Y4b+7wI+AWzAdlo+ATwAfH0aVwV6sesdKGYnTwM3ztC9lezMbmZKdrLKTTwSh3icaCRCLBInFg4Ti8exsIhEw5hWFMsMMxtjBKaEZkesaOj2HwCWhaa7QPdg6C40XUd3GWi6gaaBYRi43AYGFroVt7tpGsSjtsWQ7VYuD7rLAy43mm6ApqFpBlrm2gYz+b2jUCjmOHPKGJglvIRdyXgQOD3DY1FMnpmcoVOyM7uZKdlRcjP7USsDCoXioqGMAYVCoVAoFAqFYp4y54uOKRQKhUKhUCgUiswoY0ChUCgUCoVCoZinKGNAoVAoFAqFQqGYpyhjQKFQKBQKhUKhmKcoY0ChUCgUCoVCoZinKGNAoVAoFAqFQqGYpyhjQKFQKBQKhUKhmKcoY0ChUCgUCoVCoZinKGNAoVAoFAqFQqGYpyhjQKFQKBQKhUKhmKcoY0ChUCgUCoVCoZinKGNAoVAoFAqFQqGYpyhjQKFQKBQKhUKhmKcoY0ChUCgUCoVCoZinuGZ6AIpXhxDiRuCpCTavklI2j+j/LuBeYCNgACeA7wJfl1Ka0zjUMRFCLAE+BdwCLAM04CywG/iclLI+S7/LYvxzCSGEG7gBuB3YAdQAPqADeAH4ipTyj2P0n9I7EULcBvwlcJVzv3rgQeD/SSnD0/BcfwZsBzYA5UAB0Au8DHwP+LGU0srQT3ee5/3AGiAOvAJ8TUr54Dj3nFPyOVXZEEJ8D3jvGJeWUso10z3ei8VUn+fVyJJCoVBcLDTLGvXbp5hFCCHWAH8zRpOrgbXAGaA6VdkRQnwV+DgQwla6o8AuIB/4FXDXpVBYhBCbgSeBIuAccNA5dRWwGBgEbpVSPj+i32Ux/rmGEOJm4HFntxX7fQSAdcB65/g/Syn/IUPfKb0TIcQngc9iK0d/BHqwlc0yYC+wS0o59Cqf6xy2EXAUOO88UxVwDbbx+RvgranjE0IYwC+BO4B+55m8zjN5gS9JKf88y/3mnHxOVTZSlOfngNMZLt0ipfzbizHmi8FUnufVyJJCoVBcTNTKwCxHSnkCeF+280KIY86fD4wwBO7EVlRagRuklKec4xXYKw1vAf4M+OLFGXkaX8U2BL4NfEJKGXXG4ga+AXwA+Dqw6TId/1zDBB4Cviil3JN6QgjxduDHwKeFEE9JKZ9KOTeldyKEuAr4DDAE7JRS7nOO5wG/x56J/lfgL17lc70DeElKGRhx/1psxexN2Ared1NO34+tvB1zxtbm9KkG9gD3CSGelFL+ZsQ156p8Tkk2UvgvKeX3Lv4wLxmTeZ4pyZJCoVBcbFTMwBxGCHEt9qpAHNsNIpXErNWnEooKgPMDda+z+zfOsvbFHKMPuNbZ/ceEIeCMJQr8vbO7UQjhT+l6WYx/LiKlfFJKeddIZc8591OGZendI05P9Z38DfbM/GcThoDTbxDbncIEPi6EKJriIyWu9+xIQ8A5XodtkAK8NnHcmcn9pLN7b0J5c/qcwnZrA/i7DLebk/L5KmRjXvMqZUmhUCguKrPqh0gxaT7gbB+VUl5IHHT887cAEeDnIztJKZ/GdqOoBLZd5DHGgdgE2gWAIFx245+PvORslyQOTPWdCCE8wOuc3R9n6FeP7YvuwfZTv1gkZDA1NuFabLeic1LKZzL0+Tm2689WIcTixMF5Lp+jZEMBTFGWFAqF4lKgjIE5ijOL/nZn9zsjTm92tnVSymCWS+wf0fai4Mz+73Z2/7fjGgQk3YT+2dn9Toqb02Uz/nlKtbNtSTk21XciAD/QLaU8M4l+04YQYgXwMWf34ZRTifvtJwNODEOds3tFhn7zUT4zyUYqNwkh/kMI8S0hxD8LIW6dbasjI5jo80xVlhQKheKio2IG5i53YwcqtgO/G3FuhbNtGqN/IuvQijHaTBcfBx4FPgy8TghxwDm+FSgGvsDwEnvqmC6X8c8bhBCVDMeoPJRyaqrvZMWIcxPtN2WEEO/HDk52Y89gX4c9MfJvUspfZRjbeM90BZmfaV7J5xiykcp7Mhw7JoR4h5TyyEUZ2MVlos8zVVlSKBSKi85snpFRjE3CRegHqX74DnnOdpT/dAqDzjZ/WkeVAccV5DrgEWzl7M3Ov8XYwXZ7RjzDZTX++YIQwgX8CCgEdkspf5tyeqrvZCbe5fXYgcLvwg5OBvg0w6tQr3Zs804+x5ENgMPAfdhZh/KARcAbsNO6rgOemGXuMZN9nnknEwqFYvagVgbmIEKI1QwrOQ/M5FgmghDiOuyUe/3YGV0SKUSvBz4PPCSE+Ecp5f+ZoSEqbL6BnQbxLLM4QFRK+SHgQ0KIHOxZ2PcD/wS8TQhxe2p8jWLCjCkbUsovjDgUAH4vhHgceBo7duJvgT+9yOOcFuba8ygUivmNWhmYmyRWBV6QUh7PcD4xA5U7xjUSM1kD0zaqDDgZYn6NPRt2m5TyYSllp/PvN8Bt2IHDn3ZS8MFlNP75ghDii8AHsVNl7pJSto5oMtV3MmPvUkoZlFIek1L+Nbbitgn4yjSMbV7J5wRkIytSygjw787uxQwQvySM8TzzSiYUCsXsQhkDcwwnhV3Cj3Vk4HCCRmdbNcallo5oe7F4PU5hqUxVhqWUp4F92KtYN44Y0+Uw/jmPEOLz2C4RHdjK3qkMzRqd7WTfSeLvZZPsN918z9m+MSWIPXG/qT7TnJfPCcrGeJxwtrPJTWgsMj1Po7Od8zKhUChmH8oYmHvcynDV3p9maZNI/1fruEpkYuuItheLhBLYN0abXmdb4mwvp/HPaYQQnwP+EugCbpZSHsvSdKrv5AT2yk+JEGJVln5XZ+g33fRgpxd1MSxnh5zt1kwdnIxdiaq7qWObF/I5CdkYj1JnOzhmq9lDpueZqiwpFArFRUcZA3OPDzrbnzlFm0YhpTyL/ePkwc46lIYQYgd2IG8rdo73i0nCP3tLalrRlLG4sXO2AzTAZTf+OYsQ4jPAX2Mryq+VUr6Sre1U34njVvGIs/snGfqtxM7RHsGuRnyxuAHbEOgFOp1jL2DPeC8RQtyQoc/d2BmJ9kspzycOzgf5nIxsTIC3OduMaTdnIZmeZ0qypFAoFJcCZQzMIYQQC4A3OrvZXIQSJPxaP+sEHCeuUQ58zdn9jJTSnN5RjuIRYAh7heA/hRDelLF4gS9hL5/3AI+l9Ltcxj8nEUL8C3ZV1F5sZW8is5VTfSefASzgU0KIq1P65WEHwOvA16SUvUwRIcRrhBBvcLLejDx3PcP/X74jpYwDONvPOce/7jxHok+1M26Af81wyzkrn5OVDSHEFc5nb4w47hJC/BW2mxHAf16UAU8zU3meVylLCoVCcVHRLMsav5ViViCE+AvgP4ATUsq1E2j/NeBeIAQ8gV0BcxdQgB3Ue1dCMbqYCCHei62MGdgrBYkl9S3AQuyqsO+QUv76chz/XEMIcQfwG2f3AMPFkEZyQkr5mdQDU30nQohPAp/Frkj9JLaiuQO7aus+YKdTmGmqz/Q+4LvOdQ9hz8rnA6uwU0GCvfJwd2qhMEfh+xW2kd2PXSDPDdwM+IAvSykTyt/Ie845+ZyKbAgh3oz9GXZjf/bt2K40G7BTcprA30gp/+9FHPq0MdXneTWypFAoFBcTZQzMIYQQr2D/IH1yoj+sQoh3AZ9w+hnYPtwPAF+/lLOWQogrgfuB7dgGAMB54CngP7L5I18u459LpCjO4/G0lPLGDP2n9E6EELcBfwVcha0c1QP/Dfw/KWV4ck8x6tqJFKLbsQ2AMkDDNgoOAD8aaWym9NWxC+O9H1iDbbC8gr1a8d/j3HdOyedUZMP57P8cO/ajCltxtoBzwB7gq1LKgxdjvBeDV/M8r0aWFAqF4mKhjAGFQqFQKBQKhWKeomIGFAqFQqFQKBSKeYoyBhQKhUKhUCgUinmKMgYUCoVCoVAoFIp5ijIGFAqFQqFQKBSKeYoyBhQKhUKhUCgUinmKMgYUCoVCoVAoFIp5ijIGFAqFQqFQKBSKeYprpgegmL0IIRqxi+7cJKX84zRdcznQACCl1KbjmorLFyHEzcDjwBkp5eppvO6/AH8HfEdK+aHpuq5CoVAoFHMNZQzMYoQQ3wPeS5ZKsFNtO5sRQtwPFAHfk1I2zvBwLguEEGVAu7P7Zinlb7K0+zrwMWf3TinlL7O0+zLwp0CdlHL9dI93phFCrATeA3RLKb800+OZ6wgh3gz8ytl9Qkr52pkcj0KhUMw3lJuQ4tVwBpDA0EwPJIX7gX8Els/wOC4bpJQdwAln94Yxmt6Q5e9s7Z5+NeNyCGDL0JlpuNZ0sRJbhu6b6YHME96b8vdOIcTiGRuJQqFQzEPUyoBiykgpd830GBQT5mlgDVmUfCFEKbAWaAMqxmhXBCRWA555tYOSUr7gjEsxDxFCLABej20U/gZ4F3AP8JmZHJdCoVDMJ9TKgEIxP0go7puFEHkZzm8HNOB/sGfqNwkhCrK0S3xvTMfKgGJ+807ADTwMfNM59t7szRUKhUIx3aiVAQWQDNz9K+AWYCkQB04CPwO+IqUMZOjTyBgBxEKIddjuFjcB+UAT8FPg34G/cc59X0r5vjHGtR74e+BG7FiARuDHwGellJGUdv/kXC/BU0KI1EvN6ViJCZBQ3A3geuCxEee3O9s9gAkIp90jWdqdlFK2jryJEOIO4EPA1UAJ0APsA74spXw8Q/txA4iFEO8H7gVqgTDwEvB5KeX/CCHOAYuB7VLKZzM/eto11mHL9gHg36SUu0e0S1wPYJUQwhpxqXuklD/Kdh/FpEko/j/Glr1mYI0Q4mop5YvZOgkhrgf+F3At4AVOA98FvgQ84Fz3f0sp/ylDXx34E+y4kCuAQqDTuf9/SCn3TcuTKRQKxSxBrQwoEEK8FTiOHRRaA1jYP7BXYi/XvyCEqJjkNW8GDgJvA8qACLAC+AfgKef6413jFuBF4O2AD3sGUQD/B9tISWUQ28XFdPZ7nP3Ev+7JjH+uIaU8D9Q7u5lcgBLH9jj/xmuX5iIkhPAIIR7EdvV4I7arURAod/b/IIT4t8mMWQihCSEewFbutmLLgIZtXP5eCPGnE7zO95xrbMaWjwJgpzOmN41o3o4tO2AbDW0j/gUn8wyK7AghaoEtQBfwBymlBTzonM66OiCEeA+2/N0OFGN/t6wD/hP4xTj3zMc2hH8A3AyUYr/ThdjfVc9PVK4UCoVirqCMgXmOEGIr8JP/v71zj7GrquLwV9vSaSnttIKWUCBK9KeGJpRHsA0giDpgBFuQpAQtgoAGBP5ANMUnCY1IREQeImgAlRaqQIUEMIEGbEVJBaUFZCGPQotQgbZTAam0xT/W3r2Hy7nn3hlnuMzc9SU357XOPvtm1py7115rr4V7ieYDU81se2AsMBOfQZ2G/3i22uaOqc0ufDA/zcwmAuPxGbk9qWWtqeIG4FbgfWbWjQ/i5uHGymclfToLmtkPzWwKsDqdOsrMphQ+R7Xa/2FM9g68aZCfwoamA8+b2ePAsgZy43ADsdhW5kJgDvAP4HPA+PQ3n4AbmS8D8yQd04f+ngSckPbPAyab2SR84HZNeubkJm0cjQ/yvgxMNLMJwB74d3wXcKmkkVnYzPZO8gCr6nRoipnd2If+B9XkAf8iM3s97V+XtnMkbVd/g6QPAVfhf7vb8HfDJFzPzsANz3oDr0g2Ah4AeoBxSU8n4x7ILcDFyfMQBEHQEUSY0PBgpqS3hGzUMbHB+YvwGfevmFmO2cXMtuAegR7gIeBTkvY1s7+00J/T8Rm3fwE9ZrYhtfk6sEDSZnyg34zlwJw0Y0gKVTo//VB/Bh903tZCO4HzB3xwvZ+kLjN7LZ2fiYcPLQUwsyckPQfsK2msmf2nIDc67W8zBiR9GDgNnzk/JHkhSG39G7hMUi/wKzz3/2+adTSFcnw7Hf7UzPI+ZrZW0om4UdDTpKluXIe26ZuZPSnpWNxTMhXYH7i3WZ+CgSMZYJ9PhwvyeTNbKWklPgFxBFBvfM0DtsPfSbNzqGDS0UskjQV+0OCZnwBm4WtiPm5mvYXnrgfmS9qChzHOw98xQRAEw57wDAwPRuNhGVWfrvqbJO2Bx4VvAH5R1rCZraMWN95q/u88C39lNgTq2lxELWSlivOzIVDH4rQddjnuB5k8gB+DD4AzeR1AMfRnGT7oKpNbZWarC+fn4uE7C4uGQB2LgNfxhck7tdDX/fC1KwAX1F9MevGW8yU8WTQECvevwcPYIPSoHXwSN+aeBv5Ydy17B94UKpQMxFnp8MfFNUMFLsUzE5WR27uqaAg0ePYhRY9REATBcCY8A8ODvhQdKzIzbccDa+oW3BbJ2Wd2bSRQeM4YPH4XauEmZSzD87lXsbzB+TzgnNSsP0ENM3sqLZCdiocA1YcNLS2ILwOOSdfurpOrDxHKenRimnFvRB5c7Qq80KS709N2TUXxuHvxsI6qQVuVJyv0qH18MW0Xlhj8C/HZ+cMl7ZTqZIC/L3KGq9J3i5m9Kul+yte7ZD39lqSzm/RvHDXvZhAEwbAmPAOdzc5pO4pqr8L2SW5cC21OoqZXz1XI/bNZQynEpIwc3jK6wfWgMXn2/yDwhb945p9eYGVBbmmJXPYS1BsDWY8mUK1HWS9a0aMd07ahDqUwp/WNrica6RCEHrUFSROpxfUvqL9uZs/g+jcKrzuQ2bGw3593S9bTbqr1NNOKngZBEAx5wjPQ2eTB2YNmtldbexK8XdyDD7BmSBqFGwJdwBIz21qQW4EPpD8qaTQetjM2XasvNpb16HQzu3TQeh4MF3J2MIAVFR5JcG/mxQP03Kyns81scaVkEARBBxGegc5mbdo2Df/pA+uppffcuUKu6loweOSB/PZ4WsdifYFt5AXkSW7vgtyzZvZEXZtZj3YbwH6+mLYN9URSFxHiMxTpS1Gx6ZKmpf0XC+f7824ZDD0NgiAY8oQx0Nn8KW0nS9q/UrJFzGwT8Eg6PKBC9MCKa/8P2RAZMUjtD2nM7FFqg6KDaFA3ILG0RK6s6nDWo8MHoo+Jv6btVEm7N5CZQfV6gf4SOjRISPoAtdj9vXBjrtHn1iSXjYcngY1pv/TdkrIJ7dPg8YOhp0EQBEOeMAY6mDQw/HM6vCCFg5QiaWxaHNwKN6ftySk+uL6to2m+eLi/5MFC9yC1PxzIg/yD8YHZa5QvtF1WkMt518uMhmvx2g97SvpS1YMltTqTvxxYk/a/1kCm2SLQ/pJ1qFE63qD/zE3bB83sQTPb0OhDLQXtcZJGpjC236VzZzZ4X51KLeFBPdekbY+kw6o62Qc9DYIgGPKEMRCcAWzCZ37vknRASuGHpJGSpkn6Dj4r12pozyV4uNB7gdtTpVEkjZI0B7gaT2c6GDyctsemMJLgreTZ/cPwRb/3NUjTeB+eDjTLFe/dhpmtxP/mAD+TNF/SLvm6pB0k9UhaQK3CbCVp4HdeOvyqpO9JmpDae4+knwOHMjgVgR8DNgPvLqlQHPQTSSOAL6TDm1q45VZc/6ZQqyfxfbzi8DTgxuw1ktQl6TS8Ynrpu8XM7kjPHQHcLOnsYppbSZMlzZJ0C/Cjvn6/IAiCoUoYAx2OmS0HZuPZZA7EZ41flfQiPtBaAZyL/yCX5fwva/MF4FjcyJgBPCRpA16FdmFq84okvmnAvoyT6yUcA/RKWi1plaTrB/g5Q5k8u5///5eWCaVCTvcX5NYmb1IZZ+GVYUcC5+CpanvT370XuAPXib6E9VyJFyoD+C6wTtI64HngROBMatmEBkyPzGwjXhcBYLGkDUmHVkmaVXVvUMnBQA75alrJOXkHlqTD49O5v+PVy9/Ai5KtSjqxEa8xcDNwS7qnTCfm4nVKuvA6FWslrZe0EXgp3X9EX79YEATBUCaMgQAzux34ID4T+wD+I9qN/8Dei8+27WNmT/ehzd8D+wK/xX9kxwBP4YO6Q6llphlQD4GZLcGNm3twY2YXfAAyZSCfM8RZCawrHJcaAyXXGsqZ2WYzOwX3MF2HF5Magw+6nsHDO07FM8m0RMo/fzxwEh7GlL0XS4DDzewKah6LgfY0nYxXsjX8O+yePo1CUILm5Nj/x8zs4UrJGtloOFJSN4CZXY3r2R24oTkGX6d0BjCHWnhXWcHDV8xsNl5d+CY8Dek4PL3s47gReAJeRT0IgqAjGPHGGy1N9gbBgCJpKb4I8AQzu6bN3QmGIPKclI/iax52MLPNbe5S0GZSKNLTeIa0Q8zs7vb2KAiC4J1PeAaCtx1JM3BDYCtwV5u7Ewxdvp62d4chECTm4IbARnzNSxAEQdCEKDoWDAqSTsErht4ArDKzLZLGA0cBFyWxRWa2ul19DN75SLoWDzG6x8xeSufeD3wDXzcAcGGbuhe0AUnn4AXxFuN1L7am7D9z8QXGAJenNS9BEARBE8IYCAaL3YBvAvOBLZJ68XUI2Rv1NyIuN2hODykdpaSX07li3P65Znbn296roJ18BDgO+AnwX0mv4O+WXBfiTjzpQRAEQdACYQwEg8X1+CLhjwFTgcm46/4RfFHxFTFzF7TAWcCRwHQ8VW0X8CxeQOqyiAnvPQDATwAAAGVJREFUSC7H3yUH4OmOu/EF8SuAXwO/jLCxIAiC1okFxEEQBEEQBEHQocQC4iAIgiAIgiDoUMIYCIIgCIIgCIIOJYyBIAiCIAiCIOhQwhgIgiAIgiAIgg4ljIEgCIIgCIIg6FD+B0RmJnmId/xiAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 818.45x540 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(mlb, hue=\"Position\", size=2.5);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "a955f33ac0871464e0f587912b2153b758a5bc9d"
},
"source": [
"### <span style=\"color:orange\">Thanks for Reading this notebook...🙏...🙏...🙏!!!</span>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}