{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Input/Output\" data-toc-modified-id=\"Input/Output-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Input/Output</a></span></li><li><span><a href=\"#Pandas-data-structures\" data-toc-modified-id=\"Pandas-data-structures-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Pandas data structures</a></span></li><li><span><a href=\"#Common-dataframe-functionality\" data-toc-modified-id=\"Common-dataframe-functionality-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Common dataframe functionality</a></span></li><li><span><a href=\"#Advanced-Indexing\" data-toc-modified-id=\"Advanced-Indexing-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Advanced Indexing</a></span><ul class=\"toc-item\"><li><span><a href=\"#iloc\" data-toc-modified-id=\"iloc-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span><code>iloc</code></a></span></li><li><span><a href=\"#loc\" data-toc-modified-id=\"loc-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span><code>loc</code></a></span></li><li><span><a href=\"#ix\" data-toc-modified-id=\"ix-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span><code>ix</code></a></span></li><li><span><a href=\"#Boolean-indexing\" data-toc-modified-id=\"Boolean-indexing-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span><code>Boolean indexing</code></a></span></li><li><span><a href=\"#querying\" data-toc-modified-id=\"querying-4.5\"><span class=\"toc-item-num\">4.5&nbsp;&nbsp;</span><code>querying</code></a></span></li><li><span><a href=\"#isin\" data-toc-modified-id=\"isin-4.6\"><span class=\"toc-item-num\">4.6&nbsp;&nbsp;</span><code>isin</code></a></span></li></ul></li><li><span><a href=\"#Setting/-Resetting-Index\" data-toc-modified-id=\"Setting/-Resetting-Index-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Setting/ Resetting Index</a></span><ul class=\"toc-item\"><li><span><a href=\"#set_index()\" data-toc-modified-id=\"set_index()-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span><code>set_index()</code></a></span></li><li><span><a href=\"#reset_index()\" data-toc-modified-id=\"reset_index()-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span><code>reset_index()</code></a></span></li><li><span><a href=\"#rename()\" data-toc-modified-id=\"rename()-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span><code>rename()</code></a></span></li></ul></li><li><span><a href=\"#Duplicated-data\" data-toc-modified-id=\"Duplicated-data-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Duplicated data</a></span><ul class=\"toc-item\"><li><span><a href=\"#unique()\" data-toc-modified-id=\"unique()-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span><code>unique()</code></a></span></li><li><span><a href=\"#duplicated()\" data-toc-modified-id=\"duplicated()-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span><code>duplicated()</code></a></span></li><li><span><a href=\"#drop_duplicates\" data-toc-modified-id=\"drop_duplicates-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span><code>drop_duplicates</code></a></span></li></ul></li><li><span><a href=\"#Grouping-data\" data-toc-modified-id=\"Grouping-data-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Grouping data</a></span><ul class=\"toc-item\"><li><span><a href=\"#groupby\" data-toc-modified-id=\"groupby-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span><code>groupby</code></a></span></li><li><span><a href=\"#transform\" data-toc-modified-id=\"transform-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span><code>transform</code></a></span></li></ul></li><li><span><a href=\"#Handling-missing-data\" data-toc-modified-id=\"Handling-missing-data-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Handling missing data</a></span><ul class=\"toc-item\"><li><span><a href=\"#dropna\" data-toc-modified-id=\"dropna-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span><code>dropna</code></a></span></li><li><span><a href=\"#fillna\" data-toc-modified-id=\"fillna-8.2\"><span class=\"toc-item-num\">8.2&nbsp;&nbsp;</span><code>fillna</code></a></span></li></ul></li><li><span><a href=\"#Combining-Data\" data-toc-modified-id=\"Combining-Data-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Combining Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#merge-/--join\" data-toc-modified-id=\"merge-/--join-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span><code>merge</code> /  <code>join</code></a></span></li><li><span><a href=\"#concatenate\" data-toc-modified-id=\"concatenate-9.2\"><span class=\"toc-item-num\">9.2&nbsp;&nbsp;</span><code>concatenate</code></a></span></li></ul></li><li><span><a href=\"#Date-formatting\" data-toc-modified-id=\"Date-formatting-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Date formatting</a></span></li><li><span><a href=\"#Reshaping-data\" data-toc-modified-id=\"Reshaping-data-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Reshaping data</a></span><ul class=\"toc-item\"><li><span><a href=\"#pivot--&gt;-reshape-data\" data-toc-modified-id=\"pivot-->-reshape-data-11.1\"><span class=\"toc-item-num\">11.1&nbsp;&nbsp;</span><code>pivot</code> -&gt; reshape data</a></span></li><li><span><a href=\"#stack\" data-toc-modified-id=\"stack-11.2\"><span class=\"toc-item-num\">11.2&nbsp;&nbsp;</span><code>stack</code></a></span></li></ul></li><li><span><a href=\"#Iteration\" data-toc-modified-id=\"Iteration-12\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Iteration</a></span></li><li><span><a href=\"#Apply-functions\" data-toc-modified-id=\"Apply-functions-13\"><span class=\"toc-item-num\">13&nbsp;&nbsp;</span>Apply functions</a></span></li><li><span><a href=\"#Working-with-text-data\" data-toc-modified-id=\"Working-with-text-data-14\"><span class=\"toc-item-num\">14&nbsp;&nbsp;</span>Working with text data</a></span><ul class=\"toc-item\"><li><span><a href=\"#str\" data-toc-modified-id=\"str-14.1\"><span class=\"toc-item-num\">14.1&nbsp;&nbsp;</span><code>str</code></a></span></li></ul></li><li><span><a href=\"#End\" data-toc-modified-id=\"End-15\"><span class=\"toc-item-num\">15&nbsp;&nbsp;</span>End</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/pddf.jpg\" alt=\"Drawing\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np # necessity as pandas is built on np\n",
    "from IPython.display import Image # to display images "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for the Python programming language.  \n",
    "  \n",
    "Refer to these cheatsheets:  \n",
    "https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PandasPythonForDataScience+(1).pdf\n",
    "https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input/Output\n",
    "Different types of data can be loaded in pandas dataframe. Pandas dataframe is like looks spreadsheet table (just a rough analogy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Most common input types**\n",
    "    * `pd.read_csv`\n",
    "    * `pd.read_excel/ pd.ExcelFile`\n",
    "    * `pd.read_feather` (feather format is used to reduce memory load in df as data is saved in binary form)\n",
    "    * `pd.read_json`\n",
    "    * `pd.read_html`\n",
    "    * `pd.read_pickle` (can also infer if pickled object is zipped using infer=)\n",
    "    \n",
    "* **Output types have format `to_xxx` similar to input formats**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas data structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Pandas data strctures include `series` and `dataframe` **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Series**: A one-dimensional labeled array a capable of holding any data type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/series.png', width=500) # not pandas, just showing example series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index will set the index for further reference\n",
    "# data can be passed as list\n",
    "s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b   -5\n",
       "c    7\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Can be indexed using both index name or number.**   \n",
    "number: --> filter indexes after value of number  \n",
    ":number --> filter indexes before value of number   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b   -5\n",
       "c    7\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['a'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b   -5\n",
       "c    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     3\n",
       "b    -1\n",
       "c    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series using dictionary\n",
    "\n",
    "s2 = pd.Series({'a':3, 'b': -1, 'c': 12}); s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataframe**: A two-dimensional labeled data structure with columns of potentially different types. It is similar to excel table.  \n",
    "\n",
    "Can make data frame using dictionary, list of list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/df.png', width=500) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Country': ['Belgium', 'India', 'Brazil'],\n",
    " 'Capital': ['Brussels', 'New Delhi', 'Brasília'],\n",
    " 'Population': [11190846, 1303171035, 207847528]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample = pd.DataFrame(data,\n",
    " columns=['Country', 'Capital', 'Population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "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>Capital</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11190846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1303171035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>207847528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country    Capital  Population\n",
       "0  Belgium   Brussels    11190846\n",
       "1    India  New Delhi  1303171035\n",
       "2   Brazil   Brasília   207847528"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common dataframe functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using famous `titanic data` for analysis and exploration. \n",
    "https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/titanic.jpg\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Common things to do when we get data in a dataframe (examples shown below):** \n",
    "    * see dataframe shape (number of rows, number of columns) using `df.shape`\n",
    "    * see top 5 rows using `pd.head()`\n",
    "    * check datatype of each column using `pd.dtypes`\n",
    "    * check column names using `pd.columns`\n",
    "    * count unique values of each column to see cardinality levels using `pd.nunique()`\n",
    "    * number of non null in each column, memory usage of  df, datatype (especially for large df) using `pd.info()`\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/train.csv') # read csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # see top 5 rows of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes # see datatype of each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    891\n",
       "Survived         2\n",
       "Pclass           3\n",
       "Name           891\n",
       "Sex              2\n",
       "Age             88\n",
       "SibSp            7\n",
       "Parch            7\n",
       "Ticket         681\n",
       "Fare           248\n",
       "Cabin          147\n",
       "Embarked         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique() # unique value for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # not null part is very useful to see how many nulls are there in data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `iloc` \n",
    "Select based on integer location (**that's why i**). Can select single or multiple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'male'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 4] # 0 row, 4 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age\n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0\n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0\n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:4, 2:6] # indexes are maintained. Can reset_index() to start index from 0  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `loc`\n",
    "Select based on label name of column (can select single or multiple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex   Age\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0\n",
       "2                             Heikkinen, Miss. Laina  female  26.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1:2,'Name':\"Age\"] # here row indexes are numbers but column indexes are name of columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    Heikkinen, Miss. Laina\n",
       "Age                         26\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2,['Name',\"Age\"]] # here row indexes are numbers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `ix`\n",
    "ix has been deprecated in latest pandas library. It was used to select by label or position. But we can always use `loc` to select with labels and `iloc` to select with integers/position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Boolean indexing`\n",
    "Returns rows where the stated condition returns true\n",
    "\n",
    "* or -> condition 1 `|` condition 2 (`or` also works but throws ambiguity error for multiple conditions)\n",
    "* and -> condition 1 `&` condition 2 (`and` also works but throws ambiguity error for multiple conditions\n",
    "* not -> `~` (not condition)\n",
    "* equal -> `==` Satisfying condition \n",
    "* `any()` -> columns/rows with any value matching condition\n",
    "* `all()` > columns/rows with all values matching some condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select rows with either sex as female or Pclass as 1\n",
    "df[(df.Sex == 'female') | (df.iloc[:,2] == 1) ].iloc[:3] # () are important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</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>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  \\\n",
       "0                            Braund, Mr. Owen Harris    male   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                             Heikkinen, Miss. Laina  female   \n",
       "\n",
       "             Ticket Cabin Embarked  \n",
       "0         A/5 21171   NaN        S  \n",
       "1          PC 17599   C85        C  \n",
       "2  STON/O2. 3101282   NaN        S  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 3 rows of gives all columns which have all string values or all int > 1 values\n",
    "df.loc[:,(df > 1).all()][:3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    male      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
       "2                             Heikkinen, Miss. Laina  female      0      0   \n",
       "\n",
       "             Ticket     Fare  \n",
       "0         A/5 21171   7.2500  \n",
       "1          PC 17599  71.2833  \n",
       "2  STON/O2. 3101282   7.9250  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 3 rows of all columns which have all not null values\n",
    "df.loc[:,(df.notnull().all() )][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked\n",
       "0  22.0   NaN        S\n",
       "1  38.0   C85        C\n",
       "2  26.0   NaN        S"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 3 rows of all columns which have atleast 1 null value\n",
    "df.loc[:, df.isnull().any()][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(94, 12)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.iloc[:,2] == 1) & (df.Sex == 'female')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3075196408529742"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fraction of males with Age > 25, df.shape[0] -> number of rows \n",
    "\n",
    "sum((df.Age > 25) & (df.Sex == 'male'))/df.shape[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of people who survived and were not in class 3\n",
    "\n",
    "sum((df.Survived != 0) & (~(df.Pclass == 3)) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `querying`\n",
    "Query columns (filter rows) of dataframe with boolean expression (Filter based on condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Uruchurtu, Don. Manuel E</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holverson, Mr. Alexander Oskar</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "6             7         0       1   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "15           16         1       2   \n",
       "18           19         0       3   \n",
       "20           21         0       2   \n",
       "21           22         1       2   \n",
       "23           24         1       1   \n",
       "25           26         1       3   \n",
       "30           31         0       1   \n",
       "33           34         0       2   \n",
       "35           36         0       1   \n",
       "54           55         0       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "20                               Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                              Beesley, Mr. Lawrence    male  34.0      0   \n",
       "23                       Sloper, Mr. William Thompson    male  28.0      0   \n",
       "25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "30                           Uruchurtu, Don. Manuel E    male  40.0      0   \n",
       "33                              Wheadon, Mr. Edward H    male  66.0      0   \n",
       "35                     Holverson, Mr. Alexander Oskar    male  42.0      1   \n",
       "54                     Ostby, Mr. Engelhart Cornelius    male  65.0      0   \n",
       "\n",
       "    Parch            Ticket     Fare Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500   NaN        S  \n",
       "1       0          PC 17599  71.2833   C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3       0            113803  53.1000  C123        S  \n",
       "4       0            373450   8.0500   NaN        S  \n",
       "6       0             17463  51.8625   E46        S  \n",
       "8       2            347742  11.1333   NaN        S  \n",
       "9       0            237736  30.0708   NaN        C  \n",
       "11      0            113783  26.5500  C103        S  \n",
       "12      0         A/5. 2151   8.0500   NaN        S  \n",
       "13      5            347082  31.2750   NaN        S  \n",
       "15      0            248706  16.0000   NaN        S  \n",
       "18      0            345763  18.0000   NaN        S  \n",
       "20      0            239865  26.0000   NaN        S  \n",
       "21      0            248698  13.0000   D56        S  \n",
       "23      0            113788  35.5000    A6        S  \n",
       "25      5            347077  31.3875   NaN        S  \n",
       "30      0          PC 17601  27.7208   NaN        C  \n",
       "33      0        C.A. 24579  10.5000   NaN        S  \n",
       "35      0            113789  52.0000   NaN        S  \n",
       "54      1            113509  61.9792   B30        C  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter all rows which have Age > Passenger ID\n",
    "df.query('Age > PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`filter`  \n",
    "\n",
    "Filter dataframe on column names or row names (labels) by regrex or just item names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex\n",
       "0  22.0    male\n",
       "1  38.0  female"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter only sex and age columns (first 2 rows)\n",
    "\n",
    "df.filter(items=['Age', 'Sex'])[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                     Name   Sex   Age  SibSp  \\\n",
       "0            1         0       3  Braund, Mr. Owen Harris  male  22.0      1   \n",
       "5            6         0       3         Moran, Mr. James  male   NaN      0   \n",
       "\n",
       "   Parch     Ticket    Fare Cabin Embarked  \n",
       "0      0  A/5 21171  7.2500   NaN        S  \n",
       "5      0     330877  8.4583   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter only 0 and 5 row index\n",
    "\n",
    "df.filter(items=[0,5], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>Survived</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Embarked\n",
       "0         0        S\n",
       "1         1        C"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 2 rows of column names ending with \"ed\" (think of past tense)\n",
    "\n",
    "df.filter(like = 'ed', axis=1)[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Name, dtype: object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can use same thing as above using regex also\n",
    "\n",
    "df.filter(regex='ed$', axis=1)[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `isin` \n",
    "\n",
    "Filter rows of column based on list of multiple values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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>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>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>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "11           12         1       1   \n",
       "23           24         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "23                       Sloper, Mr. William Thompson    male  28.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  \n",
       "1       0  PC 17599  71.2833   C85        C  \n",
       "3       0    113803  53.1000  C123        S  \n",
       "6       0     17463  51.8625   E46        S  \n",
       "11      0    113783  26.5500  C103        S  \n",
       "23      0    113788  35.5000    A6        S  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Pclass.isin([0,1])].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting/ Resetting Index\n",
    "Setting and resetting index are important when we merge/groupby 2 dataframe and want to do further analysis on new dataframe. A dataframe with repeated indexes can cause problems in filtering. Apart from this we cvan set a column into index which makes merging much faster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `set_index()`\n",
    "Set any column you want as index of df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "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>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</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>A/5 21171</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>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC 17599</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>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PassengerId  Survived  Pclass  \\\n",
       "Ticket                                     \n",
       "A/5 21171            1         0       3   \n",
       "PC 17599             2         1       1   \n",
       "\n",
       "                                                        Name     Sex   Age  \\\n",
       "Ticket                                                                       \n",
       "A/5 21171                            Braund, Mr. Owen Harris    male  22.0   \n",
       "PC 17599   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "\n",
       "           SibSp  Parch     Fare Cabin Embarked  \n",
       "Ticket                                           \n",
       "A/5 21171      1      0   7.2500   NaN        S  \n",
       "PC 17599       1      0  71.2833   C85        C  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting \n",
    "df.set_index('Ticket')[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "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>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>Ticket</th>\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>A/5 21171</th>\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>PC 17599</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              PassengerId  \\\n",
       "Ticket    Name                                                              \n",
       "A/5 21171 Braund, Mr. Owen Harris                                       1   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...            2   \n",
       "\n",
       "                                                              Survived  \\\n",
       "Ticket    Name                                                           \n",
       "A/5 21171 Braund, Mr. Owen Harris                                    0   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...         1   \n",
       "\n",
       "                                                              Pclass     Sex  \\\n",
       "Ticket    Name                                                                 \n",
       "A/5 21171 Braund, Mr. Owen Harris                                  3    male   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female   \n",
       "\n",
       "                                                               Age  SibSp  \\\n",
       "Ticket    Name                                                              \n",
       "A/5 21171 Braund, Mr. Owen Harris                             22.0      1   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...  38.0      1   \n",
       "\n",
       "                                                              Parch     Fare  \\\n",
       "Ticket    Name                                                                 \n",
       "A/5 21171 Braund, Mr. Owen Harris                                 0   7.2500   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...      0  71.2833   \n",
       "\n",
       "                                                             Cabin Embarked  \n",
       "Ticket    Name                                                               \n",
       "A/5 21171 Braund, Mr. Owen Harris                              NaN        S  \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can set multiple columns as index also. Just pass them in list\n",
    "# Setting Ticket and Name as index\n",
    "\n",
    "df.set_index(['Ticket', 'Name'])[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('A/5 21171', 'Braund, Mr. Owen Harris')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can see what are values of index. \n",
    "# checking index of 1st row\n",
    "\n",
    "df.set_index(['Ticket', 'Name']).index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `reset_index()`\n",
    "Can reset index back to 0....nrows-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index = df.set_index(['Ticket', 'Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "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>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>Ticket</th>\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>A/5 21171</th>\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>PC 17599</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              PassengerId  \\\n",
       "Ticket    Name                                                              \n",
       "A/5 21171 Braund, Mr. Owen Harris                                       1   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...            2   \n",
       "\n",
       "                                                              Survived  \\\n",
       "Ticket    Name                                                           \n",
       "A/5 21171 Braund, Mr. Owen Harris                                    0   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...         1   \n",
       "\n",
       "                                                              Pclass     Sex  \\\n",
       "Ticket    Name                                                                 \n",
       "A/5 21171 Braund, Mr. Owen Harris                                  3    male   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female   \n",
       "\n",
       "                                                               Age  SibSp  \\\n",
       "Ticket    Name                                                              \n",
       "A/5 21171 Braund, Mr. Owen Harris                             22.0      1   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...  38.0      1   \n",
       "\n",
       "                                                              Parch     Fare  \\\n",
       "Ticket    Name                                                                 \n",
       "A/5 21171 Braund, Mr. Owen Harris                                 0   7.2500   \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...      0  71.2833   \n",
       "\n",
       "                                                             Cabin Embarked  \n",
       "Ticket    Name                                                               \n",
       "A/5 21171 Braund, Mr. Owen Harris                              NaN        S  \n",
       "PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "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>Ticket</th>\n",
       "      <th>Name</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\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>1</th>\n",
       "      <td>PC 17599</td>\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>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ticket                                               Name  PassengerId  \\\n",
       "0  A/5 21171                            Braund, Mr. Owen Harris            1   \n",
       "1   PC 17599  Cumings, Mrs. John Bradley (Florence Briggs Th...            2   \n",
       "\n",
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked  \n",
       "0         0       3    male  22.0      1      0   7.2500   NaN        S  \n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.reset_index()[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above case, index is back to 0,1..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `rename()` \n",
    "Renaming column names or row indexes of dataframe. Default is index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "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>Whats_name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Price</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                          Whats_name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket    Price Cabin Embarked  \n",
       "0      0  A/5 21171   7.2500   NaN        S  \n",
       "1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'Name': 'Whats_name', 'Fare':'Price'})[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "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",
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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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passengerid  survived  pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                name     sex   age  sibsp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   parch     ticket     fare cabin embarked  \n",
       "0      0  A/5 21171   7.2500   NaN        S  \n",
       "1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can use some mapper function also. default axis='index' (0)\n",
    "\n",
    "df.rename(mapper=str.lower, axis='columns')[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicated data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `unique()`\n",
    "Number of unique values in a column of df (Use nunique() for count of unique in each column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `duplicated()`\n",
    "Check duplicated in column. Returns True/False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.PassengerId.duplicated()) # there are no duplicate passegerid. good thing to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can check duplicates in index also.\n",
    "# useful if doubtful about duplicates in index doing bad things \n",
    "\n",
    "sum(df.index.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `drop_duplicates`\n",
    "Drop rows which have duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>female</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex Embarked\n",
       "0     male        S\n",
       "1   female        C\n",
       "2   female        S\n",
       "5     male        Q\n",
       "22  female        Q\n",
       "26    male        C\n",
       "61  female      NaN"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can help in getting unique combination of multiple columns\n",
    "# unique() dosn't work in this case\n",
    "df.loc[:,['Sex', 'Embarked']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping data\n",
    "Group by some column/columns, then we can aggregate to get mean, count, sum or custom function based on the group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `groupby`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "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>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>Sex</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>female</th>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>261</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>97</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>453</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>107</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId  Survived  Pclass  Name  Age  SibSp  Parch  Ticket  Fare  \\\n",
       "Sex                                                                            \n",
       "female          314       314     314   314  261    314    314     314   314   \n",
       "male            577       577     577   577  453    577    577     577   577   \n",
       "\n",
       "        Cabin  Embarked  \n",
       "Sex                      \n",
       "female     97       312  \n",
       "male      107       577  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by sex then count. \n",
    "# returns count in each column. difference in some cases because of nulls in those columns\n",
    "# can do iloc[:,0] to only get first column \n",
    "\n",
    "df.groupby(by = ['Sex']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0           25.046875\n",
       "        1           28.847716\n",
       "male    0           31.618056\n",
       "        1           27.276022\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can use multiple conditions\n",
    "# group by sex and survived -> mean of age\n",
    "\n",
    "df.groupby(by = ['Sex', 'Survived']).mean().loc[:,'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "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>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>34.611765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.970121</td>\n",
       "      <td>28.722973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.118810</td>\n",
       "      <td>21.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>1</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>41.281386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.741782</td>\n",
       "      <td>30.740707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.661633</td>\n",
       "      <td>26.507589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fare        Age\n",
       "Sex    Pclass                       \n",
       "female 1       106.125798  34.611765\n",
       "       2        21.970121  28.722973\n",
       "       3        16.118810  21.750000\n",
       "male   1        67.226127  41.281386\n",
       "       2        19.741782  30.740707\n",
       "       3        12.661633  26.507589"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can group by indexes also by using levels= \n",
    "# useful when we have multindexes\n",
    "# can use agg function with lambda func\n",
    "\n",
    "df_index = df.set_index(['Sex', 'Pclass'])\n",
    "df_index.groupby(level=[0,1]).agg({'Fare': lambda x: sum(x)/len(x), # this is also just mean actually\n",
    "                                  'Age' : np.mean})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! Ticket price of 1st class female is approximately double of 1st class male"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `transform`\n",
    "Can apply such functions for all columns also using transform which transforms all rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</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>male</th>\n",
       "      <th>3</th>\n",
       "      <td>455.515850</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498559</td>\n",
       "      <td>0.224784</td>\n",
       "      <td>12.661633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>469.212766</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>106.125798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>399.729167</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>0.798611</td>\n",
       "      <td>16.118810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>469.212766</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>106.125798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <th>3</th>\n",
       "      <td>455.515850</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498559</td>\n",
       "      <td>0.224784</td>\n",
       "      <td>12.661633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PassengerId  Survived  Age     SibSp     Parch        Fare\n",
       "Sex    Pclass                                                            \n",
       "male   3        455.515850  0.135447  NaN  0.498559  0.224784   12.661633\n",
       "female 1        469.212766  0.968085  NaN  0.553191  0.457447  106.125798\n",
       "       3        399.729167  0.500000  NaN  0.895833  0.798611   16.118810\n",
       "       1        469.212766  0.968085  NaN  0.553191  0.457447  106.125798\n",
       "male   3        455.515850  0.135447  NaN  0.498559  0.224784   12.661633"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of below code is same as original df\n",
    "\n",
    "df_index.groupby(level=[0,1]).transform(lambda x: sum(x)/len(x)).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `dropna`\n",
    "Drop rows with na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 12)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how=any -> row with any column = NA\n",
    "\n",
    "df.dropna(axis=0,  how='any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how=any -> row with all columns = NA\n",
    "\n",
    "df.dropna(axis=0, how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Age', 'Cabin', 'Embarked'}]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drops column which have any row of NA\n",
    "\n",
    "[set(df.columns) - set(df.dropna(axis=1, how='any').columns)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three columns have been removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `fillna`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.25</td>\n",
       "      <td>&lt;function mean at 0x10c7d7d90&gt;</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                     Name   Sex Age  SibSp  \\\n",
       "0            1         0       3  Braund, Mr. Owen Harris  male  22      1   \n",
       "\n",
       "   Parch     Ticket  Fare                           Cabin Embarked  \n",
       "0      0  A/5 21171  7.25  <function mean at 0x10c7d7d90>        S  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace with mean of that column\n",
    "# can put any specific value also\n",
    "# would not work for columns with string type like Cabin\n",
    "\n",
    "df.fillna(np.mean)[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/combine.png\" alt=\"Drawing\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `merge` /  `join`\n",
    "\n",
    "* **how** = 'left', 'right', 'outer', 'inner'\n",
    "* **on**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame({'x1': list('abc'), 'x2': [11.432, 1.303, 99.906]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.DataFrame({'x1': list('abd'), 'x3': [20.784,  np.NaN, 20.784]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2\n",
       "0  a  11.432\n",
       "1  b   1.303\n",
       "2  c  99.906"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "scrolled": true
   },
   "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>x1</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x3\n",
       "0  a  20.784\n",
       "1  b     NaN\n",
       "2  d  20.784"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "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",
       "    .dataframe tbody tr th {\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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2      x3\n",
       "0  a  11.432  20.784\n",
       "1  b   1.303     NaN"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inner join when both table have that key (like sql)\n",
    "\n",
    "data1.merge(data2, how='inner', on='x1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "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",
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       "      <th>x1</th>\n",
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       "      <th>x3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2      x3\n",
       "0  a  11.432  20.784\n",
       "1  b   1.303     NaN\n",
       "2  c  99.906     NaN\n",
       "3  d     NaN  20.784"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outer joins on all keys in both df and creates NA\n",
    "\n",
    "data1.merge(data2, how='outer', on='x1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "can also use `join` but `merge` is faster. just use merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "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",
       "    .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>x1L</th>\n",
       "      <th>x2</th>\n",
       "      <th>x1</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1L      x2   x1  x3\n",
       "0   a  11.432  NaN NaN\n",
       "1   b   1.303  NaN NaN\n",
       "2   c  99.906  NaN NaN"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if columns overlap, have to specify suffix as it makes for all\n",
    "\n",
    "data1.join(data2, on='x1', how='left', lsuffix='L')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `concatenate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2       x3\n",
       "0  a  11.432      NaN\n",
       "1  b   1.303      NaN\n",
       "2  c  99.906      NaN\n",
       "0  a     NaN   20.784\n",
       "1  b     NaN      NaN\n",
       "2  d     NaN   20.784\n",
       "3  g     NaN  500.000"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join over axis=0, i.e rows combine \n",
    "# also adds all columns with na\n",
    "\n",
    "pd.concat([data1, data2], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that it has index duplicates as it maintain original df index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can use `ignore_index=True` to make index start from 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2      x3\n",
       "0  a  11.432     NaN\n",
       "1  b   1.303     NaN\n",
       "2  c  99.906     NaN\n",
       "3  a     NaN  20.784\n",
       "4  b     NaN     NaN\n",
       "5  d     NaN  20.784"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([data1, data2], axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>d</td>\n",
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       "      <th>3</th>\n",
       "      <td>g</td>\n",
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      "text/plain": [
       "  x1       x3\n",
       "0  a   20.784\n",
       "1  b      NaN\n",
       "2  d   20.784\n",
       "3  g  500.000"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.loc[3] = ['g', 500] # adding new row\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x1</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "      <td>a</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>d</td>\n",
       "      <td>20.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>g</td>\n",
       "      <td>500.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x1      x2 x1       x3\n",
       "0    a  11.432  a   20.784\n",
       "1    b   1.303  b      NaN\n",
       "2    c  99.906  d   20.784\n",
       "3  NaN     NaN  g  500.000"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join over axis=1, i.e columns combine \n",
    "\n",
    "pd.concat([data1, data2], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date formatting\n",
    "\n",
    "* `to_datetime()` -> convert whatever format argument to datetime (obviously that can be parsed to datetime)\n",
    "* `date_range()` -> generates datetime data\n",
    "* `Datetimeindex` -> datetypeindex data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-02-19 00:00:00')"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('2018-2-19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-04-18', '2018-04-19', '2018-04-20', '2018-04-21',\n",
       "               '2018-04-22', '2018-04-23'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gives datetimeindex format\n",
    "\n",
    "pd.date_range('2018-4-18', periods=6, freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['date'] = pd.date_range('2018-4-18', periods=3, freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "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",
       "    .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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>11.432</td>\n",
       "      <td>2018-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1.303</td>\n",
       "      <td>2018-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>99.906</td>\n",
       "      <td>2018-04-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  x1      x2       date\n",
       "0  a  11.432 2018-04-18\n",
       "1  b   1.303 2018-04-19\n",
       "2  c  99.906 2018-04-20"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-04-18\n",
       "1   2018-04-19\n",
       "2   2018-04-20\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-04-18', '2018-04-19', '2018-04-20'], dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DatetimeIndex(data1.date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `pivot` -> reshape data\n",
    "Ever used pivot table in excel? It's same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "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",
       "    .dataframe tbody tr th {\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>PassengerId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>882</th>\n",
       "      <th>883</th>\n",
       "      <th>884</th>\n",
       "      <th>885</th>\n",
       "      <th>886</th>\n",
       "      <th>887</th>\n",
       "      <th>888</th>\n",
       "      <th>889</th>\n",
       "      <th>890</th>\n",
       "      <th>891</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</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>female</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 891 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "PassengerId   1     2     3     4     5    6     7    8     9     10   ...   \\\n",
       "Sex                                                                    ...    \n",
       "female        NaN  38.0  26.0  35.0   NaN  NaN   NaN  NaN  27.0  14.0  ...    \n",
       "male         22.0   NaN   NaN   NaN  35.0  NaN  54.0  2.0   NaN   NaN  ...    \n",
       "\n",
       "PassengerId   882   883   884   885   886   887   888  889   890   891  \n",
       "Sex                                                                     \n",
       "female        NaN  22.0   NaN   NaN  39.0   NaN  19.0  NaN   NaN   NaN  \n",
       "male         33.0   NaN  28.0  25.0   NaN  27.0   NaN  NaN  26.0  32.0  \n",
       "\n",
       "[2 rows x 891 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index = new index, columns = new_columns, values = values to put\n",
    "\n",
    "df.pivot(index='Sex', columns = 'PassengerId', values = 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above case, use of pivot doesn't make sense but this is just an example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `stack` \n",
    "\n",
    "Convert whole df into 1 long format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    PassengerId                                                    1\n",
       "     Survived                                                       0\n",
       "     Pclass                                                         3\n",
       "     Name                                     Braund, Mr. Owen Harris\n",
       "     Sex                                                         male\n",
       "     Age                                                           22\n",
       "     SibSp                                                          1\n",
       "     Parch                                                          0\n",
       "     Ticket                                                 A/5 21171\n",
       "     Fare                                                        7.25\n",
       "     Embarked                                                       S\n",
       "1    PassengerId                                                    2\n",
       "     Survived                                                       1\n",
       "     Pclass                                                         1\n",
       "     Name           Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "     Sex                                                       female\n",
       "     Age                                                           38\n",
       "     SibSp                                                          1\n",
       "     Parch                                                          0\n",
       "     Ticket                                                  PC 17599\n",
       "     Fare                                                     71.2833\n",
       "     Cabin                                                        C85\n",
       "     Embarked                                                       C\n",
       "2    PassengerId                                                    3\n",
       "     Survived                                                       1\n",
       "     Pclass                                                         3\n",
       "     Name                                      Heikkinen, Miss. Laina\n",
       "     Sex                                                       female\n",
       "     Age                                                           26\n",
       "     SibSp                                                          0\n",
       "                                          ...                        \n",
       "888  Name                    Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "     Sex                                                       female\n",
       "     SibSp                                                          1\n",
       "     Parch                                                          2\n",
       "     Ticket                                                W./C. 6607\n",
       "     Fare                                                       23.45\n",
       "     Embarked                                                       S\n",
       "889  PassengerId                                                  890\n",
       "     Survived                                                       1\n",
       "     Pclass                                                         1\n",
       "     Name                                       Behr, Mr. Karl Howell\n",
       "     Sex                                                         male\n",
       "     Age                                                           26\n",
       "     SibSp                                                          0\n",
       "     Parch                                                          0\n",
       "     Ticket                                                    111369\n",
       "     Fare                                                          30\n",
       "     Cabin                                                       C148\n",
       "     Embarked                                                       C\n",
       "890  PassengerId                                                  891\n",
       "     Survived                                                       0\n",
       "     Pclass                                                         3\n",
       "     Name                                         Dooley, Mr. Patrick\n",
       "     Sex                                                         male\n",
       "     Age                                                           32\n",
       "     SibSp                                                          0\n",
       "     Parch                                                          0\n",
       "     Ticket                                                    370376\n",
       "     Fare                                                        7.75\n",
       "     Embarked                                                       Q\n",
       "Length: 9826, dtype: object"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You won't generally use it. I have never come across its use over my experience with python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iteration\n",
    "To get column/row indexes, series pair. \n",
    "\n",
    "* `iteritems()` for column-index, series\n",
    "* `iterrows()` for row-index, series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'male'), (1, 'female'), (2, 'female'), (3, 'female'), (4, 'male')]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.Sex.iteritems())[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, PassengerId                          1\n",
       " Survived                             0\n",
       " Pclass                               3\n",
       " Name           Braund, Mr. Owen Harris\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: 0, dtype: object)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.iterrows())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply functions\n",
    "\n",
    "* `apply` -> apply function over df\n",
    "* `apply_map` -> apply function elementwise (for each series of df. think of column wise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function squares when type(x) = float, cubes when type(x) = int, return same when other\n",
    "\n",
    "f = lambda x: x**2 if type(x) == float else x**3 if type(x) == int else x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      52.562500\n",
       "1    5081.308859\n",
       "2      62.805625\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whole series is passed\n",
    "\n",
    "df.Fare.apply(f)[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "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>27</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>52.562500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</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>1444.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>5081.308859</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>676.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>62.805625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0      27   \n",
       "1            8         1       1   \n",
       "2           27         1      27   \n",
       "\n",
       "                                                Name     Sex     Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   484.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  1444.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female   676.0      0   \n",
       "\n",
       "   Parch            Ticket         Fare Cabin Embarked  \n",
       "0      0         A/5 21171    52.562500   NaN        S  \n",
       "1      0          PC 17599  5081.308859   C85        C  \n",
       "2      0  STON/O2. 3101282    62.805625   NaN        S  "
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# elements are passed\n",
    "\n",
    "df.applymap(f)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with text data\n",
    "What all can we do when we have string datatype in pandas dataframe/series ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `str` \n",
    "\n",
    "Working with string format in pandas series/df \n",
    "\n",
    "We can do:\n",
    "* `str.upper()/lower()` to convert string into upper or lower case\n",
    "* `str.len()` to find the length of sting\n",
    "* `str.strip()/lstrip()/rstrip()` to strip spaces\n",
    "* `str.replace()` to replace anything from string\n",
    "* `str.split()` to split words of string or using some other delimiter\n",
    "* `str.get()` to access elements in slit list \n",
    "* `str.resplit()` spit in reverse order of string based on some delimiter\n",
    "* `str.extract()` extract specific thing from string. alphabet or number\n",
    "\n",
    "Let's see how to use all that in pandas series. Keep in mind pandas DataFrame has no attribute called `str` and works on Series object only. So, grab column of df, then apply `str`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converts all rows into lower\n",
    "\n",
    "df.Name.str.lower().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      MALE\n",
       "1    FEMALE\n",
       "2    FEMALE\n",
       "3    FEMALE\n",
       "4      MALE\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converts all rows into upper \n",
    "\n",
    "df.Sex.str.upper().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    51\n",
       "2    22\n",
       "3    44\n",
       "4    24\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counts all the characters including spaces\n",
    "\n",
    "df.Name.str.len().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr. William Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First_Name                                    Last_Name\n",
       "0     Braund                              Mr. Owen Harris\n",
       "1    Cumings   Mrs. John Bradley (Florence Briggs Thayer)\n",
       "2  Heikkinen                                  Miss. Laina\n",
       "3   Futrelle           Mrs. Jacques Heath (Lily May Peel)\n",
       "4      Allen                            Mr. William Henry"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splits strings in each row over whitespaces ()\n",
    "# expand=True : expand columns\n",
    "# pat = regex to split on\n",
    "\n",
    "df.Name.str.split(pat=',',expand=True).head().rename(columns={0:'First_Name', 1: 'Last_Name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                         [Braund,, Mr., Owen, Harris]\n",
       "1    [Cumings,, Mrs., John, Bradley, (Florence, Bri...\n",
       "2                           [Heikkinen,, Miss., Laina]\n",
       "3    [Futrelle,, Mrs., Jacques, Heath, (Lily, May, ...\n",
       "4                        [Allen,, Mr., William, Henry]\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splits strings in each row over whitespaces ()\n",
    "# expand=False : doesn't expand columns\n",
    "# pat = regex to split on\n",
    "\n",
    "df.Name.str.split(expand=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund,  Owen Harris\n",
       "1    Cumings, . John Bradley (Florence Briggs Thayer)\n",
       "2                              Heikkinen, Miss. Laina\n",
       "3           Futrelle, . Jacques Heath (Lily May Peel)\n",
       "4                               Allen,  William Henry\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace Mr. with empty space\n",
    "\n",
    "df.Name.str.replace('Mr.', '').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cumings,', 'Mrs.', 'John', 'Bradley', '(Florence', 'Briggs', 'Thayer)']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get() is used to get particular row of split\n",
    "\n",
    "df.Name.str.split().get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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",
       "5                                     Moran, Mr. James\n",
       "6                              McCarthy, Mr. Timothy J\n",
       "7                       Palsson, Master. Gosta Leonard\n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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>Last_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Last_Name\n",
       "0     Braund\n",
       "1    Cumings\n",
       "2  Heikkinen\n",
       "3   Futrelle\n",
       "4      Allen"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract just last name\n",
    "\n",
    "df.Name.str.extract('(?P<Last_Name>[a-zA-Z]+)', expand=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End"
   ]
  }
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