{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Liquor Sales + Linear Regression\n", "\n", "_Marco Tavora_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1) Problem Statement\n", "\n", "The primary goal of this project is to contribute to the expansion plans of a liquor store owner in Iowa, by investigating the market data for potential new locations. \n", "\n", "Furthermore, he is interested in understanding details of the best model that we can fit to the data so that her team can optimally evaluate possible sites for a new storefront.\n", "\n", "More concretely, we will predict total sales by county. Note that this is a cross-sectional analysis and no temporal behavior is considered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2) Preamble\n", "\n", "Expansion plans traditionally use subsets of the following mix of data:\n", "\n", "### 2.1) Demographics\n", "\n", "Demographic information can be introduced in a high level of detail (see for example Ref. [1] for an interesting analysis and useful public datasets). Here we will not delve so deeply into that, but some simple demographical data will be used (namely information about income and population). \n", "\n", "We will be interested in several ratios. \n", "\n", "i) The first one is the ratio $r_{{\\rm{sv}}}^{(c)}$ between sales and volume for each county i.e. the amount of dollars per liter sold. If $r_{{\\rm{sv}}}^{(c)}$ is high in county $(c)$, the stores in that county are, on average, high-end stores. The ratio is given by:\n", "\n", "\\begin{eqnarray}\n", "r_{{\\text{sv}}}^{(c)} = \\frac{{{\\text{sales in }}c}}{{{\\text{volume consumed in }}c}}\\,\\,\\,\\,\\,\\,(1)\\nonumber\n", "\\end{eqnarray}\n", "\n", "\n", "(where the upper index $(c)$ is for county). \n", "\n", "ii) Another relevant ratio is the number of stores per area:\n", "\n", "\\begin{eqnarray}\n", "r_{\\text{sa}}^{(c)}=\\frac{{\\text{number of stores in }}c}{{\\text{area of }}c}\\,\\,\\,\\,\\,(2)\\nonumber\n", "\\end{eqnarray}\n", " \n", "The meaning of a high value of $r_{\\rm{sa}}^{(c)}$ is not so straightforward since it may indicate either \n", " - That the market saturated or \n", " - That the county has an extremely strong market which would welcome a new store (an example would be a county close to some major university as discussed in Ref. [3]). \n", " \n", "In contrast, a low value of $r_{\\rm{sa}}^{(c)}$ may indicate a market with untapped potential or a market with a population not very interested in this type of store (for example, a county with highly religious population).\n", "\n", "iii) Another important ratio is consumption/population i.e. the consumption *per capita*:\n", "\n", "\\begin{eqnarray}\n", "r_{\\text{cp}}^{(c)}=\\frac{{\\text{number of liters consumed in }}c}{{\\text{population of }}c}\\,\\,\\,\\,\\,(3)\\nonumber\n", "\\end{eqnarray}\n", "\n", "The knowledge of the profile of the population in the county (if they are \"light\" or \"heavy\" drinkers) would certainly help the owner decide whether to open or not a new storefront there (see Ref. [1,2,3]).\n", "\n", " \n", "### 2.2) Nearby businesses\n", "\n", "Competition is a critical component, and can be indirectly measured by the ratio of the number of stores and the population in a given county $c$:\n", " \n", "\\begin{eqnarray}\n", "r_{\\text{sp}}^{(c)}=\\frac{{\\text{number of stores in }}c}{{\\text{population of }}c}\\,\\,\\,\\,\\,(4)\\nonumber\n", "\\end{eqnarray}\n", "\n", "### 2.3) Aggregated human flow/foot traffic\n", "\n", "For this information to be useful we would need more granular data (such as apps check-ins as discussed in [7]). Population and population density will be used as proxies.\n", "\n", "There is an issue with the meaning of the data that is discussed in the appendix (see the end of the notebook)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3) Getting data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us take a look at the data. The dataset contains the spirits purchase information of Iowa Class “E” liquor licensees by product and date of purchase." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "
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Invoice/Item NumberDateStore NumberStore NameAddressCityZip CodeStore LocationCounty NumberCountyCategoryCategory NameVendor NumberVendor NameItem NumberItem DescriptionPackBottle Volume (ml)State Bottle CostState Bottle RetailBottles SoldSale (Dollars)Volume Sold (Liters)Volume Sold (Gallons)
0S2919880000111/20/20152191Keokuk Spirits1013 MAINKEOKUK526321013 MAIN\\nKEOKUK 52632\\n(40.39978, -91.387531)56.0LeeNaNNaN255Wilson Daniels Ltd.297Templeton Rye w/Flask6750$18.09$27.146$162.844.501.19
1S2919540000211/21/20152205Ding's Honk And Holler900 E WASHINGTONCLARINDA51632900 E WASHINGTON\\nCLARINDA 51632\\n(40.739238, ...73.0PageNaNNaN255Wilson Daniels Ltd.297Templeton Rye w/Flask6750$18.09$27.1412$325.689.002.38
2S2905030000111/16/20153549Quicker Liquor Store1414 48TH STFORT MADISON526271414 48TH ST\\nFORT MADISON 52627\\n(40.624226, ...56.0LeeNaNNaN130Disaronno International LLC249Disaronno Amaretto Cavalli Mignon 3-50ml Pack20150$6.40$9.602$19.200.300.08
3S2886770000111/04/20152513Hy-Vee Food Store #2 / Iowa City812 S 1ST AVEIOWA CITY52240812 S 1ST AVE\\nIOWA CITY 52240\\n52.0JohnsonNaNNaN65Jim Beam Brands237Knob Creek w/ Crystal Decanter31750$35.55$53.343$160.025.251.39
4S2905080000111/17/20153942Twin Town Liquor104 HIGHWAY 30 WESTTOLEDO52342104 HIGHWAY 30 WEST\\nTOLEDO 52342\\n(41.985887,...86.0TamaNaNNaN130Disaronno International LLC249Disaronno Amaretto Cavalli Mignon 3-50ml Pack20150$6.40$9.602$19.200.300.08
\n", "
" ], "text/plain": [ " Invoice/Item Number Date Store Number \\\n", "0 S29198800001 11/20/2015 2191 \n", "1 S29195400002 11/21/2015 2205 \n", "2 S29050300001 11/16/2015 3549 \n", "3 S28867700001 11/04/2015 2513 \n", "4 S29050800001 11/17/2015 3942 \n", "\n", " Store Name Address City \\\n", "0 Keokuk Spirits 1013 MAIN KEOKUK \n", "1 Ding's Honk And Holler 900 E WASHINGTON CLARINDA \n", "2 Quicker Liquor Store 1414 48TH ST FORT MADISON \n", "3 Hy-Vee Food Store #2 / Iowa City 812 S 1ST AVE IOWA CITY \n", "4 Twin Town Liquor 104 HIGHWAY 30 WEST TOLEDO \n", "\n", " Zip Code Store Location County Number \\\n", "0 52632 1013 MAIN\\nKEOKUK 52632\\n(40.39978, -91.387531) 56.0 \n", "1 51632 900 E WASHINGTON\\nCLARINDA 51632\\n(40.739238, ... 73.0 \n", "2 52627 1414 48TH ST\\nFORT MADISON 52627\\n(40.624226, ... 56.0 \n", "3 52240 812 S 1ST AVE\\nIOWA CITY 52240\\n 52.0 \n", "4 52342 104 HIGHWAY 30 WEST\\nTOLEDO 52342\\n(41.985887,... 86.0 \n", "\n", " County Category Category Name Vendor Number \\\n", "0 Lee NaN NaN 255 \n", "1 Page NaN NaN 255 \n", "2 Lee NaN NaN 130 \n", "3 Johnson NaN NaN 65 \n", "4 Tama NaN NaN 130 \n", "\n", " Vendor Name Item Number \\\n", "0 Wilson Daniels Ltd. 297 \n", "1 Wilson Daniels Ltd. 297 \n", "2 Disaronno International LLC 249 \n", "3 Jim Beam Brands 237 \n", "4 Disaronno International LLC 249 \n", "\n", " Item Description Pack Bottle Volume (ml) \\\n", "0 Templeton Rye w/Flask 6 750 \n", "1 Templeton Rye w/Flask 6 750 \n", "2 Disaronno Amaretto Cavalli Mignon 3-50ml Pack 20 150 \n", "3 Knob Creek w/ Crystal Decanter 3 1750 \n", "4 Disaronno Amaretto Cavalli Mignon 3-50ml Pack 20 150 \n", "\n", " State Bottle Cost State Bottle Retail Bottles Sold Sale (Dollars) \\\n", "0 $18.09 $27.14 6 $162.84 \n", "1 $18.09 $27.14 12 $325.68 \n", "2 $6.40 $9.60 2 $19.20 \n", "3 $35.55 $53.34 3 $160.02 \n", "4 $6.40 $9.60 2 $19.20 \n", "\n", " Volume Sold (Liters) Volume Sold (Gallons) \n", "0 4.50 1.19 \n", "1 9.00 2.38 \n", "2 0.30 0.08 \n", "3 5.25 1.39 \n", "4 0.30 0.08 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.set_option('display.max_columns', None) # Used to display all columns\n", "df_raw = pd.read_csv('iowa_liquor_sales_proj_2.csv')\n", "df_raw.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from __future__ import division\n", "import datetime\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "from sklearn import linear_model\n", "import seaborn as sns\n", "%matplotlib inline\n", "import math\n", "import itertools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1) Data Munging and EDA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### a) Checking the time span of the data and dropping 2016 data\n", "\n", "It is often convenient to eliminate spaces, commas, etc:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df_raw.copy()\n", "df.columns = [c.replace('/','_').replace(' ','_').replace(')','').replace('(','').lower() for c in df.columns.tolist()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us see how many years and months for each year we have. \n", "\n", "- We first convert the data to `datatime`. \n", "- Using the class attribute `.year` we see that there is data for only two years, namely, 2015 and 2016. \n", "- Using the class attribute `.month` we see that the data includes 12 months of 2015 but only 3 months of 2016. \n", "\n", "For simplicity we will work only with 2015 data (this restriction can be easily generalized if need be)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The years in the data are: [2015 2016]\n" ] } ], "source": [ "df[\"date\"] = pd.to_datetime(df[\"date\"], format=\"%m/%d/%Y\")\n", "print \"The years in the data are:\", df['date'].dt.year.unique()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The months in year 2015 are [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "The months in year 2016 are [1, 2, 3]\n" ] } ], "source": [ "for yr in [2015,2016]:\n", " print \"The months in year {}\".format(yr),\"are\", sorted(df[df['date'].dt.year == yr]['date'].dt.month.unique())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2184483, 24)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[(df['date'] < '2016-01-01')]\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The fraction of 2016 data is: 0.81\n" ] } ], "source": [ "print \"The fraction of 2016 data is:\", round(df.shape[0]/df_raw.shape[0],2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### b) Selecting rows and columns to keep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a rather large dataset and we can eliminate rows and columns that will not be used. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of rows and number of columns are:\n", "\n", "2184483 and 24\n" ] } ], "source": [ "print \"The number of rows and number of columns are:\"\n", "print \"\"\n", "print df.shape[0],\"and\",df.shape[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will, for now, keep the following columns for the following reasons:\n", "\n", " ['store_number','county','bottle_volume_ml', 'state_bottle_cost', 'state_bottle_retail',\\\n", " 'bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "\n", "The date will be dropped since we will not perform any time series analysis." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cols_to_keep = ['store_number','county','bottle_volume_ml', 'state_bottle_cost', 'state_bottle_retail',\\\n", " 'bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "\n", "df = df[cols_to_keep]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### c) Eliminating symbols in the data, dropping `NaNs` and converting objects to floats\n", "\n", "We will:\n", "\n", "- Exclude symbols from the DataFrame. Some of the columns include the symbol $\\$$ which naturally does not allow for algebraic manipulation. There are several elegant ways to exclude it (see [4]). \n", "- Convert columns of objects into columns of floats.\n", "- Drop `NaN` values. \n", "\n", "Two ways of excluding dollar signs are:\n", "\n", " 1) [x[1:] for x in df['state_bottle_cost']]\n", " \n", " 2) df['state_bottle_cost'].apply(lambda x: x.strip('$'))\n", " \n", "We will go with option 2." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [], "source": [ "cols_with_dollar = ['state_bottle_cost','state_bottle_retail','sale_dollars']\n", "for col in cols_with_dollar:\n", " df[col] = df[col].apply(lambda x: x.strip('$')).astype('float')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "store_number int64\n", "county object\n", "bottle_volume_ml int64\n", "state_bottle_cost float64\n", "state_bottle_retail float64\n", "bottles_sold int64\n", "sale_dollars float64\n", "volume_sold_liters float64\n", "dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The commands in the cells below check for:\n", "- The existence of `NaN` values\n", "- How many `NaN` values there are \n", "- How frequent they are. \n", "\n", "We find that only a tiny fraction of the entries in the `county` column is null, which is not problematic. We will drop them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Which colune has null values? county\n", "Number of null values in county is: 1119\n", "Frequency of null values: 5.122%\n" ] } ], "source": [ "null = df.isnull().any() # Column of boolens determining presence of nulls\n", "print \"Which colune has null values?\",null[null == True].index[0]\n", "print \"Number of null values in\", null[null == True].index[0], \"is:\",df.isnull().sum().loc['county']\n", "print \"Frequency of null values:\", str(100*round(100 * df.isnull().sum().loc['county']/df.shape[0],5)) +'%'" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df.dropna(inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### d) Convert store numbers to strings\n", "\n", "We convert `store_numbers` to strings:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df[['store_number']] = df[['store_number']].astype(str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our `DataFrame` is now:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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store_numbercountybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_liters
02191Lee75018.0927.146162.844.50
12205Page75018.0927.1412325.689.00
23549Lee1506.409.60219.200.30
32513Johnson175035.5553.343160.025.25
43942Tama1506.409.60219.200.30
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" ], "text/plain": [ " store_number county bottle_volume_ml state_bottle_cost \\\n", "0 2191 Lee 750 18.09 \n", "1 2205 Page 750 18.09 \n", "2 3549 Lee 150 6.40 \n", "3 2513 Johnson 1750 35.55 \n", "4 3942 Tama 150 6.40 \n", "\n", " state_bottle_retail bottles_sold sale_dollars volume_sold_liters \n", "0 27.14 6 162.84 4.50 \n", "1 27.14 12 325.68 9.00 \n", "2 9.60 2 19.20 0.30 \n", "3 53.34 3 160.02 5.25 \n", "4 9.60 2 19.20 0.30 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### e) Look for problematic issues with the data\n", "\n", "We use `.describe( )` below to better understand the data. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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state_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_liters
mean9.81639614.7422469.877354130.1826138.981275
std14.62642921.93939323.699125405.56893728.355651
min0.8900001.3400001.0000001.3400000.000000
25%5.5400008.3100002.00000030.7200001.600000
50%8.18000012.3000006.00000070.5600005.250000
75%11.96000017.94000012.000000135.36000010.500000
max6100.0000009150.0000003960.000000106326.0000003960.000000
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" ], "text/plain": [ " state_bottle_cost state_bottle_retail bottles_sold sale_dollars \\\n", "mean 9.816396 14.742246 9.877354 130.182613 \n", "std 14.626429 21.939393 23.699125 405.568937 \n", "min 0.890000 1.340000 1.000000 1.340000 \n", "25% 5.540000 8.310000 2.000000 30.720000 \n", "50% 8.180000 12.300000 6.000000 70.560000 \n", "75% 11.960000 17.940000 12.000000 135.360000 \n", "max 6100.000000 9150.000000 3960.000000 106326.000000 \n", "\n", " volume_sold_liters \n", "mean 8.981275 \n", "std 28.355651 \n", "min 0.000000 \n", "25% 1.600000 \n", "50% 5.250000 \n", "75% 10.500000 \n", "max 3960.000000 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the 'count' row is not very useful and the store_number column is meaningless in this table\n", "df.describe().iloc[1:,1:] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us look at histograms. We will use the following function (adapted from [1]):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def draw_histograms(df,col,bins):\n", " df[col].hist(bins=bins);\n", " plt.title(col);\n", " plt.xlabel(col);\n", " plt.xticks(rotation=90);\n", " plt.show();\n", " print" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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M1uuh2vuHaaO1/7rO3bX6+/uYOnVknWb9/VOe8Ge3ss7u0it1Qu/Uap3dZTzq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\nCbgHUR5tfx7+Y1AaM3+IJI3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "cols = ['bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "for col in cols:\n", " draw_histograms(df, col,bins=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some obvious problems:\n", "- In the columns `bottle_volume_ml` and `volume_sold_liters` there are zero values which can be dropped\n", "- The maximum values are many $\\sigma$s larger than the mean for all columns, indicating outliers. Let us drop some outliers. We will choose to restrict some of the columns. We will drop values roughly 2$\\sigma$ larger than the means.\n", "\n", "\n", "### f) Exclude outliers\n", "\n", "\n", "Keeping outliers in our analysis will inflate the predicted sales. Also, we intend to predict the most likely performance for each store. In other words, we do not want to use exceptionally well-performing stores to make recommendations. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "57.2755933042\n", "941.320301085\n", "65.6925625834\n" ] } ], "source": [ "df1 = df.copy()\n", "print np.mean(df1['bottles_sold'])+ 2*np.std(df1['bottles_sold'])\n", "print np.mean(df1['sale_dollars'])+ 2*np.std(df1['sale_dollars'])\n", "print np.mean(df1['volume_sold_liters'])+ 2*np.std(df1['volume_sold_liters'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us correct these:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df1 = df.copy()\n", "cutoffs = (df1['bottle_volume_ml'] > 0) & (df1['bottles_sold'] < 50) & (df1['volume_sold_liters'] < 60) & (df1['sale_dollars'] < 900)\n", "df1 = df1[cutoffs]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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state_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_liters
mean9.74547214.6356777.96878699.3640866.864374
std6.99516410.4923407.830878100.7846626.635848
min0.8900001.3400001.0000001.3400000.100000
25%5.5100008.2700002.00000030.0000001.500000
50%8.00000012.0000006.00000068.6400004.800000
75%11.83000017.75000012.000000132.78000010.500000
max498.640000747.96000048.000000899.98000054.000000
\n", "
" ], "text/plain": [ " state_bottle_cost state_bottle_retail bottles_sold sale_dollars \\\n", "mean 9.745472 14.635677 7.968786 99.364086 \n", "std 6.995164 10.492340 7.830878 100.784662 \n", "min 0.890000 1.340000 1.000000 1.340000 \n", "25% 5.510000 8.270000 2.000000 30.000000 \n", "50% 8.000000 12.000000 6.000000 68.640000 \n", "75% 11.830000 17.750000 12.000000 132.780000 \n", "max 498.640000 747.960000 48.000000 899.980000 \n", "\n", " volume_sold_liters \n", "mean 6.864374 \n", "std 6.635848 \n", "min 0.100000 \n", "25% 1.500000 \n", "50% 4.800000 \n", "75% 10.500000 \n", "max 54.000000 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.describe().iloc[1:,1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us look again at histograms. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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BucDaEeVrgdnN9+OVz207Hqt8tDImKJ+NJEmq2pQ93dMSEadR1pH8SWbeHhFr\nKKMa7WYDq5vv1/DM0NAPrGjKGKV8NvAUZapntDKa8jXAs8e4dtL6+sbOcrNmTbOnuHu63YCZra+v\nd9q9J1rv7/He55pa9nnn2eedNxV9PaUhJSLOAf438PbMvKx5+WHgpSNOXcj6aZiHKdMyI8tvBpZT\ngsZC4K6mjlmU0LMM6AO2j4jezBxqu3YAeLy5966j3Hvk9NK45s+fM2bZypVz6e2ZPn/5p1Nbp6P5\n8+ew3Xbzut2MjTLe+1ybh33eefb59DKV+6ScTHny5i2ZeWlb0RLgQxHRn5mtkZF9Wb8Ydklz3LrP\nXMpU0UmZORwRNwAvbzt/MfA0cCtluurp5rVr2+69tLl2CXDCOHVPyqpVAwwODo1atnLlUwwND2/I\n7bpqOrV1Olq1aoAVK1ZPfGJF+vp6mT9/zrjvc00t+7zz7PPOa/X5ppiSkBIRLwE+BnwSuDYiFrYV\nXwU8CFwYEadS9k/ZC3hXU/4l4LiIOAH4JuXR5Xsz86qm/HPABRFxG2UE5Dzg863QEREXAedHxCGU\nBbHHAgc31/5wgronZXBwiHXrRn9Tj/V6tcwom9V475XaTee2T1f2eefZ59PLVE3O/VFzr49RpmF+\n1nw93EzDvIEypXMjcBDwpsx8CCAz76fsFnsIsBTYDnhj68aZeTHwV8AFwHco+5wc31b3McBNwA+A\ncygjMJc3145btyRJqtdUPYJ8GnDaOOX3AK8Yp/xK4MUbc/9mD5SDWT96skF1S5KkOrnMWZIkVcmQ\nIkmSqmRIkSRJVTKkSJKkKhlSJElSlQwpkiSpSoYUSZJUJUOKJEmqkiFFkiRVyZAiSZKqZEiRJElV\nMqRIkqQqGVIkSVKVDCmSJKlKhhRJklQlQ4okSaqSIUWSJFXJkCJJkqo0q9sNkNQdq1ev5q677qSv\nr5f58+ewatUAg4ND3W7WuF70ohczb968bjdDUocYUqQt1F133cnxZ13K1gt26HZTJuWJ5Q9w+jEH\nsPvue3a7KZI6xJAibcG2XrAD2y7cpdvNkKRRuSZFkiRVyZAiSZKqZEiRJElVMqRIkqQqGVIkSVKV\nDCmSJKlKhhRJklQlQ4okSaqSIUWSJFXJkCJJkqpkSJEkSVUypEiSpCoZUiRJUpUMKZIkqUqGFEmS\nVCVDiiRJqpIhRZIkVcmQIkmSqmRIkSRJVTKkSJKkKhlSJElSlQwpkiSpSoYUSZJUJUOKJEmqkiFF\nkiRVyZBoqbesAAAL70lEQVQiSZKqNKvbDZAkaaZavXo1d911Z7ebsUFe9KIXM2/evG43AzCkSJK0\n2dx1150cf9albL1gh243ZVKeWP4Apx9zALvvvme3mwIYUiRJ2qy2XrAD2y7cpdvNmJa2iJASEf3A\nucABwABwZmae1d1WSZKk8WwRIQU4A9gD2B/YEbgoIu7PzK93tVWaUQbX/ReZ02fueTq1VZuX6yZU\nqxkfUiJiHvBu4Pcy8xbglog4HXg/YEjRlHlq5SN88Vs/Z+slT3a7KZPyyL038Jydf7PbzVAFXDeh\nWs34kALsBmwFXNf22rXAid1pjmay6TT3/MTyB7vdBFVkOr13teXYEkLKIuCxzFzX9tojQH9ELMjM\n5V1ql6QZaqLpk76+XubPn8OqVQMMDg51sGWjc+pPtdoSQspcYO2I11rHszvcFklbgOk2feLUn2q1\nJYSUNTwzjLSOn5rMDfr6xt6Yd86cfvrX3s/s5QMb17oO22rwcZ5Y/kC3mzFpT638OTDc7WZMynRq\nK0y/9j6x/AHuvnvrcf9/rMXdd2e3m7DBptPvhY15L/T29vArv9LPk0+uYWioc+/7u+/Oade3fX3/\ni1mzNv3/s6n4f7VneHj6/JLaGBHxW8BVwOzMHGpe2x/4Zma6NFySpErV/0+STXcL8DSwuO21fYGl\n3WmOJEmajBk/kgIQEedRgskhwPOBvwMOzszLu9kuSZI0ti1hTQrAMcB5wA+Ax4GTDCiSJNVtixhJ\nkSRJ08+WsCZFkiRNQ4YUSZJUJUOKJEmqkiFFkiRVaUt5umfSImIBZUfapzLz8W63R5KkLZVP9wAR\ncSDwfmBvoL+taICy6dtnfWRZkqTO2uJDSkQcA5wMnA5cS/mE5LWU0ZSFlE3gjqXsrfLX3WrnTBMR\nLwAOpewE/Hya0SvgZ8AS4IuZ+VD3Wjjz2OedZ593nn3eeZuzzw0pET8D3jveSElEvBE4JzNf0LmW\nzVwR8RrgMuB64BrgUZ4ZDPcC3pSZ3+9WO2cS+7zz7PPOs887b3P3uWtSYA7w0wnOeQjYdvM3ZYvx\nGeDUzPzUWCdExIea8/5nx1o1s9nnnWefd5593nmbtc99ugcuBS6MiP0i4r+FtojojYjfBi4Evt6V\n1s1ML6Qk7/F8A9ilA23ZUtjnnWefd5593nmbtc8NKfA+yhDVlcBARCyLiJ9GxDLKkNV3m/L3dLGN\nM80S4MSImDNaYUT0Ax8FftzRVs1s9nnn2eedZ5933mbt8y1+uicz1wBHNsNRuwGLgLnAGuBh4JbM\nfKqLTZyJDgcuBx6NiJuAViBszWHuATwIvKFrLZx52vv8ZsqCNvt88/J93nn2eedt1j7f4hfOqjsi\nogfYH9iH9cFwgBIMfwz8MDOHutfCmSkiRvZ5K4wvAa6yz6fWKO/zOcB/UX6RX4N9PuXG+N2yhrK2\n0Pf5ZjDO+/znwI/YhD43pEhbgIiYDZwCHARsA/wbcGJm3t52zkLg4czs604rZ56IeBvl6YYfUNa/\nnQ0cATyL8hTEJzPznO61cMsREU8Au2Xmvd1uy0wSEZcAh2XmquZ4K+AM4M8o+449BpyemWduzP23\n+OkedV5E/A4wqXScmVdv5uZsKf4SeD3wQaCHsnnhDRHxjsxsX/TW043GzUQR8UHKXPz3gPOAPwV2\nB94O3AHsCZweEfPGezJCkxcRF7L+d0vPiO9nA6dFxJPAcGYe2oUmzkRvpvw+WdUcfwI4AHgHcCfw\nMsr7fE5mnrKhNzekqBvOBXad5Lku7p4abwHempnXAETExZQNDC+JiLdn5iVdbd3MdCTwtsz8dvOU\n4I+A12fmt5ry2yNiOfAFwJAyNX4N+H3gBuB2yu+PYdaH7x4M4pvbHwN/3rb32O0RsQL4W8po7gYx\npKgb9gT+CdgZWJyZA11uz5ZgDrC8ddDMD38wIgaBr0TEOsqOy5o6zwbuar6/jrJ4cNmIc+4D5nWy\nUTNZZr4uIt5KmW74LmX/jjXwy48/OSEz7+lmG7cAg8DIKbV7gK035mb+K1Udl5lrKWsjAE7tZlu2\nID8AzoiIX21/MTNPAM4Hvgq8txsNm8GuA05upnOGM/OFmXlzqzAinktZo/K9rrVwBsrMr1Ke1Hwu\n8JNmR9QWF2FuHp+PiFMj4p3ATcDRrYLm0eSTKYuWN5ghRV3R/OvmIODubrdlC3EUsAB4ZMQvbTLz\nSOCTwIndaNgM9j7Kh5b+7ciC5qM2HqSMtry/w+2a8TLzP5s1J/8bODci/hFwQfjmcSBlem1Hyu+Z\nNwAHR0Rrl/YHgf1oCy4bwqd7pC1E85hgAMsyc+Uo5S8B/igzT+t442aoiOgFnpOZy0a8/muU6c6l\nPg67eTWbiZ1MWZf1isx8oMtNmtGa3zM7ZOb9zfFrgWsz88mNuZ8hRZIkVcnpHkmSVCVDiiRJqpIh\nRZIkVcmQIkmSqmRIkSRJVTKkSBpTRAw1GzRtyj12iIi3tB0viIhD245/2HzmyrQQEa9o+mWHcc6Z\nVj+TVCtDiqTN7SLg99qOz6R82F7LMDNvJ9CZ+DNJHWdIkbS5jfxANz/gTdKk+AGDkibykoi4DtiD\n8sFhJ2Xm11qFEfE64GPAS4EnKB8eeWJmromIH1K2xN4vIl4B/BB4Z3PdYGb2MSK0NDvffhp4eXO/\n7wPHZuYjTfkuwDnAPpR/aF0HfDAzb5vMDxMRc4G/Bl4HbAvcAZySmZc15X3An1O2VN8BuB84OzMv\nGON+symfYnwQMJvyWUj+A1CaAv6PJGkiRwMXAr8OfA24OCL2AIiINwH/AvwrsDtwBGX78X9qrn0T\ncD1wMbAX5bM9LqEEi0XNOb+cFmk+dO9HQFI+Lft1wDbA9U24gPJhiA825XtTPnX1sg34eU4BfgP4\nfeDFwLebn6m1xuTTwEcpW6n/OnAu8NmIOGqM+/018CfAu4DfAl5ACViSNpEjKZImcm5mfqH5/qSI\neCXwAcq6kg8Bl2bmXzbl/7f57I7LI+LFmXlnRDwNDGTmcoCIWAM8nZmPNte0j6S8B3gwMz/QeqFZ\ndPsL4M3A31M+8+Y7wP2Zua5ZhBsR0ZOZk1kHsjNlhOa+zFwZER+jjPA8HhHzmzZ8oPk0XYBzImIn\n4MPAZ9tvFBFbU8LJezLzyua1Q4FXTqIdkibgSIqkiVwz4ngpZWoHykjDyPKrm//+xkbUtQfw6xHx\nROsLeIQyjfKS5pwTgWOB5RHxL8ABwE8mGVAATgN2A34RET9q7ndvZq6ijKxsNcbP9GsR8asjXg/g\nWZRPgQUgM9cCN0+yLZLGYUiRNJHBEcd9wNrm+9EWwbZ+rzy9EXX1At+jhIj2r6A8FURmfg54HmXd\nyErK9M3tzScLTygzl1CmZA6khIl3AXc0I0RjLeod62caHlHesm4ybZE0PkOKpInsNeL4t4HWItWf\n8Mz1F63jO5r/jhzhGO/4P4BdgYcy897MvBd4nDLN8hsR8asR8TfAszLzosx8J/A/gYWUBboTioiP\nA/tm5jcy8yjgRcA9lBGZ2ylBZLSfaVlmPj7i9QTWAPu23X8W8LLJtEXS+FyTImkix0TEPcCPKU+8\nvBR4a1N2OvDPEXEi8M+UP/h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3nrVrN/xCH+yzqcx66tVJtYD11KyTaoHOq2c8JmLi6xDKEogvUUJG/59/h19f/XMI5TLl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\nRLwT2I0yhzYLWA0sA25vkuB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "cols = ['bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "for col in cols:\n", " draw_histograms(df1, col,bins=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5) Mine the data\n", "\n", "Now you are ready to compute the variables you will use for your regression from the data. For example, you may want to compute total sales per store from Jan to March of 2015, mean price per bottle, etc. Refer to the readme for more ideas appropriate to your scenario.\n", "\n", "Pandas is your friend for this task. Take a look at the operations here for ideas on how to make the best use of pandas and feel free to search for blog and Stack Overflow posts to help you group data by certain variables and compute sums, means, etc. You may find it useful to create a new data frame to house this summary data.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### a) Aggregating data by county, computing total number of stores and total sales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obtain the ratios (1)-(4) we need the counties' areas, their populations and the number of stores in them. Let us count the number of stores per county:" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2143329\n" ] }, { "data": { "text/plain": [ "county\n", "Adair 4420\n", "Adams 1797\n", "Allamakee 8595\n", "Appanoose 8582\n", "Audubon 2045\n", "Name: store_number, dtype: int64" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df1.copy()\n", "stores_per_county = df2.groupby(['county'])['store_number'].count()\n", "print stores_per_county.sum()\n", "stores_per_county.head()" ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([u'store_number', u'county', u'bottle_volume_ml', u'state_bottle_cost',\n", " u'state_bottle_retail', u'bottles_sold', u'sale_dollars',\n", " u'volume_sold_liters'],\n", " dtype='object')" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us computer total sales per county:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### b) Create profit column\n", "\n", "Let us first include the profit:" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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store_numbercountybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofit
02191Lee75018.0927.146162.844.5054.30
12205Page75018.0927.1412325.689.00108.60
23549Lee1506.409.60219.200.306.40
32513Johnson175035.5553.343160.025.2553.37
43942Tama1506.409.60219.200.306.40
\n", "
" ], "text/plain": [ " store_number county bottle_volume_ml state_bottle_cost \\\n", "0 2191 Lee 750 18.09 \n", "1 2205 Page 750 18.09 \n", "2 3549 Lee 150 6.40 \n", "3 2513 Johnson 1750 35.55 \n", "4 3942 Tama 150 6.40 \n", "\n", " state_bottle_retail bottles_sold sale_dollars volume_sold_liters profit \n", "0 27.14 6 162.84 4.50 54.30 \n", "1 27.14 12 325.68 9.00 108.60 \n", "2 9.60 2 19.20 0.30 6.40 \n", "3 53.34 3 160.02 5.25 53.37 \n", "4 9.60 2 19.20 0.30 6.40 " ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['profit'] = (df2['state_bottle_retail'] - df2['state_bottle_cost'])*df2[\"bottles_sold\"]\n", "df2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### c) Count stores per county" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "cols = ['county', 'bottle_volume_ml', 'state_bottle_cost', \\\n", " 'state_bottle_retail', 'bottles_sold', 'sale_dollars', 'volume_sold_liters', 'profit']" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [], "source": [ "df_county = df2[cols].groupby(['county']).sum()" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofit
county
Adair445587540182.6260373.6933807410805.6232522.35137466.12
Adams175740318103.8227171.398446100596.807547.6233614.78
Allamakee907917585371.49128238.3857833772843.4661269.76258630.44
Appanoose836017582458.44123839.9758261711376.1553184.66237792.71
Audubon204317517737.1126656.0713978159547.6313215.4353393.70
\n", "
" ], "text/plain": [ " bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "county \n", "Adair 4455875 40182.62 60373.69 \n", "Adams 1757403 18103.82 27171.39 \n", "Allamakee 9079175 85371.49 128238.38 \n", "Appanoose 8360175 82458.44 123839.97 \n", "Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit \n", "county \n", "Adair 33807 410805.62 32522.35 137466.12 \n", "Adams 8446 100596.80 7547.62 33614.78 \n", "Allamakee 57833 772843.46 61269.76 258630.44 \n", "Appanoose 58261 711376.15 53184.66 237792.71 \n", "Audubon 13978 159547.63 13215.43 53393.70 " ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.head()" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_stores
0Adair445587540182.6260373.6933807410805.6232522.35137466.124420
1Adams175740318103.8227171.398446100596.807547.6233614.781797
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.448595
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.718582
4Audubon204317517737.1126656.0713978159547.6313215.4353393.702045
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" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 " ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county['num_stores'] = stores_per_county\n", "df_county.reset_index(level=0, inplace=True)\n", "df_county.head()" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "bottle_volume_ml False\n", "state_bottle_cost False\n", "state_bottle_retail False\n", "bottles_sold False\n", "sale_dollars False\n", "volume_sold_liters False\n", "profit False\n", "num_stores False\n", "dtype: bool" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### d) Calculation of the profit per store" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [], "source": [ "df_county['average_store_profit'] = df_county['profit']/df_county['num_stores']" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profit
0Adair445587540182.6260373.6933807410805.6232522.35137466.12442031.100932
1Adams175740318103.8227171.398446100596.807547.6233614.78179718.706055
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.44859530.090802
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.71858227.708309
4Audubon204317517737.1126656.0713978159547.6313215.4353393.70204526.109389
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 \n", "\n", " average_store_profit \n", "0 31.100932 \n", "1 18.706055 \n", "2 30.090802 \n", "3 27.708309 \n", "4 26.109389 " ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.head()" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "bottle_volume_ml False\n", "state_bottle_cost False\n", "state_bottle_retail False\n", "bottles_sold False\n", "sale_dollars False\n", "volume_sold_liters False\n", "profit False\n", "num_stores False\n", "average_store_profit False\n", "dtype: bool" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### e) Calculation sales per volume $r_{{\\rm{sv}}}^{(c)}$" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_county['sales_per_litters'] = df_county['sale_dollars']/df_county['volume_sold_liters']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### f) Cut-offs in the aggregate DataFrame" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litters
mean1.994879e+072.109874e+053.168593e+051.725225e+052.151211e+061.486122e+057.188647e+0521649.78787930.17001613.382942
std4.068975e+074.620609e+056.938726e+054.054011e+054.974648e+063.237530e+051.662030e+0646543.9444876.0684820.999601
max3.376040e+083.868235e+065.808917e+063.319741e+064.132447e+072.673001e+061.380706e+07383547.00000044.85988815.770199
\n", "
" ], "text/plain": [ " bottle_volume_ml state_bottle_cost state_bottle_retail bottles_sold \\\n", "mean 1.994879e+07 2.109874e+05 3.168593e+05 1.725225e+05 \n", "std 4.068975e+07 4.620609e+05 6.938726e+05 4.054011e+05 \n", "max 3.376040e+08 3.868235e+06 5.808917e+06 3.319741e+06 \n", "\n", " sale_dollars volume_sold_liters profit num_stores \\\n", "mean 2.151211e+06 1.486122e+05 7.188647e+05 21649.787879 \n", "std 4.974648e+06 3.237530e+05 1.662030e+06 46543.944487 \n", "max 4.132447e+07 2.673001e+06 1.380706e+07 383547.000000 \n", "\n", " average_store_profit sales_per_litters \n", "mean 30.170016 13.382942 \n", "std 6.068482 0.999601 \n", "max 44.859888 15.770199 " ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.describe().loc[['mean','std','max']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider histograms again:" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['county', 'bottle_volume_ml', 'state_bottle_cost', 'state_bottle_retail', 'bottles_sold', 'sale_dollars', 'volume_sold_liters', 'profit', 'num_stores', 'average_store_profit', 'sales_per_litters']\n" ] } ], "source": [ "print df_county.columns.tolist()" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "image/png": 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kSeorG4IZyJsxSZL6zTkEkiTJhkCSJNkQSJIkbAgkSRI2BJIkCRsCSZJEzZcd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\nJ7AncAflhkjHZObGiPgOsC+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HwHs7+Bx91+lz6ffefUvHjjXTbKnzErbU1y1Nl/92uquT6xBsB9yVmetatt0J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\nL8NrgRcB32v4tBdm5j8DRMQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "cols = ['bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "for col in cols:\n", " draw_histograms(df_county, col,bins=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two clear outliers which we can remove:" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "76 3319741\n", "56 1500058\n", "81 1237025\n", "6 1093412\n", "51 927749\n", "Name: bottles_sold, dtype: int64\n", "\n", "Polk\n" ] } ], "source": [ "print df_county['bottles_sold'].sort_values(ascending=False).head()\n", "print \"\"\n", "print df_county.iloc[76].loc['county']" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "35 2512\n", "25 7746\n", "92 8283\n", "1 8446\n", "86 9360\n", "Name: bottles_sold, dtype: int64\n", "\n", "Fremont\n" ] } ], "source": [ "print df_county['bottles_sold'].sort_values(ascending=True).head()\n", "print \"\"\n", "print df_county.iloc[35].loc['county']" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [], "source": [ "conditions = (df_county['county'] != 'Polk') & (df_county['county'] != 'Fremont')\n", "df_county = df_county[conditions]" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "data": { "image/png": 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/P522qupnnxX77CqfBq4Flmbm8MQnI2Jr4CzgTOA501xbnewT+2zFdF5lcB5w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\nAB4P3JKZ12fm9cDdlKH6J0X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If6S8mO+0sU2d8jjKt6DxXAjs1Ia2dJJ1qLEWhXWoWQQcO9Z//gCZuRr4CPCP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\n0g1/ZnXXzymT0x4UEU8C+oB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "cols = ['bottles_sold', 'sale_dollars', 'volume_sold_liters']\n", "for col in cols:\n", " draw_histograms(df_county, col,bins=20)" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litters
0Adair445587540182.6260373.6933807410805.6232522.35137466.12442031.10093212.631486
1Adams175740318103.8227171.398446100596.807547.6233614.78179718.70605513.328281
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.44859530.09080212.613783
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.71858227.70830913.375589
4Audubon204317517737.1126656.0713978159547.6313215.4353393.70204526.10938912.072829
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 \n", "\n", " average_store_profit sales_per_litters \n", "0 31.100932 12.631486 \n", "1 18.706055 13.328281 \n", "2 30.090802 12.613783 \n", "3 27.708309 13.375589 \n", "4 26.109389 12.072829 " ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_county.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### g) Including population data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obtain both $r_{\\rm{sp}}^{(c)}$ (stores per person) and $r_{\\rm{cp}}^{(c)}$ (alcohol consumption per person) we need data about the population of each county. This dataset is taken from [5]. These are 2016 population estimates (which will be discussed later)." ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countypopulation
0Adair7092
1Adams3693
2Allamakee13884
3Appanoose12462
4Audubon5678
\n", "
" ], "text/plain": [ " county population\n", "0 Adair 7092\n", "1 Adams 3693\n", "2 Allamakee 13884\n", "3 Appanoose 12462\n", "4 Audubon 5678" ] }, "execution_count": 230, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop = pd.read_csv('pop_iowa_per_county.csv')\n", "del pop['Unnamed: 0']\n", "pop = pop[pop['county'] != 'Polk']\n", "pop = pop[pop['county'] != 'Fremont']\n", "pop.head()" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "population False\n", "dtype: bool" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now append to our dataset columns of population, population per store and consumption per store:" ] }, { "cell_type": "code", "execution_count": 263, "metadata": {}, "outputs": [], "source": [ "df_new = df_county.copy()\n", "df_new = pd.merge(df_new, pop, on= 'county', how='outer')" ] }, { "cell_type": "code", "execution_count": 264, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "bottle_volume_ml False\n", "state_bottle_cost False\n", "state_bottle_retail False\n", "bottles_sold False\n", "sale_dollars False\n", "volume_sold_liters False\n", "profit False\n", "num_stores False\n", "average_store_profit False\n", "sales_per_litters False\n", "population False\n", "dtype: bool" ] }, "execution_count": 264, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### h) Calculation of ratios $r_{\\rm{cp}}^{(c)}$ and $r_{\\rm{sp}}^{(c)}$" ] }, { "cell_type": "code", "execution_count": 265, "metadata": {}, "outputs": [], "source": [ "df_new['store_population_ratio'] = df_new['num_stores']/df_new['population']\n", "df_new['consumption_per_capita'] = df_new['volume_sold_liters']/df_new['population']" ] }, { "cell_type": "code", "execution_count": 266, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "bottle_volume_ml False\n", "state_bottle_cost False\n", "state_bottle_retail False\n", "bottles_sold False\n", "sale_dollars False\n", "volume_sold_liters False\n", "profit False\n", "num_stores False\n", "average_store_profit False\n", "sales_per_litters False\n", "population False\n", "store_population_ratio False\n", "consumption_per_capita False\n", "dtype: bool" ] }, "execution_count": 266, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is a good idea to export this DataFrame to keep after so many changes: " ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_new.to_csv('df_new.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### i) Including area data\n", "\n", "We not import a table containing among other things, areas per county (source [1]):" ] }, { "cell_type": "code", "execution_count": 268, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countyarea
0Adair1146.149874
1Adams950.420482
2Allamakee1640.663925
3Appanoose1439.625361
4Audubon1011.305224
5Benton1589.039934
6Black Hawk1658.463133
7Boone1172.618576
8Bremer1254.612988
9Buchanan1437.306002
10Buena Vista1641.768588
11Butler1714.432829
12Calhoun1593.242060
13Carroll1790.407255
14Cass1776.544213
15Cedar1232.378198
16Cerro Gordo1497.770139
17Cherokee1404.657609
18Chickasaw1332.800885
19Clarke954.543677
20Clay1305.389452
21Clayton2086.377422
22Clinton1701.089895
23Crawford1794.525720
24Dallas1727.389531
25Davis1202.497109
26Decatur1443.427332
27Delaware1275.461107
28Des Moines928.456413
29Dickinson1024.114722
30Dubuque1445.975306
31Emmet1130.769046
32Fayette1955.698767
33Floyd1245.579774
34Franklin1346.541996
36Greene1385.511780
37Grundy1097.912646
38Guthrie1659.326695
39Hamilton1425.376163
40Hancock1435.986414
41Hardin1749.403531
42Harrison2000.716609
43Henry1374.637080
44Howard1553.199575
45Humboldt1325.822767
46Ida1061.883800
47Iowa1448.032410
48Jackson2075.514783
49Jasper1677.247032
50Jefferson1228.460352
51Johnson1374.398467
52Jones1603.902991
53Keokuk1568.990538
54Kossuth2171.379858
55Lee1207.589119
56Linn2164.866374
57Louisa980.907015
58Lucas1154.264969
59Lyon1520.471701
60Madison1409.747269
61Mahaska1216.496254
62Marion1528.879832
63Marshall1520.107767
64Mills1220.953340
65Mitchell1086.794650
66Monona1656.794562
67Monroe960.814655
68Montgomery1140.359482
69Muscatine1700.941831
70O'Brien1620.431744
71Osceola967.452680
72Page1398.118634
73Palo Alto1596.128377
74Plymouth2259.953641
75Pocahontas1297.431413
77Pottawattamie2303.225231
78Poweshiek1580.076857
79Ringgold1198.624350
80Sac1412.599164
81Scott1211.649675
82Shelby1299.197014
83Sioux2056.178194
84Story1943.690095
85Tama2088.226617
86Taylor1571.159542
87Union1292.945614
88Van Buren1549.129170
89Wapello1104.862750
90Warren1617.493665
91Washington1666.506696
92Wayne1438.930980
93Webster1793.295141
94Winnebago1295.390700
95Winneshiek1587.428273
96Woodbury2591.144872
97Worth925.069678
98Wright1542.560045
\n", "
" ], "text/plain": [ " county area\n", "0 Adair 1146.149874\n", "1 Adams 950.420482\n", "2 Allamakee 1640.663925\n", "3 Appanoose 1439.625361\n", "4 Audubon 1011.305224\n", "5 Benton 1589.039934\n", "6 Black Hawk 1658.463133\n", "7 Boone 1172.618576\n", "8 Bremer 1254.612988\n", "9 Buchanan 1437.306002\n", "10 Buena Vista 1641.768588\n", "11 Butler 1714.432829\n", "12 Calhoun 1593.242060\n", "13 Carroll 1790.407255\n", "14 Cass 1776.544213\n", "15 Cedar 1232.378198\n", "16 Cerro Gordo 1497.770139\n", "17 Cherokee 1404.657609\n", "18 Chickasaw 1332.800885\n", "19 Clarke 954.543677\n", "20 Clay 1305.389452\n", "21 Clayton 2086.377422\n", "22 Clinton 1701.089895\n", "23 Crawford 1794.525720\n", "24 Dallas 1727.389531\n", "25 Davis 1202.497109\n", "26 Decatur 1443.427332\n", "27 Delaware 1275.461107\n", "28 Des Moines 928.456413\n", "29 Dickinson 1024.114722\n", "30 Dubuque 1445.975306\n", "31 Emmet 1130.769046\n", "32 Fayette 1955.698767\n", "33 Floyd 1245.579774\n", "34 Franklin 1346.541996\n", "36 Greene 1385.511780\n", "37 Grundy 1097.912646\n", "38 Guthrie 1659.326695\n", "39 Hamilton 1425.376163\n", "40 Hancock 1435.986414\n", "41 Hardin 1749.403531\n", "42 Harrison 2000.716609\n", "43 Henry 1374.637080\n", "44 Howard 1553.199575\n", "45 Humboldt 1325.822767\n", "46 Ida 1061.883800\n", "47 Iowa 1448.032410\n", "48 Jackson 2075.514783\n", "49 Jasper 1677.247032\n", "50 Jefferson 1228.460352\n", "51 Johnson 1374.398467\n", "52 Jones 1603.902991\n", "53 Keokuk 1568.990538\n", "54 Kossuth 2171.379858\n", "55 Lee 1207.589119\n", "56 Linn 2164.866374\n", "57 Louisa 980.907015\n", "58 Lucas 1154.264969\n", "59 Lyon 1520.471701\n", "60 Madison 1409.747269\n", "61 Mahaska 1216.496254\n", "62 Marion 1528.879832\n", "63 Marshall 1520.107767\n", "64 Mills 1220.953340\n", "65 Mitchell 1086.794650\n", "66 Monona 1656.794562\n", "67 Monroe 960.814655\n", "68 Montgomery 1140.359482\n", "69 Muscatine 1700.941831\n", "70 O'Brien 1620.431744\n", "71 Osceola 967.452680\n", "72 Page 1398.118634\n", "73 Palo Alto 1596.128377\n", "74 Plymouth 2259.953641\n", "75 Pocahontas 1297.431413\n", "77 Pottawattamie 2303.225231\n", "78 Poweshiek 1580.076857\n", "79 Ringgold 1198.624350\n", "80 Sac 1412.599164\n", "81 Scott 1211.649675\n", "82 Shelby 1299.197014\n", "83 Sioux 2056.178194\n", "84 Story 1943.690095\n", "85 Tama 2088.226617\n", "86 Taylor 1571.159542\n", "87 Union 1292.945614\n", "88 Van Buren 1549.129170\n", "89 Wapello 1104.862750\n", "90 Warren 1617.493665\n", "91 Washington 1666.506696\n", "92 Wayne 1438.930980\n", "93 Webster 1793.295141\n", "94 Winnebago 1295.390700\n", "95 Winneshiek 1587.428273\n", "96 Woodbury 2591.144872\n", "97 Worth 925.069678\n", "98 Wright 1542.560045" ] }, "execution_count": 268, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('display.max_rows', None)\n", "\n", "areas = pd.read_csv('ia_zip_city_county_sqkm.csv')\n", "del areas['Unnamed: 0']\n", "areas.columns = ['zip_code', 'city', 'county', 'state', 'county_number', 'area']\n", "areas = areas[['county','area']].groupby(['county'])[['area']].sum()\n", "areas.reset_index(level=0, inplace=True)\n", "areas = areas[(areas['county'] != 'Polk') & (areas['county'] != 'Fremont')]\n", "areas" ] }, { "cell_type": "code", "execution_count": 269, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_new = pd.merge(df_new, areas, on= 'county', how='outer')" ] }, { "cell_type": "code", "execution_count": 270, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaarea
0Adair445587540182.626.037369e+04338074.108056e+0532522.351.374661e+05442031.10093212.63148670920.6232374.5857801146.149874
1Adams175740318103.822.717139e+0484461.005968e+057547.623.361478e+04179718.70605513.32828136930.4865962.043764950.420482
2Allamakee907917585371.491.282384e+05578337.728435e+0561269.762.586304e+05859530.09080212.613783138840.6190584.4129761640.663925
3Appanoose836017582458.441.238400e+05582617.113762e+0553184.662.377927e+05858227.70830913.375589124620.6886544.2677471439.625361
4Audubon204317517737.112.665607e+04139781.595476e+0513215.435.339370e+04204526.10938912.07282956780.3601622.3274801011.305224
5Benton779072571349.041.071595e+05476995.803559e+0547612.841.940870e+05773125.10503212.189062256990.3008291.8527121589.039934
6Black Hawk1008012271107541.371.663275e+0610934121.192076e+07793399.133.982880e+0611822933.68784015.0249191329040.8895825.9697161658.463133
7Boone16005831154407.112.319393e+051202711.534234e+06115554.845.129844e+051651531.06172413.277107265320.6224564.3553011172.618576
8Bremer17767453173326.872.603925e+051135511.487074e+06114021.434.976283e+051774628.04171713.042060247980.7156224.5980091254.612988
9Buchanan12168450121504.751.824907e+05894291.140914e+0684484.183.814875e+051265330.14996113.504469209920.6027534.0245891437.306002
10Buena Vista20832831226554.993.402451e+051141471.532283e+06105358.285.121251e+052159823.71169014.543544203321.0622665.1818951641.768588
11Butler334880027994.244.206102e+04246152.786351e+0525010.209.331105e+04330828.20769311.140858147910.2236501.6909071714.432829
12Calhoun378745030301.414.553630e+04272143.215042e+0527977.641.075994e+05362529.68260411.49146898460.3681702.8415231593.242060
13Carroll14460725143097.042.149119e+051142461.556718e+06115511.565.202122e+051456835.70923613.476729204370.7128255.6520801790.407255
14Cass1003867598657.961.481477e+05745831.005639e+0675880.713.360579e+05998833.64616513.252889131570.7591405.7673261776.544213
15Cedar742712567426.721.013055e+05445504.927014e+0538720.951.647957e+05816020.19554512.724413184540.4421812.0982421232.378198
16Cerro Gordo50439956514818.967.732206e+053778604.892826e+06354343.001.635296e+065156631.71267113.808162430701.1972608.2271421497.770139
17Cherokee763840072195.691.084669e+05584236.855181e+0552491.702.294092e+05740830.96777013.059553115080.6437264.5613231404.657609
18Chickasaw432597537139.095.580234e+04270033.508606e+0530429.561.173801e+05403129.11936011.530256120230.3352742.5309461332.800885
19Clarke565645054720.088.218209e+04372865.035890e+0536768.251.683819e+05560030.06820013.69630093090.6015683.949753954.543677
20Clay15084525151187.652.270741e+051046451.367561e+06104603.094.569864e+051527629.91532013.073807163330.9352846.4044021305.389452
21Clayton11180875108046.711.622809e+05499746.927068e+0552494.392.315675e+051048022.09613213.195825175900.5957932.9843312086.377422
22Clinton25808600255757.843.841091e+052451802.928261e+06214882.769.784796e+052836434.49723713.627250473090.5995484.5421121701.089895
23Crawford917822891880.621.379796e+05657908.643920e+0562535.952.889041e+05936030.86581813.822321169400.5525383.6916151794.525720
24Dallas19782703208239.383.128030e+051660322.295133e+06151828.967.673762e+052128436.05413615.116572845160.2518341.7964521727.389531
25Davis162350014206.102.134244e+0477469.618596e+048004.003.217266e+04155520.68981412.01723688600.1755080.9033861202.497109
26Decatur179825017836.602.679681e+04143891.763645e+0512243.115.898266e+04199429.58007014.40520381410.2449331.5038831443.427332
27Delaware592965056982.708.557961e+04552127.547227e+0554845.422.522007e+05574943.86862213.760907173270.3317943.1653151275.461107
28Des Moines28965403311376.144.674807e+052942573.579586e+06235900.791.195418e+063247436.81152615.174117397390.8171825.936254928.456413
29Dickinson27326006291810.424.382073e+052227443.145660e+06224310.441.050838e+062811037.38307214.023690172431.63022713.0087831024.114722
30Dubuque57322060601825.159.038367e+055012746.621999e+06460080.112.212785e+066075336.42265314.393144970030.6263004.7429471445.975306
31Emmet598252560879.979.144739e+04386935.019728e+0539072.451.678386e+05607127.64595512.84723096580.6285984.0456051130.769046
32Fayette910912881374.001.222869e+05702318.602044e+0568175.752.878219e+05873032.96929112.617454200540.4353253.3996091955.698767
33Floyd805867879010.191.186743e+05826891.032124e+0674542.853.450133e+05822341.95710313.846056158730.5180504.6962041245.579774
34Franklin614632560180.129.038277e+04363134.522624e+0535596.581.512945e+05605224.99909112.705219101700.5950843.5001551346.541996
35Greene520052553392.208.019890e+04355114.774988e+0533966.781.596316e+05524830.41761114.05781890110.5823993.7694801385.511780
36Grundy436925036670.315.510175e+04262103.018362e+0526267.961.009891e+05434123.26402711.490660123130.3525542.1333521097.912646
37Guthrie349360031390.414.717285e+04246782.940075e+0523023.479.837735e+04339828.95154512.769903106250.3198122.1669151659.326695
38Hamilton788232577319.081.161721e+05610117.369458e+0556950.352.466456e+05860328.66972112.940146150760.5706423.7775501425.376163
39Hancock350942528221.144.241179e+04268203.061684e+0528072.831.025907e+05310133.08308910.906219108350.2862022.5909401435.986414
40Hardin15096700136991.102.058020e+051197721.657238e+06127383.225.542669e+051359240.77890813.009862172260.7890407.3948231749.403531
41Harrison833390082437.451.238054e+05423805.046314e+0538339.841.687135e+05913118.47699913.162064141490.6453462.7097212000.716609
42Henry821442584936.531.275347e+05729829.634271e+0567508.463.218625e+05902635.65947914.271205197730.4564813.4141741374.637080
43Howard550185350667.537.612342e+04389465.268240e+0542435.811.762160e+05519733.90726412.41460993320.5569014.5473441553.199575
44Humboldt483867546974.267.055336e+04400135.050238e+0538779.601.687570e+05496733.97563713.02292594870.5235594.0876571325.822767
45Ida490715049504.887.433259e+04357714.983864e+0534949.871.665311e+05477734.86101514.26003769850.6838945.0035601061.883800
46Iowa11862800102657.531.542814e+05744519.711115e+0577025.043.251289e+051116229.12819212.607737163110.6843234.7222761448.032410
47Jackson12728325114843.671.724907e+05890981.130876e+0688084.973.778944e+051259330.00828712.838469194720.6467244.5236732075.514783
48Jasper21363900214003.363.214453e+051318281.577656e+06121386.175.274477e+052304622.88673412.996996367080.6278203.3068041677.247032
49Jefferson637702566329.729.959432e+04568317.276337e+0551721.612.430598e+05644637.70707414.068272180900.3563292.8591271228.460352
50Johnson942011431094410.171.643462e+069277491.237021e+07784403.934.133645e+0610595539.01321315.7701991465470.7230105.3525761374.398467
51Jones15309100144067.532.164019e+05691018.898679e+0568061.752.974959e+051554519.13772513.074420204390.7605563.3299941603.902991
52Keokuk272090023814.273.576359e+04135541.495608e+0513026.404.999794e+04279717.87556011.481359101190.2764111.2873211568.990538
53Kossuth14172628142789.092.144583e+051058811.479812e+06108007.854.948291e+051372036.06626313.700965151140.9077687.1462122171.379858
54Lee24974375267566.064.017307e+052284662.902471e+06201627.379.692238e+052726135.55349314.395224346150.7875495.8248551207.589119
55Linn1583534521731106.012.599613e+0615000581.806775e+071221208.856.036068e+0618409232.78832114.7949752216610.8305115.5093542164.866374
56Louisa316585031041.024.661010e+04206022.071461e+0514809.686.917951e+04373018.54678613.987210111420.3347691.329176980.907015
57Lucas396867540531.536.087117e+04290113.761450e+0527391.101.257357e+05406430.93889813.73237986470.4699903.1677001154.264969
58Lyon977687598319.841.476554e+05553187.766658e+0556613.632.593880e+05964726.88794313.718707117540.8207424.8165421520.471701
59Madison739647574647.411.121233e+05544116.856734e+0551462.232.293326e+05779529.42048113.323817158480.4918603.2472381409.747269
60Mahaska939855096610.211.451289e+05708618.393837e+0562911.852.806475e+05967029.02249513.342219221810.4359592.8362951216.496254
61Marion19736978199812.053.000452e+051331751.735448e+06123141.995.798673e+052095827.66806714.093066331890.6314743.7103251528.879832
62Marshall22552328231546.743.477674e+051926552.504138e+06168340.478.368677e+052350735.60078714.875434403120.5831274.1759391520.107767
63Mills431720038409.795.770141e+04375474.345716e+0534164.641.452974e+05396836.61729312.719923149720.2650282.2819021220.953340
64Mitchell819800080007.931.201944e+05323354.147482e+0534245.821.387916e+05822716.87025812.110916107630.7643783.1818101086.794650
65Monona967138195956.981.440858e+05420205.197005e+0538043.921.736055e+051002317.32071313.66053988981.1264334.2755591656.794562
66Monroe289152528812.614.327483e+04219712.896006e+0520937.969.680632e+04300032.26877313.83136778700.3811942.660478960.814655
67Montgomery645707867065.991.006981e+05451675.570222e+0541624.631.861282e+05671327.72653413.382034102250.6565284.0708681140.359482
68Muscatine28497706294376.854.421020e+052082962.626114e+06188612.008.775137e+053159127.77733113.923365429400.7357014.3924551700.941831
69O'Brien13896728132245.411.986505e+05830291.057797e+0684579.603.539820e+051395425.36778212.506521140200.9952926.0327821620.431744
70Osceola310747529141.204.377315e+04192442.562448e+0520104.988.574306e+04298128.76318712.74534160640.4915903.315465967.452680
71Page10230328103322.961.551729e+05811161.057103e+0675315.103.532156e+051065133.16266714.035741153910.6920284.8934511398.118634
72Palo Alto939670089935.751.350478e+05419495.750134e+0544627.151.921719e+05866722.17283112.88483490470.9579974.9328121596.128377
73Plymouth12560609132624.741.991814e+05943251.286709e+0693061.034.300381e+051317632.63798413.826506252000.5228573.6928982259.953641
74Pocahontas431275037584.775.645243e+04243632.971895e+0525680.479.933154e+04416723.83766311.57258768860.6051413.7293741297.431413
75Pottawattamie64280187680102.801.021141e+066183587.675324e+06521376.002.563352e+067247235.37023314.721285935820.7744225.5713282303.225231
76Poweshiek16062900169084.712.539540e+05974741.306022e+0691675.424.365483e+051749624.95132114.246155185330.9440464.9466041580.076857
77Ringgold168985014072.682.115156e+04100611.386808e+0511080.294.642018e+04147631.44998612.51599150680.2912392.1863241198.624350
78Sac793462575245.541.130183e+05395715.258452e+0542670.281.760915e+05715724.60409812.32345398760.7246864.3206031412.599164
79Scott1124821021247750.111.873693e+0612370251.397399e+07914829.354.667888e+0613041935.79146915.2749671724740.7561665.3041581211.649675
80Shelby528695353350.358.009987e+04429635.279510e+0538570.771.762560e+05562731.32325913.687852118000.4768643.2687091299.197014
81Sioux10681100102003.821.532028e+05771211.069835e+0679019.863.575951e+051030734.69439613.538806348980.2953462.2643092056.178194
82Story65551096727466.681.092625e+065018326.730961e+06456414.622.250349e+067163631.41366514.747469970900.7378314.7009441943.690095
83Tama724287569664.271.046049e+05476925.735157e+0543573.671.916543e+05765525.03648913.161979173190.4420002.5159462088.226617
84Taylor207382520885.053.135727e+0493601.105983e+058433.723.693200e+04230416.02951413.11382862160.3706561.3567761571.159542
85Union956917590467.231.358550e+05665369.021116e+0568179.743.015172e+05902433.41281513.231373124200.7265705.4895121292.945614
86Van Buren188307518970.162.848695e+04128061.742457e+0512768.445.823345e+04192030.32992213.64659272710.2640631.7560781549.129170
87Wapello24021278251492.943.776542e+051867242.292624e+06160501.097.658786e+052784327.50704314.284163349820.7959244.5881051104.862750
88Warren19568800183359.552.753840e+051590191.939043e+06146705.536.481145e+051962033.03335913.217243496910.3948402.9523561617.493665
89Washington10984531119302.231.791272e+05843891.140731e+0678241.803.810510e+051174532.44367814.579561222810.5271313.5115931666.506696
90Wayne134450012518.511.880652e+0482831.054384e+058001.093.526299e+04126827.80992913.17799864520.1965281.2400951438.930980
91Webster22537959231995.403.484066e+052282522.705835e+06189009.949.042916e+052410637.51313414.315835367690.6556075.1404701793.295141
92Winnebago800930072833.611.094025e+05559316.827292e+0556888.632.282976e+05766729.77664812.001155106310.7211935.3512021295.390700
93Winneshiek11450225107156.181.609449e+05864831.189769e+0689488.043.977413e+051054137.73279013.295288205610.5126704.3523191587.428273
94Woodbury61274387672801.781.010242e+066206807.695638e+06512631.642.570501e+066773237.95105515.0120231027790.6590064.9877082591.144872
95Worth324747528918.724.343867e+04201122.466353e+0520602.258.250434e+04300027.50144711.97128175720.3961972.720847925.069678
96Wright557515051158.247.684774e+04465175.706450e+0544906.821.908072e+05546134.93996712.707313127790.4273423.5141111542.560045
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 6.037369e+04 \n", "1 Adams 1757403 18103.82 2.717139e+04 \n", "2 Allamakee 9079175 85371.49 1.282384e+05 \n", "3 Appanoose 8360175 82458.44 1.238400e+05 \n", "4 Audubon 2043175 17737.11 2.665607e+04 \n", "5 Benton 7790725 71349.04 1.071595e+05 \n", "6 Black Hawk 100801227 1107541.37 1.663275e+06 \n", "7 Boone 16005831 154407.11 2.319393e+05 \n", "8 Bremer 17767453 173326.87 2.603925e+05 \n", "9 Buchanan 12168450 121504.75 1.824907e+05 \n", "10 Buena Vista 20832831 226554.99 3.402451e+05 \n", "11 Butler 3348800 27994.24 4.206102e+04 \n", "12 Calhoun 3787450 30301.41 4.553630e+04 \n", "13 Carroll 14460725 143097.04 2.149119e+05 \n", "14 Cass 10038675 98657.96 1.481477e+05 \n", "15 Cedar 7427125 67426.72 1.013055e+05 \n", "16 Cerro Gordo 50439956 514818.96 7.732206e+05 \n", "17 Cherokee 7638400 72195.69 1.084669e+05 \n", "18 Chickasaw 4325975 37139.09 5.580234e+04 \n", "19 Clarke 5656450 54720.08 8.218209e+04 \n", "20 Clay 15084525 151187.65 2.270741e+05 \n", "21 Clayton 11180875 108046.71 1.622809e+05 \n", "22 Clinton 25808600 255757.84 3.841091e+05 \n", "23 Crawford 9178228 91880.62 1.379796e+05 \n", "24 Dallas 19782703 208239.38 3.128030e+05 \n", "25 Davis 1623500 14206.10 2.134244e+04 \n", "26 Decatur 1798250 17836.60 2.679681e+04 \n", "27 Delaware 5929650 56982.70 8.557961e+04 \n", "28 Des Moines 28965403 311376.14 4.674807e+05 \n", "29 Dickinson 27326006 291810.42 4.382073e+05 \n", "30 Dubuque 57322060 601825.15 9.038367e+05 \n", "31 Emmet 5982525 60879.97 9.144739e+04 \n", "32 Fayette 9109128 81374.00 1.222869e+05 \n", "33 Floyd 8058678 79010.19 1.186743e+05 \n", "34 Franklin 6146325 60180.12 9.038277e+04 \n", "35 Greene 5200525 53392.20 8.019890e+04 \n", "36 Grundy 4369250 36670.31 5.510175e+04 \n", "37 Guthrie 3493600 31390.41 4.717285e+04 \n", "38 Hamilton 7882325 77319.08 1.161721e+05 \n", "39 Hancock 3509425 28221.14 4.241179e+04 \n", "40 Hardin 15096700 136991.10 2.058020e+05 \n", "41 Harrison 8333900 82437.45 1.238054e+05 \n", "42 Henry 8214425 84936.53 1.275347e+05 \n", "43 Howard 5501853 50667.53 7.612342e+04 \n", "44 Humboldt 4838675 46974.26 7.055336e+04 \n", "45 Ida 4907150 49504.88 7.433259e+04 \n", "46 Iowa 11862800 102657.53 1.542814e+05 \n", "47 Jackson 12728325 114843.67 1.724907e+05 \n", "48 Jasper 21363900 214003.36 3.214453e+05 \n", "49 Jefferson 6377025 66329.72 9.959432e+04 \n", "50 Johnson 94201143 1094410.17 1.643462e+06 \n", "51 Jones 15309100 144067.53 2.164019e+05 \n", "52 Keokuk 2720900 23814.27 3.576359e+04 \n", "53 Kossuth 14172628 142789.09 2.144583e+05 \n", "54 Lee 24974375 267566.06 4.017307e+05 \n", "55 Linn 158353452 1731106.01 2.599613e+06 \n", "56 Louisa 3165850 31041.02 4.661010e+04 \n", "57 Lucas 3968675 40531.53 6.087117e+04 \n", "58 Lyon 9776875 98319.84 1.476554e+05 \n", "59 Madison 7396475 74647.41 1.121233e+05 \n", "60 Mahaska 9398550 96610.21 1.451289e+05 \n", "61 Marion 19736978 199812.05 3.000452e+05 \n", "62 Marshall 22552328 231546.74 3.477674e+05 \n", "63 Mills 4317200 38409.79 5.770141e+04 \n", "64 Mitchell 8198000 80007.93 1.201944e+05 \n", "65 Monona 9671381 95956.98 1.440858e+05 \n", "66 Monroe 2891525 28812.61 4.327483e+04 \n", "67 Montgomery 6457078 67065.99 1.006981e+05 \n", "68 Muscatine 28497706 294376.85 4.421020e+05 \n", "69 O'Brien 13896728 132245.41 1.986505e+05 \n", "70 Osceola 3107475 29141.20 4.377315e+04 \n", "71 Page 10230328 103322.96 1.551729e+05 \n", "72 Palo Alto 9396700 89935.75 1.350478e+05 \n", "73 Plymouth 12560609 132624.74 1.991814e+05 \n", "74 Pocahontas 4312750 37584.77 5.645243e+04 \n", "75 Pottawattamie 64280187 680102.80 1.021141e+06 \n", "76 Poweshiek 16062900 169084.71 2.539540e+05 \n", "77 Ringgold 1689850 14072.68 2.115156e+04 \n", "78 Sac 7934625 75245.54 1.130183e+05 \n", "79 Scott 112482102 1247750.11 1.873693e+06 \n", "80 Shelby 5286953 53350.35 8.009987e+04 \n", "81 Sioux 10681100 102003.82 1.532028e+05 \n", "82 Story 65551096 727466.68 1.092625e+06 \n", "83 Tama 7242875 69664.27 1.046049e+05 \n", "84 Taylor 2073825 20885.05 3.135727e+04 \n", "85 Union 9569175 90467.23 1.358550e+05 \n", "86 Van Buren 1883075 18970.16 2.848695e+04 \n", "87 Wapello 24021278 251492.94 3.776542e+05 \n", "88 Warren 19568800 183359.55 2.753840e+05 \n", "89 Washington 10984531 119302.23 1.791272e+05 \n", "90 Wayne 1344500 12518.51 1.880652e+04 \n", "91 Webster 22537959 231995.40 3.484066e+05 \n", "92 Winnebago 8009300 72833.61 1.094025e+05 \n", "93 Winneshiek 11450225 107156.18 1.609449e+05 \n", "94 Woodbury 61274387 672801.78 1.010242e+06 \n", "95 Worth 3247475 28918.72 4.343867e+04 \n", "96 Wright 5575150 51158.24 7.684774e+04 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 4.108056e+05 32522.35 1.374661e+05 4420 \n", "1 8446 1.005968e+05 7547.62 3.361478e+04 1797 \n", "2 57833 7.728435e+05 61269.76 2.586304e+05 8595 \n", "3 58261 7.113762e+05 53184.66 2.377927e+05 8582 \n", "4 13978 1.595476e+05 13215.43 5.339370e+04 2045 \n", "5 47699 5.803559e+05 47612.84 1.940870e+05 7731 \n", "6 1093412 1.192076e+07 793399.13 3.982880e+06 118229 \n", "7 120271 1.534234e+06 115554.84 5.129844e+05 16515 \n", "8 113551 1.487074e+06 114021.43 4.976283e+05 17746 \n", "9 89429 1.140914e+06 84484.18 3.814875e+05 12653 \n", "10 114147 1.532283e+06 105358.28 5.121251e+05 21598 \n", "11 24615 2.786351e+05 25010.20 9.331105e+04 3308 \n", "12 27214 3.215042e+05 27977.64 1.075994e+05 3625 \n", "13 114246 1.556718e+06 115511.56 5.202122e+05 14568 \n", "14 74583 1.005639e+06 75880.71 3.360579e+05 9988 \n", "15 44550 4.927014e+05 38720.95 1.647957e+05 8160 \n", "16 377860 4.892826e+06 354343.00 1.635296e+06 51566 \n", "17 58423 6.855181e+05 52491.70 2.294092e+05 7408 \n", "18 27003 3.508606e+05 30429.56 1.173801e+05 4031 \n", "19 37286 5.035890e+05 36768.25 1.683819e+05 5600 \n", "20 104645 1.367561e+06 104603.09 4.569864e+05 15276 \n", "21 49974 6.927068e+05 52494.39 2.315675e+05 10480 \n", "22 245180 2.928261e+06 214882.76 9.784796e+05 28364 \n", "23 65790 8.643920e+05 62535.95 2.889041e+05 9360 \n", "24 166032 2.295133e+06 151828.96 7.673762e+05 21284 \n", "25 7746 9.618596e+04 8004.00 3.217266e+04 1555 \n", "26 14389 1.763645e+05 12243.11 5.898266e+04 1994 \n", "27 55212 7.547227e+05 54845.42 2.522007e+05 5749 \n", "28 294257 3.579586e+06 235900.79 1.195418e+06 32474 \n", "29 222744 3.145660e+06 224310.44 1.050838e+06 28110 \n", "30 501274 6.621999e+06 460080.11 2.212785e+06 60753 \n", "31 38693 5.019728e+05 39072.45 1.678386e+05 6071 \n", "32 70231 8.602044e+05 68175.75 2.878219e+05 8730 \n", "33 82689 1.032124e+06 74542.85 3.450133e+05 8223 \n", "34 36313 4.522624e+05 35596.58 1.512945e+05 6052 \n", "35 35511 4.774988e+05 33966.78 1.596316e+05 5248 \n", "36 26210 3.018362e+05 26267.96 1.009891e+05 4341 \n", "37 24678 2.940075e+05 23023.47 9.837735e+04 3398 \n", "38 61011 7.369458e+05 56950.35 2.466456e+05 8603 \n", "39 26820 3.061684e+05 28072.83 1.025907e+05 3101 \n", "40 119772 1.657238e+06 127383.22 5.542669e+05 13592 \n", "41 42380 5.046314e+05 38339.84 1.687135e+05 9131 \n", "42 72982 9.634271e+05 67508.46 3.218625e+05 9026 \n", "43 38946 5.268240e+05 42435.81 1.762160e+05 5197 \n", "44 40013 5.050238e+05 38779.60 1.687570e+05 4967 \n", "45 35771 4.983864e+05 34949.87 1.665311e+05 4777 \n", "46 74451 9.711115e+05 77025.04 3.251289e+05 11162 \n", "47 89098 1.130876e+06 88084.97 3.778944e+05 12593 \n", "48 131828 1.577656e+06 121386.17 5.274477e+05 23046 \n", "49 56831 7.276337e+05 51721.61 2.430598e+05 6446 \n", "50 927749 1.237021e+07 784403.93 4.133645e+06 105955 \n", "51 69101 8.898679e+05 68061.75 2.974959e+05 15545 \n", "52 13554 1.495608e+05 13026.40 4.999794e+04 2797 \n", "53 105881 1.479812e+06 108007.85 4.948291e+05 13720 \n", "54 228466 2.902471e+06 201627.37 9.692238e+05 27261 \n", "55 1500058 1.806775e+07 1221208.85 6.036068e+06 184092 \n", "56 20602 2.071461e+05 14809.68 6.917951e+04 3730 \n", "57 29011 3.761450e+05 27391.10 1.257357e+05 4064 \n", "58 55318 7.766658e+05 56613.63 2.593880e+05 9647 \n", "59 54411 6.856734e+05 51462.23 2.293326e+05 7795 \n", "60 70861 8.393837e+05 62911.85 2.806475e+05 9670 \n", "61 133175 1.735448e+06 123141.99 5.798673e+05 20958 \n", "62 192655 2.504138e+06 168340.47 8.368677e+05 23507 \n", "63 37547 4.345716e+05 34164.64 1.452974e+05 3968 \n", "64 32335 4.147482e+05 34245.82 1.387916e+05 8227 \n", "65 42020 5.197005e+05 38043.92 1.736055e+05 10023 \n", "66 21971 2.896006e+05 20937.96 9.680632e+04 3000 \n", "67 45167 5.570222e+05 41624.63 1.861282e+05 6713 \n", "68 208296 2.626114e+06 188612.00 8.775137e+05 31591 \n", "69 83029 1.057797e+06 84579.60 3.539820e+05 13954 \n", "70 19244 2.562448e+05 20104.98 8.574306e+04 2981 \n", "71 81116 1.057103e+06 75315.10 3.532156e+05 10651 \n", "72 41949 5.750134e+05 44627.15 1.921719e+05 8667 \n", "73 94325 1.286709e+06 93061.03 4.300381e+05 13176 \n", "74 24363 2.971895e+05 25680.47 9.933154e+04 4167 \n", "75 618358 7.675324e+06 521376.00 2.563352e+06 72472 \n", "76 97474 1.306022e+06 91675.42 4.365483e+05 17496 \n", "77 10061 1.386808e+05 11080.29 4.642018e+04 1476 \n", "78 39571 5.258452e+05 42670.28 1.760915e+05 7157 \n", "79 1237025 1.397399e+07 914829.35 4.667888e+06 130419 \n", "80 42963 5.279510e+05 38570.77 1.762560e+05 5627 \n", "81 77121 1.069835e+06 79019.86 3.575951e+05 10307 \n", "82 501832 6.730961e+06 456414.62 2.250349e+06 71636 \n", "83 47692 5.735157e+05 43573.67 1.916543e+05 7655 \n", "84 9360 1.105983e+05 8433.72 3.693200e+04 2304 \n", "85 66536 9.021116e+05 68179.74 3.015172e+05 9024 \n", "86 12806 1.742457e+05 12768.44 5.823345e+04 1920 \n", "87 186724 2.292624e+06 160501.09 7.658786e+05 27843 \n", "88 159019 1.939043e+06 146705.53 6.481145e+05 19620 \n", "89 84389 1.140731e+06 78241.80 3.810510e+05 11745 \n", "90 8283 1.054384e+05 8001.09 3.526299e+04 1268 \n", "91 228252 2.705835e+06 189009.94 9.042916e+05 24106 \n", "92 55931 6.827292e+05 56888.63 2.282976e+05 7667 \n", "93 86483 1.189769e+06 89488.04 3.977413e+05 10541 \n", "94 620680 7.695638e+06 512631.64 2.570501e+06 67732 \n", "95 20112 2.466353e+05 20602.25 8.250434e+04 3000 \n", "96 46517 5.706450e+05 44906.82 1.908072e+05 5461 \n", "\n", " average_store_profit sales_per_litters population \\\n", "0 31.100932 12.631486 7092 \n", "1 18.706055 13.328281 3693 \n", "2 30.090802 12.613783 13884 \n", "3 27.708309 13.375589 12462 \n", "4 26.109389 12.072829 5678 \n", "5 25.105032 12.189062 25699 \n", "6 33.687840 15.024919 132904 \n", "7 31.061724 13.277107 26532 \n", "8 28.041717 13.042060 24798 \n", "9 30.149961 13.504469 20992 \n", "10 23.711690 14.543544 20332 \n", "11 28.207693 11.140858 14791 \n", "12 29.682604 11.491468 9846 \n", "13 35.709236 13.476729 20437 \n", "14 33.646165 13.252889 13157 \n", "15 20.195545 12.724413 18454 \n", "16 31.712671 13.808162 43070 \n", "17 30.967770 13.059553 11508 \n", "18 29.119360 11.530256 12023 \n", "19 30.068200 13.696300 9309 \n", "20 29.915320 13.073807 16333 \n", "21 22.096132 13.195825 17590 \n", "22 34.497237 13.627250 47309 \n", "23 30.865818 13.822321 16940 \n", "24 36.054136 15.116572 84516 \n", "25 20.689814 12.017236 8860 \n", "26 29.580070 14.405203 8141 \n", "27 43.868622 13.760907 17327 \n", "28 36.811526 15.174117 39739 \n", "29 37.383072 14.023690 17243 \n", "30 36.422653 14.393144 97003 \n", "31 27.645955 12.847230 9658 \n", "32 32.969291 12.617454 20054 \n", "33 41.957103 13.846056 15873 \n", "34 24.999091 12.705219 10170 \n", "35 30.417611 14.057818 9011 \n", "36 23.264027 11.490660 12313 \n", "37 28.951545 12.769903 10625 \n", "38 28.669721 12.940146 15076 \n", "39 33.083089 10.906219 10835 \n", "40 40.778908 13.009862 17226 \n", "41 18.476999 13.162064 14149 \n", "42 35.659479 14.271205 19773 \n", "43 33.907264 12.414609 9332 \n", "44 33.975637 13.022925 9487 \n", "45 34.861015 14.260037 6985 \n", "46 29.128192 12.607737 16311 \n", "47 30.008287 12.838469 19472 \n", "48 22.886734 12.996996 36708 \n", "49 37.707074 14.068272 18090 \n", "50 39.013213 15.770199 146547 \n", "51 19.137725 13.074420 20439 \n", "52 17.875560 11.481359 10119 \n", "53 36.066263 13.700965 15114 \n", "54 35.553493 14.395224 34615 \n", "55 32.788321 14.794975 221661 \n", "56 18.546786 13.987210 11142 \n", "57 30.938898 13.732379 8647 \n", "58 26.887943 13.718707 11754 \n", "59 29.420481 13.323817 15848 \n", "60 29.022495 13.342219 22181 \n", "61 27.668067 14.093066 33189 \n", "62 35.600787 14.875434 40312 \n", "63 36.617293 12.719923 14972 \n", "64 16.870258 12.110916 10763 \n", "65 17.320713 13.660539 8898 \n", "66 32.268773 13.831367 7870 \n", "67 27.726534 13.382034 10225 \n", "68 27.777331 13.923365 42940 \n", "69 25.367782 12.506521 14020 \n", "70 28.763187 12.745341 6064 \n", "71 33.162667 14.035741 15391 \n", "72 22.172831 12.884834 9047 \n", "73 32.637984 13.826506 25200 \n", "74 23.837663 11.572587 6886 \n", "75 35.370233 14.721285 93582 \n", "76 24.951321 14.246155 18533 \n", "77 31.449986 12.515991 5068 \n", "78 24.604098 12.323453 9876 \n", "79 35.791469 15.274967 172474 \n", "80 31.323259 13.687852 11800 \n", "81 34.694396 13.538806 34898 \n", "82 31.413665 14.747469 97090 \n", "83 25.036489 13.161979 17319 \n", "84 16.029514 13.113828 6216 \n", "85 33.412815 13.231373 12420 \n", "86 30.329922 13.646592 7271 \n", "87 27.507043 14.284163 34982 \n", "88 33.033359 13.217243 49691 \n", "89 32.443678 14.579561 22281 \n", "90 27.809929 13.177998 6452 \n", "91 37.513134 14.315835 36769 \n", "92 29.776648 12.001155 10631 \n", "93 37.732790 13.295288 20561 \n", "94 37.951055 15.012023 102779 \n", "95 27.501447 11.971281 7572 \n", "96 34.939967 12.707313 12779 \n", "\n", " store_population_ratio consumption_per_capita area \n", "0 0.623237 4.585780 1146.149874 \n", "1 0.486596 2.043764 950.420482 \n", "2 0.619058 4.412976 1640.663925 \n", "3 0.688654 4.267747 1439.625361 \n", "4 0.360162 2.327480 1011.305224 \n", "5 0.300829 1.852712 1589.039934 \n", "6 0.889582 5.969716 1658.463133 \n", "7 0.622456 4.355301 1172.618576 \n", "8 0.715622 4.598009 1254.612988 \n", "9 0.602753 4.024589 1437.306002 \n", "10 1.062266 5.181895 1641.768588 \n", "11 0.223650 1.690907 1714.432829 \n", "12 0.368170 2.841523 1593.242060 \n", "13 0.712825 5.652080 1790.407255 \n", "14 0.759140 5.767326 1776.544213 \n", "15 0.442181 2.098242 1232.378198 \n", "16 1.197260 8.227142 1497.770139 \n", "17 0.643726 4.561323 1404.657609 \n", "18 0.335274 2.530946 1332.800885 \n", "19 0.601568 3.949753 954.543677 \n", "20 0.935284 6.404402 1305.389452 \n", "21 0.595793 2.984331 2086.377422 \n", "22 0.599548 4.542112 1701.089895 \n", "23 0.552538 3.691615 1794.525720 \n", "24 0.251834 1.796452 1727.389531 \n", "25 0.175508 0.903386 1202.497109 \n", "26 0.244933 1.503883 1443.427332 \n", "27 0.331794 3.165315 1275.461107 \n", "28 0.817182 5.936254 928.456413 \n", "29 1.630227 13.008783 1024.114722 \n", "30 0.626300 4.742947 1445.975306 \n", "31 0.628598 4.045605 1130.769046 \n", "32 0.435325 3.399609 1955.698767 \n", "33 0.518050 4.696204 1245.579774 \n", "34 0.595084 3.500155 1346.541996 \n", "35 0.582399 3.769480 1385.511780 \n", "36 0.352554 2.133352 1097.912646 \n", "37 0.319812 2.166915 1659.326695 \n", "38 0.570642 3.777550 1425.376163 \n", "39 0.286202 2.590940 1435.986414 \n", "40 0.789040 7.394823 1749.403531 \n", "41 0.645346 2.709721 2000.716609 \n", "42 0.456481 3.414174 1374.637080 \n", "43 0.556901 4.547344 1553.199575 \n", "44 0.523559 4.087657 1325.822767 \n", "45 0.683894 5.003560 1061.883800 \n", "46 0.684323 4.722276 1448.032410 \n", "47 0.646724 4.523673 2075.514783 \n", "48 0.627820 3.306804 1677.247032 \n", "49 0.356329 2.859127 1228.460352 \n", "50 0.723010 5.352576 1374.398467 \n", "51 0.760556 3.329994 1603.902991 \n", "52 0.276411 1.287321 1568.990538 \n", "53 0.907768 7.146212 2171.379858 \n", "54 0.787549 5.824855 1207.589119 \n", "55 0.830511 5.509354 2164.866374 \n", "56 0.334769 1.329176 980.907015 \n", "57 0.469990 3.167700 1154.264969 \n", "58 0.820742 4.816542 1520.471701 \n", "59 0.491860 3.247238 1409.747269 \n", "60 0.435959 2.836295 1216.496254 \n", "61 0.631474 3.710325 1528.879832 \n", "62 0.583127 4.175939 1520.107767 \n", "63 0.265028 2.281902 1220.953340 \n", "64 0.764378 3.181810 1086.794650 \n", "65 1.126433 4.275559 1656.794562 \n", "66 0.381194 2.660478 960.814655 \n", "67 0.656528 4.070868 1140.359482 \n", "68 0.735701 4.392455 1700.941831 \n", "69 0.995292 6.032782 1620.431744 \n", "70 0.491590 3.315465 967.452680 \n", "71 0.692028 4.893451 1398.118634 \n", "72 0.957997 4.932812 1596.128377 \n", "73 0.522857 3.692898 2259.953641 \n", "74 0.605141 3.729374 1297.431413 \n", "75 0.774422 5.571328 2303.225231 \n", "76 0.944046 4.946604 1580.076857 \n", "77 0.291239 2.186324 1198.624350 \n", "78 0.724686 4.320603 1412.599164 \n", "79 0.756166 5.304158 1211.649675 \n", "80 0.476864 3.268709 1299.197014 \n", "81 0.295346 2.264309 2056.178194 \n", "82 0.737831 4.700944 1943.690095 \n", "83 0.442000 2.515946 2088.226617 \n", "84 0.370656 1.356776 1571.159542 \n", "85 0.726570 5.489512 1292.945614 \n", "86 0.264063 1.756078 1549.129170 \n", "87 0.795924 4.588105 1104.862750 \n", "88 0.394840 2.952356 1617.493665 \n", "89 0.527131 3.511593 1666.506696 \n", "90 0.196528 1.240095 1438.930980 \n", "91 0.655607 5.140470 1793.295141 \n", "92 0.721193 5.351202 1295.390700 \n", "93 0.512670 4.352319 1587.428273 \n", "94 0.659006 4.987708 2591.144872 \n", "95 0.396197 2.720847 925.069678 \n", "96 0.427342 3.514111 1542.560045 " ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new" ] }, { "cell_type": "code", "execution_count": 271, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "county False\n", "bottle_volume_ml False\n", "state_bottle_cost False\n", "state_bottle_retail False\n", "bottles_sold False\n", "sale_dollars False\n", "volume_sold_liters False\n", "profit False\n", "num_stores False\n", "average_store_profit False\n", "sales_per_litters False\n", "population False\n", "store_population_ratio False\n", "consumption_per_capita False\n", "area False\n", "dtype: bool" ] }, "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### j) Calculate stores per area $r_{\\rm{sa}}^{(c)}$" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_new['stores_per_area'] = df_new['num_stores']/df_new['area']" ] }, { "cell_type": "code", "execution_count": 273, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaareastores_per_area
0Adair445587540182.6260373.6933807410805.6232522.35137466.12442031.10093212.63148670920.6232374.5857801146.1498743.856389
1Adams175740318103.8227171.398446100596.807547.6233614.78179718.70605513.32828136930.4865962.043764950.4204821.890742
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.44859530.09080212.613783138840.6190584.4129761640.6639255.238733
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.71858227.70830913.375589124620.6886544.2677471439.6253615.961273
4Audubon204317517737.1126656.0713978159547.6313215.4353393.70204526.10938912.07282956780.3601622.3274801011.3052242.022139
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" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 \n", "\n", " average_store_profit sales_per_litters population \\\n", "0 31.100932 12.631486 7092 \n", "1 18.706055 13.328281 3693 \n", "2 30.090802 12.613783 13884 \n", "3 27.708309 13.375589 12462 \n", "4 26.109389 12.072829 5678 \n", "\n", " store_population_ratio consumption_per_capita area \\\n", "0 0.623237 4.585780 1146.149874 \n", "1 0.486596 2.043764 950.420482 \n", "2 0.619058 4.412976 1640.663925 \n", "3 0.688654 4.267747 1439.625361 \n", "4 0.360162 2.327480 1011.305224 \n", "\n", " stores_per_area \n", "0 3.856389 \n", "1 1.890742 \n", "2 5.238733 \n", "3 5.961273 \n", "4 2.022139 " ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.head()" ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Taking care of outliers:\n", "#df4 = df4[df4['County'] != 'Fremont']\n", "#df4 = df2[df4['County'] != 'Davis']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### l) Download a database about incomes:" ] }, { "cell_type": "code", "execution_count": 275, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countyper_capita_incomemedian_household_incomemedian_family_incomenumber_of_households
0Adair2349745202572873292
1Adams2354940368527821715
2Allamakee2134946623559265845
3Appanoose2008434689412505627
4Audubon2420742717586412617
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" ], "text/plain": [ " county per_capita_income median_household_income \\\n", "0 Adair 23497 45202 \n", "1 Adams 23549 40368 \n", "2 Allamakee 21349 46623 \n", "3 Appanoose 20084 34689 \n", "4 Audubon 24207 42717 \n", "\n", " median_family_income number_of_households \n", "0 57287 3292 \n", "1 52782 1715 \n", "2 55926 5845 \n", "3 41250 5627 \n", "4 58641 2617 " ] }, "execution_count": 275, "metadata": {}, "output_type": "execute_result" } ], "source": [ "income = pd.read_excel('iowa_incomes.xls')\n", "col_names = [c.replace('/','_').replace(' ','_').replace(')','').replace('(','').lower() for c in income.columns.tolist()]\n", "income.columns = col_names\n", "income.head(5)" ] }, { "cell_type": "code", "execution_count": 276, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaareastores_per_areaper_capita_incomemedian_household_incomemedian_family_incomenumber_of_households
0Adair445587540182.6260373.6933807410805.6232522.35137466.12442031.10093212.63148670920.6232374.5857801146.1498743.8563892349745202572873292
1Adams175740318103.8227171.398446100596.807547.6233614.78179718.70605513.32828136930.4865962.043764950.4204821.8907422354940368527821715
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.44859530.09080212.613783138840.6190584.4129761640.6639255.2387332134946623559265845
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.71858227.70830913.375589124620.6886544.2677471439.6253615.9612732008434689412505627
4Audubon204317517737.1126656.0713978159547.6313215.4353393.70204526.10938912.07282956780.3601622.3274801011.3052242.0221392420742717586412617
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 \n", "\n", " average_store_profit sales_per_litters population \\\n", "0 31.100932 12.631486 7092 \n", "1 18.706055 13.328281 3693 \n", "2 30.090802 12.613783 13884 \n", "3 27.708309 13.375589 12462 \n", "4 26.109389 12.072829 5678 \n", "\n", " store_population_ratio consumption_per_capita area \\\n", "0 0.623237 4.585780 1146.149874 \n", "1 0.486596 2.043764 950.420482 \n", "2 0.619058 4.412976 1640.663925 \n", "3 0.688654 4.267747 1439.625361 \n", "4 0.360162 2.327480 1011.305224 \n", "\n", " stores_per_area per_capita_income median_household_income \\\n", "0 3.856389 23497 45202 \n", "1 1.890742 23549 40368 \n", "2 5.238733 21349 46623 \n", "3 5.961273 20084 34689 \n", "4 2.022139 24207 42717 \n", "\n", " median_family_income number_of_households \n", "0 57287 3292 \n", "1 52782 1715 \n", "2 55926 5845 \n", "3 41250 5627 \n", "4 58641 2617 " ] }, "execution_count": 276, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new = df_new.merge(income, how='left', on='county')\n", "df_new.head()" ] }, { "cell_type": "code", "execution_count": 277, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Series([], dtype: bool)" ] }, "execution_count": 277, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.isnull().any()[df_new.isnull().any() == True]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6) Refine the data\n", "\n", "Look for any statistical relationships, correlations, or other relevant properties of the dataset." ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_final = df_new.copy()" ] }, { "cell_type": "code", "execution_count": 279, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaareastores_per_areaper_capita_incomemedian_household_incomemedian_family_incomenumber_of_households
0Adair445587540182.6260373.6933807410805.6232522.35137466.12442031.10093212.63148670920.6232374.5857801146.1498743.8563892349745202572873292
1Adams175740318103.8227171.398446100596.807547.6233614.78179718.70605513.32828136930.4865962.043764950.4204821.8907422354940368527821715
2Allamakee907917585371.49128238.3857833772843.4661269.76258630.44859530.09080212.613783138840.6190584.4129761640.6639255.2387332134946623559265845
3Appanoose836017582458.44123839.9758261711376.1553184.66237792.71858227.70830913.375589124620.6886544.2677471439.6253615.9612732008434689412505627
4Audubon204317517737.1126656.0713978159547.6313215.4353393.70204526.10938912.07282956780.3601622.3274801011.3052242.0221392420742717586412617
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" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 60373.69 \n", "1 Adams 1757403 18103.82 27171.39 \n", "2 Allamakee 9079175 85371.49 128238.38 \n", "3 Appanoose 8360175 82458.44 123839.97 \n", "4 Audubon 2043175 17737.11 26656.07 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 410805.62 32522.35 137466.12 4420 \n", "1 8446 100596.80 7547.62 33614.78 1797 \n", "2 57833 772843.46 61269.76 258630.44 8595 \n", "3 58261 711376.15 53184.66 237792.71 8582 \n", "4 13978 159547.63 13215.43 53393.70 2045 \n", "\n", " average_store_profit sales_per_litters population \\\n", "0 31.100932 12.631486 7092 \n", "1 18.706055 13.328281 3693 \n", "2 30.090802 12.613783 13884 \n", "3 27.708309 13.375589 12462 \n", "4 26.109389 12.072829 5678 \n", "\n", " store_population_ratio consumption_per_capita area \\\n", "0 0.623237 4.585780 1146.149874 \n", "1 0.486596 2.043764 950.420482 \n", "2 0.619058 4.412976 1640.663925 \n", "3 0.688654 4.267747 1439.625361 \n", "4 0.360162 2.327480 1011.305224 \n", "\n", " stores_per_area per_capita_income median_household_income \\\n", "0 3.856389 23497 45202 \n", "1 1.890742 23549 40368 \n", "2 5.238733 21349 46623 \n", "3 5.961273 20084 34689 \n", "4 2.022139 24207 42717 \n", "\n", " median_family_income number_of_households \n", "0 57287 3292 \n", "1 52782 1715 \n", "2 55926 5845 \n", "3 41250 5627 \n", "4 58641 2617 " ] }, "execution_count": 279, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_final.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### a) Choosing predictors\n", "\n", "- We must look for strong correlations between predictors to avoid problems with multicollinearity. \n", "- Predictors that change very little may have little impact.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### a) Correlations between sales and predictors" ] }, { "cell_type": "code", "execution_count": 280, "metadata": {}, "outputs": [], "source": [ "cols_to_keep = ['sale_dollars', 'num_stores', 'population', 'store_population_ratio', 'area', u'per_capita_income', u'median_household_income', u'median_family_income']" ] }, { "cell_type": "code", "execution_count": 281, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_stores 0.995047\n", "population 0.974590\n", "per_capita_income 0.348343\n", "store_population_ratio 0.344187\n", "median_family_income 0.340245\n", "area 0.281804\n", "median_household_income 0.190911\n", "Name: sale_dollars, dtype: float64" ] }, "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_final[cols_to_keep].corr().loc['sale_dollars'].sort_values(ascending=False).iloc[1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### d) Correlations between predictors\n", "\n", "Observations:\n", "\n", "- We see from the correlation matrices below that `num_stores` and `stores_per_area` are highly correlated. Furthermore, both are highly correlated to the target variable `sale_dollars`.\n", "- Both things also happen with `store_population_ratio` and `consumption_per_capita`." ] }, { "cell_type": "code", "execution_count": 282, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_storesstore_population_ratioconsumption_per_capitastores_per_areaper_capita_income
num_stores1.0000000.3669900.3827470.9552230.345534
store_population_ratio0.3669901.0000000.8917110.3956770.214897
consumption_per_capita0.3827470.8917111.0000000.4183150.276315
stores_per_area0.9552230.3956770.4183151.0000000.358652
per_capita_income0.3455340.2148970.2763150.3586521.000000
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" ], "text/plain": [ " num_stores store_population_ratio \\\n", "num_stores 1.000000 0.366990 \n", "store_population_ratio 0.366990 1.000000 \n", "consumption_per_capita 0.382747 0.891711 \n", "stores_per_area 0.955223 0.395677 \n", "per_capita_income 0.345534 0.214897 \n", "\n", " consumption_per_capita stores_per_area \\\n", "num_stores 0.382747 0.955223 \n", "store_population_ratio 0.891711 0.395677 \n", "consumption_per_capita 1.000000 0.418315 \n", "stores_per_area 0.418315 1.000000 \n", "per_capita_income 0.276315 0.358652 \n", "\n", " per_capita_income \n", "num_stores 0.345534 \n", "store_population_ratio 0.214897 \n", "consumption_per_capita 0.276315 \n", "stores_per_area 0.358652 \n", "per_capita_income 1.000000 " ] }, "execution_count": 282, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols_to_keep = ['num_stores', 'store_population_ratio', 'consumption_per_capita', 'stores_per_area', u'per_capita_income']\n", "df_final[cols_to_keep].corr()" ] }, { "cell_type": "code", "execution_count": 283, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 283, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VvrcCd1TrPw9slJkX12Jp+QNliPVVlFE6G1OSvr9TRgbvRyklr4AkSbPJGI1a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\n3S1k7HIiZ9aZWeSborVoV2PPrIueBN4oaR6wFVm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sale_dollars
num_stores0.995047
store_population_ratio0.344187
consumption_per_capita0.388819
stores_per_area0.961106
per_capita_income0.348343
median_household_income0.190911
median_family_income0.340245
\n", "
" ], "text/plain": [ " sale_dollars\n", "num_stores 0.995047\n", "store_population_ratio 0.344187\n", "consumption_per_capita 0.388819\n", "stores_per_area 0.961106\n", "per_capita_income 0.348343\n", "median_household_income 0.190911\n", "median_family_income 0.340245" ] }, "execution_count": 284, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols_to_keep_2 = ['sale_dollars','num_stores', 'store_population_ratio', 'consumption_per_capita', 'stores_per_area', u'per_capita_income', u'median_household_income', u'median_family_income']\n", "df_final[cols_to_keep_2].corr()[['sale_dollars']].iloc[1:]" ] }, { "cell_type": "code", "execution_count": 286, "metadata": {}, "outputs": [ { "data": { "image/png": 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BngcBl+XdoyLiaEnHRMQg0sDR1YDXgJMkjch1jMzlVyfNDFoPmAicBWwJvA1c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\nEHi3tK3+ubj0HBHxTdJT1I5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m82+T3+/UwHWcApwG3E3qoh4DnAmsBGwMrJLbfUFEFAP1\n4bn+zfO57gLOzm1bGjisULa1B3FfkdtZv8ZF8vn+0ECby3UOyu1dD9gPODAivpnr3ZMU8B4KrA5M\nBv6cjxtGuucH5n3PAzdFxCc7e50RMZAUlB8DfAW4DLgxIr7Y4PWYmZlZSTMEhMdKekzSlaSu3Ssk\n3SrpbuCfpEzhocDxkm6Q9GzOfD0P7JgDoO+RMlJjJd1MCj5aC/37kLKRh0h6TtJDwMXAlwpl5st1\nPZ7ruglYq6OLyN2v7wDvS3pN0vvAaGBPSY9IepYUMC4OLFU49AxJ90saDTwE3CzpKkkPA1fl669r\n7Zr+BiwZEevlz1ul5ujJjtrcSp29SdnSZyRdDjwMrJn37QmcLunPkv5DChhvi4g+pCD8F5KukyRS\ncP8haUxlZ6/zEOB8SVdKGifpLNJ3sHeD12NmZmYlc22XccG4wvupwPjS5wWAAcDJEXFCYd8CpGxW\nf1IgM7aw74HWTiRpSkSMAH4SEWsCAawBvFIq+kzh/WRSkNgVlwBbR8Re+Vxfzdt7F8q0d/3TSNfZ\nJkmTIuJGUlB8N6lr9soutvdVSW8XPhevvT+pa7d+3teAwyNiKWAx4N7Cvg/i/7d39zF2VGUcx79r\nS6uFYIghCIKQQvoANVGMxaAiSyEGNSVBKcYXDC8CMQgiLwHEmFJEQ4PUUCwSrJgWqICBIKggFEuK\nEAIoplZ4UKptDFCSgkCwpSDXP86sDsPe7e0udXc7309yszsz586c2W5zfznnPLMRD1P+3QZs7j4n\nVd/vB8yuRiQHTKKEQkmS3hoWlYw5rzW2Xx+kzUTKdOSy2r4+SmDZq7Y9YND1fhGxA2XN4LPAL4Dr\nKAHk7Hq7zGz2abi/NUso07CLKSOTz1Cmkut6uf/NWQpcWq0JPIwyejccg/3cBu791S7v2dhl/0Te\nGHx7vc8JlDWcixt92NClvSRJW2y8Pj5muMZDIOxFAntk5n9Hmarn/N1MmZZ9FTgQuKc6fECX8/RT\n1qtNr6qPiYgjGH7g66oqdPk8cGBmPlLt+1R1+K2+3m2UQpqzgT9m5oifUTiIv1DWb/4SICLeBTxG\nmU5fRwm+K6tj21FGQ+8cxnUSmNr4t55X7V80gv5LkvQ/VhmPK32UoofLKMUYT1BG2E4Gjga+k5kv\nRcRiYEFEHA9MAeZ0Od96YAfKNO4jwOGUQpUXe+jDltpIWVN4dESsp0wZX1Ed6zYN3McwwmJmboiI\nWymFHRcMo6/d1PtyOfCDiFgJPE6pAl6dmWsi4jJgbkQ8RSnYOZcyzXvDEOdt3ufA9nxgRUQ8BPwK\nmAV8AzgUSZLeIlYZjy2bC1odoJOZN1KCzkWUUahDgVlVoQaUoob7gbuAayjhpdM4D5n5AKV6dSGl\nYOLLlEC4c0Ts2qVPnR76+aa21WNqvkQJrquAS6v+P0X3Eczmterbgx2ru5ESNLuFsF6v1zwOQGZe\nS7mHhZS1hJMp9walWObq6vUwsBvQn5ndHs7d9T4z80HgWMqjb1YBX6E8kua+LbgvSZJU09fpDGdw\nS+NNRJwEfKH5nMK2Wbdiub/wkrQN2OXg/q06hPfyP54c9ufF9rvvPe6GF8f7lPGYUa0JnDJEk5dG\n4y9/VM/nm0EZQf1mbf9kSvVvN5sy87mt3D1JksYmi0o0TPMoaxe7mUOZjv5/m0opKLklM6+v7T+K\n8tDqbpYDM7divyRJGrPaVmXslLFaxSljSdo2bO0p4389vWbYnxdTdt1z3KVJRwglSZKarDKWJElS\nmxgIJUmSWs4pY0mSpIa2FZUYCCVJkpr803WSJEnt1rYRwnbFX0mSJL2JI4SSJElNLZsybtfdSpIk\n6U0cIZQkSWroa9mDqQ2EkiRJTS0rKjEQSpIkNfS5hlCSJElt0tfpdEa7D5IkSRpFjhBKkiS1nIFQ\nkiSp5QyEkiRJLWcglCRJajkDoSRJUssZCCVJklrOQChJktRyBkJJkqSWMxBKkiS1nIFQkiSp5QyE\nkjRKImJORPx2K527PyJe77HtcRHxty19n6Rth4FQkiSp5QyEkiRJLTdxtDsgSduCiDgdOBPYBfgT\ncEZm/i4ijgQuBPYFNgK/Bk7KzJcHOcfBwHxgf+CvwJzMvLnH6+8IXAV8Gnga+HHj+O7AZcBhwOvA\n9cA5mblpM+f9KHAJcADQAe4FTszMZyLiOOAk4FngUOCrwJ+BK4H3A88DV2XmRb3cg6TR4wihJI1Q\nRBwAzKMEogBWADdFxFTgJuCKav8xwOHAyYOc493AbcBPgPdRQthPI+JjPXbjR8A04OPAacBZlABH\nREwC7gHeUR0/hhIc523mvt4J3A7cQQmpnwD2Ac6vNTsIWAl8GPgNsBh4pGp/InBuRBzR4z1IGiWO\nEErSyO1FCV9rM3NtRHyLEu7eBnwtMxdV7dZGxDJKWGo6Fbg7MxdW26sj4oPAGcB9Q128Cm6zgf7M\nfOCgK7MAAALlSURBVLTadyHww6rJEcBuwIzMfAFYFRGnArdFxAVDnPrtwNzMnF9tr4mIm4EZtTYd\n4OLMfKW67p7Ac9XPYk1EHAb8faj+Sxp9BkJJGrk7KKNkKyPiD8CtwNWZ+XREbKpC1/Taa/Eg59gP\nmBURL9X2bQdkD9efBkwAHq3te7hx7ieqMDjgAcpnwD7dTpqZ6yJiSUScSZkC3r/6Wg+ozw6Ewcp3\nge8Bp0TE7cCSzFzXwz1IGkVOGUvSCGXmBsqU6UxgOXA88PuIOARYRVk/eC9wAvCzxts71deJwBJK\n4Bp4TQdmbUFX+mrf19cGbhyk7YTqa9fPgYh4DyXo9lMC5hnA9xvXecO5M3MesDdlynsqcE9EnNhb\n9yWNFkcIJWmEIuIgYGZmXgwsj4jzKYUW1wDLM/PYWttplJA4YCBcPQ58JDNX19qeBUyijLgNJYFX\ngQMpawWhFIHUj0+LiJ0y8/lq30HAa8CTlPA5mKOA9Zl5ZK1PX+/WiYiYTFmXeEk1zTw/Iq4EPgss\n6vY+SaPPQChJI7cB+HZEPAMsAw4BtqcEuVMjYgbwAnAK8CFKCGtaCJweERdRppRnABdTRhuHlJkv\nRsRiYEFEHA9MAebUmtwFrAaWRMR5wM7AAuC66r3dTr0eeG9EzKSsA5wNfAZ4qEs/XqmqkhdUoXhH\nShHLLZu7B0mjyyljSRqhqpDjBOAc4DHgPOCLlND1AHA3pfJ4D2Au8IHqrZ3qRWaupUwPf5IyTTsX\nODMzl/bYjdOA+ynh7xrg8tq5/w0MjPI9CCylhLRTmv2obQPcAFwL/JwSAvsp1cv7RsR2jbYDPkcJ\nww8Bd1Kmyn3sjDTG9XU6zf/LkiRJahOnjCVpjIuInYDJQzT5Z2YOVjgiST0xEErS2LeU8lDobo5j\n8EfZSFJPnDKWJElqOYtKJEmSWs5AKEmS1HIGQkmSpJYzEEqSJLWcgVCSJKnlDISSJEktZyCUJElq\nOQOhJElSy/0HwiuHh4nI1zoAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(df_final[cols_to_keep_2].corr()[['sale_dollars']].iloc[1:]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us generate scatter plots for all the predictors. They provide similar information as the correlation matrices." ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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KRyRFVC5FRPqXrruSDfQ9zlyayENERJKmYkoBD99b2WnAXxFJHZVLEZH+peuuZAN9jzOT\ngn4iIpI0g7w5zC0vpLK8EFDzcpF0oHIpItK/dN2VbKDvcWZS0E9ERJJONwUi6UflUkSkf+m6K9lA\n3+PMovMlIiIiIiIiIiKSZRT0ExERERERERERyTIK+omISFrzB/9EJDX8gC8QSHU2RGQAUd0vA5W+\n+5JoGtNPRETSUpvPT1VtU6cZwgZ59bxKpD9ElsEVt05jdmmBnhiLSNKo7peBSt99SZY+f4OMMYON\nMfOMMSMSkSERERGAqtomVq7aSV1DM3UNzaxctZOq2qZUZ0tkwIgsg4/+Zju7ahpTnS0RyWKq+2Wg\n0ndfkqXXQT9jzGRjzAvGmAXGmKHAdmAzUGuMuT7hORQRkQHHD+EnnW5r1tWoy4NIP4hVBlevVRkU\nkeRQ3S8Dlb77kkzxtPR7FBgFnALuA0qARcBTwCOJy5qIiIiIiIiIiIjEI56g323Ax6y1h4B3As9Z\na18Hvg68JZGZExGRgSkHZyyTSMsWlWk8MZF+EKsMLl+sMigiyaG6XwYqffclmeKZyGMQcMYY4wFu\nBz4fXJ4DtCcqYyIiMrBVTCng4XsrOw1oLCL9I7IMhibyEBFJFtX9MlDpuy/JEk/QbwfwUaABKACe\nMcYMAT4XTBMREemzQd4c5pYXUlleCCRg5ikR6RV3GfR6PYwdM4Kmpou0t2uEIRFJDtX9MlDpuy/J\nEk/Q71PA00Ah8Ii19qgx5vvAcuAdicyciIiIbnpEUisH8Ho8qc6GiAwgqvtloNJ3XxKt198pa+1m\noBgYa639v8HFjwLTrLUbE5k5ERERERERERER6b24AsnWWh9wnTHmY8aYkTjj/J1LaM5ERCRj+YN/\nIpLeVFZFJBPp2iWSnlQ200+vu/caY/KB54GbgADwIvBVoNwYs9RaeyyxWRQRkUzR5vNTVdvUaRDi\nQV51VhBJJyqrIpKJdO0SSU8qm+krnjPwn8F/y4FLOIG/TwOtwDcSlC8REclAVbVNrFy1k7qGZuoa\nmlm5aidVtU2pzpaIRFBZFZFMpGuXSHpS2Uxf8UzkcTfwAWvtIWMMANbavcaYvwdWx5uR4AzA24B/\nsNa+Glw2FfgxcDNQB3zSWvuCa52lwLeAqcBG4CFr7SFX+idxApL5wBPAw9balmBaHvBdYAXQAnzD\nWvtN17pd7ltERDryQ/jpntuadTVUlhdqYGKRNKGyKiKZqLtrl4ikhu4r0ls8n/844HiU5WeBEfFk\nIhiA+w0wC6flIMYYD/AH4BhwI/BL4CljzORgekkw/afAPOBU8HVom/cAXwL+FrgNJ3j3iGu3Xwdu\nAJYAfw98KbhOt/sWERERERERERFJZ/EE/bYC90ZZ/g/AG73dmDFmFk4rvbKIpCXBZR+zjq8CG4CP\nBNMfAjZbax+11u4BHgSmGGNuCab/I/CotfZP1tqtwMeAjxhj8owxw4GPAv9ord1hrf0DTkDw4z3c\nt4iIRMjBGb8j0rJFZXrCJ5JGVFZFJBPp2iWSnlQ201s83Xs/B7xgjLkJGAx83hgzG6fV3Nvj2N4t\nwF+AfwYuupbfDGwLdccNWgcsdKW/Fkqw1rYYY94AFhpj1uO0/vuia91NwfxWAl6cGYdfd6WvDx6L\npwf7FhGRKCqmFPDwvZWdBvEVkfSisioimUjXLpH0pLKZvnod9LPWvm6MWYgzVt4BnEBYFU6ruU1x\nbO8Hof+HxggMKgYaIt5+EpgU/H8RTvdbtxPB9FFAnjvdWttujGl0rX/aWtsesW4eUNiDfYuISBSD\nvDnMLS8Mj62jp3si6UllVUQyka5dIulJZTN99TroZ4z5L2CltfZDSciP2zCcGYHdWoEhPUgf5nod\nLd0bIw3X+l3tu0e8aTw9dShvHk8fNuLxkJub3GMM5TMTPkvlse/SJX/JyEcyz4G2rW2ny7ZTrb/z\nkYpra6qu5zrW7NtnKvYXi85xdu1Xx9p/+021dMlHX2XKb6WeyqbjyaZjgdQcRzzdez8MPJrgfETT\ngtPqzm0IV7sAX6ZzEC4PaAqmESV9CHAJp2tvtDSC6ZeBMTHW7bGRI4f25u0pkZvr7dO6BQXDE5ib\n2DLhs1Qes0cyPydtW9vO5m2nWqqOLRX71bFm536zuXx2Rec4O/erY81+2XbcOp70lU3H0t/iCfo9\nC3zCGPMla21zojPkUg/MjlhWxNVut/U43XAj098AGnECd0XAPgBjTC5OELEBp6XfWGNMjrXW71q3\nBWcW4nqcmYQjtx3ZnbhL58+34PP5u39jCni9OYwcOZT2dl8v2y9e1d7uo6npYvdv7INQPjPhs1Qe\n+y6Uz1RLxueUzHOgbWvb6bLtVOvva1wqrq2pup7rWLNvn+79pprOcXbtV8faf/tNtXT/bdFTmfJb\nqaey6Xiy6VggNWU3nqBfMXAf8EljzEmcQFlIwFrbedqW+GwCPmeMybPWhlruLeLq5B0bg68BMMYM\nA64HvmitDRhjtgCLXe9fCLQBO3G6mLcFl613bXtzcN2NwGe72HeP+Hx+2tvT+4sZCPRt5f46vkz4\nLJXH7JHMz0nb1razeduplqpjS8V+dazZud9sLp9d0TnOzv3qWLNfth23jid9ZdOx9Ld4gn4vB/+i\n6UsIKdIrwBHgMWPMV4C7cWbkfSCY/jPg08aYzwJP48zUW2OtfTWY/j3gh8aYKpwWet8HfhQK4hlj\nHgd+YIx5EGeCjk/hdF3uyb5FRERERERERETSVjyz9/5LEvIRbT9+Y8xy4KfAVmA/8F5r7dFgep0x\nZgXwLZyA33rgPa71f2uMmQL8EKcD6++Az7h28U84gcCXcbr0ftFa+4ee7FtERERERERERCSdxdPS\nD2PMPODTwHXAFaAa+Ja1dnNfMmOtzYl4fRC4tYv3Pwdc20X614CvxUhrwWnZ9+EY6V3uW0RERERE\nREREJF31er5gY8zbcFrVTQOexxnn7lpgrTFmUVfrioiIiIiIiIiISPLF09Lv34GfWWv/LrTAGOMB\nvgN8GViSoLyJiIiIiIiIiIhIHHrd0g+4Afgv9wJrbQAn6Dc/EZkSERERERERERGR+MUT9DsNjIuy\nfBzQ2rfsiIiIiIiIiIiISF/FE/T7I7DSGDMrtMAYMxtYGUwTERERERERERGRFIpnTL8v4EzgUWWM\nORdcNgrYAfyfRGVMRERERERERERE4tProJ+19owx5ibgTuA6wAPsAv5srfUnOH8iIiIiIiIiIiLS\nS70O+hljXgJWWGufBZ51Lb/GGPNna+3cRGZQREREREREREREeqdHQT9jzDuBeTit+m4F/p8x5kLE\n22YAUxOaOxEREREREREREem1nrb0qwO+63r9PsDneh0ALqAx/URERERERERERFKuR0E/a+1ugq34\njDG1wDxr7enkZUtERERERERERETiFc9EHlPcr40xg4E5wF5rbWSXXxEREREREREREeln8UzkMRn4\nGfB54E1gKzATOGOMWWqt3ZHYLIqIiIiIiIiIiEhv5MSxzqPAKOAUcB9QAiwCngIeSVzWREQk2/iD\nfyKSflQ+RdKbyqjIwKNyL33V65Z+wG3A7dbaQ8aYrwLPWWtfN8acBt5IbPZERCSThW5SfD4/VbVN\nrFlXA8CyRWVUlhemLmMiA0Co/HX3hLctSvmsmFLAIG88z4ZFJNF6WkZ7WuZFJP0lsm7WtWFgiyfo\nNwinK68HuB2nmy8436H2RGVMREQyV+SNyh0LSlm7vZ66hmYAVq7aySfuq+SOcfmpzKZIVurtD4Wq\n2iZWrtoZfr1y1U4evreSuQrMi6SF7sqoAvci2ScRdfOVdj87Dzbq2jDAxXO2dwAfBf4XUAA8Y4wZ\nAnwumCYiIgNc6EalrqGZuoZmfrK6itnlheR6PeH3rF5bw+UrelYkkmiR5W/lqp1U1TZFfa8fwj8G\n3Nasq1F3IpE00JMy2psyLyLpL1F1866aRl0bJK6g36dwAn7fAR6x1h4FvgUsxwn8iYjIABBrjJFY\nNypb95xgVplaDokkU6zy98zrh/ChcYFE0lk8Y3cpcC+SnlI9Ft/lK+2sXqtrg8QR9LPWbgaKgbHW\n2v8bXPwtYJq1diOAMWaQMebOxGVTRETSRZvPz/aDjXz58S18+fEtbD/YSJuv97cPyxeXkTc4nlEm\nRKQ3TEkB82cW8ZUoZTYHp7tPpGWLyjT2j0g/6apeVRkVySyJuE9WuZdEius7Y631WWubXK+ttbbR\n9ZZC4Nm+Zk5ERNJPd92IYt2o3HlTKRcuXaG0OJ+H761kjlr9iSRcZPnL9XqYXV7Ib1/cF7PMVkwp\n4OF7Kyktzg+Xz4opBSnIvcjA1F292lUZVXBAJL0kqrt9X+vmvMG5LF+sa4PEN5FHT3m6f4uIiGSS\nrroRVZYXhm8iQjcqkQMH3zzzGsD5kZKbq1sOkWRwl7/pk0azbc+JTu9xl9lB3hzmlheGZ9RWyRTp\nPz2pV7sro7HqXBHpXz29T+6JRNTNc8oKdW2QpAb9RERkgFIQQSR13OUvAHzl8S09Wk/lVCS9xSqj\nqnNFsldfyvPgXF0bROddRER6obfdiHJQRSOSKjmAF3X9E0lnieyeqzpXJLXStbu9rg0Dm1r6iYhI\nj4SGIFY3IpHMoDIrkhn6q4yGrgn68S+SPOlQ5/oCAS5fae/XfUr6UtBPRES6dPFyG1v3nWL12o43\nL5Xl8wH9eBBJN20+P1W1TZ1+cKjMiqSnZHfPjVWPD/LqaiCSaKnsbu+u/z3AssVlzC5VWR/odPZF\nRKRLm6oa+PYTnWchU1cBkfQUa+ZAlVmR9JasMhqrHheR5ElFneuu/2sbmvn2Eyrrons/ERHpgi8Q\n4KlXDnRavmZdTbibkIikj65mDlSZFRl4VI+LDAyq/yWWZAX9LgE/S9K2RUREREREREREpAu9HtPP\nGJMHPATMBoa4kjxAwFr7EWvt+eB7REQkg3k9Ht576zQe/c32DstTPQuZiEQXmjlw5aqdHZarzIoM\nTKrHRQYG1f8SSzwTefwceA+wA2hxLfcAgQTkSURE0shNFcV84j5/pwHARSQ9pcPMgSKSPlSPiwwM\n7vrfPZGHDGzxBP3eAXzAWvtkojMTjTHmvcDvIxb/zlp7nzFmKvBj4GagDviktfYF17pLgW8BU4GN\nwEPW2kOu9E8CnwbygSeAh621LcG0POC7wAqc4OY3rLXfTM5Rioikr+F5g5g3Yxxzyvp/FjIR6b1U\nzhwoIulH9bjIwBCq/2+YMZYRI/JoudhKe7tG9Bvo4rnmnwX2JjojXZgFrAGKXH8PGWM8wB+AY8CN\nwC+Bp4wxkwGMMSXB9J8C84BTwdcE0+8BvgT8LXAbTuDwEdd+vw7cACwB/h74UnAdEZEBSTN/OnyB\nAJevtKc6GyLdyrYy6w/+ichVvSkX2XZNEJHovB4PeYOvtu9S/TmwxdPS79+Bbxpj/sFaezDRGYpi\nJlBlrT3pXmiMuQ0oA24Ots77qjHmduAjwL/ijCm42Vr7aPD9DwLHjTG3WGtfA/4ReNRa+6dg+seA\n540xnwa8wEeBu6y1O4AdxphHgI/TudWhiIgMAG0+P1W1TZ26TAzy6ieUSDJdafez82Bjp+7KKnsy\nkLnrJFC5EJHOVH8KxPew501gPrDfGOOP+PMlOH/gBP32RVl+M7At1B03aB2w0JX+Wigh+L43gIXG\nGC9O67/XXOtuAgYDlcG/QcDrrvT1wE19OhIREclYVbVNrFy1k7qGZmobmvn2Ezupqm1KdbZEst6u\nmsZw2atraGblKpU9EXedpHIhItGo/hSIr6XfT3GCcL8CLiY2Ox0Fu/BeC9xljPk8Tgu8VcAXgWKg\nIWKVk8Ck4P+LcLr+up0Ipo8C8tzp1tp2Y0yja/3T1tr2iHXzjDGF1trGvh6biIhkDj+En5K6rVlX\nQ2V5obpLiSTJ5Svt4ckH3FT2ZCBTnSQi3VH9KSHxBP2mApXW2mit7xKtBBgKXAbuxenO++3gsqFA\na8T7W4Ehwf8P6yJ9mOt1tHRvjDRc2++WN42bzYby5vH0YSMeD7m5yT3GUD4z4bNUHvsuXfKXjHwk\n8xxo28nfti8Qe3J6r9eDt08XU/e2MuczibbtVOvvfKTi2pqq63kqjzV2euLKXrT9DqTzmmoD6fuc\niH32pk6zxzJqAAAgAElEQVTK9GNN9/2m+lhTLV3y0VeZ8lupp1JVfybDQDs3yRBP0G8rMJ3oXW4T\nylpbZ4wZY609G1y0yxiTg9PK8OfA8IhVhnC19eFlOgfo8oCmYBpR0ocAl3C69kZLI5jeIyNHDu3p\nW1MmN9fbp3ULCiJPQXJkwmepPGaPZH5O2nbmbnvFrdN49DfbOy0bO2ZEwvYRkimfSbpJ1bGlYr8D\n6Vj7s+y5DaTzmmoD6fucqH32tlxk8rFmwn5VdrNDth1PqurPZMi2c9Of4gn6/QJ4zBjzU+AA0OZO\ntNb+IhEZc23vbMSivTjBu+M44/25FXG1y289ThfgyPQ3gEacwF8RweClMSYXKAyu7wXGGmNyrLV+\n17otUfIT0/nzLfh86TlPjtebw8iRQ2lv9/Wi7WJH7e0+mpqS2sM7nM9M+CyVx74L5TPVkvE5JfMc\naNv9s+3ZpQV84r5KVq8NTeRRzuzSgoReBzPtM4ncdqr19zUuFdfWVF3PU3ms15UVhssewPLgJDrJ\nugcZiOc11QbS9zlR+3TXSRC7XGTDsabzflN9rKmW7r8teipTfiv1VKrqz2TI1nPTn+IJ+v0w+O9n\nY6QnLOhnjHk78GtgsmvCjuuB08Ba4FPGmDxrbajl3iKuTs6xMfg6tK1hwXW/aK0NGGO2AItd71+I\nE8DciTPBSVtw2XrXtjf3Jv8+n5/29vT+YnbRO6BHK/fX8WXCZ6k8Zo9kfk7aduZuOwe4vqyQudPH\nMmJEHi0XW2lvT07eM+UzSTepOrZU7HcgHWuuxyl7c8oKgauz0CU7HwPpvKbaQPo+J2qfoTqpp+Ui\nk481E/arspsdsu14UlV/JkO2nZv+1Ougn7W2PzshrwdagJ8YY/4VKAceCf69ChzBaXX4FeBunBl5\nHwiu+zPg08aYzwJP40z+UWOtfTWY/j3gh8aYKpwJPb4P/CgUQDTGPA78wBjzIM7kHp8CPpzcwxUR\nkXTn9XjIG5xLy8XIoV9FJJmyYzQfkcRSuRCR7ug6MbCl9fm31l4A3g6MwxlL8CfAD6213wh2u12O\n04V3K/AB4L3W2qPBdeuAFcCDOC30CoD3uLb9W+A/cVouPg9sAD7j2v0/AduAl4GVOC0E/5C0gxUR\nEREREREREUmQXrf0M8YcirI4AHiAgLW2rM+5crHWVgN3xkg7CNzaxbrPAdd2kf414Gsx0lpwWvZ9\nuMeZFRERERERERERSQPxjOn3eJRtTAfuAr7U5xyJiIiIiIiIiIhIn8Qzpt+/RFtujPkYsBT4Vh/z\nJCIiIiIiIiIiIn2QyDH9/gy8M4HbExERERERERERkTgkMuh3D3A+gdsTERERERERERGROMQ7kUdo\n4o6QfGAMGtNPREREREREREQk5RIxkQfAFeB1a+0rfcuOiIgMNL5AAD+JbXouIonjxymnIpJ+/MF/\nVYeKpD+VV0mFhE3kISIiEqmrm5sr7X5e2nqYJ185AMCyRWVUTClgkFe3QiK9lYwfEm0+P1W1TaxZ\nVwPAilunMbu0QD9WRHrB3/1b4hJZPlWHiqSvRJZXBQ6lt+Jp6YcxZjHwFmAwHbv5Yq39twTkS0RE\nMlhPbm521TTy7Sd2hl+vXLWTh++tZG55Yb/nVyRTJfOHf1VtEytXXS2jj/5mO5+4r5Lry1RGRboT\nWTaXLy7jLZWDE7b9yPKpOlQkfSWivCrQL/Hq9TfEGPMF4FXgs8BHgAeDf6H/i4jIABe6ualraKau\noZmVq3ZSVdsUTvcDq9fWdFpvzbqapLWKEMlG3ZW1ePkh/MPCbfValVGRnogsm99+YiebqhoSsu1Y\n5VN1qEj6SVR5TVZ9L9kvnpZ+fwd83lr7n4nOjIiIpBdfIMDlK+29Wqerm5vK8kJycGaDmj5pNPUn\nL9Du01hh/U1dQ7JDtLKW6/Wwp/YMc8oL8aYmW9LPVJ7Tj4/o9eBTrxxgTtmYqOu4z6POqUh2mT5p\nNKNGDKG6prHH972R14Tu7q2zga59yRFP0G808N+JzoiIiKQPdxcCD7BscRmzS/vehaDN56fa3d3p\nlnKqDjZiDztPKpctKlNFn0QXL7exdd+pcCtLdQ3JLqakgNnlhWzdc4KvPL6FuxeVcV2c5zcH+Ou3\nX8vT6w91+JGyfLHKaLpQV6/0Ezone2rP0N3cN6Eft76I83jHglJs3RkOn2iOeU5zcM63u7sgqA4V\n6W/dBalC14T9R84SAJbdUs7u4H1vrPIa9do+tSAJuU8fqs+SK55P8XXgrYnOiIiIpA93F4LaYLek\nnnYhCP0YibR8cRm7I7om/P7lA8yfPZ6yiSN5+N5KKqZk901Nqm2qauDbT6hrSLZwl7Vcr4fZ5YU8\n+fIBDh93zu934jy/bT4/2w828us/7+Vccyv3LJnO4usn8L/fP5c5Gs8vbairV/oJnZNX3jjKvJnj\nO6W/99Zp+HwBth9s5MuPb+E/frmVLftOdziPP1ldxbiCYdSfvNDlOa2YUsDD91ZSWpxPaXG+6lCR\nfhSqJ7/8+Ba+/PgWth9spM3XubNu+Dp9vJnDx5t58uUDXD9jHJ+4L3Z5jXZt313bFPXeOlsC/arP\nkiueln6/Br5jjJkH7AFa3YnW2l8kImMiIpIaPelC0N2TzdCPEfcTu9lTCviPX2zr9N61O+r55wfm\nqytikvkCAZ4KzpTslm1dQwaaUFmrrj3D1j0nOqVHO7/dld/IAcfrjjfz8H2V3DavhKami7S3a9Sw\nVBsoXb0yhR9n2IpnXj8EQLsvwO6DjaxYMo2te07g8TgPvm6qKOb1nfXh8jVn+lie31TXaXtb95xg\nVlkhu/afjnlOB3lzmFteSGVwIgCdc5H+05OJOWINwdF4roWl8yZFve/tajzdz33wxk731tkQ6O+u\nPpO+iyfo99Pgv5+Mka6gn4hIhosceyTX62H6pNGduufGan4f7ceID+dHUaTuukCJDATxjGPjB7ze\nHCrLCzl3oZX9R852+f6edJ+JefO9toZb5k7qRe5Esl9kmVpYUcyMkgKOnb5IdU0jB+vPcusNk7j/\n9ukMyc3Bm+OJOolVXyjYJ9K/4n3oEjkER2QdHHp44M3xRF1fgX6JV6+DftZafb9ERLJUtLFHzjW3\nMip/CDvsSV7Zfozfvrgv/P5oTzbd3BWGB+cH0eHjzR3es7CimOi3N5JIXo+H9946jUd/s73D8mzp\nGpKp4hnHJtrYjMVjhzFv5vhO5ct9fnvSMkEyg8Z0Sw9v1jbxHXer2IZm7l86A1vXFB67a+aUMTFb\nslfXNLLslvJO5XbezPGsee0goHMqkqnc12n3EBwhoTq4YkpBp3E9B3nrw+NdQ8frQLZdD1SfJZ8+\nRxGRFPJztYVPOog29khJUT5rXjvIqBGD2VDV0GmdNetqenQMOUBp0QhWLJlGSVE+JUX5rFgyjdKi\nEf1aGaXbZ96fbqoo5hP3aQyovkj096e7cWyi7S/a2IwXL/uYNnEk9y+dQUlRPqVF+XzcdX67apng\n3n5XY3LmDY6ng4gki8Z0Sy0/8McorfY2VDWQP3wwT758gMVzJ3Y4J3mDc1m++Gr5CnUDfmh5Rfg8\nPrS8glNNl5h4zQidU5E0FKuejBakqphSwEPLK3jbDZNiDsFRd+pip3E9F8+dSNnEkT2+tmf6vW06\n12eZ/tlCfN17RUSkjxI1S5UfZ6y2RIgVFPjL1iPcsaCUkqJ8nt1Q26d9lBeP5PIVH+cvjAZgSnE+\nMyaN7tM2e0ozg8HwvEHMmzEuPBnDwDnyvov2/Yl3rBn3zWOsQNysKQVRu9J7vJ6oYzP+cV0NX3hg\nPjMmjWbpvEl46OH5DYDP7ycn5+q7o43JqQk80o+6eqVOm8/PqebWbt/3wuY6bp55TYdlc8oKO5Sv\nO28qoWJKQfh9OdDh/yKSXtp8foYO8XL/0hlsqGrAA9y9OPr4el5vDi9tPUxF+diY29u8+3inZS9s\nruOfH5jfbV0e6942Nzd9rx7RhjNJx/osm343KOgnIpICfe1mF1kRrbh1GrNLC5JWSZYU5fPTNVVR\nuyH1pvn9IG8O15cVMqesEK/Xw9gxI/ptYgB1bbwq825XUi/a9+cT91Vyx7j8Hm8j2g1kyfh86ho6\nlilvjofqGN/XG2bE/uEAsc9trO4zN84cz+7aJq53BfWi3Xyn8w+IgU5npv8dbDjPsdMXubmimLou\nuuZGMzi3+x+3Oqci6St0P5Dr9TAr9BDVQ8xgkM8f4E/rD8W8hw5NABSpJw/vYt3bzjfjenw8/aUn\nQbR0uvZl0++GdPpcRUQGhJ52s+tKZJfAR3+znV01jX3KV6zuCu9YOIXnN9V1mI0w1D03WvP7njSD\nz8EZY64vetPcPhGfuQxcXc2md/lKe4+3E60rrykdQ663Y1l43x0m5vcV4L23TuuU5g6+RysbfmDW\nlALed8eMDt3rdx9sZPXa6OUgB90oikTyAw2NLfzqOcuOfafCdWJpUT73BMtUu89pgd/VQzGVL5HM\n474faPcF2LX/NLv2n+6yHl22qKzTPXSp6x76XW+Z2mm9njxQ7+reNtQLyBcI9Po+N1ndWbsbziSd\nZNvvBrX0ExHJMF0FIOaUxZ41rCciu/UtX1xG5fRxPLexFgB7uImD9WeZVVbINaOHdpilrL+awWdT\nc3sZOGKV2xc21/GZD83j13/eCzjf59Jrhne5LWdsRn+HiTxmTSmIWjZmloxmz+Gz4WVvqShm9tRC\n6k9fYM1rB2n3BSgt7nlrRZGBrM3np+7URV7dfhToWCeOGz2UcQVDabnSTmlxfrhuEpGBzX1vvWPf\nST5417WUXjOcQcFhNaINqdHXa0dbu5+Xth7myeBwILHuld1dbZN5fx3vjMeSGAr6iYj0s3SepSqy\nW9/g3BwGDx3MnQtK+fHqKuDqk82H763skN+umsFHG78jXvE0t0/nz1zSX6zvT2hyi5aL3Y/t1ZWy\nonz++YH5AOFZPmN9X70eT3hsxtlTCqg7eZH/ecHyzOuHuG1eCT8JllOA7z+5iw+/e3aHZXUNzaxY\nMo3qmp61RupOIsu2SLqrqm3i1R31HZaF6sSSonzOnGvhnQunMGZUHtOKFEwXyTbx3E92NV6dH2fc\nv3jGs+vq3qTq0Bm+/UTse+XIAN/yxWX4A3SYjTyTu7P2Vbb9blDQT0QkBfryVK+rSj5RFZF7O5uq\nGnhtez0rlkwLzzx2502lHfIbeoLnHt+kuqaRHftPAXQ6Tq83p9cTkPhxJhzo7klhrMlNkvEkVQaO\nvk5uEavc3r2ojF01jR1a7VVMKejR97Xq0NUA+JzpY3l+U12H9FllhZ2WAWzbc4Jbb5jE/qNno243\nWiDPFwh06MocT4sA93YVLJR05+7C1eb3c/pcK6vX1nDs1IWoY3MtrCgmN8fDy9uOcvlKO1/48Hx9\nv0WyULz3k+7rQV9a1bnrz1Bennn9EGUTRrFgdhGl1wznP365rdN6q9fWkD98MKXjhnd6gP7K9nrO\nRZmcKFZLvN7W4ZkYRMum3w0K+omIpEBfZ6mKrIhCE3n0Rk8q7DZ/gKoDpzlYf5aD9We5rryQyUUj\nOXz8fKcZCUvG53PjtePDgcH3vG0aeUO8nVrlPbS8gpe2HsbnD/RoAhL3jdH0SaOJFSv0+f3sPBR7\ncpN0nBlMMkciJreIdgM5dIiXR371Rvg97ifrc8sLmRPcn9e1nctX2mnzBzoN/u3N8TBnujPRR3VX\nY3x64P7bp3caJLy77sEeYNniMmaXFvSqxW3kdu9YUIqtO8PhE83qni9pJ9SFd/Pu47T5/JRNGMVL\nW48wbbIz07x7bK5QffeOhVOoP9nMcxud8W9LivLp3WMtEckUibifjKd3TKxAYSgQtWZdDfuPnuWv\n335tzP0+vf4Qb7t+YtTJQ3pyzepJsDJW/jMtiJZNvxsU9BMRSaF4KxB3RdTbWXBDFXaHp4Ljhneo\nsN2VeiAAy24p51xzK6Pyh4R/5Ow82Nihor9pdhHf+p/t4S6Do/OHRH1q+PymOkbnD2HX/tM8+pvt\nfOK+yg4zh0Zy3xjVn4zewmLZojJ2R9xAxdp2Jlfaknp9+f5E3kACfPnxLR3ek+v1sKf2DLOmFrCn\ntqlDC8CZJaPZW9PImrU1BICbZxczyOvFHm6irc3H2+ZO4qVtRwBYfks5La3tLKwo5odPvdlhH8sW\nlXUIIoZE/giJ1j3420/sDLcqiBSrRUDkdn+yuooVS6ax4c2GAd19SNJPm8/Pln2neX5THd4cD++5\npZy9dU3Un7pAvauFn3ssv4UVxfxly2GGDR0Urv8WVhTTt6mqRCTd9WVYjKi9VtbWUDgqj8eeqQY6\nB9TqTl3k1R311J+8QLsvwMpVO/nEfZUEAnSoYx/55VY+eNdMfh7cTkhoZvFzF1qZPmk0NfXnw2nV\nNY3cs2R6+P461HPn3W+d2uOhfLoLCGZqEC1T8tmVbDgGEZGs0ptZs+KZBXd3XRMHjp7jjgWlHKw/\nx6+e28uWfadp813da1VtE99/chejRgxhdP4Q/rS+hsnj83ny5QMcPt7M4ePOrFtv1jaxo6aR//jl\nVrbvO8VHl1Uwa8qY8HZ68tQw1oxn0PnGKNTC4v6lV2cgvX/pDGZNKQgHR3q67Vj7y8RZuSQ99PX7\nY0oKWHZLOfuOnOWrv9jG0VMXyRuUG57l7o0DjXzv97uoDc589/uX97Nk3iTuubWMW2+YxK+e2xMu\nn79/+QD5wwaz7/AZPvbe6ygtzqe0OPqM26G8h8partdpMfjOt06N2j14zboayiaM6vFnEu3HzdY9\nJ8JDAWTqbHiSfXbXNbG5+jizpo5h3rXj2XXwNG3tPt53xwymTx7dof6ZMG4EFWWFvLLtKFfa/Ywb\nPTQ8K3Zp0Qj9yBLJYv6Iv8jlsd4fEmqZP2f6WHK9zn18AGhovET9yQvUn7zAqzvqqTt1kTafn+0H\nG/nVc3s529zKslvKMSVOPV596EynOrbdF+BA/VkeeNfM8L3yioiZxRfMLuq0TmnRCB6+t5LF10/g\nniXTOdfcyq//vJftBxtp8/m7ndG2p7Pzauby/qeWfiIiaSJRs2b5gv966dzEvtXn52RTC7sPNbL7\nUCPzZo5n98FGfrK6iqH3Voafvu3Yf4plt5SHW/U98K7ZPLuhttO+/ri2hplTx3D9jGvYuucE+4+c\n5bYbJ5OTA14PvOdt5XzvdzvDNxlw9UkjOMGF6ZNG96oblD3cxJV2H9fPGEdtw3nGjxna566BmhFY\n+uJKu5+dBxtjfn9C5dDn94cn3vD5AyxfXMbdi8r4zqqd5Ho9zC4v5MmXD4S3W3fcmXTjYP1Z2n0B\nnt9Ux6yyQnbtP40pKeCtlRNoOt/Klr2n8HCKe2+bwRv2JPawc5Pd0HiRqRNG8vymOqZPGh21VW9I\nAJg+aTTDhwzi2qlj2LrnBBdHt8VsrbRgdhEvbjnSYVk6j80j0p3L7T5OnGmh+eIVJs8uovHcZfYf\nOQvAyBFDuH1eCS9tPcLm6uNcP2McxYXDefyZ3Vy+4udvl1dQd/w8MyaPZtK44ZQXj0zx0YhIX3TX\nxXbH/lNMGDuCjVUN4HHG5y0ZP4KXth1l3+Em3vWWqeEHbNGGzbhtXkn4odqyW8rZfbCR2eXOOLxL\nF5QwLG8QW/ec4FfP7eXOm0pZu70+3Arv8PFmPvB2w7ChueTkRK+lDx9v5vLlNt6xcAobqhpY89rB\nDpN3lY4b3qmrbXnxyPD9QbTWfJVdtMgPoNl505mCfiIiaSKeWWndLra2s23faf6y5TBlE0dx/fRx\nbN17gtqG8+EgRHVtE//zwr7wOoddQYXVa2uwh5uYN3M8N1cU8ehvrnbV3VDVEPXHf06OhzEj8/jv\nP9vwss27j3PXwlJ2HjjN0+tq+OBdM6k5do5BuTnMKR/Ln4NjHpmSAmaXF7Jt7wm+8viWqIG2WAP/\nLphVxJY9x3n3W6cyO3hTtXxxWYeZykLLenKj0dfPXgYW99P6y1fa2VnT2Klb7Gc+NI/Sa4azu7aJ\n7fuu/jgIQDjY/r3f7+ILH1nAlz+2kANHzvLyG0c77SvUIm7X/tPhZbleD9dNG8vZC60RQULLPcHy\nPGNyAeUTR/HY09Xkej3kDx/M0+sPcevciR26vLdFBCKX3DCZDW82cPh4c3jCgroo3emj/WCI1oIw\nVhl2B/8VLJRUa/P5qT1xgT11ZygcOYTmi1c6lK3Dx5u5f+kMbrlhIpcut5HrzaH6UCPjC4eHv/s3\nBce51XdZJDNdvtLO5TYfb9acifkQ783aJn7w5C6W3VLOb1+8ej/9nVU7uX/pDApGDOGO+aU8v+kw\nHg+dut5+/8ldPHj3bDZXH2d0/hD21Z3hUP1Z7ls6nR37TuONcl8dGhIj9AAQYN3OY0yfPJrS4pHM\nKCnoMOsuXK1jmy+1M7u80Blux+PcF8+eUoA3J3pX265a81WWF8acjEPDGaQ3Bf1ERNJAV+N7VEwt\nYFBOxxZD0X5UvLHvNGu31zPXdGx1d/LMJZ7fdJghg71Ru8CGggpnm1uZOmEUv3puLx6cMcGqDjZi\nDzexr+4M77/zWh572hkfJDTWx7veMoVfu25MZk4pYMHsIn4X/LE0b+Z4NrzZwFvmFPPKG0fZf+Qs\nt8+bzLgxQykaM7zDDVOsQFu0gX9nTSng1rkTqK5tCs9QdveiMj7zwRv47V/2Az2f3KS7Gxz9gJMQ\nd4vQkvH5XFs6hkPHzrEv2BoICAezf/XcXjwemHfteKZPLuBnf9wdfs/h48185O7Z3HDtNfzsj9UE\ngLsXTe32pvmOBaVsrW7gnW+dytAhXl7dXt/pPVv2nOC68kIWzC7m2Q214fyEWu2eONNCa6mfHIKt\nD4LjA4YCkT9/prrDj4tQd8aNuxs6TOTRm7F5IstwaCKPideMSPuBvCX7tfn8vHGgMdya/YF3zeQX\nz+zp9L4NVQ0suWESk8bnc6nlCg/c5QyWrzpCJLO1+fzsqGnkmfWHmDezKOa9qR+nh8ussqt1qtuG\nqgZG5w9h2qTRzJk2lr11TeGW9yFLF5Rw/sIVzgbHvH7/ndey/+hZHv/THhZWFHPf0hn86rm9nbYd\n7QHgqbMt/GXLEf7PB2/go8sqeGGz03LwjgWlTBg7jK17TtBypZ3CUXl89oM34vHQ4b45nl4tsSbj\nyMTZeQcSBf1ERNJYADh5rpWxI4dQ3UX30/MXW3l52xFun1/ChqoGjp1yBvn9+TPV/PXbDfnDB3cI\nTERz54JSNlcfD69bF+w+UHf8HDOnjGH/0bOsWDKNk02XmDh2BBuqGthSffWmJ9frYcGsog4DB4da\nEr7yxlHyhw9m1/7TPPZ0da8mAogVXNh+sGPrqu8Eb8y+8MD8Xk9uItIToRahuV4PN147nh+vrgrP\nlgtE76Lb4LQQyvV6wk/oc70eLra08dsX94UD6K+/2cCSeZP51bN7wmPdVdc0srCimM3Vx1mxZBq1\nx84xf3YxL2yqY9rk0VGDhB7guvKxHDh6Fm9O5/wcPt7M+DFDO7U+cLf6df+4sIebaPP5+OKDCxiZ\nn0fLxdYOZaonN/PRyvDNahUlaaKqtomfrqliVlkhXg+cu9AadcgJD06dvLX6BPe8TT9kRbJFqG6f\nM30sG6oaOqWH7k0D9Gys6q17TjB98mgmjh0eDvrlDc7hnQunMrZgGD/6w9XJtR57upq/um06Fy9d\nYU/dGQpGDulRqzl3a/lVL+5nzKg8RucPAeDxZ3bzwbtmck3BUK60+3nsj7sZes+cTvV+tIft3QXv\ncrp44Jdps/MOJKqvRETSQKiSjXTbjZN5/JlqXtl+rMvBcVuvtLPwugk8u6G20yC/63YeY/+Rs7x5\n4DS33Ti50z4WVhQzf+Z49h4+Q+O5yx3W3fhmA5/7mwW8e3E59Scv8Kf1Tgun3764j8PHm3nljaPM\nmzkegOvKC3klRvfEsokdB/3vzUQA7s+oJ90PoHeTm8T67PV0Utzc3zn3U/59dWdYVDmh03K3DVUN\n4UBe6H0bqhrCE3ecbW6l8dxlRo0YzN+8axZnm1s529zKh94xkynF+Vw3bSzPbzzEqPwh/GR1FXXH\nm1m/s54lUcrz7fNLGD1yMIfqz3HnTaVR8xNt4G/oOLmG27veMpVBOR7yBvftWbG7DGsgb0kHvkAg\nPIbt2eZWGs+3cvJMC0vnl3R6723zJjNy+CAAvDn69opkAz/wzOuHmDN9LFO6GYvTA7zlumKqaxrD\n975u82eOp7qmMfx60vh8li0q4/Z5k3n/nddy7tIVnttY22EdU1KAN8dD4/nWcN2//JbovweaL16h\npCifj9w9m311Z2j3Bcj1epg2eTSTx+dTXdPIrv2nafcFeGnbESaMGxF+HavejzaRVih4554AbFZE\n8C5aHR56wPeFB+bzhQfmM7e8UGNjpwm19BMR6WfRuuj6gVlTCvibd84MB85umzeZ440XqSgfy+lz\nLcyZPpbqmsZwJb+n9gxzygvxBALsPNjIL5+92h3J3WongNMF4NCx8wzObeDBd8/iL1udAfiXzp/M\n+IJhrH6thuraMx3WzcmBOdPG8djTTrfEJTdO5viZi6zbeSy8H3f3vyGDcnglSndDgFlTx/D93+/q\nsCydJgLQ00lxi1ZGQxNd1J+8EF5mSgq4btpYfH4/f/POmRw7dSHcZSck1+thxuTRjBg6KFx+AXIj\nWuHlej0cOXGhQ6u8x56u5v6lM3jzwGmW3TKNwyeujq03o3QMB+qd1rehwN68mePZd6SJi5euMNdc\nEx70u7fuvKmUl7YeprQ4X2VBslq7L8CEsSM6dOc7fLyZ999h+KvbprO5+jjgPBwrGjOM9VUNLJoz\nQQFrkSzR5vMzf2YRG6oaePOi83Dc3WMFOt6bmtIC7lkynfrTF7h/6Yxwy8CFFcWcu9hKuy/AvJnj\nGTsqj9Jxw2HccC61+vjpmo49A+Bq74DI68+D757F+++YwetVDQQCTt2+aXcD+cMHA/DytiNcN20s\nbcBuvDwAACAASURBVO0BZ2zsPScIcHVCkMguxbleDxPGDgeg/uSFDpPrRTPIm0NleSEVUwvCY/6u\nWVcTvh/wBgN5sa6Duj6mHwX9umCMyQO+C6wAWoBvWGu/mdpciUimuni5ja37TrF6bQ3eHA/vu8Mw\naewwjp6+xObdx6k5do7FlRN565wJjC8YyqUr7VxoaWPc6GG8YU8CToXefPEKZRNHcfbCZf6y7Sin\nz7ZE7bobarUzbdLocBeA0My377tjBu2+AN/73Q4uX/GzYsk09h1pCt8IbLcnWXhdcYeBhH/+TDUf\nXVaBret4MxHa5tIFJSysKO4UaLh93mQOHj3X4SZj2aIyyory+d/vn8uTrxwIL+tpcCHRY4f0Znwy\nyV5tPj+765qoPuQEwGdNHcO1JaPZe/gsT68/RNmEUXx0WQWv7zzGgllFjBw+mLMXWjl+6hKTx43A\nk+PhbXMnhQPw4clqgkG5991pqG04T/GY4ZQU5bPqpf3hfXc3RtB//9l26iZ8+HgzG9+82opwzWsH\nmTBuBKPzh1B1sJEbzDjecl3nMjk6P487F5Ty49VVHZYvrChm/JihzC4tUPdbyXpHTzbja2tn4+7O\n3fnWv3mMwpFDwt3lRgwbxB/X1XDzdROcH/IiknJdjXPdU9W1TeGgW67Xw4kzl/inD9zAU68cwOcP\ncOdNpcwsGR1+/4QxQ/nls3sYMWwwl1vPcsOMcfiBbXtPUFo8kvuXzqCkaATTikfi9ebgh/BYe9U1\njSy7pTxcJ8eq9/+y9QiFI4dQOX0cl1raOsy8C1BSlE9hfh7XzxjXKWAYeuB/242T+Z8X9obvQ17d\nUQ+BzoHByPtm99jFBODGmeMZ5PVSU9/E85sO03LFFz6eeMYElNRQ0K9rXwduAJYAU4DHjTF11trf\npzRXIpIR3DcjvkCA3QdP873f76J84mhmlxeybucxyieOCre6mzdzPJurj3PXzVPYd+Qsw/NyGV8w\nrEMLvuF5uSy8bgLPb6pjwawi/ufFfZ2eHLotrpzAi5uPdLhZ8PkDPLexjtlTCsPLIwcILps4ivWu\nFn0hL2yuY/ktZayMmCV3rrmGX/6pmg+8fWaHlkeLKicwbnQe+cMGUVqcD1y9SRicm8Nt80qYUzYG\nny/Q65u2ZLTO023LwOIuo20+P3uOnKW1zU8AqKk/x8gRQ2i54iPgD3DTrGLWv3mMfcHJaPIGeTnT\n3Mqf1tfwwLtms6GqgeqaRsonjuaeJdPYbk92uiGve3YvH3i7Yd/hJnJzPb2e7W5jsJvwrv2nwz8e\nnjze3GFg79A4P+2+AFfafdz3/9m79/goyzv//6/JJCEBEg7hFDkEEuDiEIkgRwsonmprCy1dte73\n21bbbnddq9t2f9/+ut32u91fv93udvvtQbur2227ak+rtrbiardarQooCgrBAF5gwklOSggQICFk\nZn5/TCZMJjPJTDIz9z33vJ+Phw/JPfdch7nv676u+3Mfrmtm8LH3zeaFrW93f/7GW8cZN7o07gQ5\nQzR4lzxw8mwH+4+d4d2TbXFf0uUDxowcyu6DLbx3SRV7DrZw5eWTmacTXBHH9QhMET/4lExAMPq1\nHdGTXu3Y28yqyydx7MQ5HvivHZSundf93ruigvDYdf3WQ0yfNLJ7vHvd4ilMn1jO6K4LBZE75Kov\nGdHd10eejomMk8eNLA3PqhtHIARPbdzLR1bN6HVn3jULJzOyrJhn/3iw1/e27DrG5z46n5JiPxPH\nDu89DumaifxCIMCNV0zrNW6OvN8wev21q6az/+gp5tZU8KOoi4WJJuAT91HQLwFjzDDgU8AN1tpt\nwDZjzLeAzwIK+olIQrEzfJqq0Tzz6n5CofCMuCOGD+Ghp3Zy87Uzu2fDhfAVuttunMNvX2wkGAxx\n5YJJNB0+1f15od/HfDOOnzyxo8fLhmOvHEZctWAS77a09brNPxIUONl6vtdMYBFzpo1mT4KJP946\neIrbPzCH51472P3YwY7GZto7gmysP8wCM5aZk8NXRceMKKGmspwifwHzquPfRef3+Qgl9WrknnR3\nngxU7AnDmhXVjB5RwqkzHT2C8Du67pbzFfj45TMX73r9j//ayWc+VMuRE2dZu2pG96yfkSvoTYdP\n8Z66S+LOrruh/jC3XjeTR5/dw3vqLmH/0XC6idpx9Mu6Q4QD+ZFHiEcOH8Kt1xk2vnEYnw8unxUu\nc+QE4eqFU2g7f4GN2w9137EUCQi2X+jkEzfMUvuRvHMhEKS+8QTPvLofUzWKpbWV7I9pd0trK5lV\nNYolteMZNqSQxbPGqY2IuERsYCo6+JRMQDBWvEm4HnxyF2tXTQd6TjIXBPYcbGHZpZU9HgP+8bod\nfPamOg43n2Pdi02ECPffb+49weWzx3cfY+yBFhoPneSumy+jrb2TMSNLex1/oi/eHTp+psfMvFdf\nPplTZzsYO6o0bl18Ppg1eSTFhQX8f39+BV++b2OvdTbtOMJXPrEIf8zyRO/L3rLrGNcvnRr3rsR4\nE/CJ+yjol1gdUAS8FLVsI/C3zhRHRHJF7Ayf0VfFDnRdYbt0+pge78aLeO61g4wsG8L2Pcd56Kld\nPR7nm1NdEfc7sVcOfb7wIKd0iJ/HX9zLn77XdH8vEsiIvWoY/Q6v6xZXsf2t43Ef1V1WW0lRoY+q\nCWV89ROLaDrayrd+uqU7PXughfcuncI1l08CegYSMjUg0EBDUhV7wrC+/jDzZ47tFYRfu2o6x0+3\n93qkHeC/N+3ng8un8S+/2t7rO3sPn+Lw8bMJ83928wGuqLuE4yfberwTqEcQj/BjNdHt9dpFk+no\nDHYH8B56KlzeL35sIVXjhrFl93HaOjqZMqGM5XWX8NL2Q1y3eAo3XjEtbY/Ci+S6N7pm6wUYWTaE\ntvOdvd6Nefj4GZbWTsAePMElU3VCK+IWfU3kVldT0WdAMFbkVTEvbDsUN6AVeQrm1Jmed+MV+Qt4\n7rXed9k9sb6JEWVDuoN4kTHBrr3NPfr6qy+fzPY9x3n3VBvlw4p7HX+i+/3pE0fy8DO7uHpRFQD/\n+cyb3Hr9LE6cbo87To/u24uLYsN6F6X6pIHkNgX9EqsEjltrO6OWHQNKjDEV1trmBN8TkTyWaIbP\naC83HOGymWNpPtXeb3ovRz3OFy32rqDIlcMvfmwh1RPKuh9XvH7JFH738j4+uHwa6+sP93gvyLLa\nSjbvOspdN9VRO7XnO7yWzh7HhUCQcaNKu+uz8rJJXOjsZHR5KZWjSikAqsYO446183pcUZ1bNUon\nSOJa8U4YJo8v6zWpDIQH/FfNnxg36AfEXb5l1zEqyocwZXwZI4YP6TUgXzR7PI+/2MjVCyczumwI\n77a0xQ3iTR43jF37WrreE1TW/fitv8DH0JJCfvtCIxPHDWf18mqqxg6jqKCAhTPGMHZkKa/uOMqm\nhiPceMU05laFH93RRDUi4fb/RFT7j/Sl615s7PFuzE9+cC5Ffh+LZ4xzqKQikqoQfQcE441Na6eO\nomxYMT/77zcTphsdSCsgPBFdvHdpx3tmZcuuY4wsG8LmXUf5k6umU1payP/9+Wu0dwQp9Pt6HH8m\njhlOgc9He9fFu4Wzx3PwWCvvXTqNLV2TdaxdNYNNDUfYczB8t+DH33/x9R2xfXtJcSFrVlRzzyPJ\nXfRL9L7sZbWVvG6Pcf2Sqh43MvSVlriLgn6JDQViH7KP/D0kmQT8Ln7nR6RsvsGE+X0+CgszW8dI\nOXPht1QZB88t5RtMOQKh/h9T9QEHj55m4ezxfT7KF7Gi7hJOnTnPmXMdXLe4ih+va+hxd99ru46B\nD9asqKF6QhnFXe2ysLCARWYsZcOKed2+w4xJI7sfC3zvkioWzRrLDUsm409wICguLGDpnPGsXDCJ\nc+c6CAbDb0iJXj+Sx4KZY3p91p9M7pdK25m0nZZMOeK10YNHTye86j2ybAiL4rTV5XWX8PIbvScA\nAHjPvIn4C8LtI3J13wesmD+R/UdO85cfmcfMSeHHb9ovBBgzqpR165uYOG44a1ZUd7fjhTPHMn9G\nz7bl9xdw7aIqFswYSzAY7NUeZ00awYyJ5T2+Awy4nUY40Y841XflY12dlq1yxLb/SF/6kVUzuif0\n+NTqWi6tHk15aVFa83Z6G+fT/pxPdXWaE+WIF8has6KaQn/ivs3v98Xt+yL95odW1nDPI9t6fLas\ntpIJFaVcOq2ixzlv9YQy3rsk/mRYv/7jHuK58T3TuLRmNBc6g3zmQ5fy+PpwcHLyuOF89HrDi1sP\ncerMedasqOb2D8zhsRcauy/UF/p9fPWTizlz7gK/+mN4gpHVK2t4auM+blg2ha99cnG4jlH1i2yX\ny2aM5e6b67rzW7OimnnVFQnP4etqKnqtXzttNDcsmUwgEGLokOTTSpdcOY9NlhP18IWSOEHNR8aY\nm4B7rLWVUctmAzuA0dba+C+7uignftjbP/u/OT7k8gF9t+TkJh798TfTXCIRx+84H3TbfW7LAb77\ny63dV/Ci3xECcPfNl1Hgh9d2vcPEMcN5uetE45qFU9hYf7jHO/j+6qOXsbxuIgDtHZ3s2tvM28fO\n8vzWt/H54ENX1rBw9nhKigspKY5/Heds+wVeaTjCuhcbqb5kBEsvrWRuzRiGlaT3hEbyXs603Ugb\njSj0+/jU6lr+7Tdv9Fjvkx+cQ+OhU1xRW8nZ9k4eX9+Ij/C7OYeWFLL30Gn+M+oF2QAff99sNjUc\n5gMrqplvxlHQNQiPfswmXltt7+hM+JlIhuVM202H2PYP8KnVc5l2STkTRg9lfMXwbBZHZDDyqu1G\nRMa1v3k+PL7+8FXTWVJbybCSorjt+/O3zufqhVNSSnPNlTVcPms8o8pKEq7/8huH+e0L4XHBh6+a\nTkmRn28+tKXHerdcO5ORZcWsWjilx7g7us9v7+ik40KA4iI/JcWFCesHsKPxOJveOELT4VOsXlnT\nXe/+pDrG6Gt9jVfSIqttV0G/BIwxVwAvAEOstcGuZauA/7LWDksiidDp020EAsH+13SA319AeXkp\nH/uLv+XksMUDSqPw+EZ+8r2/S3PJeoqUMxd+S5Vx8LrK6fgAZrC/U0dnkO1NzTy+PjyRx6yq0Tz9\n6n58wOoVNcyrHk1xYUGvOw4udAZ5Y+8J1sVcQSuOuYIWCIUIhqDAF76ql+z2jeSX6t14mdp3lLbn\n0s6ZthvdRiHc1mZXjWLX/pbuZatXVHPptNEUFRZ0t5nYNnQ+EKJhbzO/faGxO53amO+km1PHcyfy\nVV2zlm/OtN106OgMsq2xmSe62vo1iyYzYngxMyeNZNiQzJ3Ean/2Zr5qu86dW8Qb18br3+ONpWNF\ntuOJk+d63UWfbBk6OoPUNzazbkMToRBcOX8S1RPLmTxmWL/5J1u/vpbHq4/bz/2S4aW6gDNtV+HZ\nxLYBF4BlhCfwAFgOvJpsAoFAkM5Od++Yg4r5hkJZq18u/JYqo3cM9ncqAC6rrugxW+0VteMZPryE\ntrPn6eyMn36hz8f86grqYma5jbeuj/Dl1c6oi6zJlrtzABdmM7nvKG3vpO20ZOsWr42SYFkoEOrV\nZiJ/DykMP2p7WU0FgUCoz++km1Pb0Yl8VVfvy2a9C4DFZixXLpjEqdPthEKh7lkss1EG7c/ezFdt\n1znR/W2i/j3ZMvpCoQH14Z2ExwDzayqoq6kgRHisnmr+idJOZXk0N2yfdPFSXbJNQb8ErLXnjDEP\nAvcbY24HJgF/DdzmaMFEJGdEX9Pz+3yUFBfSdjb2VaF9f09EMifRi6xT5ff5COXGWz1EpEtJcSFt\nfh+dnWq7Il7j5Fha43hxGwX9+vYF4D7gj8BJ4H9ba3/rbJFERERERERERET6pqBfH6y1bYTv7LvN\n2ZKIiIiIiIiIiIgkT3efioiIiIiIiIiIeIzu9JMB67zQwdatrw34+zNnzmLYsGQmQhYRERERERER\nkVQo6CcD1nqqmS9+5zHKKqak/t3mA3zrC2uZP//yDJRMRERERERERCS/Kegng1JWMYWRE2Y4XQwR\nEREREREREYmid/qJiIiIiIiIiIh4jIJ+IiIiIiIiIiIiHqOgn4iIiIiIiIiIiMco6CciIiIiIiIi\nIuIxCvqJiIiIiIiIiIh4jIJ+IiIiIiIiIiIiHqOgn4iIiIiIiIiIiMco6CciIiIiIiIiIuIxCvqJ\niIiIiIiIiIh4jIJ+IiIiIiIiIiIiHqOgn4iIiIiIiIiIiMco6CciIiIiIiIiIuIxCvqJiIiIiIiI\niIh4jIJ+IiIiIiIiIiIiHqOgn4iIiIiIiIiIiMco6CciIiIiIiIiIuIxCvqJiIiIiIiIiIh4jIJ+\nIiIiIiIiIiIiHqOgn4iIiIiIiIiIiMco6CciIiIiIiIiIuIxCvqJiIiIiIiIiIh4TKHTBRBJ1dmz\nZ9m9+80Bf3/mzFkMGzYsjSUSEREREREREXEXBf0k5+ze/SZf/M5jlFVMSfm7rc0H+NYX1jJ//uUZ\nKJmIiIiIiIiIiDso6Cc5qaxiCiMnzHC6GCIiIiIiIiIirqR3+omIiIiIiIiIiHiMgn4iIiIiIiIi\nIiIe4+rHe40x84HXYhZvsdYu7vq8AvghcB1wHPiqtfbnMd+/H6gFdgB/Ya19PerzW4H/A0wAfg/8\nmbW2OerzfwQ+CfiBHwFfstaG0l1PERERERERERGRdHL7nX5zgK2Eg3KR/94b9fkDQBmwlHDw7kfG\nmEUAxphhwFPAC8AC4CXgSWPM0K7PFxMO5P1d1/dHdaVH1+d/DdwKfAj4CPA/gC9kpJYiIiIiIiIi\nIiJp5Oo7/YDZwC5r7TuxHxhjaoAbganW2gPATmPMMuAvgduBW4Cz1tovdn3lc8aY9wM3AQ8CnwUe\nttb+rCu9jwH7jTFV1tr9wF8BX7HWvtT1+f9LOLD4fzNXXRERERERERERkcFze9BvDlCf4LMlwMGu\ngF/ERuBLXf9eCmyI+c5GYBnhoN8S4JuRD6y1bxtjDgBLjTEXgEnAizHfrTLGjLfWHhtgfaRLoLMD\na9/sdz2/v4Dy8lJOn24jEAgCJPW9TDh79iy7d/fOO14ZY7W3txEK+SgtLUk538F8F2DmzFmMGFGW\n8vcS1TeVfIcNGzbg74uIiIiIiIjIwLk96Dcb8BljtgMjgN8B/8ta2wpUAodj1j9GOFhH1+dvxHz+\nDuFAYuTzRN+v7Pr7cMxndH2uoN8gnTt1jB8/eZSyTWdS/u6xps2Mr16UgVL1bffuN/nidx6jrGJK\nyt891rSZoSPGZ/27rc0H+NYX1rJoUeq/12DqG8l3/vzLU/6uiIiIiIiIiAyeo0E/Y0wJF4N0sd4F\nqoFG4DZgNPBd4KeE37M3FDgf853zwJCufw/m86EA1tqOmM+I+n6//H73vjIxUjafbxCJ+Hy0Nh/o\nf704zp06ytAR4wec9UDzbW0+wJ49ZQPaNnv22AHl6bQ9eyxFRX6GDy/hzJl2gsHk5qIZbH39/gIK\nC1P7nd3SZjJRjkiaSltpezltp2W7HJn8Td2Up1P5qq7Zy9dp2sbeyld1zV6+TnNLOQbLqe2YKV6q\nj5fqAs7Uw+k7/ZYCz8VZHgI+DFQA7dbaTgBjzCeALcaYSqCd3gG4IcC5rn+3AbHPQ0Z/3tf327ry\nK44K/EXWPUdyfOXlpUmu6pyf3v+NQXx7TdrKkQuuuWYld97pdCmyJ9/qGyWjbVdpK20vp+0wx/pd\nJ/JVXb2Zr4fbZ1/Udj2ar+rqeTlxvpsK1ce9vFSXbHM06GetfZ7UZhCOvGDsEuBtwrP5RpvAxUdy\nDyX4/EgSnx+O+vtA1L+J+r6IiIiIiIiIiIgrufYeSWPMHGNMqzFmatTiy4BO4C3gFcITa0yM+nw5\nsKnr35uAK6LS8wHvifl8RdTnk4HJwCZr7WHCwb7uz7vS3q9JPERERERERERExO2cfry3L7uAPcC/\nG2M+B4wC/g34obX2FHDKGPN74KfGmL8CFgO3Aiu7vv8r4B+NMd8Dfgj8OVAKPNL1+X3A88aYl4Et\nwPeBJ6y1+6M+/ydjzNuAj/BMv9/OZIVFRERERERERETSwbV3+llrQ8Bq4DSwHvgt8Azw+ajVPg60\nEr7r72+A2621W7q+3wp8gPDdelsIBwXfb61t6/p8E+FA4N8BG4Fm4PaotP8ZeBj4DeFA4UPW2u9l\noq4iIiIiIiIiIiLp5AuFkpvJU0RERERERERERHKDa+/0ExERERERERERkYFR0E9ERERERERERMRj\nFPQTERERERERERHxGAX9REREREREREREPEZBPxEREREREREREY9R0E9ERERERERERMRjFPQTERER\nERERERHxGAX9REREREREREREPEZBPxEREREREREREY9R0E9ERERERERERMRjFPQTERERERERERHx\nGAX9REREREREREREPEZBPxEREREREREREY9R0E9ERERERERERMRjFPQTERERERERERHxGAX9RERE\nREREREREPKbQ6QI4wRgzBHgNuNNa+0KCdT4M/AMwCdgG3G2t3Zq9UoqIiIiIiIiIiAxM3t3pZ4wp\nAX4JzAFCCdaZC/wc+AYwj3DQ70ljTGm2yikiIiIiIiIiIjJQeRX0M8bMATYB1f2sej2ww1r7M2vt\nXuDLwARgdoaLKCIiIiIiIiIiMmh5FfQDVgLPAsv6We84MNcYc4UxpgC4HTgFNGa4fCIiIiIiIiIi\nIoOWV+/0s9beH/m3MaavVR8GVgMbgAAQBN5vrT2V0QKKiIiIiIiIiIikQb7d6ZesMYQf570TWAw8\nBDxgjBnraKlERERERERERESSkFd3+qXgn4Dt1tr7AIwxnwF2EX7M91vJJBAKhUI+ny9zJRTxLkcb\njtquyICp7YrkJrVdkdyktiuSm7LacBT0i28B8P3IH9bakDGmHpiSbAI+n4/Tp9sIBIKZKN+g+f0F\nlJeXurqMkBvlVBnTJ1JOJ2Wq7WZyGyhtpe2WtJ3kRL/rxLHVqeO56uq9PKPzdZLarvfyVV2zl6+T\n3H6+m4pcOVdKlpfq46W6gDNtV0G/+A4Dc2OWzQJeTSWRQCBIZ6e7d8xcKCPkRjlVRu/I5O+ktJW2\nl9N2mlN1cyJf1dWb+Xq5ffZF29ib+aqu3ue1eqs+7uWlumSbgn5djDETgJPW2nbg3wm/w28zsAn4\nNDAZeNDBIoqIiIiIiIiIiCRFE3lcdBi4GcBa+wjwWeDLwOvAMuBqa+1x54onIiIiIiIiIiKSnLy9\n089aW9DP3z8BfpLVQomIiIiIiIiIiKSB7vQTERERERERERHxGAX9REREREREREREPEZBPxERERER\nEREREY9R0E9ERERERERERMRjFPQTERERERERERHxGAX9REREREREREREPEZBPxEREREREREREY9R\n0E9ERERERERERMRjFPQTERERERERERHxGAX9REREREREREREPEZBP8mIYNd/IiIiIpI5GnN5j7ap\niOQjHfsyo9DpAjjBGDMEeA2401r7QoJ1LgXuAxYAbwF3W2ufz1ohc9SFQJCGfS2s29AEwOrl1dRO\nHUWRX/FlERERkXTRmMt7tE1FJB/p2JdZefcrGmNKgF8Cc4BQgnVGAM8ADUAt8BjwG2PM2GyVM1c1\n7Gvh3kfr2X+klf1HWrn30Xoa9rU4XSwRERERT9GYy3u0TUUkH+nYl1l5FfQzxswBNgHV/az6CeA0\ncIe1tsla+zVgD3B5ZkuY24LQHZ2Ptm5Dk27TFREREUkTjbm8R9tURPKRjn2Zl2+P964EngW+Apzt\nY72rgMettd13AlprF2e2aCIiIiIiIiIiIumRV3f6WWvvt9b+tbW2rZ9VpwHHjTE/NMYcMca8bIy5\nIhtlzGUFhJ+/j7V6eXV+7WgiIiIiGaQxl/dom4pIPtKxL/Py7U6/ZJUBXwK+B9wA3Ao8bYyZZa19\nO9lE/C5+8WSkbOkuY11NBXffXMfj68O36K5ZUc286goKCweWT6bKmU4qY/q4pXyZKEcmt4HSVtpu\nSdtp2S6HE8dWp47nqqv78kzHmEtt113bON3j6GTzTTe13ezl6zS3lGOwcuVcKVm5Vp++jn25Vpf+\nOFEPXygUdy4LzzPGBIGrrLUvxvnsTeCQtfaaqGWvA49aa7+ZZBb5+cN2ae/oBKCkWHFlSZnP4fzz\nuu2KDILarogD0jDmUtt1GY2jJUlqu+IpeXTsy2rb9fyvOUCHgTdjlu0GJqWSyOnTbQQC7nz9pN9f\nQHl5acbL2Hb2/KC+n61yDobKmD6RcjotE79TJreB0lbabknbadk+xjlxbHXqeK66uj/PgYy51Hbd\nvY0HO44eaL65mKdT+TpdV6e5/dwiWblyrpSsXK9P9LEv1+sSy4m2q6BffJuAK2OWzQZ+lkoigUCQ\nzk5375i5UEbIjXKqjN6Ryd9JaSttL6ftNKfq5kS+qqs38/Vy++yLtrE381Vdvc9r9VZ93MtLdck2\nBf26GGMmACette3A/cBdxpi/A34OfByYSopBPxERERERERERESd4422I6XEYuBnAWnsAeC/wQeAN\n4EbgRmvtEeeKJyIiIiIiIiIikpy8vdPPWlvQz98vAQuzWigREREREREREZE00J1+IiIiIiIiIiIi\nHqOgn4iIiIiIiIiIiMco6CciIiIiIiIiIuIxCvqJiIiIiIiIiIh4jOsn8jDGFBCeSbcWuADsBJ61\n1gYcLZiIiIiIiIiIiIhLuTroZ4wZDTwNLABOAT6gHHjdGHOttfakk+UTERERERERERFxI7c/3vtt\noBS4zFo7ylo7EpgPlAD/6GjJREREREREREREXMrtQb8PAndaa7dHFlhr64HPAh92rFQiIiIiIiIi\nIiIu5vagXxFwJM7yY4Qf8xUREREREREREZEYbg/6vQ78ZZzldwBbs1wWERERERERERGRnODqiTyA\nvwWeN8YsBTZ2LVsB1AE3OFYqERERERERERERF3P1nX7W2pcJB/n2EQ7y3QA0Asuttc85WDQRJ7BW\n8AAAIABJREFUERERERERERHXcvudflhrXwVuSWeaxpghwGuEJwl5oZ91pwINwPuttS+msxwiIiIi\nIiIiIiKZ4LqgnzHmJ8BfWWtbjTH/AYTirOYDQtbaTw4g/RLgF8CcBGnHug8Ymmo+IiIiIiIiIiIi\nTnFd0A+oBvxd/55GODDni/o88ncyAbsejDFzCAf8kl3/fwDDU81HRERERERERETESa4L+llrr4r3\n71jGmMoBJL8SeBb4CnC2rxWNMRXAPwHXE368V0REREREREREJCe4LugXzRgTACqtte/ELJ9KOBCX\n0l141tr7o9Lob/XvAA9Ya3cmsa6IiIiIiIiIiIhruC7oZ4z5JPCxrj99wGPGmAsxq10CtGSwDNcC\nVwC1g0nH73fv5MiRsrm5jJAb5VQZ08ct5ctEOTK5DZS20nZL2k7LdjmcOLY6dTxXXb2XpxP5JaJt\n7K18Vdfs5es0t5RjsHLlXClZXqqPl+oCztTDFwql/Gq8jOp6rPbbhAN+HwceAdqjVgkBrcCD1trX\nBpFPELgqdkZeY0wp4bsI77DWPm2M8QEBYFV/M/3GcNcPK5I7fP2vklFquyIDo7YrkpvUdkVyk9qu\nSG7Katt13Z1+1tpm4HbofgT3bmvt6SwWYTHhCUR+HfNY7++MMQ9Ya/8y2YROn24jEAimu3xp4fcX\nUF5e6uoyQm6UU2VMn0g5nZaJ3ymT20BpK223pO20bB/jnDi2OnU8V129l2d0vk7TNvZWvqpr9vJ1\nmtvPLZKVK+dKyfJSfbxUF3Cm7bou6BfNWntbvOXGmGJgkbV2YwayfQWYHvW3D9gDfAp4JpWEAoEg\nnZ3u3jFzoYyQG+VUGb0jk7+T0lbaXk7baU7VzYl8VVdv5uvl9tkXbWNv5qu6ep/X6q36uJeX6pJt\nrg76GWMuB34EXEo4+BZ9G2QI8KcxrwnASWttO9AU8xnAIWvt8XTlJyIiIiIiIiIikilufxvid4EL\nwGeBDuDOrmUdwK1pzuswcHOa0xQREREREREREck6V9/pBywArrHWvmKMuR14w1p7nzHmbeDPCE/y\nMSDW2oK+/k72MxEREREREREREbdxezCrgPAdeBB+r96lXf9eB1zmSIlERERERERERERczu1Bv7eA\nFV3/tsCirn+XA0McKZGIiIiIiIiIiIjLuf3x3nuAHxtjQsCvgHpjTBuwHNjkaMlERERERERERERc\nytV3+llrfwT8KfC2tXYXcBvhO/8OAp9xsGgiIiIiIiIiIiKu5eo7/Ywx3wfutda+BWCt/QXwC2dL\nJSIiIiIiIiIi4m6uvtOP8J19nU4XQkREREREREREJJe4Pej3O+BuY0yZ0wURERERERERERHJFa5+\nvBeoBG4GPmeMeQdoi/osZK2tdqZYIiIiIiIiIiIi7uX2oN8fu/6LJ5TNgoiIiIiIiIiIiOQKVwf9\nrLVf628dY0w5cL+19k8zXyIRERERERERERH3c/s7/ZIxFPio04UQERERERERERFxC1ff6Zcpxpgh\nwGvAndbaFxKscyPwDaAGaAK+Yq19InulFBERERERERERGRgv3OmXEmNMCfBLYA4J3gtojJkH/Br4\nEVAH/Bvwq67lIiIiIiIiIiIirpZXd/oZY+YAv0hi1T8FnrXW/qDr7381xqwmPJPw9kyVT0RERERE\nREREJB3yKugHrASeBb4CnO1jvQeAophlPqA8M8WSvrR3dBIIabJmERGRfBbs+n/ePaYi0ge1CxHp\nj44T+S2vgn7W2vsj/zbG9LXem9F/G2PmAlcD/5qxwkkvFwJBtjU1s259EyFg9fJqaqeOosivw5WI\niEi+6OgMUt/YzLoNTYDGAyIQHic37GtRuxCRhNR/CijY2y9jzBjC7/fbYK193Ony5JOGfS3c80g9\n+460sv9IK/c+Wk/DvhaniyUiIiJZtL2pmXsfrWe/xgMi3Rr2tahdiEif1H8K5NmdfqkyxowHnun6\n809S/b7fxRH0SNncWsZAKNR9RSLaug1NLJg5Br/P50Cp4nP7bwm5UUZwT/kyUY5MbgOlrbTdkrbT\nsl0OJ46tTh3Pnapre0cnj6/P7nggH7er0/Jlf05XnqmMk3O9rm7P1+m6Os0t5RisXDlXSpZT/Wcm\neHHbZJsXgn7HCb+rL62MMROB5wg/An+VtbY51TTKy0vTXay0c2sZ2zs6iXcY8gHDh5dQUuy+Xdet\nv2W0XCijG2Tyd1LaStvLaTvNqbo5kW++1NXJ8UA+bVen5cv+nK48B9IucrWuuZKv2q43eKk+uXg+\n3RcvbZtsc/WWNsbMAv4FeA9QHPNxyFrrt9Z2AhvSnO8w4L+BTmCVtfadgaRz+nQbgUCw/xUd4PcX\nUF5e6uoyrl5RzT2P1Pda1nb2PG1nzztUqt5y4bfMhTLCxXI6LRO/Uya3gdJW2m5J22nZPsY5cWx1\n6njuZF1Xr6jhnke29fgsk+OBfNyuTsun/TldeSY7TvZCXd2cr9N1dZrbzy2SlSvnSslyqv/MBK9u\nm2xyddAPuB8YB3wROJ3JjIwxE4CT1tp24MtANXAVUND1GcA5a23S5QgEgnR2unvHdHMZ51aN4u6b\n63pM5DG3apRry+vm3zIiF8roBpn8nZS20vZy2k5zqm5O5JtPdZ1XPZq7bqrr8SLybIwH8mm7Oi2f\n9ud05Tm3alRK7SKX65oL+arteoPX6uNU/5kJXts22eT2oN8SYLm19rUs5HUYuA14CFgLlACvxKzz\nAPDJLJRFgCJ/AQtnjmXF/EmcOdNOKBByukgiIiKSZcWFBcyvqaCupgLQLHQiEB4nq12ISF/Ufwq4\nP+h3HOjIRMLW2oJEf1trZ2ciz1wTiaM7fXAoKS6kzeejEwX9REREormlr86GfKijuEeutC23l09E\n4svmMUbHifzm9qDfD4BvGGM+Zq095XRh8sWFQJCGfS09bgOunTqKIo/MmCMiIpLrOjqD1Dc2q68W\nSTONg0Ukk3SMkWxze9DvWmAFcMIYcwyIfttkyFpb7UyxvK1hXwv3PnrxxcD3PlrPXTfVMb/rtmAR\nERFx1vamZvXVIhmgcbCIZJKOMZJtbg/6bez6Lx4965kBQei+6hBt3YYm6moqdGuwiIiIw9o7Onl8\nvfpqkXTTOFhEMknHGHGCq4N+1tqvOV0GERERERERERGRXOP6YLIxZqEx5mFjzE5jzDZjzC+MMYud\nLpdXFRB+r0Cs1cur3b+ziIiI5IGS4kLWrFBfLZJuGgeLSCbpGCNOcPW+ZYy5kvDjvdOBp4EXgVnA\nemPMcifL5mW1U0dx1011VFWWUVVZxl031VE7dZTTxRIREZEu86or1FeLZIDGwSKSSTrGSLa5+vFe\n4BvAT6y1d0QWGGN8hGf1/TqwyqmCeVmRv4D5NRXUdb1M1NWRYRERkTxUXKi+WiQTNA4WkUzSMUay\nze372ALg+9ELrLUhwkG/RY6UKI8U0P8OEuz6T0RERLIvmb46HvXfIn1Ltm2pLYnkp8G2/YH23yKp\ncvudfseBscCbMcvHAuezXxyJuBAI0rCvpXv2odXLq6mdOooivw5dIiIibqX+WyQ91JZE8pPavuQa\nt++ZTwD3GmPmRBYYY+YC93Z9Jg5p2NfCvY/Ws/9IK/uPtHLvo/U07GtxulgiIiLSB/XfIumhtiSS\nn9T2Jde4Pej3VaATaDDGtBhjWoA3gADw/zhasjwWhO4rG9HWbWjS4w0iIiIupf5bJD3UlkTyk9q+\n5CJXP95rrT1hjFkCXA9cCviA7cDvrbVqVyIiIiIiIiIiInG4OugHYK0NAL/r+i8tjDFDgNeAO621\nLyRYZz5wP1AL7AD+wlr7errKkMsKCL+74N5H63ssX7282vW3joqIiOQr9d8i6aG2JJKf1PYlF7ku\n6GeMCQITrLXvdP07kZC11j+A9EuAXwBzgFCCdYYBTwE/BT4O3AE8aYypsdaeSzVPL6qdOoq7bqrr\n9QJTERERcS/13yLpobYkkp/U9iXXuC7oB3wSOB3170TiBuz60jUhyC+SWPUW4Ky19otdf3/OGPN+\n4CbgwVTz9aIifwHzayqoq6kA3P9ySBEREVH/LZIuaksi+UltX3KN64J+1toHov4MAQ9ba9uj1+m6\nE+8zA0h+JfAs8BXgbB/rLQU2xCzbCCwjT4N+kVsuYw9qOsiJiIjknlT770AoRHtHZ0bKIuJ2icbB\niZaJiPe5re33dZyS/Oa6oJ8xZixQSnjSjv8gPHPvuzGrzQe+CXw3lbSttfdH5dPXqhOAhphl7wBz\nU8nPCy4EgjTsa+l1+3KRX4cTERERr4seB/iA1SuqmVulcYDkB42DRcTt4h2nInchioALg37A+wkH\n+yI2J1jvqQyWYShwPmbZeWBIKon4XTwgiJStvzJua2ru8aLSex+t5+6b61g4c2xGyxeRbDmdpDKm\nj1vKl4lyZHIbKG2l7Za0nZbtcjhxbM12nrHjgHseyd44wKm+Kx+2a2y+TnPrNk7nONjpbZxP+3M+\n1dVpbinHYOXKuVI88Y9Tl3Hd2LKcrE+sXN428ThRD9cF/ay1Dxpj9hG+0+854CNAS9QqIeAMsD2D\nxWgHSmKWDQFSmsSjvLw0bQXKlL7K2N7Rybr1Tb2Wr1vfxIr5kygpzt7uk+u/pVvkQhndIJO/k9JW\n2l5O22lO1c2JfLORp1vGAdqu3ufGbZyp/d+NdfVSnk7lq7brDblWn8THqUZWzJ+Yc/Xpi5fqkm2u\nC/oBWGtfADDGXA1stNZeyHIRDhF+xDfaBOBwKomcPt1GINDXBMTO8fsLKC8vTVjGQChEMAQFBb5e\nn4WAM2faafP1/izb5XQDlTF9IuV0WiZ+p0xuA6WttN2SttOyfYxz4tg60DwDofD8Z/4U+u5AKBR3\n1rRsjQOc6rtyabumK1+nuW0bZ2Ic7PQ2zqf9OZ/q6jS3n1sky+lzpYH00ZHvJeqnwRvbx+ltk25O\ntF1XBv0irLXPG2PqjDG1gL9rsY/wXXgLrbV/lqGsNwFfivxhjPEB7wG+nkoigUCQzk5375ixZYx9\nJ8B1i6so8h/CHrh4s+Xq5dWEAiE6U59AOW3ldCOV0Tsy+TspbaXt5bSd5lTdnMg32TwH+06y1cur\nezw2FFmWzXGAtqv3uWUbZ2Mc7Ja6ejVPp/JV2/WGbNcnHe8NjddPr1lRTUlxIW1nz3tm+3htX8sm\nVwf9jDFfAL6d4OMX05zXBOBk10zBvwL+0RjzPeCHwJ8TnlzkkXTm6UYN+1p6HDR+9HgDn15Ty4VA\ngEAwxJoV1cydOoogmhlIRETE7Rr2tXDfY9uZUx1+qfd9j23njrXzmJ/kS75rp47irpvqek3kIeJF\n/Y2DIyfkmiVTRNIh9phz76P13HVTXdJ9NPTspyEcBJzX1ecHQiGdt4u7g37AncC3gK8B+4EFwGjg\nP4Hfpjmvw8BtwEPW2lZjzAeA+4HPAPXA+621bWnO01WC0H2wiPbMq/v5yicW0RkIsnNfC//w09cA\nzWAmIiLiZkFg2553Wb2yhi27jgGwemUN2/a8S11NRVInAUX+AubXVLBg5hiGDy/pddeAgh/iFf2N\ng32E7zTRbL4ikg6JjjnrNjQl3UfDxX46MmNvQVfaz205wGPPvwWEj1Vzpo5iiI5VecntQb9JwL9b\na9uNMfWEH+l93BjzeeA7wPcGmrC1tqCfvzcDlw80fa/Zd6yVtw6e4uE/7O5eNpArESIiIpI9l4wZ\n3qPvPnC0lVuunZlyOn6fr/tRIUjPI0kiucJH+ES6Ps5dOZ9eU8uimWO074uIo6KPQNubmrnnkZ7H\nqluuncn40aXMrVJfnW/cvrXPcjEw2QjM7fr3m8A0R0rkYQWEB+2xrltcxVMb9/Jyw5Fen63b0ISe\nrBcREXGnTXH67njLUhV5JGn/kVb2H2nl3kfradjX0v8XRVwq0Th49fLq7jtn4t2V8/Qr+9n/7tmM\nl09EvKW/Y85ABYHH48zo+3LDEZ7fekh9dR5ye9DvJeBLxpihwFZgtTHGT3hSjdOOlsyjIu8EqKos\no6qyjLtuqmPPwRYmTyh3umgiIiKSqngTAQ5y0t2+Hknq60JgEHShUFwt3ji4dmr/77B8dcdR7dsi\nkrKBHnMGIx037ag/zy1uf7z3S8AzhN/tdz/wZeAEMAz4ZwfL5Vmx7wS4EAjyTksbr+48yrLaSg4c\nbe2x/mCvRIiIiEhmRO4iiDf7bjb7bj0KLLmiyF9A7dRRlA2bxas7jvLkS3sBuvfXeO1p4ezxbNv9\njhPFFZEcF+99fINVQHj23ujHeyF8rFr3YiMTxw0fcNrqz3OTq7eOtbYBqAH+w1rbCiwF/h641Vr7\nN44WzuMKuv7bua+Fh/+wm72HT7Nt97usXTWdKROydyXC63SVREREMikddxEECc8AGJHqI0l6FFhy\nScO+Fv7hgc38YfNBmg6d7rG/1k4dxafX1DJlQhlTJpSxdtV0djQ2c+MV09x9UpWDNEaWfBI594b0\n7Pvzqiv4/K3zqarseazqDIQGdeFP/XlucvudflhrzwHnuv59lPAEHpIFsY/v2AMtNB46yVULJnHT\nqhqKCjS8GShdJRERkWwYzF0EsX3V2qumM7dqFAVcDCbG9mOx0jU7oUg29Le/FvkLWDRzDONGlfLq\njqNs2/0ON14xTRfB00hjZMlX6dz3iwsLuHrhFOZUjWLv0Vb+8xlLIBga1E076s9zl+uCfsaYvUCI\n/t84E7LW9r7MLBnVGQix5+2T+BXwG5SGOLO/aSZkERHJlIH02rF91Xd/uZW7b67jsuqKjDySJJIL\nivwFTJ9QRvWEMkD7frppjCz5KhP7/pDC8PHqyx9bCOh4la/cuN0fBB7q+n9//0kGZWpGoXw30Beg\ni4iIZEuivurx9T37quhHkuLRWEJySSr7a3/7vqROY2TJV5ne99NxvFJ/nrtcd6eftfZrTpdBLkr2\n8R0RERGReDSWkFyi/VVEJD4dH3OT64J+0YwxH+/rc2vtQ9kqS77S4zvp55bZFEVERBJJ1FetWZF6\nX6WxhOQS7a/O0RhZ8lWu7Ps6PuYmVwf9gAcSLD8PvE34MWDJAjXo9NJVEhERcbvYvioykcdAaSwh\nuUT7qzM0RpZ8lUv7vo6PucXVQT9rbY/9yRhTCMwA7gN+6EihRNLA61dJIu+e8Fq9RES8Kt5xO7qv\n8vt9jBk9nJaWs3R26u1aIpIZmRoja2wqbue180O1OfdwddAvlrW2E9hljPk88Cjwi1S+b4wpAf4F\nWAu0Ad+21n4nwbofBv4BmARsA+621m4dRPFzihppdnjt903nVPMiIjJ4/fXnyRy3CwC/z5fRcoq4\njcbCzkrX766xqWRLuo4Zub5nqs25T04F/aIEgYkD+N4/AwuAVcBU4EFjzH5r7a+jVzLGzAV+DnwG\n2Ah8AXjSGFNjrW0bTMHdLhONVIOm/JGJqeZFRCR1yfbnyRy3g0AgFMpKuUWclmsnrBpn901jU8m0\nVI4Z+dBe1ebcx9VBvwQTeYwA/gx4JcW0hgGfAm6w1m4DthljvgV8Fvh1zOrXAzustT/r+u6XgTuB\n2cDrKVUix6SzkebaoEkGp6+p5utqKjzduYmIuE2ywby+jtuBmH488k4/Hc/Fy3LlhFXj7P5pbCrZ\nkMwxI1/aq9qcO7n9d38gzn/fBk4Ad6SYVh1QBLwUtWwjsCTOuseBucaYK4wxBcDtwCmgMcU8c0pf\njTSVt/cEu/7bsT98ANx/pJX9R1q599F6Gva1pKm0IiIiEk8A2LXvBIX+no/kptqfR05kIv34d3+5\nle1NzWktq4ibpGssPNC8B9M+Nc4Wyb5kjxn73z3LC9sOceidM2qvknWuDvpZawvi/DfEWnuVtXZX\nislVAse73gsYcQwoMcbEXrp7GHgS2EB4puB/Bv7EWntqoHXJdSH6H4hcCATZ2tjM1x/czNcf3Mw7\nLW2YKT1nHMrGoEmcEZlqPpbbppoXEfGqSD/8fx7czO6DJ1m9sqZXPxytr+M2hAOH82aM6RE8fHx9\nevrxVAMcIl4TaQOx4+etjc1cCPTdOpwMTuYSjU0l0/p78UWkff/sv9/kZOv5Hv3yQNurm/tPtTl3\ncvXjvRHGmBnApYQvXr9urT04gGSGEg7gRYv8PSRm+RhgAuFHejcBfwk8YIxZYK19dwB554RII42+\nPRngusVVfPOnWwgEQ33eivzGvhZ+EPXd/UdaufU6Q+Ohk3QG9C6gfJBLU82LiHhN7CNGB462snbV\n9O5+ON6gO95xe/aUkdQ3NrP74MnwspU17Ghsxh4Y/F0J+fKIk+SmRGPhpXMr2d7UzNyqwe+rHZ1B\n6hubWbehCX+Bj6sXTuFHjzd0f+7Wx4lzlcamkgmRvuzJl/aydG4l+4+09vg80t/W99EvDzRPt/ef\nanPu4+qgnzGmjPBddzfELP9P4DZrbUcKybXTO7gX+ftczPJ/ArZba+/ryu8zwC7Cj/l+K9kM/S5r\ngNEiZYstY11NBXffXMfj68ON9PrFVby49RBNh04D4YHI3TfXsXDm2B7fC4RCPBHniuPGNw5zaU0F\nW3cfB2DNimqKC5P/XRKVM1WRF5BnYvbBdJUxk7JVxsLCAhaZsSyYOSacX4q/t1t+w0yUI5PbQGkr\nbbek7bRsl8OJ43+iPAOhUNw7f7bsOsZVCyYxZ9po5lVXUBjTB0cft4MhKPDBtreOJzxJWbOiJqV+\nPNa2puZe7z6KN67oq66Z5qbtmq18neambRw9Fg6FYOHs8Wzb/S72QEvCfTWVPLc3nehuA/NmjOHp\nV/b3WnfdhiYWzBzTaxwVGc8W+3ysWVHNPY/0DE7GG2fn4/4cne9gx6YDyTMb8rXtZkqq2zG6Lyvy\n+1m7ajqvvXkMCLfDedUV+Py+hP3ynOoKrpo/Man+NNLud+7t/e5At/WfkP42lwvn2qlwZJtkPcfU\nfB+YCbwPeBnwA1cAPwD+kfCsusk6BIwxxhRYayN3xE4A2qy1saH2BV15A2CtDRlj6oEpqRS+vLw0\nldUdEa+MK0aUsqS2ks7OIH//4000HTpNod/HnOrwFccnN+5lxfxJlBRf3H1Onz1Poon9Lq0ZQ0vr\neT581XSW1FYyrKQoLeVMxtn2C7zScITfPP8WwKDK0J9c3d7SWyZ/J6WttL2cttOcqttg8m3v6Ozx\nd3Tfmkqe7R2dxBtW+3xw+wfnUj4s9rrnRdF9ZQhYNrcSM2VUjzv7tuw6xt98YhFza8YMuA9t7+hk\n3fo4jySub+o1roiWi9s1l/J0A7dt4yVDi9m59wTNp9vZe+gkQ4b4KSkuYNfeEyypreyzPfUl3Ab6\nf0W4Dxg+vKS7TcQbz14+azyfv3V+0mPcfNqf86muTvNavZOpT2xfZg+00HjoJKsWTOJ/vm82Q0uL\nKCkuTNgvA6xZWc3MKaMo8PkoLvLH7f+S6Zvd2n9mgpfqkm1uD/p9GPiQtfaFqGVPGmPOA78gtaDf\nNuACsIzwBB4Ay4FX46x7GJgbs2xWgnUTOn26jUA/7+Rwit9fQHl5aY8ydnQG2d7U3H2X38rLJnHJ\n2OEU+f3Mralgy67w1YtltZW0nDxHSZG/O71AKMSy2koOHO15a/Oy2kqunD+RVQsm4vf56GjroKMt\n+Rs045UzFVt2v9vjKuh3f7mVu28ODvgqbSbKmIzB3qmYjTKmQ6ScTsvE75TJbaC0lbZb0nZato9x\ng/lNY/vcpXMrOXz8DPNnjmVedUXCq/995bk6wZ0/gY5OWmKCi3Cxb9n21nG+/3DP13NEPxYM4eDh\nvBlj6Wi/QEsK/XhsfvGuD4aAM2faaYvp45zqu5zI1+m6Os0t2zjSLnfuPUH7hQDTLhnRPf796HWz\neOvQSb5838buO3lSfXqluKSoRxvY2dTM6pU1vcbPq1dU03b2PG1nw28iij+eDd/hM696dDj9BOPs\nfNyf3VzXdD19lK9tN1NS2Y7x+rLOQIjdb5/kp7/bxZ63T3YfIxL1y6daz/OV+18iFAqfL1dNGM7M\nSSN7HFNi2328vtmp/jOTT9HFypXz2GQ50XbdHvTrJDxrbqwjpFh2a+05Y8yDwP3GmNuBScBfA7cB\nGGMmACette3AvxN+h99mwu/0+zQwGXgwlTwDgSCdne7eMaPLWN/Y85Gbnx7ZxW0fmMOE0cP41XN7\nupcfONrKuFGlvd41UjVhOGtXTe8eHC2cPZ6qCcPxBcMHxs5+X3WaXDmTFYTuk6loj69vYl51+qcM\nz8T2Tve7G3Jhn3SDTP5OSltpezltpzlVt4HkG9vnRgbz//rr7dyxdl6/7/OKl+fcqt7v0ZlbNarX\nerF9y9IEd/bNqa5g+56Lr+coKS6k7ez5Qf3G8d6Xtnp5NaFAKOE4IZe2ay7m6QZu2caRdllSXMBH\nr5vFA0/u7P7sgSd3snbVdDa9cYR7HhnYe/dKigt7PJbbGQixo7GZT6+p5ZlXw4/5xrbbZMez/Y2z\n82l/dmNdc+V9bKny2jEr2frE68sunzWedS820hkIdR8j4r3frnSIn2/97PXu70VeodHeEeCyrifr\nErX72L452/2nk/ux1/a1bHJ70O8e4B5jzM3W2qMAxphy4BtEPX6bgi8A9wF/BE4C/9ta+9uuzw4T\nDgA+ZK19xBgzHPgy4eDgVuBqa+3xwVTGzRLNAvbcloPMmDyy1/J1G5qoq+kZOKupLKe9I8DpM+H1\nJ40dRk1leffsQk51adGPJu9sanaoFAMT+1J2vdxZRCT3JepzI4P5eH1sMmn6/QXMr6mgrp8+IrZv\niXf3AMDYkaVMmVDGstpKaqeNTqE0iekF3+JW0e1yZtVonnut97yB0SfcybbT2FPUedUVPdrA9Uum\nUDt1FEtnjwMGN152eswtiWlM7y2xfdnSuZVs2/1ujz40fIxY1Ktf/vqDm3ult2XXMU6fGdnvjSk+\nH1w2fQzjRpYyZ9po5lYl13+m69ig/Tg3uT3odz2wCNhrjLGE7/ybCQwH5htjbutaL2R0MtK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kxPL9W1HyVNOh6RZDSU4bDhBjqeG46ejnwDMHfGFDasLaW98xwPPXeA9+vbONc/7Dd4u0BgEk4/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\nur7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XpS2zBh1F2NNgow2P9fixlASHhoxDDZRGBvWcpBTgjZfO46xTpo9pklFJva1a0dJr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\n2vC4LUmSKsV2RTql7vH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sWtHCknkzmNBYj33ZUnrtP3iE9Vue5fa11jWpHnh+lCRp/Ol5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.pairplot(df_final[cols_to_keep])\n", "for ax in g.axes.flatten(): # from [6]\n", " for tick in ax.get_xticklabels(): \n", " tick.set(rotation=90);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 7) Build your models\n", "\n", "Using scikit-learn or statsmodels, build the necessary models for your scenario. Evaluate model fit." ] }, { "cell_type": "code", "execution_count": 289, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import r2_score,mean_squared_error\n", "from sklearn.cross_validation import cross_val_score\n", "from sklearn.model_selection import cross_val_predict\n", "from sklearn import linear_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us first take a look at the 10 best performing counties (recall that we dropped Polk as an outlier):" ] }, { "cell_type": "code", "execution_count": 290, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countysale_dollars
55Linn1.806775e+07
79Scott1.397399e+07
50Johnson1.237021e+07
6Black Hawk1.192076e+07
94Woodbury7.695638e+06
75Pottawattamie7.675324e+06
82Story6.730961e+06
30Dubuque6.621999e+06
16Cerro Gordo4.892826e+06
28Des Moines3.579586e+06
\n", "
" ], "text/plain": [ " county sale_dollars\n", "55 Linn 1.806775e+07\n", "79 Scott 1.397399e+07\n", "50 Johnson 1.237021e+07\n", "6 Black Hawk 1.192076e+07\n", "94 Woodbury 7.695638e+06\n", "75 Pottawattamie 7.675324e+06\n", "82 Story 6.730961e+06\n", "30 Dubuque 6.621999e+06\n", "16 Cerro Gordo 4.892826e+06\n", "28 Des Moines 3.579586e+06" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_final_sorted = df_final.copy()\n", "df_final_sorted.sort_values(\"sale_dollars\", inplace=True, ascending=False)\n", "df_final_sorted[['county','sale_dollars']].head(10)" ] }, { "cell_type": "code", "execution_count": 291, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countysale_dollars
56Louisa207146.11
26Decatur176364.48
86Van Buren174245.69
4Audubon159547.63
52Keokuk149560.78
77Ringgold138680.81
84Taylor110598.35
90Wayne105438.35
1Adams100596.80
25Davis96185.96
\n", "
" ], "text/plain": [ " county sale_dollars\n", "56 Louisa 207146.11\n", "26 Decatur 176364.48\n", "86 Van Buren 174245.69\n", "4 Audubon 159547.63\n", "52 Keokuk 149560.78\n", "77 Ringgold 138680.81\n", "84 Taylor 110598.35\n", "90 Wayne 105438.35\n", "1 Adams 100596.80\n", "25 Davis 96185.96" ] }, "execution_count": 291, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_final_sorted[['county','sale_dollars']].tail(10)" ] }, { "cell_type": "code", "execution_count": 292, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['county', 'bottle_volume_ml', 'state_bottle_cost', 'state_bottle_retail', 'bottles_sold', 'sale_dollars', 'volume_sold_liters', 'profit', 'num_stores', 'average_store_profit', 'sales_per_litters', 'population', 'store_population_ratio', 'consumption_per_capita', 'area', 'stores_per_area', u'per_capita_income', u'median_household_income', u'median_family_income', u'number_of_households']\n" ] } ], "source": [ "print df_final.columns.tolist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Building" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features = ['num_stores','population', 'store_population_ratio', \\\n", " 'consumption_per_capita', 'stores_per_area', u'per_capita_income']" ] }, { "cell_type": "code", "execution_count": 294, "metadata": {}, "outputs": [], "source": [ "X = df_final[features]\n", "y = df_final['sale_dollars']" ] }, { "cell_type": "code", "execution_count": 295, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [], "source": [ "combs = []\n", "for num in range(1,len(features)+1):\n", " combs.append([i[0] for i in list(itertools.combinations(features, num))])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now create the training and testing data, instantiate the models and test all models you want:" ] }, { "cell_type": "code", "execution_count": 297, "metadata": {}, "outputs": [], "source": [ "lr = linear_model.LinearRegression(normalize=True)\n", "ridge = linear_model.RidgeCV(cv=5)\n", "lasso = linear_model.LassoCV(cv=5)\n", "models = [lr,lasso,ridge]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a list of $R^2$ combinations:" ] }, { "cell_type": "code", "execution_count": 298, "metadata": {}, "outputs": [], "source": [ "r2_comb_lst = []\n", "for comb in combs: \n", " for m in models:\n", " model = m.fit(X_train[comb],y_train)\n", " r2 = m.score(X_test[comb], y_test)\n", " r2_comb_lst.append([round(r2,3),comb,str(model).split('(')[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the best predictors using `itemgetter`:" ] }, { "cell_type": "code", "execution_count": 299, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.974, ['num_stores', 'population', 'store_population_ratio', 'consumption_per_capita', 'stores_per_area', u'per_capita_income'], 'RidgeCV']\n" ] } ], "source": [ "import operator\n", "r2_comb_lst.sort(key=operator.itemgetter(1))\n", "print r2_comb_lst[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best predictors are:" ] }, { "cell_type": "code", "execution_count": 300, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['num_stores',\n", " 'population',\n", " 'store_population_ratio',\n", " 'consumption_per_capita',\n", " 'stores_per_area',\n", " u'per_capita_income']" ] }, "execution_count": 300, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_comb_lst[-1][1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** Using\n", "\n", " str(model).split('(')[0]\n", " \n", " \n", "on something like:\n", " \n", " LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))\n", " \n", "we can extract only the model since we convert it to a string, split on `(` and get the first element" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dropping highly correlated predictors:" ] }, { "cell_type": "code", "execution_count": 312, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features=['population','store_population_ratio','stores_per_area',u'per_capita_income']" ] }, { "cell_type": "code", "execution_count": 313, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r-squared: 0.98031775254\n" ] } ], "source": [ "X = df_final[features]\n", "y = df_final['sale_dollars']\n", "ridge = linear_model.RidgeCV(cv=5)\n", "model = ridge.fit(X,y)\n", "print 'r-squared: {}'.format(model.score(X,y))" ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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featurecoefficients
0population58.8369
1store_population_ratio1.05083e+06
2stores_per_area49967.5
3per_capita_income-71.6356
\n", "
" ], "text/plain": [ " feature coefficients\n", "0 population 58.8369\n", "1 store_population_ratio 1.05083e+06\n", "2 stores_per_area 49967.5\n", "3 per_capita_income -71.6356" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefficients = pd.DataFrame([features, model.coef_.tolist()], index=['feature', 'coefficients']).T\n", "coefficients" ] }, { "cell_type": "code", "execution_count": 315, "metadata": {}, "outputs": [], "source": [ "coefficients['coefficients'] = coefficients['coefficients'].astype(float)\n", "coefficients = coefficients.sort_values(by='coefficients', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot your results\n", "\n", "Again make sure that you record any valuable information. For example, in the tax scenario, did you find the sales from the first three months of the year to be a good predictor of the total sales for the year? Plot the predictions versus the true values and discuss the successes and limitations of your models" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [ { "data": { "image/png": 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hKyIiEhOFrIiISEwUsiIiIjFRyIqIiMREISsiIhIThayIiEhMFLIiIiIxUciKiIjERCEr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\nn19cRrJXMut2dQO54K1Ho4C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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predicted = model.predict(X)\n", "fig, ax = plt.subplots(figsize=(5,5));\n", "plt.scatter(predicted, y);\n", "plt.plot([min(y), max(y)], [min(y), max(y)], '-');\n", "plt.title('Predicted and actual sales');\n", "plt.xlabel('Predicted sales');\n", "plt.ylabel('Actual sales');" ] }, { "cell_type": "code", "execution_count": 317, "metadata": {}, "outputs": [], "source": [ "pred = [round(p,0) for p in predicted]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Present the Results\n", "\n", "Present your conclusions and results. If you have more than one interesting model feel free to include more than one along with a discussion. Use your work in this notebook to prepare your write-up.\n", "\n", "\n", "### Recommended counties\n", "\n", "The counties with highest predicted total sales are below:" ] }, { "cell_type": "code", "execution_count": 326, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaareastores_per_areaper_capita_incomemedian_household_incomemedian_family_incomenumber_of_householdssales_prediction_dollars
0Adair445587540182.626.037369e+04338074.108056e+0532522.351.374661e+05442031.10093212.63148670920.6232374.5857801146.1498743.8563892349745202572873292221482.0
1Adams175740318103.822.717139e+0484461.005968e+057547.623.361478e+04179718.70605513.32828136930.4865962.043764950.4204821.8907422354940368527821715-224036.0
2Allamakee907917585371.491.282384e+05578337.728435e+0561269.762.586304e+05859530.09080212.613783138840.6190584.4129761640.6639255.2387332134946623559265845839656.0
3Appanoose836017582458.441.238400e+05582617.113762e+0553184.662.377927e+05858227.70830913.375589124620.6886544.2677471439.6253615.9612732008434689412505627955846.0
4Audubon204317517737.112.665607e+04139781.595476e+0513215.435.339370e+04204526.10938912.07282956780.3601622.3274801011.3052242.0221392420742717586412617-280677.0
5Benton779072571349.041.071595e+05476995.803559e+0547612.841.940870e+05773125.10503212.189062256990.3008291.8527121589.0399344.86520225111547266497010302912250.0
6Black Hawk1008012271107541.371.663275e+0610934121.192076e+07793399.133.982880e+0611822933.68784015.0249191329040.8895825.9697161658.46313371.2882902335744178574955247011283189.0
7Boone16005831154407.112.319393e+051202711.534234e+06115554.845.129844e+051651531.06172413.277107265320.6224564.3553011172.61857614.083864259984957866872107281696331.0
8Bremer17767453173326.872.603925e+051135511.487074e+06114021.434.976283e+051774628.04171713.042060247980.7156224.5980091254.61298814.14460126522556766860293851657708.0
9Buchanan12168450121504.751.824907e+05894291.140914e+0684484.183.814875e+051265330.14996113.504469209920.6027534.0245891437.3060028.80327523437519616142181611269271.0
10Buena Vista20832831226554.993.402451e+051141471.532283e+06105358.285.121251e+052159823.71169014.543544203321.0622665.1818951641.76858813.15532521256431825338275222087010.0
11Butler334880027994.244.206102e+04246152.786351e+0525010.209.331105e+04330828.20769311.140858147910.2236501.6909071714.4328291.9295012403047702596416120120103.0
12Calhoun378745030301.414.553630e+04272143.215042e+0527977.641.075994e+05362529.68260411.49146898460.3681702.8415231593.2420602.275235230494161150037424268571.0
13Carroll14460725143097.042.149119e+051142461.556718e+06115511.565.202122e+051456835.70923613.476729204370.7128255.6520801790.4072558.13669625094475076196086831200276.0
14Cass1003867598657.961.481477e+05745831.005639e+0675880.713.360579e+05998833.64616513.252889131570.7591405.7673261776.5442135.6221512178740820488845980931866.0
15Cedar742712567426.721.013055e+05445504.927014e+0538720.951.647957e+05816020.19554512.724413184540.4421812.0982421232.3781986.6213442474254321638937511748698.0
16Cerro Gordo50439956514818.967.732206e+053778604.892826e+06354343.001.635296e+065156631.71267113.808162430701.1972608.2271421497.77013934.428514254634474160148193504328297.0
17Cherokee763840072195.691.084669e+05584236.855181e+0552491.702.294092e+05740830.96777013.059553115080.6437264.5613231404.6576095.2738832450744635566965207501313.0
18Chickasaw432597537139.095.580234e+04270033.508606e+0530429.561.173801e+05403129.11936011.530256120230.3352742.5309461332.8008853.0244582244741372505305204242653.0
19Clarke565645054720.088.218209e+04372865.035890e+0536768.251.683819e+05560030.06820013.69630093090.6015683.949753954.5436775.8666782327145596547073701445791.0
20Clay15084525151187.652.270741e+051046451.367561e+06104603.094.569864e+051527629.91532013.073807163330.9352846.4044021305.38945211.70225525398435425646072821348963.0
21Clayton11180875108046.711.622809e+05499746.927068e+0552494.392.315675e+051048022.09613213.195825175900.5957932.9843312086.3774225.0230602230345873539057599954141.0
22Clinton25808600255757.843.841091e+052451802.928261e+06214882.769.784796e+052836434.49723713.627250473090.5995484.5421121701.08989516.674016235734617058681202233197853.0
23Crawford917822891880.621.379796e+05657908.643920e+0562535.952.889041e+05936030.86581813.822321169400.5525383.6916151794.5257205.2158632118144377537946413960452.0
24Dallas19782703208239.383.128030e+051660322.295133e+06151828.967.673762e+052128436.05413615.116572845160.2518341.7964521727.38953112.321483330516703784018252404125161.0
25Davis162350014206.102.134244e+0477469.618596e+048004.003.217266e+04155520.68981412.01723688600.1755080.9033861202.4971091.2931422197046597528553201-163676.0
26Decatur179825017836.602.679681e+04143891.763645e+0512243.115.898266e+04199429.58007014.40520381410.2449331.5038831443.4273321.3814341819537138480153223141811.0
27Delaware592965056982.708.557961e+04552127.547227e+0554845.422.522007e+05574943.86862213.760907173270.3317943.1653151275.4611074.5073892257847078598027062615781.0
28Des Moines28965403311376.144.674807e+052942573.579586e+06235900.791.195418e+063247436.81152615.174117397390.8171825.936254928.45641334.976332225554193753946170033968601.0
29Dickinson27326006291810.424.382073e+052227443.145660e+06224310.441.050838e+062811037.38307214.023690172431.63022713.0087831024.11472227.44809729459501745964875542628643.0
30Dubuque57322060601825.159.038367e+055012746.621999e+06460080.112.212785e+066075336.42265314.393144970030.6263004.7429471445.97530642.015240250454857361138368157310597.0
31Emmet598252560879.979.144739e+04386935.019728e+0539072.451.678386e+05607127.64595512.84723096580.6285984.0456051130.7690465.3689122437142286558444236391058.0
32Fayette910912881374.001.222869e+05702318.602044e+0568175.752.878219e+05873032.96929112.617454200540.4353253.3996091955.6987674.4638782156641055526278634955344.0
33Floyd805867879010.191.186743e+05826891.032124e+0674542.853.450133e+05822341.95710313.846056158730.5180504.6962041245.5797746.6017452141639467528086886913846.0
34Franklin614632560180.129.038277e+04363134.522624e+0535596.581.512945e+05605224.99909112.705219101700.5950843.5001551346.5419964.4944752250744863529174332475800.0
35Greene520052553392.208.019890e+04355114.774988e+0533966.781.596316e+05524830.41761114.05781890110.5823993.7694801385.5117803.7877702394743286601333996255811.0
36Grundy436925036670.315.510175e+04262103.018362e+0526267.961.009891e+05434123.26402711.490660123130.3525542.1333521097.9126463.95386626916561846815151314175.0
37Guthrie349360031390.414.717285e+04246782.940075e+0523023.479.837735e+04339828.95154512.769903106250.3198122.1669151659.3266952.0478192659050090619514544-201436.0
38Hamilton788232577319.081.161721e+05610117.369458e+0556950.352.466456e+05860328.66972112.940146150760.5706423.7775501425.3761636.0356002476546188614726540654023.0
39Hancock350942528221.144.241179e+04268203.061684e+0528072.831.025907e+05310133.08308910.906219108350.2862022.5909401435.9864142.159491227134731855922474158912.0
40Hardin15096700136991.102.058020e+051197721.657238e+06127383.225.542669e+051359240.77890813.009862172260.7890407.3948231749.4035317.76950524154446945761272961140430.0
41Harrison833390082437.451.238054e+05423805.046314e+0538339.841.687135e+05913118.47699913.162064141490.6453462.7097212000.7166094.5638652422151303632835987643413.0
42Henry821442584936.531.275347e+05729829.634271e+0567508.463.218625e+05902635.65947914.271205197730.4564813.4141741374.6370806.5660972305641983539857666959348.0
43Howard550185350667.537.612342e+04389465.268240e+0542435.811.762160e+05519733.90726412.41460993320.5569014.5473441553.1995753.3459962241746068555823944335431.0
44Humboldt483867546974.267.055336e+04400135.050238e+0538779.601.687570e+05496733.97563713.02292594870.5235594.0876571325.8227673.7463532456845282570634209175430.0
45Ida490715049504.887.433259e+04357714.983864e+0534949.871.665311e+05477734.86101514.26003769850.6838945.0035601061.8838004.4986092384144521586353052286374.0
46Iowa11862800102657.531.542814e+05744519.711115e+0577025.043.251289e+051116229.12819212.607737163110.6843234.7222761448.0324107.7083912672156053645786677789613.0
47Jackson12728325114843.671.724907e+05890981.130876e+0688084.973.778944e+051259330.00828712.838469194720.6467244.5236732075.5147836.06741023008424895421082891120072.0
48Jasper21363900214003.363.214453e+051318281.577656e+06121386.175.274477e+052304622.88673412.996996367080.6278203.3068041677.24703213.740373231604639656484148062486830.0
49Jefferson637702566329.729.959432e+04568317.276337e+0551721.612.430598e+05644637.70707414.068272180900.3563292.8591271228.4603525.2472192385344167553526846632088.0
50Johnson942011431094410.171.643462e+069277491.237021e+07784403.934.133645e+0610595539.01321315.7701991465470.7230105.3525761374.39846777.0919082800851380745475271511867678.0
51Jones15309100144067.532.164019e+05691018.898679e+0568061.752.974959e+051554519.13772513.074420204390.7605563.3299941603.9029919.69198322873479555916781811487368.0
52Keokuk272090023814.273.576359e+04135541.495608e+0513026.404.999794e+04279717.87556011.481359101190.2764111.2873211568.9905381.782675220884269853456440832440.0
53Kossuth14172628142789.092.144583e+051058811.479812e+06108007.854.948291e+051372036.06626313.700965151140.9077687.1462122171.3798586.3185632741548277610126697834826.0
54Lee24974375267566.064.017307e+052284662.902471e+06201627.379.692238e+052726135.55349314.395224346150.7875495.8248551207.58911922.574731213244244450630146103104487.0
55Linn1583534521731106.012.599613e+0615000581.806775e+071221208.856.036068e+0618409232.78832114.7949752216610.8305115.5093542164.86637485.0361952823953674692508613416780528.0
56Louisa316585031041.024.661010e+04206022.071461e+0514809.686.917951e+04373018.54678613.987210111420.3347691.329176980.9070153.8026032036750457549234346378171.0
57Lucas396867540531.536.087117e+04290113.761450e+0527391.101.257357e+05406430.93889813.73237986470.4699903.1677001154.2649693.5208551996743005566473689388043.0
58Lyon977687598319.841.476554e+05553187.766658e+0556613.632.593880e+05964726.88794313.718707117540.8207424.8165421520.4717016.3447422161349506573484442962622.0
59Madison739647574647.411.121233e+05544116.856734e+0551462.232.293326e+05779529.42048113.323817158480.4918603.2472381409.7472695.5293602571153183670996025523595.0
60Mahaska939855096610.211.451289e+05708618.393837e+0562911.852.806475e+05967029.02249513.342219221810.4359592.8362951216.4962547.94905921568450255787789751255159.0
61Marion19736978199812.053.000452e+051331751.735448e+06123141.995.798673e+052095827.66806714.093066331890.6314743.7103251528.87983213.708075246135337065817127232177923.0
62Marshall22552328231546.743.477674e+051926552.504138e+06168340.478.368677e+052350735.60078714.875434403120.5831274.1759391520.10776715.464035224074523255716155382791982.0
63Mills431720038409.795.770141e+04375474.345716e+0534164.641.452974e+05396836.61729312.719923149720.2650282.2819021220.9533403.2499192540059481735325605142072.0
64Mitchell819800080007.931.201944e+05323354.147482e+0534245.821.387916e+05822716.87025812.110916107630.7643783.1818101086.7946507.5699672282048506633564395819843.0
65Monona967138195956.981.440858e+05420205.197005e+0538043.921.736055e+051002317.32071313.66053988981.1264334.2755591656.7945626.04963422774413985109840501017900.0
66Monroe289152528812.614.327483e+04219712.896006e+0520937.969.680632e+04300032.26877313.83136778700.3811942.660478960.8146553.1223502122843245530523213138773.0
67Montgomery645707867065.991.006981e+05451675.570222e+0541624.631.861282e+05671327.72653413.382034102250.6565284.0708681140.3594825.8867402130138624505954558699564.0
68Muscatine28497706294376.854.421020e+052082962.626114e+06188612.008.775137e+053159127.77733113.923365429400.7357014.3924551700.94183118.572652241385102561445164123138265.0
69O'Brien13896728132245.411.986505e+05830291.057797e+0684579.603.539820e+051395425.36778212.506521140200.9952926.0327821620.4317448.61128524771440185939160691166399.0
70Osceola310747529141.204.377315e+04192442.562448e+0520104.988.574306e+04298128.76318712.74534160640.4915903.315465967.4526803.081288230634388958286268215017.0
71Page10230328103322.961.551729e+05811161.057103e+0675315.103.532156e+051065133.16266714.035741153910.6920284.8934511398.1186347.61809521204407785279163931134280.0
72Palo Alto939670089935.751.350478e+05419495.750134e+0544627.151.921719e+05866722.17283112.88483490470.9579974.9328121596.1283775.4300142307142800572083994797432.0
73Plymouth12560609132624.741.991814e+05943251.286709e+0693061.034.300381e+051317632.63798413.826506252000.5228573.6928982259.9536415.8302082806056379692619875953171.0
74Pocahontas431275037584.775.645243e+04243632.971895e+0525680.479.933154e+04416723.83766311.57258768860.6051413.7293741297.4314133.2117302338542105562503233166156.0
75Pottawattamie64280187680102.801.021141e+066183587.675324e+06521376.002.563352e+067247235.37023314.721285935820.7744225.5713282303.22523131.465442237824872860354367756828297.0
76Poweshiek16062900169084.712.539540e+05974741.306022e+0691675.424.365483e+051749624.95132114.246155185330.9440464.9466041580.07685711.07287925218509986574475551469057.0
77Ringgold168985014072.682.115156e+04100611.386808e+0511080.294.642018e+04147631.44998612.51599150680.2912392.1863241198.6243501.2314122185842336512692047-260232.0
78Sac793462575245.541.130183e+05395715.258452e+0542670.281.760915e+05715724.60409812.32345398760.7246864.3206031412.5991645.0665472383742986543044482528002.0
79Scott1124821021247750.111.873693e+0612370251.397399e+07914829.354.667888e+0613041935.79146915.2749671724740.7561665.3041581211.649675107.6375482740849964645136676514997255.0
80Shelby528695353350.358.009987e+04429635.279510e+0538570.771.762560e+05562731.32325913.687852118000.4768643.2687091299.1970144.3311372238944085555235085447766.0
81Sioux10681100102003.821.532028e+05771211.069835e+0679019.863.575951e+051030734.69439613.538806348980.2953462.2643092056.1781945.012698213335155760043115841725739.0
82Story65551096727466.681.092625e+065018326.730961e+06456414.622.250349e+067163631.41366514.747469970900.7378314.7009441943.69009536.855670254504824874278347367146093.0
83Tama724287569664.271.046049e+05476925.735157e+0543573.671.916543e+05765525.03648913.161979173190.4420002.5159462088.2266173.6657902304146288550116947655898.0
84Taylor207382520885.053.135727e+0493601.105983e+058433.723.693200e+04230416.02951413.11382862160.3706561.3567761571.1595421.4664332133540300481562679-60024.0
85Union956917590467.231.358550e+05665369.021116e+0568179.743.015172e+05902433.41281513.231373124200.7265705.4895121292.9456146.97941220435408795054652711018948.0
86Van Buren188307518970.162.848695e+04128061.742457e+0512768.445.823345e+04192030.32992213.64659272710.2640631.7560781549.1291701.2394062020940073500643108-40646.0
87Wapello24021278251492.943.776542e+051867242.292624e+06160501.097.658786e+052784327.50704314.284163349820.7959244.5881051104.86275025.200415223764009349309145523190720.0
88Warren19568800183359.552.753840e+051590191.939043e+06146705.536.481145e+051962033.03335913.217243496910.3948402.9523561617.49366512.129877287986203474042172622521533.0
89Washington10984531119302.231.791272e+05843891.140731e+0678241.803.810510e+051174532.44367814.579561222810.5271313.5115931666.5066967.04767623979507106046687411139096.0
90Wayne134450012518.511.880652e+0482831.054384e+058001.093.526299e+04126827.80992913.17799864520.1965281.2400951438.9309800.8812101879535425447842652-76407.0
91Webster22537959231995.403.484066e+052282522.705835e+06189009.949.042916e+052410637.51313414.315835367690.6556075.1404701793.29514113.442294226534080654129155802541044.0
92Winnebago800930072833.611.094025e+05559316.827292e+0556888.632.282976e+05766729.77664812.001155106310.7211935.3512021295.3907005.9186782268441871587004597693928.0
93Winneshiek11450225107156.181.609449e+05864831.189769e+0689488.043.977413e+051054137.73279013.295288205610.5126704.3523191587.4282736.64030023608506936155879971028921.0
94Woodbury61274387672801.781.010242e+066206807.695638e+06512631.642.570501e+066773237.95105515.0120231027790.6590064.9877082591.14487226.139797220694434355957390527104739.0
95Worth324747528918.724.343867e+04201122.466353e+0520602.258.250434e+04300027.50144711.97128175720.3961972.720847925.0696783.2429992724049673566593172-287641.0
96Wright557515051158.247.684774e+04465175.706450e+0544906.821.908072e+05546134.93996712.707313127790.4273423.5141111542.5600453.5402192306844035538905625365167.0
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "0 Adair 4455875 40182.62 6.037369e+04 \n", "1 Adams 1757403 18103.82 2.717139e+04 \n", "2 Allamakee 9079175 85371.49 1.282384e+05 \n", "3 Appanoose 8360175 82458.44 1.238400e+05 \n", "4 Audubon 2043175 17737.11 2.665607e+04 \n", "5 Benton 7790725 71349.04 1.071595e+05 \n", "6 Black Hawk 100801227 1107541.37 1.663275e+06 \n", "7 Boone 16005831 154407.11 2.319393e+05 \n", "8 Bremer 17767453 173326.87 2.603925e+05 \n", "9 Buchanan 12168450 121504.75 1.824907e+05 \n", "10 Buena Vista 20832831 226554.99 3.402451e+05 \n", "11 Butler 3348800 27994.24 4.206102e+04 \n", "12 Calhoun 3787450 30301.41 4.553630e+04 \n", "13 Carroll 14460725 143097.04 2.149119e+05 \n", "14 Cass 10038675 98657.96 1.481477e+05 \n", "15 Cedar 7427125 67426.72 1.013055e+05 \n", "16 Cerro Gordo 50439956 514818.96 7.732206e+05 \n", "17 Cherokee 7638400 72195.69 1.084669e+05 \n", "18 Chickasaw 4325975 37139.09 5.580234e+04 \n", "19 Clarke 5656450 54720.08 8.218209e+04 \n", "20 Clay 15084525 151187.65 2.270741e+05 \n", "21 Clayton 11180875 108046.71 1.622809e+05 \n", "22 Clinton 25808600 255757.84 3.841091e+05 \n", "23 Crawford 9178228 91880.62 1.379796e+05 \n", "24 Dallas 19782703 208239.38 3.128030e+05 \n", "25 Davis 1623500 14206.10 2.134244e+04 \n", "26 Decatur 1798250 17836.60 2.679681e+04 \n", "27 Delaware 5929650 56982.70 8.557961e+04 \n", "28 Des Moines 28965403 311376.14 4.674807e+05 \n", "29 Dickinson 27326006 291810.42 4.382073e+05 \n", "30 Dubuque 57322060 601825.15 9.038367e+05 \n", "31 Emmet 5982525 60879.97 9.144739e+04 \n", "32 Fayette 9109128 81374.00 1.222869e+05 \n", "33 Floyd 8058678 79010.19 1.186743e+05 \n", "34 Franklin 6146325 60180.12 9.038277e+04 \n", "35 Greene 5200525 53392.20 8.019890e+04 \n", "36 Grundy 4369250 36670.31 5.510175e+04 \n", "37 Guthrie 3493600 31390.41 4.717285e+04 \n", "38 Hamilton 7882325 77319.08 1.161721e+05 \n", "39 Hancock 3509425 28221.14 4.241179e+04 \n", "40 Hardin 15096700 136991.10 2.058020e+05 \n", "41 Harrison 8333900 82437.45 1.238054e+05 \n", "42 Henry 8214425 84936.53 1.275347e+05 \n", "43 Howard 5501853 50667.53 7.612342e+04 \n", "44 Humboldt 4838675 46974.26 7.055336e+04 \n", "45 Ida 4907150 49504.88 7.433259e+04 \n", "46 Iowa 11862800 102657.53 1.542814e+05 \n", "47 Jackson 12728325 114843.67 1.724907e+05 \n", "48 Jasper 21363900 214003.36 3.214453e+05 \n", "49 Jefferson 6377025 66329.72 9.959432e+04 \n", "50 Johnson 94201143 1094410.17 1.643462e+06 \n", "51 Jones 15309100 144067.53 2.164019e+05 \n", "52 Keokuk 2720900 23814.27 3.576359e+04 \n", "53 Kossuth 14172628 142789.09 2.144583e+05 \n", "54 Lee 24974375 267566.06 4.017307e+05 \n", "55 Linn 158353452 1731106.01 2.599613e+06 \n", "56 Louisa 3165850 31041.02 4.661010e+04 \n", "57 Lucas 3968675 40531.53 6.087117e+04 \n", "58 Lyon 9776875 98319.84 1.476554e+05 \n", "59 Madison 7396475 74647.41 1.121233e+05 \n", "60 Mahaska 9398550 96610.21 1.451289e+05 \n", "61 Marion 19736978 199812.05 3.000452e+05 \n", "62 Marshall 22552328 231546.74 3.477674e+05 \n", "63 Mills 4317200 38409.79 5.770141e+04 \n", "64 Mitchell 8198000 80007.93 1.201944e+05 \n", "65 Monona 9671381 95956.98 1.440858e+05 \n", "66 Monroe 2891525 28812.61 4.327483e+04 \n", "67 Montgomery 6457078 67065.99 1.006981e+05 \n", "68 Muscatine 28497706 294376.85 4.421020e+05 \n", "69 O'Brien 13896728 132245.41 1.986505e+05 \n", "70 Osceola 3107475 29141.20 4.377315e+04 \n", "71 Page 10230328 103322.96 1.551729e+05 \n", "72 Palo Alto 9396700 89935.75 1.350478e+05 \n", "73 Plymouth 12560609 132624.74 1.991814e+05 \n", "74 Pocahontas 4312750 37584.77 5.645243e+04 \n", "75 Pottawattamie 64280187 680102.80 1.021141e+06 \n", "76 Poweshiek 16062900 169084.71 2.539540e+05 \n", "77 Ringgold 1689850 14072.68 2.115156e+04 \n", "78 Sac 7934625 75245.54 1.130183e+05 \n", "79 Scott 112482102 1247750.11 1.873693e+06 \n", "80 Shelby 5286953 53350.35 8.009987e+04 \n", "81 Sioux 10681100 102003.82 1.532028e+05 \n", "82 Story 65551096 727466.68 1.092625e+06 \n", "83 Tama 7242875 69664.27 1.046049e+05 \n", "84 Taylor 2073825 20885.05 3.135727e+04 \n", "85 Union 9569175 90467.23 1.358550e+05 \n", "86 Van Buren 1883075 18970.16 2.848695e+04 \n", "87 Wapello 24021278 251492.94 3.776542e+05 \n", "88 Warren 19568800 183359.55 2.753840e+05 \n", "89 Washington 10984531 119302.23 1.791272e+05 \n", "90 Wayne 1344500 12518.51 1.880652e+04 \n", "91 Webster 22537959 231995.40 3.484066e+05 \n", "92 Winnebago 8009300 72833.61 1.094025e+05 \n", "93 Winneshiek 11450225 107156.18 1.609449e+05 \n", "94 Woodbury 61274387 672801.78 1.010242e+06 \n", "95 Worth 3247475 28918.72 4.343867e+04 \n", "96 Wright 5575150 51158.24 7.684774e+04 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "0 33807 4.108056e+05 32522.35 1.374661e+05 4420 \n", "1 8446 1.005968e+05 7547.62 3.361478e+04 1797 \n", "2 57833 7.728435e+05 61269.76 2.586304e+05 8595 \n", "3 58261 7.113762e+05 53184.66 2.377927e+05 8582 \n", "4 13978 1.595476e+05 13215.43 5.339370e+04 2045 \n", "5 47699 5.803559e+05 47612.84 1.940870e+05 7731 \n", "6 1093412 1.192076e+07 793399.13 3.982880e+06 118229 \n", "7 120271 1.534234e+06 115554.84 5.129844e+05 16515 \n", "8 113551 1.487074e+06 114021.43 4.976283e+05 17746 \n", "9 89429 1.140914e+06 84484.18 3.814875e+05 12653 \n", "10 114147 1.532283e+06 105358.28 5.121251e+05 21598 \n", "11 24615 2.786351e+05 25010.20 9.331105e+04 3308 \n", "12 27214 3.215042e+05 27977.64 1.075994e+05 3625 \n", "13 114246 1.556718e+06 115511.56 5.202122e+05 14568 \n", "14 74583 1.005639e+06 75880.71 3.360579e+05 9988 \n", "15 44550 4.927014e+05 38720.95 1.647957e+05 8160 \n", "16 377860 4.892826e+06 354343.00 1.635296e+06 51566 \n", "17 58423 6.855181e+05 52491.70 2.294092e+05 7408 \n", "18 27003 3.508606e+05 30429.56 1.173801e+05 4031 \n", "19 37286 5.035890e+05 36768.25 1.683819e+05 5600 \n", "20 104645 1.367561e+06 104603.09 4.569864e+05 15276 \n", "21 49974 6.927068e+05 52494.39 2.315675e+05 10480 \n", "22 245180 2.928261e+06 214882.76 9.784796e+05 28364 \n", "23 65790 8.643920e+05 62535.95 2.889041e+05 9360 \n", "24 166032 2.295133e+06 151828.96 7.673762e+05 21284 \n", "25 7746 9.618596e+04 8004.00 3.217266e+04 1555 \n", "26 14389 1.763645e+05 12243.11 5.898266e+04 1994 \n", "27 55212 7.547227e+05 54845.42 2.522007e+05 5749 \n", "28 294257 3.579586e+06 235900.79 1.195418e+06 32474 \n", "29 222744 3.145660e+06 224310.44 1.050838e+06 28110 \n", "30 501274 6.621999e+06 460080.11 2.212785e+06 60753 \n", "31 38693 5.019728e+05 39072.45 1.678386e+05 6071 \n", "32 70231 8.602044e+05 68175.75 2.878219e+05 8730 \n", "33 82689 1.032124e+06 74542.85 3.450133e+05 8223 \n", "34 36313 4.522624e+05 35596.58 1.512945e+05 6052 \n", "35 35511 4.774988e+05 33966.78 1.596316e+05 5248 \n", "36 26210 3.018362e+05 26267.96 1.009891e+05 4341 \n", "37 24678 2.940075e+05 23023.47 9.837735e+04 3398 \n", "38 61011 7.369458e+05 56950.35 2.466456e+05 8603 \n", "39 26820 3.061684e+05 28072.83 1.025907e+05 3101 \n", "40 119772 1.657238e+06 127383.22 5.542669e+05 13592 \n", "41 42380 5.046314e+05 38339.84 1.687135e+05 9131 \n", "42 72982 9.634271e+05 67508.46 3.218625e+05 9026 \n", "43 38946 5.268240e+05 42435.81 1.762160e+05 5197 \n", "44 40013 5.050238e+05 38779.60 1.687570e+05 4967 \n", "45 35771 4.983864e+05 34949.87 1.665311e+05 4777 \n", "46 74451 9.711115e+05 77025.04 3.251289e+05 11162 \n", "47 89098 1.130876e+06 88084.97 3.778944e+05 12593 \n", "48 131828 1.577656e+06 121386.17 5.274477e+05 23046 \n", "49 56831 7.276337e+05 51721.61 2.430598e+05 6446 \n", "50 927749 1.237021e+07 784403.93 4.133645e+06 105955 \n", "51 69101 8.898679e+05 68061.75 2.974959e+05 15545 \n", "52 13554 1.495608e+05 13026.40 4.999794e+04 2797 \n", "53 105881 1.479812e+06 108007.85 4.948291e+05 13720 \n", "54 228466 2.902471e+06 201627.37 9.692238e+05 27261 \n", "55 1500058 1.806775e+07 1221208.85 6.036068e+06 184092 \n", "56 20602 2.071461e+05 14809.68 6.917951e+04 3730 \n", "57 29011 3.761450e+05 27391.10 1.257357e+05 4064 \n", "58 55318 7.766658e+05 56613.63 2.593880e+05 9647 \n", "59 54411 6.856734e+05 51462.23 2.293326e+05 7795 \n", "60 70861 8.393837e+05 62911.85 2.806475e+05 9670 \n", "61 133175 1.735448e+06 123141.99 5.798673e+05 20958 \n", "62 192655 2.504138e+06 168340.47 8.368677e+05 23507 \n", "63 37547 4.345716e+05 34164.64 1.452974e+05 3968 \n", "64 32335 4.147482e+05 34245.82 1.387916e+05 8227 \n", "65 42020 5.197005e+05 38043.92 1.736055e+05 10023 \n", "66 21971 2.896006e+05 20937.96 9.680632e+04 3000 \n", "67 45167 5.570222e+05 41624.63 1.861282e+05 6713 \n", "68 208296 2.626114e+06 188612.00 8.775137e+05 31591 \n", "69 83029 1.057797e+06 84579.60 3.539820e+05 13954 \n", "70 19244 2.562448e+05 20104.98 8.574306e+04 2981 \n", "71 81116 1.057103e+06 75315.10 3.532156e+05 10651 \n", "72 41949 5.750134e+05 44627.15 1.921719e+05 8667 \n", "73 94325 1.286709e+06 93061.03 4.300381e+05 13176 \n", "74 24363 2.971895e+05 25680.47 9.933154e+04 4167 \n", "75 618358 7.675324e+06 521376.00 2.563352e+06 72472 \n", "76 97474 1.306022e+06 91675.42 4.365483e+05 17496 \n", "77 10061 1.386808e+05 11080.29 4.642018e+04 1476 \n", "78 39571 5.258452e+05 42670.28 1.760915e+05 7157 \n", "79 1237025 1.397399e+07 914829.35 4.667888e+06 130419 \n", "80 42963 5.279510e+05 38570.77 1.762560e+05 5627 \n", "81 77121 1.069835e+06 79019.86 3.575951e+05 10307 \n", "82 501832 6.730961e+06 456414.62 2.250349e+06 71636 \n", "83 47692 5.735157e+05 43573.67 1.916543e+05 7655 \n", "84 9360 1.105983e+05 8433.72 3.693200e+04 2304 \n", "85 66536 9.021116e+05 68179.74 3.015172e+05 9024 \n", "86 12806 1.742457e+05 12768.44 5.823345e+04 1920 \n", "87 186724 2.292624e+06 160501.09 7.658786e+05 27843 \n", "88 159019 1.939043e+06 146705.53 6.481145e+05 19620 \n", "89 84389 1.140731e+06 78241.80 3.810510e+05 11745 \n", "90 8283 1.054384e+05 8001.09 3.526299e+04 1268 \n", "91 228252 2.705835e+06 189009.94 9.042916e+05 24106 \n", "92 55931 6.827292e+05 56888.63 2.282976e+05 7667 \n", "93 86483 1.189769e+06 89488.04 3.977413e+05 10541 \n", "94 620680 7.695638e+06 512631.64 2.570501e+06 67732 \n", "95 20112 2.466353e+05 20602.25 8.250434e+04 3000 \n", "96 46517 5.706450e+05 44906.82 1.908072e+05 5461 \n", "\n", " average_store_profit sales_per_litters population \\\n", "0 31.100932 12.631486 7092 \n", "1 18.706055 13.328281 3693 \n", "2 30.090802 12.613783 13884 \n", "3 27.708309 13.375589 12462 \n", "4 26.109389 12.072829 5678 \n", "5 25.105032 12.189062 25699 \n", "6 33.687840 15.024919 132904 \n", "7 31.061724 13.277107 26532 \n", "8 28.041717 13.042060 24798 \n", "9 30.149961 13.504469 20992 \n", "10 23.711690 14.543544 20332 \n", "11 28.207693 11.140858 14791 \n", "12 29.682604 11.491468 9846 \n", "13 35.709236 13.476729 20437 \n", "14 33.646165 13.252889 13157 \n", "15 20.195545 12.724413 18454 \n", "16 31.712671 13.808162 43070 \n", "17 30.967770 13.059553 11508 \n", "18 29.119360 11.530256 12023 \n", "19 30.068200 13.696300 9309 \n", "20 29.915320 13.073807 16333 \n", "21 22.096132 13.195825 17590 \n", "22 34.497237 13.627250 47309 \n", "23 30.865818 13.822321 16940 \n", "24 36.054136 15.116572 84516 \n", "25 20.689814 12.017236 8860 \n", "26 29.580070 14.405203 8141 \n", "27 43.868622 13.760907 17327 \n", "28 36.811526 15.174117 39739 \n", "29 37.383072 14.023690 17243 \n", "30 36.422653 14.393144 97003 \n", "31 27.645955 12.847230 9658 \n", "32 32.969291 12.617454 20054 \n", "33 41.957103 13.846056 15873 \n", "34 24.999091 12.705219 10170 \n", "35 30.417611 14.057818 9011 \n", "36 23.264027 11.490660 12313 \n", "37 28.951545 12.769903 10625 \n", "38 28.669721 12.940146 15076 \n", "39 33.083089 10.906219 10835 \n", "40 40.778908 13.009862 17226 \n", "41 18.476999 13.162064 14149 \n", "42 35.659479 14.271205 19773 \n", "43 33.907264 12.414609 9332 \n", "44 33.975637 13.022925 9487 \n", "45 34.861015 14.260037 6985 \n", "46 29.128192 12.607737 16311 \n", "47 30.008287 12.838469 19472 \n", "48 22.886734 12.996996 36708 \n", "49 37.707074 14.068272 18090 \n", "50 39.013213 15.770199 146547 \n", "51 19.137725 13.074420 20439 \n", "52 17.875560 11.481359 10119 \n", "53 36.066263 13.700965 15114 \n", "54 35.553493 14.395224 34615 \n", "55 32.788321 14.794975 221661 \n", "56 18.546786 13.987210 11142 \n", "57 30.938898 13.732379 8647 \n", "58 26.887943 13.718707 11754 \n", "59 29.420481 13.323817 15848 \n", "60 29.022495 13.342219 22181 \n", "61 27.668067 14.093066 33189 \n", "62 35.600787 14.875434 40312 \n", "63 36.617293 12.719923 14972 \n", "64 16.870258 12.110916 10763 \n", "65 17.320713 13.660539 8898 \n", "66 32.268773 13.831367 7870 \n", "67 27.726534 13.382034 10225 \n", "68 27.777331 13.923365 42940 \n", "69 25.367782 12.506521 14020 \n", "70 28.763187 12.745341 6064 \n", "71 33.162667 14.035741 15391 \n", "72 22.172831 12.884834 9047 \n", "73 32.637984 13.826506 25200 \n", "74 23.837663 11.572587 6886 \n", "75 35.370233 14.721285 93582 \n", "76 24.951321 14.246155 18533 \n", "77 31.449986 12.515991 5068 \n", "78 24.604098 12.323453 9876 \n", "79 35.791469 15.274967 172474 \n", "80 31.323259 13.687852 11800 \n", "81 34.694396 13.538806 34898 \n", "82 31.413665 14.747469 97090 \n", "83 25.036489 13.161979 17319 \n", "84 16.029514 13.113828 6216 \n", "85 33.412815 13.231373 12420 \n", "86 30.329922 13.646592 7271 \n", "87 27.507043 14.284163 34982 \n", "88 33.033359 13.217243 49691 \n", "89 32.443678 14.579561 22281 \n", "90 27.809929 13.177998 6452 \n", "91 37.513134 14.315835 36769 \n", "92 29.776648 12.001155 10631 \n", "93 37.732790 13.295288 20561 \n", "94 37.951055 15.012023 102779 \n", "95 27.501447 11.971281 7572 \n", "96 34.939967 12.707313 12779 \n", "\n", " store_population_ratio consumption_per_capita area \\\n", "0 0.623237 4.585780 1146.149874 \n", "1 0.486596 2.043764 950.420482 \n", "2 0.619058 4.412976 1640.663925 \n", "3 0.688654 4.267747 1439.625361 \n", "4 0.360162 2.327480 1011.305224 \n", "5 0.300829 1.852712 1589.039934 \n", "6 0.889582 5.969716 1658.463133 \n", "7 0.622456 4.355301 1172.618576 \n", "8 0.715622 4.598009 1254.612988 \n", "9 0.602753 4.024589 1437.306002 \n", "10 1.062266 5.181895 1641.768588 \n", "11 0.223650 1.690907 1714.432829 \n", "12 0.368170 2.841523 1593.242060 \n", "13 0.712825 5.652080 1790.407255 \n", "14 0.759140 5.767326 1776.544213 \n", "15 0.442181 2.098242 1232.378198 \n", "16 1.197260 8.227142 1497.770139 \n", "17 0.643726 4.561323 1404.657609 \n", "18 0.335274 2.530946 1332.800885 \n", "19 0.601568 3.949753 954.543677 \n", "20 0.935284 6.404402 1305.389452 \n", "21 0.595793 2.984331 2086.377422 \n", "22 0.599548 4.542112 1701.089895 \n", "23 0.552538 3.691615 1794.525720 \n", "24 0.251834 1.796452 1727.389531 \n", "25 0.175508 0.903386 1202.497109 \n", "26 0.244933 1.503883 1443.427332 \n", "27 0.331794 3.165315 1275.461107 \n", "28 0.817182 5.936254 928.456413 \n", "29 1.630227 13.008783 1024.114722 \n", "30 0.626300 4.742947 1445.975306 \n", "31 0.628598 4.045605 1130.769046 \n", "32 0.435325 3.399609 1955.698767 \n", "33 0.518050 4.696204 1245.579774 \n", "34 0.595084 3.500155 1346.541996 \n", "35 0.582399 3.769480 1385.511780 \n", "36 0.352554 2.133352 1097.912646 \n", "37 0.319812 2.166915 1659.326695 \n", "38 0.570642 3.777550 1425.376163 \n", "39 0.286202 2.590940 1435.986414 \n", "40 0.789040 7.394823 1749.403531 \n", "41 0.645346 2.709721 2000.716609 \n", "42 0.456481 3.414174 1374.637080 \n", "43 0.556901 4.547344 1553.199575 \n", "44 0.523559 4.087657 1325.822767 \n", "45 0.683894 5.003560 1061.883800 \n", "46 0.684323 4.722276 1448.032410 \n", "47 0.646724 4.523673 2075.514783 \n", "48 0.627820 3.306804 1677.247032 \n", "49 0.356329 2.859127 1228.460352 \n", "50 0.723010 5.352576 1374.398467 \n", "51 0.760556 3.329994 1603.902991 \n", "52 0.276411 1.287321 1568.990538 \n", "53 0.907768 7.146212 2171.379858 \n", "54 0.787549 5.824855 1207.589119 \n", "55 0.830511 5.509354 2164.866374 \n", "56 0.334769 1.329176 980.907015 \n", "57 0.469990 3.167700 1154.264969 \n", "58 0.820742 4.816542 1520.471701 \n", "59 0.491860 3.247238 1409.747269 \n", "60 0.435959 2.836295 1216.496254 \n", "61 0.631474 3.710325 1528.879832 \n", "62 0.583127 4.175939 1520.107767 \n", "63 0.265028 2.281902 1220.953340 \n", "64 0.764378 3.181810 1086.794650 \n", "65 1.126433 4.275559 1656.794562 \n", "66 0.381194 2.660478 960.814655 \n", "67 0.656528 4.070868 1140.359482 \n", "68 0.735701 4.392455 1700.941831 \n", "69 0.995292 6.032782 1620.431744 \n", "70 0.491590 3.315465 967.452680 \n", "71 0.692028 4.893451 1398.118634 \n", "72 0.957997 4.932812 1596.128377 \n", "73 0.522857 3.692898 2259.953641 \n", "74 0.605141 3.729374 1297.431413 \n", "75 0.774422 5.571328 2303.225231 \n", "76 0.944046 4.946604 1580.076857 \n", "77 0.291239 2.186324 1198.624350 \n", "78 0.724686 4.320603 1412.599164 \n", "79 0.756166 5.304158 1211.649675 \n", "80 0.476864 3.268709 1299.197014 \n", "81 0.295346 2.264309 2056.178194 \n", "82 0.737831 4.700944 1943.690095 \n", "83 0.442000 2.515946 2088.226617 \n", "84 0.370656 1.356776 1571.159542 \n", "85 0.726570 5.489512 1292.945614 \n", "86 0.264063 1.756078 1549.129170 \n", "87 0.795924 4.588105 1104.862750 \n", "88 0.394840 2.952356 1617.493665 \n", "89 0.527131 3.511593 1666.506696 \n", "90 0.196528 1.240095 1438.930980 \n", "91 0.655607 5.140470 1793.295141 \n", "92 0.721193 5.351202 1295.390700 \n", "93 0.512670 4.352319 1587.428273 \n", "94 0.659006 4.987708 2591.144872 \n", "95 0.396197 2.720847 925.069678 \n", "96 0.427342 3.514111 1542.560045 \n", "\n", " stores_per_area per_capita_income median_household_income \\\n", "0 3.856389 23497 45202 \n", "1 1.890742 23549 40368 \n", "2 5.238733 21349 46623 \n", "3 5.961273 20084 34689 \n", "4 2.022139 24207 42717 \n", "5 4.865202 25111 54726 \n", "6 71.288290 23357 44178 \n", "7 14.083864 25998 49578 \n", "8 14.144601 26522 55676 \n", "9 8.803275 23437 51961 \n", "10 13.155325 21256 43182 \n", "11 1.929501 24030 47702 \n", "12 2.275235 23049 41611 \n", "13 8.136696 25094 47507 \n", "14 5.622151 21787 40820 \n", "15 6.621344 24742 54321 \n", "16 34.428514 25463 44741 \n", "17 5.273883 24507 44635 \n", "18 3.024458 22447 41372 \n", "19 5.866678 23271 45596 \n", "20 11.702255 25398 43542 \n", "21 5.023060 22303 45873 \n", "22 16.674016 23573 46170 \n", "23 5.215863 21181 44377 \n", "24 12.321483 33051 67037 \n", "25 1.293142 21970 46597 \n", "26 1.381434 18195 37138 \n", "27 4.507389 22578 47078 \n", "28 34.976332 22555 41937 \n", "29 27.448097 29459 50174 \n", "30 42.015240 25045 48573 \n", "31 5.368912 24371 42286 \n", "32 4.463878 21566 41055 \n", "33 6.601745 21416 39467 \n", "34 4.494475 22507 44863 \n", "35 3.787770 23947 43286 \n", "36 3.953866 26916 56184 \n", "37 2.047819 26590 50090 \n", "38 6.035600 24765 46188 \n", "39 2.159491 22713 47318 \n", "40 7.769505 24154 44694 \n", "41 4.563865 24221 51303 \n", "42 6.566097 23056 41983 \n", "43 3.345996 22417 46068 \n", "44 3.746353 24568 45282 \n", "45 4.498609 23841 44521 \n", "46 7.708391 26721 56053 \n", "47 6.067410 23008 42489 \n", "48 13.740373 23160 46396 \n", "49 5.247219 23853 44167 \n", "50 77.091908 28008 51380 \n", "51 9.691983 22873 47955 \n", "52 1.782675 22088 42698 \n", "53 6.318563 27415 48277 \n", "54 22.574731 21324 42444 \n", "55 85.036195 28239 53674 \n", "56 3.802603 20367 50457 \n", "57 3.520855 19967 43005 \n", "58 6.344742 21613 49506 \n", "59 5.529360 25711 53183 \n", "60 7.949059 21568 45025 \n", "61 13.708075 24613 53370 \n", "62 15.464035 22407 45232 \n", "63 3.249919 25400 59481 \n", "64 7.569967 22820 48506 \n", "65 6.049634 22774 41398 \n", "66 3.122350 21228 43245 \n", "67 5.886740 21301 38624 \n", "68 18.572652 24138 51025 \n", "69 8.611285 24771 44018 \n", "70 3.081288 23063 43889 \n", "71 7.618095 21204 40778 \n", "72 5.430014 23071 42800 \n", "73 5.830208 28060 56379 \n", "74 3.211730 23385 42105 \n", "75 31.465442 23782 48728 \n", "76 11.072879 25218 50998 \n", "77 1.231412 21858 42336 \n", "78 5.066547 23837 42986 \n", "79 107.637548 27408 49964 \n", "80 4.331137 22389 44085 \n", "81 5.012698 21333 51557 \n", "82 36.855670 25450 48248 \n", "83 3.665790 23041 46288 \n", "84 1.466433 21335 40300 \n", "85 6.979412 20435 40879 \n", "86 1.239406 20209 40073 \n", "87 25.200415 22376 40093 \n", "88 12.129877 28798 62034 \n", "89 7.047676 23979 50710 \n", "90 0.881210 18795 35425 \n", "91 13.442294 22653 40806 \n", "92 5.918678 22684 41871 \n", "93 6.640300 23608 50693 \n", "94 26.139797 22069 44343 \n", "95 3.242999 27240 49673 \n", "96 3.540219 23068 44035 \n", "\n", " median_family_income number_of_households sales_prediction_dollars \n", "0 57287 3292 221482.0 \n", "1 52782 1715 -224036.0 \n", "2 55926 5845 839656.0 \n", "3 41250 5627 955846.0 \n", "4 58641 2617 -280677.0 \n", "5 64970 10302 912250.0 \n", "6 57495 52470 11283189.0 \n", "7 66872 10728 1696331.0 \n", "8 68602 9385 1657708.0 \n", "9 61421 8161 1269271.0 \n", "10 53382 7522 2087010.0 \n", "11 59641 6120 120103.0 \n", "12 50037 4242 68571.0 \n", "13 61960 8683 1200276.0 \n", "14 48884 5980 931866.0 \n", "15 63893 7511 748698.0 \n", "16 60148 19350 4328297.0 \n", "17 56696 5207 501313.0 \n", "18 50530 5204 242653.0 \n", "19 54707 3701 445791.0 \n", "20 56460 7282 1348963.0 \n", "21 53905 7599 954141.0 \n", "22 58681 20223 3197853.0 \n", "23 53794 6413 960452.0 \n", "24 84018 25240 4125161.0 \n", "25 52855 3201 -163676.0 \n", "26 48015 3223 141811.0 \n", "27 59802 7062 615781.0 \n", "28 53946 17003 3968601.0 \n", "29 59648 7554 2628643.0 \n", "30 61138 36815 7310597.0 \n", "31 55844 4236 391058.0 \n", "32 52627 8634 955344.0 \n", "33 52808 6886 913846.0 \n", "34 52917 4332 475800.0 \n", "35 60133 3996 255811.0 \n", "36 68151 5131 4175.0 \n", "37 61951 4544 -201436.0 \n", "38 61472 6540 654023.0 \n", "39 55922 4741 58912.0 \n", "40 57612 7296 1140430.0 \n", "41 63283 5987 643413.0 \n", "42 53985 7666 959348.0 \n", "43 55582 3944 335431.0 \n", "44 57063 4209 175430.0 \n", "45 58635 3052 286374.0 \n", "46 64578 6677 789613.0 \n", "47 54210 8289 1120072.0 \n", "48 56484 14806 2486830.0 \n", "49 55352 6846 632088.0 \n", "50 74547 52715 11867678.0 \n", "51 59167 8181 1487368.0 \n", "52 53456 4408 32440.0 \n", "53 61012 6697 834826.0 \n", "54 50630 14610 3104487.0 \n", "55 69250 86134 16780528.0 \n", "56 54923 4346 378171.0 \n", "57 56647 3689 388043.0 \n", "58 57348 4442 962622.0 \n", "59 67099 6025 523595.0 \n", "60 57877 8975 1255159.0 \n", "61 65817 12723 2177923.0 \n", "62 55716 15538 2791982.0 \n", "63 73532 5605 142072.0 \n", "64 63356 4395 819843.0 \n", "65 51098 4050 1017900.0 \n", "66 53052 3213 138773.0 \n", "67 50595 4558 699564.0 \n", "68 61445 16412 3138265.0 \n", "69 59391 6069 1166399.0 \n", "70 58286 2682 15017.0 \n", "71 52791 6393 1134280.0 \n", "72 57208 3994 797432.0 \n", "73 69261 9875 953171.0 \n", "74 56250 3233 166156.0 \n", "75 60354 36775 6828297.0 \n", "76 65744 7555 1469057.0 \n", "77 51269 2047 -260232.0 \n", "78 54304 4482 528002.0 \n", "79 64513 66765 14997255.0 \n", "80 55523 5085 447766.0 \n", "81 60043 11584 1725739.0 \n", "82 74278 34736 7146093.0 \n", "83 55011 6947 655898.0 \n", "84 48156 2679 -60024.0 \n", "85 50546 5271 1018948.0 \n", "86 50064 3108 -40646.0 \n", "87 49309 14552 3190720.0 \n", "88 74042 17262 2521533.0 \n", "89 60466 8741 1139096.0 \n", "90 44784 2652 -76407.0 \n", "91 54129 15580 2541044.0 \n", "92 58700 4597 693928.0 \n", "93 61558 7997 1028921.0 \n", "94 55957 39052 7104739.0 \n", "95 56659 3172 -287641.0 \n", "96 53890 5625 365167.0 " ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_with_pred = df_final.copy()\n", "df_with_pred['sales_prediction_dollars'] = pred\n", "df_with_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that some of the predicted values are negative. This also happened in other works [1] and the reason is not quite clear. We remove them." ] }, { "cell_type": "code", "execution_count": 332, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countybottle_volume_mlstate_bottle_coststate_bottle_retailbottles_soldsale_dollarsvolume_sold_litersprofitnum_storesaverage_store_profitsales_per_litterspopulationstore_population_ratioconsumption_per_capitaareastores_per_areaper_capita_incomemedian_household_incomemedian_family_incomenumber_of_householdssales_prediction_dollars
55Linn1583534521731106.012.599613e+0615000581.806775e+071221208.856.036068e+0618409232.78832114.7949752216610.8305115.5093542164.86637485.0361952823953674692508613416780528.0
79Scott1124821021247750.111.873693e+0612370251.397399e+07914829.354.667888e+0613041935.79146915.2749671724740.7561665.3041581211.649675107.6375482740849964645136676514997255.0
50Johnson942011431094410.171.643462e+069277491.237021e+07784403.934.133645e+0610595539.01321315.7701991465470.7230105.3525761374.39846777.0919082800851380745475271511867678.0
6Black Hawk1008012271107541.371.663275e+0610934121.192076e+07793399.133.982880e+0611822933.68784015.0249191329040.8895825.9697161658.46313371.2882902335744178574955247011283189.0
30Dubuque57322060601825.159.038367e+055012746.621999e+06460080.112.212785e+066075336.42265314.393144970030.6263004.7429471445.97530642.015240250454857361138368157310597.0
82Story65551096727466.681.092625e+065018326.730961e+06456414.622.250349e+067163631.41366514.747469970900.7378314.7009441943.69009536.855670254504824874278347367146093.0
94Woodbury61274387672801.781.010242e+066206807.695638e+06512631.642.570501e+066773237.95105515.0120231027790.6590064.9877082591.14487226.139797220694434355957390527104739.0
75Pottawattamie64280187680102.801.021141e+066183587.675324e+06521376.002.563352e+067247235.37023314.721285935820.7744225.5713282303.22523131.465442237824872860354367756828297.0
16Cerro Gordo50439956514818.967.732206e+053778604.892826e+06354343.001.635296e+065156631.71267113.808162430701.1972608.2271421497.77013934.428514254634474160148193504328297.0
24Dallas19782703208239.383.128030e+051660322.295133e+06151828.967.673762e+052128436.05413615.116572845160.2518341.7964521727.38953112.321483330516703784018252404125161.0
28Des Moines28965403311376.144.674807e+052942573.579586e+06235900.791.195418e+063247436.81152615.174117397390.8171825.936254928.45641334.976332225554193753946170033968601.0
22Clinton25808600255757.843.841091e+052451802.928261e+06214882.769.784796e+052836434.49723713.627250473090.5995484.5421121701.08989516.674016235734617058681202233197853.0
87Wapello24021278251492.943.776542e+051867242.292624e+06160501.097.658786e+052784327.50704314.284163349820.7959244.5881051104.86275025.200415223764009349309145523190720.0
68Muscatine28497706294376.854.421020e+052082962.626114e+06188612.008.775137e+053159127.77733113.923365429400.7357014.3924551700.94183118.572652241385102561445164123138265.0
54Lee24974375267566.064.017307e+052284662.902471e+06201627.379.692238e+052726135.55349314.395224346150.7875495.8248551207.58911922.574731213244244450630146103104487.0
62Marshall22552328231546.743.477674e+051926552.504138e+06168340.478.368677e+052350735.60078714.875434403120.5831274.1759391520.10776715.464035224074523255716155382791982.0
29Dickinson27326006291810.424.382073e+052227443.145660e+06224310.441.050838e+062811037.38307214.023690172431.63022713.0087831024.11472227.44809729459501745964875542628643.0
91Webster22537959231995.403.484066e+052282522.705835e+06189009.949.042916e+052410637.51313414.315835367690.6556075.1404701793.29514113.442294226534080654129155802541044.0
88Warren19568800183359.552.753840e+051590191.939043e+06146705.536.481145e+051962033.03335913.217243496910.3948402.9523561617.49366512.129877287986203474042172622521533.0
48Jasper21363900214003.363.214453e+051318281.577656e+06121386.175.274477e+052304622.88673412.996996367080.6278203.3068041677.24703213.740373231604639656484148062486830.0
61Marion19736978199812.053.000452e+051331751.735448e+06123141.995.798673e+052095827.66806714.093066331890.6314743.7103251528.87983213.708075246135337065817127232177923.0
10Buena Vista20832831226554.993.402451e+051141471.532283e+06105358.285.121251e+052159823.71169014.543544203321.0622665.1818951641.76858813.15532521256431825338275222087010.0
81Sioux10681100102003.821.532028e+05771211.069835e+0679019.863.575951e+051030734.69439613.538806348980.2953462.2643092056.1781945.012698213335155760043115841725739.0
7Boone16005831154407.112.319393e+051202711.534234e+06115554.845.129844e+051651531.06172413.277107265320.6224564.3553011172.61857614.083864259984957866872107281696331.0
8Bremer17767453173326.872.603925e+051135511.487074e+06114021.434.976283e+051774628.04171713.042060247980.7156224.5980091254.61298814.14460126522556766860293851657708.0
51Jones15309100144067.532.164019e+05691018.898679e+0568061.752.974959e+051554519.13772513.074420204390.7605563.3299941603.9029919.69198322873479555916781811487368.0
76Poweshiek16062900169084.712.539540e+05974741.306022e+0691675.424.365483e+051749624.95132114.246155185330.9440464.9466041580.07685711.07287925218509986574475551469057.0
20Clay15084525151187.652.270741e+051046451.367561e+06104603.094.569864e+051527629.91532013.073807163330.9352846.4044021305.38945211.70225525398435425646072821348963.0
9Buchanan12168450121504.751.824907e+05894291.140914e+0684484.183.814875e+051265330.14996113.504469209920.6027534.0245891437.3060028.80327523437519616142181611269271.0
60Mahaska939855096610.211.451289e+05708618.393837e+0562911.852.806475e+05967029.02249513.342219221810.4359592.8362951216.4962547.94905921568450255787789751255159.0
13Carroll14460725143097.042.149119e+051142461.556718e+06115511.565.202122e+051456835.70923613.476729204370.7128255.6520801790.4072558.13669625094475076196086831200276.0
69O'Brien13896728132245.411.986505e+05830291.057797e+0684579.603.539820e+051395425.36778212.506521140200.9952926.0327821620.4317448.61128524771440185939160691166399.0
40Hardin15096700136991.102.058020e+051197721.657238e+06127383.225.542669e+051359240.77890813.009862172260.7890407.3948231749.4035317.76950524154446945761272961140430.0
89Washington10984531119302.231.791272e+05843891.140731e+0678241.803.810510e+051174532.44367814.579561222810.5271313.5115931666.5066967.04767623979507106046687411139096.0
71Page10230328103322.961.551729e+05811161.057103e+0675315.103.532156e+051065133.16266714.035741153910.6920284.8934511398.1186347.61809521204407785279163931134280.0
47Jackson12728325114843.671.724907e+05890981.130876e+0688084.973.778944e+051259330.00828712.838469194720.6467244.5236732075.5147836.06741023008424895421082891120072.0
93Winneshiek11450225107156.181.609449e+05864831.189769e+0689488.043.977413e+051054137.73279013.295288205610.5126704.3523191587.4282736.64030023608506936155879971028921.0
85Union956917590467.231.358550e+05665369.021116e+0568179.743.015172e+05902433.41281513.231373124200.7265705.4895121292.9456146.97941220435408795054652711018948.0
65Monona967138195956.981.440858e+05420205.197005e+0538043.921.736055e+051002317.32071313.66053988981.1264334.2755591656.7945626.04963422774413985109840501017900.0
58Lyon977687598319.841.476554e+05553187.766658e+0556613.632.593880e+05964726.88794313.718707117540.8207424.8165421520.4717016.3447422161349506573484442962622.0
23Crawford917822891880.621.379796e+05657908.643920e+0562535.952.889041e+05936030.86581813.822321169400.5525383.6916151794.5257205.2158632118144377537946413960452.0
42Henry821442584936.531.275347e+05729829.634271e+0567508.463.218625e+05902635.65947914.271205197730.4564813.4141741374.6370806.5660972305641983539857666959348.0
3Appanoose836017582458.441.238400e+05582617.113762e+0553184.662.377927e+05858227.70830913.375589124620.6886544.2677471439.6253615.9612732008434689412505627955846.0
32Fayette910912881374.001.222869e+05702318.602044e+0568175.752.878219e+05873032.96929112.617454200540.4353253.3996091955.6987674.4638782156641055526278634955344.0
21Clayton11180875108046.711.622809e+05499746.927068e+0552494.392.315675e+051048022.09613213.195825175900.5957932.9843312086.3774225.0230602230345873539057599954141.0
73Plymouth12560609132624.741.991814e+05943251.286709e+0693061.034.300381e+051317632.63798413.826506252000.5228573.6928982259.9536415.8302082806056379692619875953171.0
14Cass1003867598657.961.481477e+05745831.005639e+0675880.713.360579e+05998833.64616513.252889131570.7591405.7673261776.5442135.6221512178740820488845980931866.0
33Floyd805867879010.191.186743e+05826891.032124e+0674542.853.450133e+05822341.95710313.846056158730.5180504.6962041245.5797746.6017452141639467528086886913846.0
5Benton779072571349.041.071595e+05476995.803559e+0547612.841.940870e+05773125.10503212.189062256990.3008291.8527121589.0399344.86520225111547266497010302912250.0
2Allamakee907917585371.491.282384e+05578337.728435e+0561269.762.586304e+05859530.09080212.613783138840.6190584.4129761640.6639255.2387332134946623559265845839656.0
53Kossuth14172628142789.092.144583e+051058811.479812e+06108007.854.948291e+051372036.06626313.700965151140.9077687.1462122171.3798586.3185632741548277610126697834826.0
64Mitchell819800080007.931.201944e+05323354.147482e+0534245.821.387916e+05822716.87025812.110916107630.7643783.1818101086.7946507.5699672282048506633564395819843.0
72Palo Alto939670089935.751.350478e+05419495.750134e+0544627.151.921719e+05866722.17283112.88483490470.9579974.9328121596.1283775.4300142307142800572083994797432.0
46Iowa11862800102657.531.542814e+05744519.711115e+0577025.043.251289e+051116229.12819212.607737163110.6843234.7222761448.0324107.7083912672156053645786677789613.0
15Cedar742712567426.721.013055e+05445504.927014e+0538720.951.647957e+05816020.19554512.724413184540.4421812.0982421232.3781986.6213442474254321638937511748698.0
67Montgomery645707867065.991.006981e+05451675.570222e+0541624.631.861282e+05671327.72653413.382034102250.6565284.0708681140.3594825.8867402130138624505954558699564.0
92Winnebago800930072833.611.094025e+05559316.827292e+0556888.632.282976e+05766729.77664812.001155106310.7211935.3512021295.3907005.9186782268441871587004597693928.0
83Tama724287569664.271.046049e+05476925.735157e+0543573.671.916543e+05765525.03648913.161979173190.4420002.5159462088.2266173.6657902304146288550116947655898.0
38Hamilton788232577319.081.161721e+05610117.369458e+0556950.352.466456e+05860328.66972112.940146150760.5706423.7775501425.3761636.0356002476546188614726540654023.0
41Harrison833390082437.451.238054e+05423805.046314e+0538339.841.687135e+05913118.47699913.162064141490.6453462.7097212000.7166094.5638652422151303632835987643413.0
49Jefferson637702566329.729.959432e+04568317.276337e+0551721.612.430598e+05644637.70707414.068272180900.3563292.8591271228.4603525.2472192385344167553526846632088.0
27Delaware592965056982.708.557961e+04552127.547227e+0554845.422.522007e+05574943.86862213.760907173270.3317943.1653151275.4611074.5073892257847078598027062615781.0
78Sac793462575245.541.130183e+05395715.258452e+0542670.281.760915e+05715724.60409812.32345398760.7246864.3206031412.5991645.0665472383742986543044482528002.0
59Madison739647574647.411.121233e+05544116.856734e+0551462.232.293326e+05779529.42048113.323817158480.4918603.2472381409.7472695.5293602571153183670996025523595.0
17Cherokee763840072195.691.084669e+05584236.855181e+0552491.702.294092e+05740830.96777013.059553115080.6437264.5613231404.6576095.2738832450744635566965207501313.0
34Franklin614632560180.129.038277e+04363134.522624e+0535596.581.512945e+05605224.99909112.705219101700.5950843.5001551346.5419964.4944752250744863529174332475800.0
80Shelby528695353350.358.009987e+04429635.279510e+0538570.771.762560e+05562731.32325913.687852118000.4768643.2687091299.1970144.3311372238944085555235085447766.0
19Clarke565645054720.088.218209e+04372865.035890e+0536768.251.683819e+05560030.06820013.69630093090.6015683.949753954.5436775.8666782327145596547073701445791.0
31Emmet598252560879.979.144739e+04386935.019728e+0539072.451.678386e+05607127.64595512.84723096580.6285984.0456051130.7690465.3689122437142286558444236391058.0
57Lucas396867540531.536.087117e+04290113.761450e+0527391.101.257357e+05406430.93889813.73237986470.4699903.1677001154.2649693.5208551996743005566473689388043.0
56Louisa316585031041.024.661010e+04206022.071461e+0514809.686.917951e+04373018.54678613.987210111420.3347691.329176980.9070153.8026032036750457549234346378171.0
96Wright557515051158.247.684774e+04465175.706450e+0544906.821.908072e+05546134.93996712.707313127790.4273423.5141111542.5600453.5402192306844035538905625365167.0
43Howard550185350667.537.612342e+04389465.268240e+0542435.811.762160e+05519733.90726412.41460993320.5569014.5473441553.1995753.3459962241746068555823944335431.0
45Ida490715049504.887.433259e+04357714.983864e+0534949.871.665311e+05477734.86101514.26003769850.6838945.0035601061.8838004.4986092384144521586353052286374.0
35Greene520052553392.208.019890e+04355114.774988e+0533966.781.596316e+05524830.41761114.05781890110.5823993.7694801385.5117803.7877702394743286601333996255811.0
18Chickasaw432597537139.095.580234e+04270033.508606e+0530429.561.173801e+05403129.11936011.530256120230.3352742.5309461332.8008853.0244582244741372505305204242653.0
0Adair445587540182.626.037369e+04338074.108056e+0532522.351.374661e+05442031.10093212.63148670920.6232374.5857801146.1498743.8563892349745202572873292221482.0
44Humboldt483867546974.267.055336e+04400135.050238e+0538779.601.687570e+05496733.97563713.02292594870.5235594.0876571325.8227673.7463532456845282570634209175430.0
74Pocahontas431275037584.775.645243e+04243632.971895e+0525680.479.933154e+04416723.83766311.57258768860.6051413.7293741297.4314133.2117302338542105562503233166156.0
63Mills431720038409.795.770141e+04375474.345716e+0534164.641.452974e+05396836.61729312.719923149720.2650282.2819021220.9533403.2499192540059481735325605142072.0
26Decatur179825017836.602.679681e+04143891.763645e+0512243.115.898266e+04199429.58007014.40520381410.2449331.5038831443.4273321.3814341819537138480153223141811.0
66Monroe289152528812.614.327483e+04219712.896006e+0520937.969.680632e+04300032.26877313.83136778700.3811942.660478960.8146553.1223502122843245530523213138773.0
11Butler334880027994.244.206102e+04246152.786351e+0525010.209.331105e+04330828.20769311.140858147910.2236501.6909071714.4328291.9295012403047702596416120120103.0
12Calhoun378745030301.414.553630e+04272143.215042e+0527977.641.075994e+05362529.68260411.49146898460.3681702.8415231593.2420602.275235230494161150037424268571.0
39Hancock350942528221.144.241179e+04268203.061684e+0528072.831.025907e+05310133.08308910.906219108350.2862022.5909401435.9864142.159491227134731855922474158912.0
52Keokuk272090023814.273.576359e+04135541.495608e+0513026.404.999794e+04279717.87556011.481359101190.2764111.2873211568.9905381.782675220884269853456440832440.0
70Osceola310747529141.204.377315e+04192442.562448e+0520104.988.574306e+04298128.76318712.74534160640.4915903.315465967.4526803.081288230634388958286268215017.0
36Grundy436925036670.315.510175e+04262103.018362e+0526267.961.009891e+05434123.26402711.490660123130.3525542.1333521097.9126463.95386626916561846815151314175.0
\n", "
" ], "text/plain": [ " county bottle_volume_ml state_bottle_cost state_bottle_retail \\\n", "55 Linn 158353452 1731106.01 2.599613e+06 \n", "79 Scott 112482102 1247750.11 1.873693e+06 \n", "50 Johnson 94201143 1094410.17 1.643462e+06 \n", "6 Black Hawk 100801227 1107541.37 1.663275e+06 \n", "30 Dubuque 57322060 601825.15 9.038367e+05 \n", "82 Story 65551096 727466.68 1.092625e+06 \n", "94 Woodbury 61274387 672801.78 1.010242e+06 \n", "75 Pottawattamie 64280187 680102.80 1.021141e+06 \n", "16 Cerro Gordo 50439956 514818.96 7.732206e+05 \n", "24 Dallas 19782703 208239.38 3.128030e+05 \n", "28 Des Moines 28965403 311376.14 4.674807e+05 \n", "22 Clinton 25808600 255757.84 3.841091e+05 \n", "87 Wapello 24021278 251492.94 3.776542e+05 \n", "68 Muscatine 28497706 294376.85 4.421020e+05 \n", "54 Lee 24974375 267566.06 4.017307e+05 \n", "62 Marshall 22552328 231546.74 3.477674e+05 \n", "29 Dickinson 27326006 291810.42 4.382073e+05 \n", "91 Webster 22537959 231995.40 3.484066e+05 \n", "88 Warren 19568800 183359.55 2.753840e+05 \n", "48 Jasper 21363900 214003.36 3.214453e+05 \n", "61 Marion 19736978 199812.05 3.000452e+05 \n", "10 Buena Vista 20832831 226554.99 3.402451e+05 \n", "81 Sioux 10681100 102003.82 1.532028e+05 \n", "7 Boone 16005831 154407.11 2.319393e+05 \n", "8 Bremer 17767453 173326.87 2.603925e+05 \n", "51 Jones 15309100 144067.53 2.164019e+05 \n", "76 Poweshiek 16062900 169084.71 2.539540e+05 \n", "20 Clay 15084525 151187.65 2.270741e+05 \n", "9 Buchanan 12168450 121504.75 1.824907e+05 \n", "60 Mahaska 9398550 96610.21 1.451289e+05 \n", "13 Carroll 14460725 143097.04 2.149119e+05 \n", "69 O'Brien 13896728 132245.41 1.986505e+05 \n", "40 Hardin 15096700 136991.10 2.058020e+05 \n", "89 Washington 10984531 119302.23 1.791272e+05 \n", "71 Page 10230328 103322.96 1.551729e+05 \n", "47 Jackson 12728325 114843.67 1.724907e+05 \n", "93 Winneshiek 11450225 107156.18 1.609449e+05 \n", "85 Union 9569175 90467.23 1.358550e+05 \n", "65 Monona 9671381 95956.98 1.440858e+05 \n", "58 Lyon 9776875 98319.84 1.476554e+05 \n", "23 Crawford 9178228 91880.62 1.379796e+05 \n", "42 Henry 8214425 84936.53 1.275347e+05 \n", "3 Appanoose 8360175 82458.44 1.238400e+05 \n", "32 Fayette 9109128 81374.00 1.222869e+05 \n", "21 Clayton 11180875 108046.71 1.622809e+05 \n", "73 Plymouth 12560609 132624.74 1.991814e+05 \n", "14 Cass 10038675 98657.96 1.481477e+05 \n", "33 Floyd 8058678 79010.19 1.186743e+05 \n", "5 Benton 7790725 71349.04 1.071595e+05 \n", "2 Allamakee 9079175 85371.49 1.282384e+05 \n", "53 Kossuth 14172628 142789.09 2.144583e+05 \n", "64 Mitchell 8198000 80007.93 1.201944e+05 \n", "72 Palo Alto 9396700 89935.75 1.350478e+05 \n", "46 Iowa 11862800 102657.53 1.542814e+05 \n", "15 Cedar 7427125 67426.72 1.013055e+05 \n", "67 Montgomery 6457078 67065.99 1.006981e+05 \n", "92 Winnebago 8009300 72833.61 1.094025e+05 \n", "83 Tama 7242875 69664.27 1.046049e+05 \n", "38 Hamilton 7882325 77319.08 1.161721e+05 \n", "41 Harrison 8333900 82437.45 1.238054e+05 \n", "49 Jefferson 6377025 66329.72 9.959432e+04 \n", "27 Delaware 5929650 56982.70 8.557961e+04 \n", "78 Sac 7934625 75245.54 1.130183e+05 \n", "59 Madison 7396475 74647.41 1.121233e+05 \n", "17 Cherokee 7638400 72195.69 1.084669e+05 \n", "34 Franklin 6146325 60180.12 9.038277e+04 \n", "80 Shelby 5286953 53350.35 8.009987e+04 \n", "19 Clarke 5656450 54720.08 8.218209e+04 \n", "31 Emmet 5982525 60879.97 9.144739e+04 \n", "57 Lucas 3968675 40531.53 6.087117e+04 \n", "56 Louisa 3165850 31041.02 4.661010e+04 \n", "96 Wright 5575150 51158.24 7.684774e+04 \n", "43 Howard 5501853 50667.53 7.612342e+04 \n", "45 Ida 4907150 49504.88 7.433259e+04 \n", "35 Greene 5200525 53392.20 8.019890e+04 \n", "18 Chickasaw 4325975 37139.09 5.580234e+04 \n", "0 Adair 4455875 40182.62 6.037369e+04 \n", "44 Humboldt 4838675 46974.26 7.055336e+04 \n", "74 Pocahontas 4312750 37584.77 5.645243e+04 \n", "63 Mills 4317200 38409.79 5.770141e+04 \n", "26 Decatur 1798250 17836.60 2.679681e+04 \n", "66 Monroe 2891525 28812.61 4.327483e+04 \n", "11 Butler 3348800 27994.24 4.206102e+04 \n", "12 Calhoun 3787450 30301.41 4.553630e+04 \n", "39 Hancock 3509425 28221.14 4.241179e+04 \n", "52 Keokuk 2720900 23814.27 3.576359e+04 \n", "70 Osceola 3107475 29141.20 4.377315e+04 \n", "36 Grundy 4369250 36670.31 5.510175e+04 \n", "\n", " bottles_sold sale_dollars volume_sold_liters profit num_stores \\\n", "55 1500058 1.806775e+07 1221208.85 6.036068e+06 184092 \n", "79 1237025 1.397399e+07 914829.35 4.667888e+06 130419 \n", "50 927749 1.237021e+07 784403.93 4.133645e+06 105955 \n", "6 1093412 1.192076e+07 793399.13 3.982880e+06 118229 \n", "30 501274 6.621999e+06 460080.11 2.212785e+06 60753 \n", "82 501832 6.730961e+06 456414.62 2.250349e+06 71636 \n", "94 620680 7.695638e+06 512631.64 2.570501e+06 67732 \n", "75 618358 7.675324e+06 521376.00 2.563352e+06 72472 \n", "16 377860 4.892826e+06 354343.00 1.635296e+06 51566 \n", "24 166032 2.295133e+06 151828.96 7.673762e+05 21284 \n", "28 294257 3.579586e+06 235900.79 1.195418e+06 32474 \n", "22 245180 2.928261e+06 214882.76 9.784796e+05 28364 \n", "87 186724 2.292624e+06 160501.09 7.658786e+05 27843 \n", "68 208296 2.626114e+06 188612.00 8.775137e+05 31591 \n", "54 228466 2.902471e+06 201627.37 9.692238e+05 27261 \n", "62 192655 2.504138e+06 168340.47 8.368677e+05 23507 \n", "29 222744 3.145660e+06 224310.44 1.050838e+06 28110 \n", "91 228252 2.705835e+06 189009.94 9.042916e+05 24106 \n", "88 159019 1.939043e+06 146705.53 6.481145e+05 19620 \n", "48 131828 1.577656e+06 121386.17 5.274477e+05 23046 \n", "61 133175 1.735448e+06 123141.99 5.798673e+05 20958 \n", "10 114147 1.532283e+06 105358.28 5.121251e+05 21598 \n", "81 77121 1.069835e+06 79019.86 3.575951e+05 10307 \n", "7 120271 1.534234e+06 115554.84 5.129844e+05 16515 \n", "8 113551 1.487074e+06 114021.43 4.976283e+05 17746 \n", "51 69101 8.898679e+05 68061.75 2.974959e+05 15545 \n", "76 97474 1.306022e+06 91675.42 4.365483e+05 17496 \n", "20 104645 1.367561e+06 104603.09 4.569864e+05 15276 \n", "9 89429 1.140914e+06 84484.18 3.814875e+05 12653 \n", "60 70861 8.393837e+05 62911.85 2.806475e+05 9670 \n", "13 114246 1.556718e+06 115511.56 5.202122e+05 14568 \n", "69 83029 1.057797e+06 84579.60 3.539820e+05 13954 \n", "40 119772 1.657238e+06 127383.22 5.542669e+05 13592 \n", "89 84389 1.140731e+06 78241.80 3.810510e+05 11745 \n", "71 81116 1.057103e+06 75315.10 3.532156e+05 10651 \n", "47 89098 1.130876e+06 88084.97 3.778944e+05 12593 \n", "93 86483 1.189769e+06 89488.04 3.977413e+05 10541 \n", "85 66536 9.021116e+05 68179.74 3.015172e+05 9024 \n", "65 42020 5.197005e+05 38043.92 1.736055e+05 10023 \n", "58 55318 7.766658e+05 56613.63 2.593880e+05 9647 \n", "23 65790 8.643920e+05 62535.95 2.889041e+05 9360 \n", "42 72982 9.634271e+05 67508.46 3.218625e+05 9026 \n", "3 58261 7.113762e+05 53184.66 2.377927e+05 8582 \n", "32 70231 8.602044e+05 68175.75 2.878219e+05 8730 \n", "21 49974 6.927068e+05 52494.39 2.315675e+05 10480 \n", "73 94325 1.286709e+06 93061.03 4.300381e+05 13176 \n", "14 74583 1.005639e+06 75880.71 3.360579e+05 9988 \n", "33 82689 1.032124e+06 74542.85 3.450133e+05 8223 \n", "5 47699 5.803559e+05 47612.84 1.940870e+05 7731 \n", "2 57833 7.728435e+05 61269.76 2.586304e+05 8595 \n", "53 105881 1.479812e+06 108007.85 4.948291e+05 13720 \n", "64 32335 4.147482e+05 34245.82 1.387916e+05 8227 \n", "72 41949 5.750134e+05 44627.15 1.921719e+05 8667 \n", "46 74451 9.711115e+05 77025.04 3.251289e+05 11162 \n", "15 44550 4.927014e+05 38720.95 1.647957e+05 8160 \n", "67 45167 5.570222e+05 41624.63 1.861282e+05 6713 \n", "92 55931 6.827292e+05 56888.63 2.282976e+05 7667 \n", "83 47692 5.735157e+05 43573.67 1.916543e+05 7655 \n", "38 61011 7.369458e+05 56950.35 2.466456e+05 8603 \n", "41 42380 5.046314e+05 38339.84 1.687135e+05 9131 \n", "49 56831 7.276337e+05 51721.61 2.430598e+05 6446 \n", "27 55212 7.547227e+05 54845.42 2.522007e+05 5749 \n", "78 39571 5.258452e+05 42670.28 1.760915e+05 7157 \n", "59 54411 6.856734e+05 51462.23 2.293326e+05 7795 \n", "17 58423 6.855181e+05 52491.70 2.294092e+05 7408 \n", "34 36313 4.522624e+05 35596.58 1.512945e+05 6052 \n", "80 42963 5.279510e+05 38570.77 1.762560e+05 5627 \n", "19 37286 5.035890e+05 36768.25 1.683819e+05 5600 \n", "31 38693 5.019728e+05 39072.45 1.678386e+05 6071 \n", "57 29011 3.761450e+05 27391.10 1.257357e+05 4064 \n", "56 20602 2.071461e+05 14809.68 6.917951e+04 3730 \n", "96 46517 5.706450e+05 44906.82 1.908072e+05 5461 \n", "43 38946 5.268240e+05 42435.81 1.762160e+05 5197 \n", "45 35771 4.983864e+05 34949.87 1.665311e+05 4777 \n", "35 35511 4.774988e+05 33966.78 1.596316e+05 5248 \n", "18 27003 3.508606e+05 30429.56 1.173801e+05 4031 \n", "0 33807 4.108056e+05 32522.35 1.374661e+05 4420 \n", "44 40013 5.050238e+05 38779.60 1.687570e+05 4967 \n", "74 24363 2.971895e+05 25680.47 9.933154e+04 4167 \n", "63 37547 4.345716e+05 34164.64 1.452974e+05 3968 \n", "26 14389 1.763645e+05 12243.11 5.898266e+04 1994 \n", "66 21971 2.896006e+05 20937.96 9.680632e+04 3000 \n", "11 24615 2.786351e+05 25010.20 9.331105e+04 3308 \n", "12 27214 3.215042e+05 27977.64 1.075994e+05 3625 \n", "39 26820 3.061684e+05 28072.83 1.025907e+05 3101 \n", "52 13554 1.495608e+05 13026.40 4.999794e+04 2797 \n", "70 19244 2.562448e+05 20104.98 8.574306e+04 2981 \n", "36 26210 3.018362e+05 26267.96 1.009891e+05 4341 \n", "\n", " average_store_profit sales_per_litters population \\\n", "55 32.788321 14.794975 221661 \n", "79 35.791469 15.274967 172474 \n", "50 39.013213 15.770199 146547 \n", "6 33.687840 15.024919 132904 \n", "30 36.422653 14.393144 97003 \n", "82 31.413665 14.747469 97090 \n", "94 37.951055 15.012023 102779 \n", "75 35.370233 14.721285 93582 \n", "16 31.712671 13.808162 43070 \n", "24 36.054136 15.116572 84516 \n", "28 36.811526 15.174117 39739 \n", "22 34.497237 13.627250 47309 \n", "87 27.507043 14.284163 34982 \n", "68 27.777331 13.923365 42940 \n", "54 35.553493 14.395224 34615 \n", "62 35.600787 14.875434 40312 \n", "29 37.383072 14.023690 17243 \n", "91 37.513134 14.315835 36769 \n", "88 33.033359 13.217243 49691 \n", "48 22.886734 12.996996 36708 \n", "61 27.668067 14.093066 33189 \n", "10 23.711690 14.543544 20332 \n", "81 34.694396 13.538806 34898 \n", "7 31.061724 13.277107 26532 \n", "8 28.041717 13.042060 24798 \n", "51 19.137725 13.074420 20439 \n", "76 24.951321 14.246155 18533 \n", "20 29.915320 13.073807 16333 \n", "9 30.149961 13.504469 20992 \n", "60 29.022495 13.342219 22181 \n", "13 35.709236 13.476729 20437 \n", "69 25.367782 12.506521 14020 \n", "40 40.778908 13.009862 17226 \n", "89 32.443678 14.579561 22281 \n", "71 33.162667 14.035741 15391 \n", "47 30.008287 12.838469 19472 \n", "93 37.732790 13.295288 20561 \n", "85 33.412815 13.231373 12420 \n", "65 17.320713 13.660539 8898 \n", "58 26.887943 13.718707 11754 \n", "23 30.865818 13.822321 16940 \n", "42 35.659479 14.271205 19773 \n", "3 27.708309 13.375589 12462 \n", "32 32.969291 12.617454 20054 \n", "21 22.096132 13.195825 17590 \n", "73 32.637984 13.826506 25200 \n", "14 33.646165 13.252889 13157 \n", "33 41.957103 13.846056 15873 \n", "5 25.105032 12.189062 25699 \n", "2 30.090802 12.613783 13884 \n", "53 36.066263 13.700965 15114 \n", "64 16.870258 12.110916 10763 \n", "72 22.172831 12.884834 9047 \n", "46 29.128192 12.607737 16311 \n", "15 20.195545 12.724413 18454 \n", "67 27.726534 13.382034 10225 \n", "92 29.776648 12.001155 10631 \n", "83 25.036489 13.161979 17319 \n", "38 28.669721 12.940146 15076 \n", "41 18.476999 13.162064 14149 \n", "49 37.707074 14.068272 18090 \n", "27 43.868622 13.760907 17327 \n", "78 24.604098 12.323453 9876 \n", "59 29.420481 13.323817 15848 \n", "17 30.967770 13.059553 11508 \n", "34 24.999091 12.705219 10170 \n", "80 31.323259 13.687852 11800 \n", "19 30.068200 13.696300 9309 \n", "31 27.645955 12.847230 9658 \n", "57 30.938898 13.732379 8647 \n", "56 18.546786 13.987210 11142 \n", "96 34.939967 12.707313 12779 \n", "43 33.907264 12.414609 9332 \n", "45 34.861015 14.260037 6985 \n", "35 30.417611 14.057818 9011 \n", "18 29.119360 11.530256 12023 \n", "0 31.100932 12.631486 7092 \n", "44 33.975637 13.022925 9487 \n", "74 23.837663 11.572587 6886 \n", "63 36.617293 12.719923 14972 \n", "26 29.580070 14.405203 8141 \n", "66 32.268773 13.831367 7870 \n", "11 28.207693 11.140858 14791 \n", "12 29.682604 11.491468 9846 \n", "39 33.083089 10.906219 10835 \n", "52 17.875560 11.481359 10119 \n", "70 28.763187 12.745341 6064 \n", "36 23.264027 11.490660 12313 \n", "\n", " store_population_ratio consumption_per_capita area \\\n", "55 0.830511 5.509354 2164.866374 \n", "79 0.756166 5.304158 1211.649675 \n", "50 0.723010 5.352576 1374.398467 \n", "6 0.889582 5.969716 1658.463133 \n", "30 0.626300 4.742947 1445.975306 \n", "82 0.737831 4.700944 1943.690095 \n", "94 0.659006 4.987708 2591.144872 \n", "75 0.774422 5.571328 2303.225231 \n", "16 1.197260 8.227142 1497.770139 \n", "24 0.251834 1.796452 1727.389531 \n", "28 0.817182 5.936254 928.456413 \n", "22 0.599548 4.542112 1701.089895 \n", "87 0.795924 4.588105 1104.862750 \n", "68 0.735701 4.392455 1700.941831 \n", "54 0.787549 5.824855 1207.589119 \n", "62 0.583127 4.175939 1520.107767 \n", "29 1.630227 13.008783 1024.114722 \n", "91 0.655607 5.140470 1793.295141 \n", "88 0.394840 2.952356 1617.493665 \n", "48 0.627820 3.306804 1677.247032 \n", "61 0.631474 3.710325 1528.879832 \n", "10 1.062266 5.181895 1641.768588 \n", "81 0.295346 2.264309 2056.178194 \n", "7 0.622456 4.355301 1172.618576 \n", "8 0.715622 4.598009 1254.612988 \n", "51 0.760556 3.329994 1603.902991 \n", "76 0.944046 4.946604 1580.076857 \n", "20 0.935284 6.404402 1305.389452 \n", "9 0.602753 4.024589 1437.306002 \n", "60 0.435959 2.836295 1216.496254 \n", "13 0.712825 5.652080 1790.407255 \n", "69 0.995292 6.032782 1620.431744 \n", "40 0.789040 7.394823 1749.403531 \n", "89 0.527131 3.511593 1666.506696 \n", "71 0.692028 4.893451 1398.118634 \n", "47 0.646724 4.523673 2075.514783 \n", "93 0.512670 4.352319 1587.428273 \n", "85 0.726570 5.489512 1292.945614 \n", "65 1.126433 4.275559 1656.794562 \n", "58 0.820742 4.816542 1520.471701 \n", "23 0.552538 3.691615 1794.525720 \n", "42 0.456481 3.414174 1374.637080 \n", "3 0.688654 4.267747 1439.625361 \n", "32 0.435325 3.399609 1955.698767 \n", "21 0.595793 2.984331 2086.377422 \n", "73 0.522857 3.692898 2259.953641 \n", "14 0.759140 5.767326 1776.544213 \n", "33 0.518050 4.696204 1245.579774 \n", "5 0.300829 1.852712 1589.039934 \n", "2 0.619058 4.412976 1640.663925 \n", "53 0.907768 7.146212 2171.379858 \n", "64 0.764378 3.181810 1086.794650 \n", "72 0.957997 4.932812 1596.128377 \n", "46 0.684323 4.722276 1448.032410 \n", "15 0.442181 2.098242 1232.378198 \n", "67 0.656528 4.070868 1140.359482 \n", "92 0.721193 5.351202 1295.390700 \n", "83 0.442000 2.515946 2088.226617 \n", "38 0.570642 3.777550 1425.376163 \n", "41 0.645346 2.709721 2000.716609 \n", "49 0.356329 2.859127 1228.460352 \n", "27 0.331794 3.165315 1275.461107 \n", "78 0.724686 4.320603 1412.599164 \n", "59 0.491860 3.247238 1409.747269 \n", "17 0.643726 4.561323 1404.657609 \n", "34 0.595084 3.500155 1346.541996 \n", "80 0.476864 3.268709 1299.197014 \n", "19 0.601568 3.949753 954.543677 \n", "31 0.628598 4.045605 1130.769046 \n", "57 0.469990 3.167700 1154.264969 \n", "56 0.334769 1.329176 980.907015 \n", "96 0.427342 3.514111 1542.560045 \n", "43 0.556901 4.547344 1553.199575 \n", "45 0.683894 5.003560 1061.883800 \n", "35 0.582399 3.769480 1385.511780 \n", "18 0.335274 2.530946 1332.800885 \n", "0 0.623237 4.585780 1146.149874 \n", "44 0.523559 4.087657 1325.822767 \n", "74 0.605141 3.729374 1297.431413 \n", "63 0.265028 2.281902 1220.953340 \n", "26 0.244933 1.503883 1443.427332 \n", "66 0.381194 2.660478 960.814655 \n", "11 0.223650 1.690907 1714.432829 \n", "12 0.368170 2.841523 1593.242060 \n", "39 0.286202 2.590940 1435.986414 \n", "52 0.276411 1.287321 1568.990538 \n", "70 0.491590 3.315465 967.452680 \n", "36 0.352554 2.133352 1097.912646 \n", "\n", " stores_per_area per_capita_income median_household_income \\\n", "55 85.036195 28239 53674 \n", "79 107.637548 27408 49964 \n", "50 77.091908 28008 51380 \n", "6 71.288290 23357 44178 \n", "30 42.015240 25045 48573 \n", "82 36.855670 25450 48248 \n", "94 26.139797 22069 44343 \n", "75 31.465442 23782 48728 \n", "16 34.428514 25463 44741 \n", "24 12.321483 33051 67037 \n", "28 34.976332 22555 41937 \n", "22 16.674016 23573 46170 \n", "87 25.200415 22376 40093 \n", "68 18.572652 24138 51025 \n", "54 22.574731 21324 42444 \n", "62 15.464035 22407 45232 \n", "29 27.448097 29459 50174 \n", "91 13.442294 22653 40806 \n", "88 12.129877 28798 62034 \n", "48 13.740373 23160 46396 \n", "61 13.708075 24613 53370 \n", "10 13.155325 21256 43182 \n", "81 5.012698 21333 51557 \n", "7 14.083864 25998 49578 \n", "8 14.144601 26522 55676 \n", "51 9.691983 22873 47955 \n", "76 11.072879 25218 50998 \n", "20 11.702255 25398 43542 \n", "9 8.803275 23437 51961 \n", "60 7.949059 21568 45025 \n", "13 8.136696 25094 47507 \n", "69 8.611285 24771 44018 \n", "40 7.769505 24154 44694 \n", "89 7.047676 23979 50710 \n", "71 7.618095 21204 40778 \n", "47 6.067410 23008 42489 \n", "93 6.640300 23608 50693 \n", "85 6.979412 20435 40879 \n", "65 6.049634 22774 41398 \n", "58 6.344742 21613 49506 \n", "23 5.215863 21181 44377 \n", "42 6.566097 23056 41983 \n", "3 5.961273 20084 34689 \n", "32 4.463878 21566 41055 \n", "21 5.023060 22303 45873 \n", "73 5.830208 28060 56379 \n", "14 5.622151 21787 40820 \n", "33 6.601745 21416 39467 \n", "5 4.865202 25111 54726 \n", "2 5.238733 21349 46623 \n", "53 6.318563 27415 48277 \n", "64 7.569967 22820 48506 \n", "72 5.430014 23071 42800 \n", "46 7.708391 26721 56053 \n", "15 6.621344 24742 54321 \n", "67 5.886740 21301 38624 \n", "92 5.918678 22684 41871 \n", "83 3.665790 23041 46288 \n", "38 6.035600 24765 46188 \n", "41 4.563865 24221 51303 \n", "49 5.247219 23853 44167 \n", "27 4.507389 22578 47078 \n", "78 5.066547 23837 42986 \n", "59 5.529360 25711 53183 \n", "17 5.273883 24507 44635 \n", "34 4.494475 22507 44863 \n", "80 4.331137 22389 44085 \n", "19 5.866678 23271 45596 \n", "31 5.368912 24371 42286 \n", "57 3.520855 19967 43005 \n", "56 3.802603 20367 50457 \n", "96 3.540219 23068 44035 \n", "43 3.345996 22417 46068 \n", "45 4.498609 23841 44521 \n", "35 3.787770 23947 43286 \n", "18 3.024458 22447 41372 \n", "0 3.856389 23497 45202 \n", "44 3.746353 24568 45282 \n", "74 3.211730 23385 42105 \n", "63 3.249919 25400 59481 \n", "26 1.381434 18195 37138 \n", "66 3.122350 21228 43245 \n", "11 1.929501 24030 47702 \n", "12 2.275235 23049 41611 \n", "39 2.159491 22713 47318 \n", "52 1.782675 22088 42698 \n", "70 3.081288 23063 43889 \n", "36 3.953866 26916 56184 \n", "\n", " median_family_income number_of_households sales_prediction_dollars \n", "55 69250 86134 16780528.0 \n", "79 64513 66765 14997255.0 \n", "50 74547 52715 11867678.0 \n", "6 57495 52470 11283189.0 \n", "30 61138 36815 7310597.0 \n", "82 74278 34736 7146093.0 \n", "94 55957 39052 7104739.0 \n", "75 60354 36775 6828297.0 \n", "16 60148 19350 4328297.0 \n", "24 84018 25240 4125161.0 \n", "28 53946 17003 3968601.0 \n", "22 58681 20223 3197853.0 \n", "87 49309 14552 3190720.0 \n", "68 61445 16412 3138265.0 \n", "54 50630 14610 3104487.0 \n", "62 55716 15538 2791982.0 \n", "29 59648 7554 2628643.0 \n", "91 54129 15580 2541044.0 \n", "88 74042 17262 2521533.0 \n", "48 56484 14806 2486830.0 \n", "61 65817 12723 2177923.0 \n", "10 53382 7522 2087010.0 \n", "81 60043 11584 1725739.0 \n", "7 66872 10728 1696331.0 \n", "8 68602 9385 1657708.0 \n", "51 59167 8181 1487368.0 \n", "76 65744 7555 1469057.0 \n", "20 56460 7282 1348963.0 \n", "9 61421 8161 1269271.0 \n", "60 57877 8975 1255159.0 \n", "13 61960 8683 1200276.0 \n", "69 59391 6069 1166399.0 \n", "40 57612 7296 1140430.0 \n", "89 60466 8741 1139096.0 \n", "71 52791 6393 1134280.0 \n", "47 54210 8289 1120072.0 \n", "93 61558 7997 1028921.0 \n", "85 50546 5271 1018948.0 \n", "65 51098 4050 1017900.0 \n", "58 57348 4442 962622.0 \n", "23 53794 6413 960452.0 \n", "42 53985 7666 959348.0 \n", "3 41250 5627 955846.0 \n", "32 52627 8634 955344.0 \n", "21 53905 7599 954141.0 \n", "73 69261 9875 953171.0 \n", "14 48884 5980 931866.0 \n", "33 52808 6886 913846.0 \n", "5 64970 10302 912250.0 \n", "2 55926 5845 839656.0 \n", "53 61012 6697 834826.0 \n", "64 63356 4395 819843.0 \n", "72 57208 3994 797432.0 \n", "46 64578 6677 789613.0 \n", "15 63893 7511 748698.0 \n", "67 50595 4558 699564.0 \n", "92 58700 4597 693928.0 \n", "83 55011 6947 655898.0 \n", "38 61472 6540 654023.0 \n", "41 63283 5987 643413.0 \n", "49 55352 6846 632088.0 \n", "27 59802 7062 615781.0 \n", "78 54304 4482 528002.0 \n", "59 67099 6025 523595.0 \n", "17 56696 5207 501313.0 \n", "34 52917 4332 475800.0 \n", "80 55523 5085 447766.0 \n", "19 54707 3701 445791.0 \n", "31 55844 4236 391058.0 \n", "57 56647 3689 388043.0 \n", "56 54923 4346 378171.0 \n", "96 53890 5625 365167.0 \n", "43 55582 3944 335431.0 \n", "45 58635 3052 286374.0 \n", "35 60133 3996 255811.0 \n", "18 50530 5204 242653.0 \n", "0 57287 3292 221482.0 \n", "44 57063 4209 175430.0 \n", "74 56250 3233 166156.0 \n", "63 73532 5605 142072.0 \n", "26 48015 3223 141811.0 \n", "66 53052 3213 138773.0 \n", "11 59641 6120 120103.0 \n", "12 50037 4242 68571.0 \n", "39 55922 4741 58912.0 \n", "52 53456 4408 32440.0 \n", "70 58286 2682 15017.0 \n", "36 68151 5131 4175.0 " ] }, "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred = df_with_pred[df_with_pred['sales_prediction_dollars']>0]\n", "df_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Higher aggregated sales\n", "\n", "Let us order the counties with higher predicted sales:" ] }, { "cell_type": "code", "execution_count": 333, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countysales_per_litterspopulationstores_per_areasales_prediction_dollars
55Linn14.79497522166185.03619516780528.0
79Scott15.274967172474107.63754814997255.0
50Johnson15.77019914654777.09190811867678.0
6Black Hawk15.02491913290471.28829011283189.0
\n", "
" ], "text/plain": [ " county sales_per_litters population stores_per_area \\\n", "55 Linn 14.794975 221661 85.036195 \n", "79 Scott 15.274967 172474 107.637548 \n", "50 Johnson 15.770199 146547 77.091908 \n", "6 Black Hawk 15.024919 132904 71.288290 \n", "\n", " sales_prediction_dollars \n", "55 16780528.0 \n", "79 14997255.0 \n", "50 11867678.0 \n", "6 11283189.0 " ] }, "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred.sort_values(\"sales_prediction_dollars\", inplace=True, ascending=False)\n", "df_pred[['county','sales_per_litters','population','stores_per_area','sales_prediction_dollars']].head(4)" ] }, { "cell_type": "code", "execution_count": 337, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countysales_per_litterspopulationstores_per_areasales_prediction_dollars
55Linn14.79497522166185.03619516780528.0
79Scott15.274967172474107.63754814997255.0
50Johnson15.77019914654777.09190811867678.0
6Black Hawk15.02491913290471.28829011283189.0
\n", "
" ], "text/plain": [ " county sales_per_litters population stores_per_area \\\n", "55 Linn 14.794975 221661 85.036195 \n", "79 Scott 15.274967 172474 107.637548 \n", "50 Johnson 15.770199 146547 77.091908 \n", "6 Black Hawk 15.024919 132904 71.288290 \n", "\n", " sales_prediction_dollars \n", "55 16780528.0 \n", "79 14997255.0 \n", "50 11867678.0 \n", "6 11283189.0 " ] }, "execution_count": 337, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred.sort_values(\"population\", inplace=True, ascending=False)\n", "df_pred[['county','sales_per_litters','population','stores_per_area','sales_prediction_dollars']].head(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linn has higher sales which in part is because it has larger population which is not very useful information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### More high-end stores\n", "\n", "Let us order by `sales_per_litters` to see which county has more high-end stores. " ] }, { "cell_type": "code", "execution_count": 338, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countysales_per_litterspopulationstores_per_areasales_prediction_dollars
50Johnson15.77019914654777.09190811867678.0
79Scott15.274967172474107.63754814997255.0
28Des Moines15.1741173973934.9763323968601.0
24Dallas15.1165728451612.3214834125161.0
\n", "
" ], "text/plain": [ " county sales_per_litters population stores_per_area \\\n", "50 Johnson 15.770199 146547 77.091908 \n", "79 Scott 15.274967 172474 107.637548 \n", "28 Des Moines 15.174117 39739 34.976332 \n", "24 Dallas 15.116572 84516 12.321483 \n", "\n", " sales_prediction_dollars \n", "50 11867678.0 \n", "79 14997255.0 \n", "28 3968601.0 \n", "24 4125161.0 " ] }, "execution_count": 338, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred.sort_values(\"sales_per_litters\", inplace=True, ascending=False)\n", "df_pred[['county','sales_per_litters','population','stores_per_area','sales_prediction_dollars']].head(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that Johnson has more high-end stores. We would recommend it *if the goal of the the owner is to build new high-end stores*. If the plan is to open more stores but with cheaper products, Johnson is not the place to choose." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Saturation\n", "\n", "The less saturated market is Decatur. But as discussed before this information does not provide have a unique recommendation and a more thorough analysis is needed." ] }, { "cell_type": "code", "execution_count": 344, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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countysales_per_litterspopulationstores_per_areasales_prediction_dollars
26Decatur14.40520381411.381434141811.0
52Keokuk11.481359101191.78267532440.0
11Butler11.140858147911.929501120103.0
39Hancock10.906219108352.15949158912.0
12Calhoun11.49146898462.27523568571.0
18Chickasaw11.530256120233.024458242653.0
70Osceola12.74534160643.08128815017.0
66Monroe13.83136778703.122350138773.0
74Pocahontas11.57258768863.211730166156.0
63Mills12.719923149723.249919142072.0
43Howard12.41460993323.345996335431.0
57Lucas13.73237986473.520855388043.0
96Wright12.707313127793.540219365167.0
83Tama13.161979173193.665790655898.0
44Humboldt13.02292594873.746353175430.0
35Greene14.05781890113.787770255811.0
56Louisa13.987210111423.802603378171.0
0Adair12.63148670923.856389221482.0
36Grundy11.490660123133.9538664175.0
80Shelby13.687852118004.331137447766.0
32Fayette12.617454200544.463878955344.0
34Franklin12.705219101704.494475475800.0
45Ida14.26003769854.498609286374.0
27Delaware13.760907173274.507389615781.0
41Harrison13.162064141494.563865643413.0
5Benton12.189062256994.865202912250.0
81Sioux13.538806348985.0126981725739.0
21Clayton13.195825175905.023060954141.0
78Sac12.32345398765.066547528002.0
23Crawford13.822321169405.215863960452.0
2Allamakee12.613783138845.238733839656.0
49Jefferson14.068272180905.247219632088.0
17Cherokee13.059553115085.273883501313.0
31Emmet12.84723096585.368912391058.0
72Palo Alto12.88483490475.430014797432.0
59Madison13.323817158485.529360523595.0
14Cass13.252889131575.622151931866.0
73Plymouth13.826506252005.830208953171.0
19Clarke13.69630093095.866678445791.0
67Montgomery13.382034102255.886740699564.0
92Winnebago12.001155106315.918678693928.0
3Appanoose13.375589124625.961273955846.0
38Hamilton12.940146150766.035600654023.0
65Monona13.66053988986.0496341017900.0
47Jackson12.838469194726.0674101120072.0
53Kossuth13.700965151146.318563834826.0
58Lyon13.718707117546.344742962622.0
42Henry14.271205197736.566097959348.0
33Floyd13.846056158736.601745913846.0
15Cedar12.724413184546.621344748698.0
93Winneshiek13.295288205616.6403001028921.0
85Union13.231373124206.9794121018948.0
89Washington14.579561222817.0476761139096.0
64Mitchell12.110916107637.569967819843.0
71Page14.035741153917.6180951134280.0
46Iowa12.607737163117.708391789613.0
40Hardin13.009862172267.7695051140430.0
60Mahaska13.342219221817.9490591255159.0
13Carroll13.476729204378.1366961200276.0
69O'Brien12.506521140208.6112851166399.0
9Buchanan13.504469209928.8032751269271.0
51Jones13.074420204399.6919831487368.0
76Poweshiek14.2461551853311.0728791469057.0
20Clay13.0738071633311.7022551348963.0
88Warren13.2172434969112.1298772521533.0
24Dallas15.1165728451612.3214834125161.0
10Buena Vista14.5435442033213.1553252087010.0
91Webster14.3158353676913.4422942541044.0
61Marion14.0930663318913.7080752177923.0
48Jasper12.9969963670813.7403732486830.0
7Boone13.2771072653214.0838641696331.0
8Bremer13.0420602479814.1446011657708.0
62Marshall14.8754344031215.4640352791982.0
22Clinton13.6272504730916.6740163197853.0
68Muscatine13.9233654294018.5726523138265.0
54Lee14.3952243461522.5747313104487.0
87Wapello14.2841633498225.2004153190720.0
94Woodbury15.01202310277926.1397977104739.0
29Dickinson14.0236901724327.4480972628643.0
75Pottawattamie14.7212859358231.4654426828297.0
16Cerro Gordo13.8081624307034.4285144328297.0
28Des Moines15.1741173973934.9763323968601.0
82Story14.7474699709036.8556707146093.0
30Dubuque14.3931449700342.0152407310597.0
6Black Hawk15.02491913290471.28829011283189.0
50Johnson15.77019914654777.09190811867678.0
55Linn14.79497522166185.03619516780528.0
79Scott15.274967172474107.63754814997255.0
\n", "
" ], "text/plain": [ " county sales_per_litters population stores_per_area \\\n", "26 Decatur 14.405203 8141 1.381434 \n", "52 Keokuk 11.481359 10119 1.782675 \n", "11 Butler 11.140858 14791 1.929501 \n", "39 Hancock 10.906219 10835 2.159491 \n", "12 Calhoun 11.491468 9846 2.275235 \n", "18 Chickasaw 11.530256 12023 3.024458 \n", "70 Osceola 12.745341 6064 3.081288 \n", "66 Monroe 13.831367 7870 3.122350 \n", "74 Pocahontas 11.572587 6886 3.211730 \n", "63 Mills 12.719923 14972 3.249919 \n", "43 Howard 12.414609 9332 3.345996 \n", "57 Lucas 13.732379 8647 3.520855 \n", "96 Wright 12.707313 12779 3.540219 \n", "83 Tama 13.161979 17319 3.665790 \n", "44 Humboldt 13.022925 9487 3.746353 \n", "35 Greene 14.057818 9011 3.787770 \n", "56 Louisa 13.987210 11142 3.802603 \n", "0 Adair 12.631486 7092 3.856389 \n", "36 Grundy 11.490660 12313 3.953866 \n", "80 Shelby 13.687852 11800 4.331137 \n", "32 Fayette 12.617454 20054 4.463878 \n", "34 Franklin 12.705219 10170 4.494475 \n", "45 Ida 14.260037 6985 4.498609 \n", "27 Delaware 13.760907 17327 4.507389 \n", "41 Harrison 13.162064 14149 4.563865 \n", "5 Benton 12.189062 25699 4.865202 \n", "81 Sioux 13.538806 34898 5.012698 \n", "21 Clayton 13.195825 17590 5.023060 \n", "78 Sac 12.323453 9876 5.066547 \n", "23 Crawford 13.822321 16940 5.215863 \n", "2 Allamakee 12.613783 13884 5.238733 \n", "49 Jefferson 14.068272 18090 5.247219 \n", "17 Cherokee 13.059553 11508 5.273883 \n", "31 Emmet 12.847230 9658 5.368912 \n", "72 Palo Alto 12.884834 9047 5.430014 \n", "59 Madison 13.323817 15848 5.529360 \n", "14 Cass 13.252889 13157 5.622151 \n", "73 Plymouth 13.826506 25200 5.830208 \n", "19 Clarke 13.696300 9309 5.866678 \n", "67 Montgomery 13.382034 10225 5.886740 \n", "92 Winnebago 12.001155 10631 5.918678 \n", "3 Appanoose 13.375589 12462 5.961273 \n", "38 Hamilton 12.940146 15076 6.035600 \n", "65 Monona 13.660539 8898 6.049634 \n", "47 Jackson 12.838469 19472 6.067410 \n", "53 Kossuth 13.700965 15114 6.318563 \n", "58 Lyon 13.718707 11754 6.344742 \n", "42 Henry 14.271205 19773 6.566097 \n", "33 Floyd 13.846056 15873 6.601745 \n", "15 Cedar 12.724413 18454 6.621344 \n", "93 Winneshiek 13.295288 20561 6.640300 \n", "85 Union 13.231373 12420 6.979412 \n", "89 Washington 14.579561 22281 7.047676 \n", "64 Mitchell 12.110916 10763 7.569967 \n", "71 Page 14.035741 15391 7.618095 \n", "46 Iowa 12.607737 16311 7.708391 \n", "40 Hardin 13.009862 17226 7.769505 \n", "60 Mahaska 13.342219 22181 7.949059 \n", "13 Carroll 13.476729 20437 8.136696 \n", "69 O'Brien 12.506521 14020 8.611285 \n", "9 Buchanan 13.504469 20992 8.803275 \n", "51 Jones 13.074420 20439 9.691983 \n", "76 Poweshiek 14.246155 18533 11.072879 \n", "20 Clay 13.073807 16333 11.702255 \n", "88 Warren 13.217243 49691 12.129877 \n", "24 Dallas 15.116572 84516 12.321483 \n", "10 Buena Vista 14.543544 20332 13.155325 \n", "91 Webster 14.315835 36769 13.442294 \n", "61 Marion 14.093066 33189 13.708075 \n", "48 Jasper 12.996996 36708 13.740373 \n", "7 Boone 13.277107 26532 14.083864 \n", "8 Bremer 13.042060 24798 14.144601 \n", "62 Marshall 14.875434 40312 15.464035 \n", "22 Clinton 13.627250 47309 16.674016 \n", "68 Muscatine 13.923365 42940 18.572652 \n", "54 Lee 14.395224 34615 22.574731 \n", "87 Wapello 14.284163 34982 25.200415 \n", "94 Woodbury 15.012023 102779 26.139797 \n", "29 Dickinson 14.023690 17243 27.448097 \n", "75 Pottawattamie 14.721285 93582 31.465442 \n", "16 Cerro Gordo 13.808162 43070 34.428514 \n", "28 Des Moines 15.174117 39739 34.976332 \n", "82 Story 14.747469 97090 36.855670 \n", "30 Dubuque 14.393144 97003 42.015240 \n", "6 Black Hawk 15.024919 132904 71.288290 \n", "50 Johnson 15.770199 146547 77.091908 \n", "55 Linn 14.794975 221661 85.036195 \n", "79 Scott 15.274967 172474 107.637548 \n", "\n", " sales_prediction_dollars \n", "26 141811.0 \n", "52 32440.0 \n", "11 120103.0 \n", "39 58912.0 \n", "12 68571.0 \n", "18 242653.0 \n", "70 15017.0 \n", "66 138773.0 \n", "74 166156.0 \n", "63 142072.0 \n", "43 335431.0 \n", "57 388043.0 \n", "96 365167.0 \n", "83 655898.0 \n", "44 175430.0 \n", "35 255811.0 \n", "56 378171.0 \n", "0 221482.0 \n", "36 4175.0 \n", "80 447766.0 \n", "32 955344.0 \n", "34 475800.0 \n", "45 286374.0 \n", "27 615781.0 \n", "41 643413.0 \n", "5 912250.0 \n", "81 1725739.0 \n", "21 954141.0 \n", "78 528002.0 \n", "23 960452.0 \n", "2 839656.0 \n", "49 632088.0 \n", "17 501313.0 \n", "31 391058.0 \n", "72 797432.0 \n", "59 523595.0 \n", "14 931866.0 \n", "73 953171.0 \n", "19 445791.0 \n", "67 699564.0 \n", "92 693928.0 \n", "3 955846.0 \n", "38 654023.0 \n", "65 1017900.0 \n", "47 1120072.0 \n", "53 834826.0 \n", "58 962622.0 \n", "42 959348.0 \n", "33 913846.0 \n", "15 748698.0 \n", "93 1028921.0 \n", "85 1018948.0 \n", "89 1139096.0 \n", "64 819843.0 \n", "71 1134280.0 \n", "46 789613.0 \n", "40 1140430.0 \n", "60 1255159.0 \n", "13 1200276.0 \n", "69 1166399.0 \n", "9 1269271.0 \n", "51 1487368.0 \n", "76 1469057.0 \n", "20 1348963.0 \n", "88 2521533.0 \n", "24 4125161.0 \n", "10 2087010.0 \n", "91 2541044.0 \n", "61 2177923.0 \n", "48 2486830.0 \n", "7 1696331.0 \n", "8 1657708.0 \n", "62 2791982.0 \n", "22 3197853.0 \n", "68 3138265.0 \n", "54 3104487.0 \n", "87 3190720.0 \n", "94 7104739.0 \n", "29 2628643.0 \n", "75 6828297.0 \n", "16 4328297.0 \n", "28 3968601.0 \n", "82 7146093.0 \n", "30 7310597.0 \n", "6 11283189.0 \n", "50 11867678.0 \n", "55 16780528.0 \n", "79 14997255.0 " ] }, "execution_count": 344, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred.sort_values(\"stores_per_area\", inplace=True, ascending=True)\n", "df_pred[['county','sales_per_litters','population','stores_per_area','sales_prediction_dollars']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Competition\n", "\n", "The county with weaker competition is Butler. This could provided untapped potential. However, the absence of a reasonable number of stores may indicate, as observed before, that the county's population is simply not interested in this category of product. Again, further investigation must be carried out." ] }, { "cell_type": "code", "execution_count": 347, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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countysales_per_litterspopulationstores_per_areasales_prediction_dollarsstore_population_ratio
11Butler11.140858147911.929501120103.00.223650
26Decatur14.40520381411.381434141811.00.244933
24Dallas15.1165728451612.3214834125161.00.251834
63Mills12.719923149723.249919142072.00.265028
52Keokuk11.481359101191.78267532440.00.276411
39Hancock10.906219108352.15949158912.00.286202
81Sioux13.538806348985.0126981725739.00.295346
5Benton12.189062256994.865202912250.00.300829
27Delaware13.760907173274.507389615781.00.331794
56Louisa13.987210111423.802603378171.00.334769
18Chickasaw11.530256120233.024458242653.00.335274
36Grundy11.490660123133.9538664175.00.352554
49Jefferson14.068272180905.247219632088.00.356329
12Calhoun11.49146898462.27523568571.00.368170
66Monroe13.83136778703.122350138773.00.381194
88Warren13.2172434969112.1298772521533.00.394840
96Wright12.707313127793.540219365167.00.427342
32Fayette12.617454200544.463878955344.00.435325
60Mahaska13.342219221817.9490591255159.00.435959
83Tama13.161979173193.665790655898.00.442000
15Cedar12.724413184546.621344748698.00.442181
42Henry14.271205197736.566097959348.00.456481
57Lucas13.73237986473.520855388043.00.469990
80Shelby13.687852118004.331137447766.00.476864
70Osceola12.74534160643.08128815017.00.491590
59Madison13.323817158485.529360523595.00.491860
93Winneshiek13.295288205616.6403001028921.00.512670
33Floyd13.846056158736.601745913846.00.518050
73Plymouth13.826506252005.830208953171.00.522857
44Humboldt13.02292594873.746353175430.00.523559
89Washington14.579561222817.0476761139096.00.527131
23Crawford13.822321169405.215863960452.00.552538
43Howard12.41460993323.345996335431.00.556901
38Hamilton12.940146150766.035600654023.00.570642
35Greene14.05781890113.787770255811.00.582399
62Marshall14.8754344031215.4640352791982.00.583127
34Franklin12.705219101704.494475475800.00.595084
21Clayton13.195825175905.023060954141.00.595793
22Clinton13.6272504730916.6740163197853.00.599548
19Clarke13.69630093095.866678445791.00.601568
9Buchanan13.504469209928.8032751269271.00.602753
74Pocahontas11.57258768863.211730166156.00.605141
2Allamakee12.613783138845.238733839656.00.619058
7Boone13.2771072653214.0838641696331.00.622456
0Adair12.63148670923.856389221482.00.623237
30Dubuque14.3931449700342.0152407310597.00.626300
48Jasper12.9969963670813.7403732486830.00.627820
31Emmet12.84723096585.368912391058.00.628598
61Marion14.0930663318913.7080752177923.00.631474
17Cherokee13.059553115085.273883501313.00.643726
41Harrison13.162064141494.563865643413.00.645346
47Jackson12.838469194726.0674101120072.00.646724
91Webster14.3158353676913.4422942541044.00.655607
67Montgomery13.382034102255.886740699564.00.656528
94Woodbury15.01202310277926.1397977104739.00.659006
45Ida14.26003769854.498609286374.00.683894
46Iowa12.607737163117.708391789613.00.684323
3Appanoose13.375589124625.961273955846.00.688654
71Page14.035741153917.6180951134280.00.692028
13Carroll13.476729204378.1366961200276.00.712825
8Bremer13.0420602479814.1446011657708.00.715622
92Winnebago12.001155106315.918678693928.00.721193
50Johnson15.77019914654777.09190811867678.00.723010
78Sac12.32345398765.066547528002.00.724686
85Union13.231373124206.9794121018948.00.726570
68Muscatine13.9233654294018.5726523138265.00.735701
82Story14.7474699709036.8556707146093.00.737831
79Scott15.274967172474107.63754814997255.00.756166
14Cass13.252889131575.622151931866.00.759140
51Jones13.074420204399.6919831487368.00.760556
64Mitchell12.110916107637.569967819843.00.764378
75Pottawattamie14.7212859358231.4654426828297.00.774422
54Lee14.3952243461522.5747313104487.00.787549
40Hardin13.009862172267.7695051140430.00.789040
87Wapello14.2841633498225.2004153190720.00.795924
28Des Moines15.1741173973934.9763323968601.00.817182
58Lyon13.718707117546.344742962622.00.820742
55Linn14.79497522166185.03619516780528.00.830511
6Black Hawk15.02491913290471.28829011283189.00.889582
53Kossuth13.700965151146.318563834826.00.907768
20Clay13.0738071633311.7022551348963.00.935284
76Poweshiek14.2461551853311.0728791469057.00.944046
72Palo Alto12.88483490475.430014797432.00.957997
69O'Brien12.506521140208.6112851166399.00.995292
10Buena Vista14.5435442033213.1553252087010.01.062266
65Monona13.66053988986.0496341017900.01.126433
16Cerro Gordo13.8081624307034.4285144328297.01.197260
29Dickinson14.0236901724327.4480972628643.01.630227
\n", "
" ], "text/plain": [ " county sales_per_litters population stores_per_area \\\n", "11 Butler 11.140858 14791 1.929501 \n", "26 Decatur 14.405203 8141 1.381434 \n", "24 Dallas 15.116572 84516 12.321483 \n", "63 Mills 12.719923 14972 3.249919 \n", "52 Keokuk 11.481359 10119 1.782675 \n", "39 Hancock 10.906219 10835 2.159491 \n", "81 Sioux 13.538806 34898 5.012698 \n", "5 Benton 12.189062 25699 4.865202 \n", "27 Delaware 13.760907 17327 4.507389 \n", "56 Louisa 13.987210 11142 3.802603 \n", "18 Chickasaw 11.530256 12023 3.024458 \n", "36 Grundy 11.490660 12313 3.953866 \n", "49 Jefferson 14.068272 18090 5.247219 \n", "12 Calhoun 11.491468 9846 2.275235 \n", "66 Monroe 13.831367 7870 3.122350 \n", "88 Warren 13.217243 49691 12.129877 \n", "96 Wright 12.707313 12779 3.540219 \n", "32 Fayette 12.617454 20054 4.463878 \n", "60 Mahaska 13.342219 22181 7.949059 \n", "83 Tama 13.161979 17319 3.665790 \n", "15 Cedar 12.724413 18454 6.621344 \n", "42 Henry 14.271205 19773 6.566097 \n", "57 Lucas 13.732379 8647 3.520855 \n", "80 Shelby 13.687852 11800 4.331137 \n", "70 Osceola 12.745341 6064 3.081288 \n", "59 Madison 13.323817 15848 5.529360 \n", "93 Winneshiek 13.295288 20561 6.640300 \n", "33 Floyd 13.846056 15873 6.601745 \n", "73 Plymouth 13.826506 25200 5.830208 \n", "44 Humboldt 13.022925 9487 3.746353 \n", "89 Washington 14.579561 22281 7.047676 \n", "23 Crawford 13.822321 16940 5.215863 \n", "43 Howard 12.414609 9332 3.345996 \n", "38 Hamilton 12.940146 15076 6.035600 \n", "35 Greene 14.057818 9011 3.787770 \n", "62 Marshall 14.875434 40312 15.464035 \n", "34 Franklin 12.705219 10170 4.494475 \n", "21 Clayton 13.195825 17590 5.023060 \n", "22 Clinton 13.627250 47309 16.674016 \n", "19 Clarke 13.696300 9309 5.866678 \n", "9 Buchanan 13.504469 20992 8.803275 \n", "74 Pocahontas 11.572587 6886 3.211730 \n", "2 Allamakee 12.613783 13884 5.238733 \n", "7 Boone 13.277107 26532 14.083864 \n", "0 Adair 12.631486 7092 3.856389 \n", "30 Dubuque 14.393144 97003 42.015240 \n", "48 Jasper 12.996996 36708 13.740373 \n", "31 Emmet 12.847230 9658 5.368912 \n", "61 Marion 14.093066 33189 13.708075 \n", "17 Cherokee 13.059553 11508 5.273883 \n", "41 Harrison 13.162064 14149 4.563865 \n", "47 Jackson 12.838469 19472 6.067410 \n", "91 Webster 14.315835 36769 13.442294 \n", "67 Montgomery 13.382034 10225 5.886740 \n", "94 Woodbury 15.012023 102779 26.139797 \n", "45 Ida 14.260037 6985 4.498609 \n", "46 Iowa 12.607737 16311 7.708391 \n", "3 Appanoose 13.375589 12462 5.961273 \n", "71 Page 14.035741 15391 7.618095 \n", "13 Carroll 13.476729 20437 8.136696 \n", "8 Bremer 13.042060 24798 14.144601 \n", "92 Winnebago 12.001155 10631 5.918678 \n", "50 Johnson 15.770199 146547 77.091908 \n", "78 Sac 12.323453 9876 5.066547 \n", "85 Union 13.231373 12420 6.979412 \n", "68 Muscatine 13.923365 42940 18.572652 \n", "82 Story 14.747469 97090 36.855670 \n", "79 Scott 15.274967 172474 107.637548 \n", "14 Cass 13.252889 13157 5.622151 \n", "51 Jones 13.074420 20439 9.691983 \n", "64 Mitchell 12.110916 10763 7.569967 \n", "75 Pottawattamie 14.721285 93582 31.465442 \n", "54 Lee 14.395224 34615 22.574731 \n", "40 Hardin 13.009862 17226 7.769505 \n", "87 Wapello 14.284163 34982 25.200415 \n", "28 Des Moines 15.174117 39739 34.976332 \n", "58 Lyon 13.718707 11754 6.344742 \n", "55 Linn 14.794975 221661 85.036195 \n", "6 Black Hawk 15.024919 132904 71.288290 \n", "53 Kossuth 13.700965 15114 6.318563 \n", "20 Clay 13.073807 16333 11.702255 \n", "76 Poweshiek 14.246155 18533 11.072879 \n", "72 Palo Alto 12.884834 9047 5.430014 \n", "69 O'Brien 12.506521 14020 8.611285 \n", "10 Buena Vista 14.543544 20332 13.155325 \n", "65 Monona 13.660539 8898 6.049634 \n", "16 Cerro Gordo 13.808162 43070 34.428514 \n", "29 Dickinson 14.023690 17243 27.448097 \n", "\n", " sales_prediction_dollars store_population_ratio \n", "11 120103.0 0.223650 \n", "26 141811.0 0.244933 \n", "24 4125161.0 0.251834 \n", "63 142072.0 0.265028 \n", "52 32440.0 0.276411 \n", "39 58912.0 0.286202 \n", "81 1725739.0 0.295346 \n", "5 912250.0 0.300829 \n", "27 615781.0 0.331794 \n", "56 378171.0 0.334769 \n", "18 242653.0 0.335274 \n", "36 4175.0 0.352554 \n", "49 632088.0 0.356329 \n", "12 68571.0 0.368170 \n", "66 138773.0 0.381194 \n", "88 2521533.0 0.394840 \n", "96 365167.0 0.427342 \n", "32 955344.0 0.435325 \n", "60 1255159.0 0.435959 \n", "83 655898.0 0.442000 \n", "15 748698.0 0.442181 \n", "42 959348.0 0.456481 \n", "57 388043.0 0.469990 \n", "80 447766.0 0.476864 \n", "70 15017.0 0.491590 \n", "59 523595.0 0.491860 \n", "93 1028921.0 0.512670 \n", "33 913846.0 0.518050 \n", "73 953171.0 0.522857 \n", "44 175430.0 0.523559 \n", "89 1139096.0 0.527131 \n", "23 960452.0 0.552538 \n", "43 335431.0 0.556901 \n", "38 654023.0 0.570642 \n", "35 255811.0 0.582399 \n", "62 2791982.0 0.583127 \n", "34 475800.0 0.595084 \n", "21 954141.0 0.595793 \n", "22 3197853.0 0.599548 \n", "19 445791.0 0.601568 \n", "9 1269271.0 0.602753 \n", "74 166156.0 0.605141 \n", "2 839656.0 0.619058 \n", "7 1696331.0 0.622456 \n", "0 221482.0 0.623237 \n", "30 7310597.0 0.626300 \n", "48 2486830.0 0.627820 \n", "31 391058.0 0.628598 \n", "61 2177923.0 0.631474 \n", "17 501313.0 0.643726 \n", "41 643413.0 0.645346 \n", "47 1120072.0 0.646724 \n", "91 2541044.0 0.655607 \n", "67 699564.0 0.656528 \n", "94 7104739.0 0.659006 \n", "45 286374.0 0.683894 \n", "46 789613.0 0.684323 \n", "3 955846.0 0.688654 \n", "71 1134280.0 0.692028 \n", "13 1200276.0 0.712825 \n", "8 1657708.0 0.715622 \n", "92 693928.0 0.721193 \n", "50 11867678.0 0.723010 \n", "78 528002.0 0.724686 \n", "85 1018948.0 0.726570 \n", "68 3138265.0 0.735701 \n", "82 7146093.0 0.737831 \n", "79 14997255.0 0.756166 \n", "14 931866.0 0.759140 \n", "51 1487368.0 0.760556 \n", "64 819843.0 0.764378 \n", "75 6828297.0 0.774422 \n", "54 3104487.0 0.787549 \n", "40 1140430.0 0.789040 \n", "87 3190720.0 0.795924 \n", "28 3968601.0 0.817182 \n", "58 962622.0 0.820742 \n", "55 16780528.0 0.830511 \n", "6 11283189.0 0.889582 \n", "53 834826.0 0.907768 \n", "20 1348963.0 0.935284 \n", "76 1469057.0 0.944046 \n", "72 797432.0 0.957997 \n", "69 1166399.0 0.995292 \n", "10 2087010.0 1.062266 \n", "65 1017900.0 1.126433 \n", "16 4328297.0 1.197260 \n", "29 2628643.0 1.630227 " ] }, "execution_count": 347, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pred.sort_values(\"store_population_ratio\", inplace=True, ascending=True)\n", "df_pred[['county','sales_per_litters','population','stores_per_area','sales_prediction_dollars','store_population_ratio']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] https://jocelyn-ong.github.io/iowa-liquor-sales\n", "\n", "[2] https://medium.com/@neil.liberman/where-to-open-a-liquor-store-in-iowa-bb85af614563\n", "\n", "[3] https://www.youtube.com/watch?v=PL_GBlcBNCk&t=725s)\n", "\n", "[4] https://goo.gl/nTwV6J\n", "\n", "[5] https://www.iowa-demographics.com/counties_by_population\n", "\n", "[6] https://github.com/mwaskom/seaborn/issues/1126\n", "\n", "[7] Karamshuk et al: Geo-spotting: mining online location-based services for optimal retail store placement " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Appendix " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appendix A\n", "\n", "Consider the following two columns of the original dataset:" ] }, { "cell_type": "code", "execution_count": 311, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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State Bottle CostState Bottle Retail
0$18.09$27.14
1$18.09$27.14
2$6.40$9.60
3$35.55$53.34
4$6.40$9.60
\n", "
" ], "text/plain": [ " State Bottle Cost State Bottle Retail\n", "0 $18.09 $27.14\n", "1 $18.09 $27.14\n", "2 $6.40 $9.60\n", "3 $35.55 $53.34\n", "4 $6.40 $9.60" ] }, "execution_count": 311, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_raw[['State Bottle Cost','State Bottle Retail']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The state of Iowa is a wholesale seller to its stores. The cost above is the amount paid per ordered bottle by the Alcoholic Beverages Division and the retail prices are actually the prices stores paid to the State. In the dataset there is no information regarding the end user (the consumer). Following previous works we will use the difference between these columns as a proxy for profit. This can be done approximately if one assumes e.g. that the profit margin is essentially flat and we then set the retail price as end user price. Also, we must assume that the delay between the purchase from the State and the sale to the end user is not too large." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }