{
"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": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Invoice/Item Number | \n",
" Date | \n",
" Store Number | \n",
" Store Name | \n",
" Address | \n",
" City | \n",
" Zip Code | \n",
" Store Location | \n",
" County Number | \n",
" County | \n",
" Category | \n",
" Category Name | \n",
" Vendor Number | \n",
" Vendor Name | \n",
" Item Number | \n",
" Item Description | \n",
" Pack | \n",
" Bottle Volume (ml) | \n",
" State Bottle Cost | \n",
" State Bottle Retail | \n",
" Bottles Sold | \n",
" Sale (Dollars) | \n",
" Volume Sold (Liters) | \n",
" Volume Sold (Gallons) | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" S29198800001 | \n",
" 11/20/2015 | \n",
" 2191 | \n",
" Keokuk Spirits | \n",
" 1013 MAIN | \n",
" KEOKUK | \n",
" 52632 | \n",
" 1013 MAIN\\nKEOKUK 52632\\n(40.39978, -91.387531) | \n",
" 56.0 | \n",
" Lee | \n",
" NaN | \n",
" NaN | \n",
" 255 | \n",
" Wilson Daniels Ltd. | \n",
" 297 | \n",
" Templeton Rye w/Flask | \n",
" 6 | \n",
" 750 | \n",
" $18.09 | \n",
" $27.14 | \n",
" 6 | \n",
" $162.84 | \n",
" 4.50 | \n",
" 1.19 | \n",
"
\n",
" \n",
" 1 | \n",
" S29195400002 | \n",
" 11/21/2015 | \n",
" 2205 | \n",
" Ding's Honk And Holler | \n",
" 900 E WASHINGTON | \n",
" CLARINDA | \n",
" 51632 | \n",
" 900 E WASHINGTON\\nCLARINDA 51632\\n(40.739238, ... | \n",
" 73.0 | \n",
" Page | \n",
" NaN | \n",
" NaN | \n",
" 255 | \n",
" Wilson Daniels Ltd. | \n",
" 297 | \n",
" Templeton Rye w/Flask | \n",
" 6 | \n",
" 750 | \n",
" $18.09 | \n",
" $27.14 | \n",
" 12 | \n",
" $325.68 | \n",
" 9.00 | \n",
" 2.38 | \n",
"
\n",
" \n",
" 2 | \n",
" S29050300001 | \n",
" 11/16/2015 | \n",
" 3549 | \n",
" Quicker Liquor Store | \n",
" 1414 48TH ST | \n",
" FORT MADISON | \n",
" 52627 | \n",
" 1414 48TH ST\\nFORT MADISON 52627\\n(40.624226, ... | \n",
" 56.0 | \n",
" Lee | \n",
" NaN | \n",
" NaN | \n",
" 130 | \n",
" Disaronno International LLC | \n",
" 249 | \n",
" Disaronno Amaretto Cavalli Mignon 3-50ml Pack | \n",
" 20 | \n",
" 150 | \n",
" $6.40 | \n",
" $9.60 | \n",
" 2 | \n",
" $19.20 | \n",
" 0.30 | \n",
" 0.08 | \n",
"
\n",
" \n",
" 3 | \n",
" S28867700001 | \n",
" 11/04/2015 | \n",
" 2513 | \n",
" Hy-Vee Food Store #2 / Iowa City | \n",
" 812 S 1ST AVE | \n",
" IOWA CITY | \n",
" 52240 | \n",
" 812 S 1ST AVE\\nIOWA CITY 52240\\n | \n",
" 52.0 | \n",
" Johnson | \n",
" NaN | \n",
" NaN | \n",
" 65 | \n",
" Jim Beam Brands | \n",
" 237 | \n",
" Knob Creek w/ Crystal Decanter | \n",
" 3 | \n",
" 1750 | \n",
" $35.55 | \n",
" $53.34 | \n",
" 3 | \n",
" $160.02 | \n",
" 5.25 | \n",
" 1.39 | \n",
"
\n",
" \n",
" 4 | \n",
" S29050800001 | \n",
" 11/17/2015 | \n",
" 3942 | \n",
" Twin Town Liquor | \n",
" 104 HIGHWAY 30 WEST | \n",
" TOLEDO | \n",
" 52342 | \n",
" 104 HIGHWAY 30 WEST\\nTOLEDO 52342\\n(41.985887,... | \n",
" 86.0 | \n",
" Tama | \n",
" NaN | \n",
" NaN | \n",
" 130 | \n",
" Disaronno International LLC | \n",
" 249 | \n",
" Disaronno Amaretto Cavalli Mignon 3-50ml Pack | \n",
" 20 | \n",
" 150 | \n",
" $6.40 | \n",
" $9.60 | \n",
" 2 | \n",
" $19.20 | \n",
" 0.30 | \n",
" 0.08 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" store_number | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2191 | \n",
" Lee | \n",
" 750 | \n",
" 18.09 | \n",
" 27.14 | \n",
" 6 | \n",
" 162.84 | \n",
" 4.50 | \n",
"
\n",
" \n",
" 1 | \n",
" 2205 | \n",
" Page | \n",
" 750 | \n",
" 18.09 | \n",
" 27.14 | \n",
" 12 | \n",
" 325.68 | \n",
" 9.00 | \n",
"
\n",
" \n",
" 2 | \n",
" 3549 | \n",
" Lee | \n",
" 150 | \n",
" 6.40 | \n",
" 9.60 | \n",
" 2 | \n",
" 19.20 | \n",
" 0.30 | \n",
"
\n",
" \n",
" 3 | \n",
" 2513 | \n",
" Johnson | \n",
" 1750 | \n",
" 35.55 | \n",
" 53.34 | \n",
" 3 | \n",
" 160.02 | \n",
" 5.25 | \n",
"
\n",
" \n",
" 4 | \n",
" 3942 | \n",
" Tama | \n",
" 150 | \n",
" 6.40 | \n",
" 9.60 | \n",
" 2 | \n",
" 19.20 | \n",
" 0.30 | \n",
"
\n",
" \n",
"
\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 \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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
"
\n",
" \n",
" \n",
" \n",
" mean | \n",
" 9.816396 | \n",
" 14.742246 | \n",
" 9.877354 | \n",
" 130.182613 | \n",
" 8.981275 | \n",
"
\n",
" \n",
" std | \n",
" 14.626429 | \n",
" 21.939393 | \n",
" 23.699125 | \n",
" 405.568937 | \n",
" 28.355651 | \n",
"
\n",
" \n",
" min | \n",
" 0.890000 | \n",
" 1.340000 | \n",
" 1.000000 | \n",
" 1.340000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 5.540000 | \n",
" 8.310000 | \n",
" 2.000000 | \n",
" 30.720000 | \n",
" 1.600000 | \n",
"
\n",
" \n",
" 50% | \n",
" 8.180000 | \n",
" 12.300000 | \n",
" 6.000000 | \n",
" 70.560000 | \n",
" 5.250000 | \n",
"
\n",
" \n",
" 75% | \n",
" 11.960000 | \n",
" 17.940000 | \n",
" 12.000000 | \n",
" 135.360000 | \n",
" 10.500000 | \n",
"
\n",
" \n",
" max | \n",
" 6100.000000 | \n",
" 9150.000000 | \n",
" 3960.000000 | \n",
" 106326.000000 | \n",
" 3960.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": {
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59i6jPPdc4GcjKHdSe+SRFSxb9uj6N6Sk3lmzZvDQQ8tZvXrN+neYpKyzu/RKndA7tVpn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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"
]
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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fGbD525n59ojYAvgSMAu4AzgkMy9uHbsz8DlgC+AaYN/MvL3VfghwOLABcB5w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\nbxhtxwAAAABJRU5ErkJggg==\n",
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
"
\n",
" \n",
" \n",
" \n",
" mean | \n",
" 9.745472 | \n",
" 14.635677 | \n",
" 7.968786 | \n",
" 99.364086 | \n",
" 6.864374 | \n",
"
\n",
" \n",
" std | \n",
" 6.995164 | \n",
" 10.492340 | \n",
" 7.830878 | \n",
" 100.784662 | \n",
" 6.635848 | \n",
"
\n",
" \n",
" min | \n",
" 0.890000 | \n",
" 1.340000 | \n",
" 1.000000 | \n",
" 1.340000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 25% | \n",
" 5.510000 | \n",
" 8.270000 | \n",
" 2.000000 | \n",
" 30.000000 | \n",
" 1.500000 | \n",
"
\n",
" \n",
" 50% | \n",
" 8.000000 | \n",
" 12.000000 | \n",
" 6.000000 | \n",
" 68.640000 | \n",
" 4.800000 | \n",
"
\n",
" \n",
" 75% | \n",
" 11.830000 | \n",
" 17.750000 | \n",
" 12.000000 | \n",
" 132.780000 | \n",
" 10.500000 | \n",
"
\n",
" \n",
" max | \n",
" 498.640000 | \n",
" 747.960000 | \n",
" 48.000000 | \n",
" 899.980000 | \n",
" 54.000000 | \n",
"
\n",
" \n",
"
\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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\nAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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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"
]
},
{
"data": {
"image/png": 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JFRuXkBIRrwX2BE6gBJT+pmhbYOmA3R8EXtS8nksZjml7oCnfCuhrl2fmauCR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\n",
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" store_number | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2191 | \n",
" Lee | \n",
" 750 | \n",
" 18.09 | \n",
" 27.14 | \n",
" 6 | \n",
" 162.84 | \n",
" 4.50 | \n",
" 54.30 | \n",
"
\n",
" \n",
" 1 | \n",
" 2205 | \n",
" Page | \n",
" 750 | \n",
" 18.09 | \n",
" 27.14 | \n",
" 12 | \n",
" 325.68 | \n",
" 9.00 | \n",
" 108.60 | \n",
"
\n",
" \n",
" 2 | \n",
" 3549 | \n",
" Lee | \n",
" 150 | \n",
" 6.40 | \n",
" 9.60 | \n",
" 2 | \n",
" 19.20 | \n",
" 0.30 | \n",
" 6.40 | \n",
"
\n",
" \n",
" 3 | \n",
" 2513 | \n",
" Johnson | \n",
" 1750 | \n",
" 35.55 | \n",
" 53.34 | \n",
" 3 | \n",
" 160.02 | \n",
" 5.25 | \n",
" 53.37 | \n",
"
\n",
" \n",
" 4 | \n",
" 3942 | \n",
" Tama | \n",
" 150 | \n",
" 6.40 | \n",
" 9.60 | \n",
" 2 | \n",
" 19.20 | \n",
" 0.30 | \n",
" 6.40 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
"
\n",
" \n",
" county | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
"
\n",
" \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
"
\n",
" \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
"
\n",
" \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
"
\n",
" \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
"
\n",
" \n",
"
\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 "
]
},
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
" 31.100932 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
" 18.706055 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
" 30.090802 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
" 27.708309 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
" 26.109389 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
"
\n",
" \n",
" \n",
" \n",
" mean | \n",
" 1.994879e+07 | \n",
" 2.109874e+05 | \n",
" 3.168593e+05 | \n",
" 1.725225e+05 | \n",
" 2.151211e+06 | \n",
" 1.486122e+05 | \n",
" 7.188647e+05 | \n",
" 21649.787879 | \n",
" 30.170016 | \n",
" 13.382942 | \n",
"
\n",
" \n",
" std | \n",
" 4.068975e+07 | \n",
" 4.620609e+05 | \n",
" 6.938726e+05 | \n",
" 4.054011e+05 | \n",
" 4.974648e+06 | \n",
" 3.237530e+05 | \n",
" 1.662030e+06 | \n",
" 46543.944487 | \n",
" 6.068482 | \n",
" 0.999601 | \n",
"
\n",
" \n",
" max | \n",
" 3.376040e+08 | \n",
" 3.868235e+06 | \n",
" 5.808917e+06 | \n",
" 3.319741e+06 | \n",
" 4.132447e+07 | \n",
" 2.673001e+06 | \n",
" 1.380706e+07 | \n",
" 383547.000000 | \n",
" 44.859888 | \n",
" 15.770199 | \n",
"
\n",
" \n",
"
\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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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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MBJIkyUAgSZIMBJIkCQOBJEnCQCBJkqh/HoIHFRGbAK+hzGa4BeVZB/cAtwAX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\nPxZFBoPPFZ7yAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"image/png": 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HwHs7+Bx91+lz6ffefUvHjjXTbKnzErbU1y1Nl/92uquT6xBsB9yVmetatt0J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\nEvD/AQRx/L+tlf/sAAAAAElFTkSuQmCC\n",
"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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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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W1GE6sQ411qKwDjXWorAORbNefyOB4ABgYUSsrG7PBYiIVwIfB3Ydtf9CylkHE9bX18v8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\nkiRhIJAkSRgIJEkSBgJJkgT8f3R2MIzUMuAQAAAAAElFTkSuQmCC\n",
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
" 18.706055 | \n",
" 13.328281 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
" 30.090802 | \n",
" 12.613783 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
" 27.708309 | \n",
" 13.375589 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
" 26.109389 | \n",
" 12.072829 | \n",
"
\n",
" \n",
"
\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",
" county | \n",
" population | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 7092 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 3693 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 13884 | \n",
"
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" \n",
" 3 | \n",
" Appanoose | \n",
" 12462 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 5678 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" county | \n",
" area | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 1146.149874 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 950.420482 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 1640.663925 | \n",
"
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" 3 | \n",
" Appanoose | \n",
" 1439.625361 | \n",
"
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" \n",
" 4 | \n",
" Audubon | \n",
" 1011.305224 | \n",
"
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" 5 | \n",
" Benton | \n",
" 1589.039934 | \n",
"
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" \n",
" 6 | \n",
" Black Hawk | \n",
" 1658.463133 | \n",
"
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" \n",
" 7 | \n",
" Boone | \n",
" 1172.618576 | \n",
"
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" \n",
" 8 | \n",
" Bremer | \n",
" 1254.612988 | \n",
"
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" \n",
" 9 | \n",
" Buchanan | \n",
" 1437.306002 | \n",
"
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" \n",
" 10 | \n",
" Buena Vista | \n",
" 1641.768588 | \n",
"
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" \n",
" 11 | \n",
" Butler | \n",
" 1714.432829 | \n",
"
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" \n",
" 12 | \n",
" Calhoun | \n",
" 1593.242060 | \n",
"
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" \n",
" 13 | \n",
" Carroll | \n",
" 1790.407255 | \n",
"
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" \n",
" 14 | \n",
" Cass | \n",
" 1776.544213 | \n",
"
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" \n",
" 15 | \n",
" Cedar | \n",
" 1232.378198 | \n",
"
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" \n",
" 16 | \n",
" Cerro Gordo | \n",
" 1497.770139 | \n",
"
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" \n",
" 17 | \n",
" Cherokee | \n",
" 1404.657609 | \n",
"
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" \n",
" 18 | \n",
" Chickasaw | \n",
" 1332.800885 | \n",
"
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" \n",
" 19 | \n",
" Clarke | \n",
" 954.543677 | \n",
"
\n",
" \n",
" 20 | \n",
" Clay | \n",
" 1305.389452 | \n",
"
\n",
" \n",
" 21 | \n",
" Clayton | \n",
" 2086.377422 | \n",
"
\n",
" \n",
" 22 | \n",
" Clinton | \n",
" 1701.089895 | \n",
"
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" \n",
" 23 | \n",
" Crawford | \n",
" 1794.525720 | \n",
"
\n",
" \n",
" 24 | \n",
" Dallas | \n",
" 1727.389531 | \n",
"
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" \n",
" 25 | \n",
" Davis | \n",
" 1202.497109 | \n",
"
\n",
" \n",
" 26 | \n",
" Decatur | \n",
" 1443.427332 | \n",
"
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" \n",
" 27 | \n",
" Delaware | \n",
" 1275.461107 | \n",
"
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" \n",
" 28 | \n",
" Des Moines | \n",
" 928.456413 | \n",
"
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" \n",
" 29 | \n",
" Dickinson | \n",
" 1024.114722 | \n",
"
\n",
" \n",
" 30 | \n",
" Dubuque | \n",
" 1445.975306 | \n",
"
\n",
" \n",
" 31 | \n",
" Emmet | \n",
" 1130.769046 | \n",
"
\n",
" \n",
" 32 | \n",
" Fayette | \n",
" 1955.698767 | \n",
"
\n",
" \n",
" 33 | \n",
" Floyd | \n",
" 1245.579774 | \n",
"
\n",
" \n",
" 34 | \n",
" Franklin | \n",
" 1346.541996 | \n",
"
\n",
" \n",
" 36 | \n",
" Greene | \n",
" 1385.511780 | \n",
"
\n",
" \n",
" 37 | \n",
" Grundy | \n",
" 1097.912646 | \n",
"
\n",
" \n",
" 38 | \n",
" Guthrie | \n",
" 1659.326695 | \n",
"
\n",
" \n",
" 39 | \n",
" Hamilton | \n",
" 1425.376163 | \n",
"
\n",
" \n",
" 40 | \n",
" Hancock | \n",
" 1435.986414 | \n",
"
\n",
" \n",
" 41 | \n",
" Hardin | \n",
" 1749.403531 | \n",
"
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" \n",
" 42 | \n",
" Harrison | \n",
" 2000.716609 | \n",
"
\n",
" \n",
" 43 | \n",
" Henry | \n",
" 1374.637080 | \n",
"
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" \n",
" 44 | \n",
" Howard | \n",
" 1553.199575 | \n",
"
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" \n",
" 45 | \n",
" Humboldt | \n",
" 1325.822767 | \n",
"
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" \n",
" 46 | \n",
" Ida | \n",
" 1061.883800 | \n",
"
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" \n",
" 47 | \n",
" Iowa | \n",
" 1448.032410 | \n",
"
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" \n",
" 48 | \n",
" Jackson | \n",
" 2075.514783 | \n",
"
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" \n",
" 49 | \n",
" Jasper | \n",
" 1677.247032 | \n",
"
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" \n",
" 50 | \n",
" Jefferson | \n",
" 1228.460352 | \n",
"
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" \n",
" 51 | \n",
" Johnson | \n",
" 1374.398467 | \n",
"
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" 52 | \n",
" Jones | \n",
" 1603.902991 | \n",
"
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" 53 | \n",
" Keokuk | \n",
" 1568.990538 | \n",
"
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" 54 | \n",
" Kossuth | \n",
" 2171.379858 | \n",
"
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" 55 | \n",
" Lee | \n",
" 1207.589119 | \n",
"
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" 56 | \n",
" Linn | \n",
" 2164.866374 | \n",
"
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" 57 | \n",
" Louisa | \n",
" 980.907015 | \n",
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" 58 | \n",
" Lucas | \n",
" 1154.264969 | \n",
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" 59 | \n",
" Lyon | \n",
" 1520.471701 | \n",
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" 60 | \n",
" Madison | \n",
" 1409.747269 | \n",
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" 61 | \n",
" Mahaska | \n",
" 1216.496254 | \n",
"
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" \n",
" 62 | \n",
" Marion | \n",
" 1528.879832 | \n",
"
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" 63 | \n",
" Marshall | \n",
" 1520.107767 | \n",
"
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" 64 | \n",
" Mills | \n",
" 1220.953340 | \n",
"
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" 65 | \n",
" Mitchell | \n",
" 1086.794650 | \n",
"
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" 66 | \n",
" Monona | \n",
" 1656.794562 | \n",
"
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" 67 | \n",
" Monroe | \n",
" 960.814655 | \n",
"
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" \n",
" 68 | \n",
" Montgomery | \n",
" 1140.359482 | \n",
"
\n",
" \n",
" 69 | \n",
" Muscatine | \n",
" 1700.941831 | \n",
"
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" \n",
" 70 | \n",
" O'Brien | \n",
" 1620.431744 | \n",
"
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" \n",
" 71 | \n",
" Osceola | \n",
" 967.452680 | \n",
"
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" \n",
" 72 | \n",
" Page | \n",
" 1398.118634 | \n",
"
\n",
" \n",
" 73 | \n",
" Palo Alto | \n",
" 1596.128377 | \n",
"
\n",
" \n",
" 74 | \n",
" Plymouth | \n",
" 2259.953641 | \n",
"
\n",
" \n",
" 75 | \n",
" Pocahontas | \n",
" 1297.431413 | \n",
"
\n",
" \n",
" 77 | \n",
" Pottawattamie | \n",
" 2303.225231 | \n",
"
\n",
" \n",
" 78 | \n",
" Poweshiek | \n",
" 1580.076857 | \n",
"
\n",
" \n",
" 79 | \n",
" Ringgold | \n",
" 1198.624350 | \n",
"
\n",
" \n",
" 80 | \n",
" Sac | \n",
" 1412.599164 | \n",
"
\n",
" \n",
" 81 | \n",
" Scott | \n",
" 1211.649675 | \n",
"
\n",
" \n",
" 82 | \n",
" Shelby | \n",
" 1299.197014 | \n",
"
\n",
" \n",
" 83 | \n",
" Sioux | \n",
" 2056.178194 | \n",
"
\n",
" \n",
" 84 | \n",
" Story | \n",
" 1943.690095 | \n",
"
\n",
" \n",
" 85 | \n",
" Tama | \n",
" 2088.226617 | \n",
"
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" \n",
" 86 | \n",
" Taylor | \n",
" 1571.159542 | \n",
"
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" \n",
" 87 | \n",
" Union | \n",
" 1292.945614 | \n",
"
\n",
" \n",
" 88 | \n",
" Van Buren | \n",
" 1549.129170 | \n",
"
\n",
" \n",
" 89 | \n",
" Wapello | \n",
" 1104.862750 | \n",
"
\n",
" \n",
" 90 | \n",
" Warren | \n",
" 1617.493665 | \n",
"
\n",
" \n",
" 91 | \n",
" Washington | \n",
" 1666.506696 | \n",
"
\n",
" \n",
" 92 | \n",
" Wayne | \n",
" 1438.930980 | \n",
"
\n",
" \n",
" 93 | \n",
" Webster | \n",
" 1793.295141 | \n",
"
\n",
" \n",
" 94 | \n",
" Winnebago | \n",
" 1295.390700 | \n",
"
\n",
" \n",
" 95 | \n",
" Winneshiek | \n",
" 1587.428273 | \n",
"
\n",
" \n",
" 96 | \n",
" Woodbury | \n",
" 2591.144872 | \n",
"
\n",
" \n",
" 97 | \n",
" Worth | \n",
" 925.069678 | \n",
"
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" \n",
" 98 | \n",
" Wright | \n",
" 1542.560045 | \n",
"
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" \n",
"
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"
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],
"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": {
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" average_store_profit | \n",
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" 24 | \n",
" Dallas | \n",
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" 2.295133e+06 | \n",
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" 14389 | \n",
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" 8141 | \n",
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" 51 | \n",
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" 54 | \n",
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" 55 | \n",
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" 67 | \n",
" Montgomery | \n",
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" 68 | \n",
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" 69 | \n",
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\n",
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" 71 | \n",
" Page | \n",
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" 73 | \n",
" Plymouth | \n",
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" 0.522857 | \n",
" 3.692898 | \n",
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\n",
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" 74 | \n",
" Pocahontas | \n",
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\n",
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" 75 | \n",
" Pottawattamie | \n",
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" 24.951321 | \n",
" 14.246155 | \n",
" 18533 | \n",
" 0.944046 | \n",
" 4.946604 | \n",
" 1580.076857 | \n",
"
\n",
" \n",
" 77 | \n",
" Ringgold | \n",
" 1689850 | \n",
" 14072.68 | \n",
" 2.115156e+04 | \n",
" 10061 | \n",
" 1.386808e+05 | \n",
" 11080.29 | \n",
" 4.642018e+04 | \n",
" 1476 | \n",
" 31.449986 | \n",
" 12.515991 | \n",
" 5068 | \n",
" 0.291239 | \n",
" 2.186324 | \n",
" 1198.624350 | \n",
"
\n",
" \n",
" 78 | \n",
" Sac | \n",
" 7934625 | \n",
" 75245.54 | \n",
" 1.130183e+05 | \n",
" 39571 | \n",
" 5.258452e+05 | \n",
" 42670.28 | \n",
" 1.760915e+05 | \n",
" 7157 | \n",
" 24.604098 | \n",
" 12.323453 | \n",
" 9876 | \n",
" 0.724686 | \n",
" 4.320603 | \n",
" 1412.599164 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 112482102 | \n",
" 1247750.11 | \n",
" 1.873693e+06 | \n",
" 1237025 | \n",
" 1.397399e+07 | \n",
" 914829.35 | \n",
" 4.667888e+06 | \n",
" 130419 | \n",
" 35.791469 | \n",
" 15.274967 | \n",
" 172474 | \n",
" 0.756166 | \n",
" 5.304158 | \n",
" 1211.649675 | \n",
"
\n",
" \n",
" 80 | \n",
" Shelby | \n",
" 5286953 | \n",
" 53350.35 | \n",
" 8.009987e+04 | \n",
" 42963 | \n",
" 5.279510e+05 | \n",
" 38570.77 | \n",
" 1.762560e+05 | \n",
" 5627 | \n",
" 31.323259 | \n",
" 13.687852 | \n",
" 11800 | \n",
" 0.476864 | \n",
" 3.268709 | \n",
" 1299.197014 | \n",
"
\n",
" \n",
" 81 | \n",
" Sioux | \n",
" 10681100 | \n",
" 102003.82 | \n",
" 1.532028e+05 | \n",
" 77121 | \n",
" 1.069835e+06 | \n",
" 79019.86 | \n",
" 3.575951e+05 | \n",
" 10307 | \n",
" 34.694396 | \n",
" 13.538806 | \n",
" 34898 | \n",
" 0.295346 | \n",
" 2.264309 | \n",
" 2056.178194 | \n",
"
\n",
" \n",
" 82 | \n",
" Story | \n",
" 65551096 | \n",
" 727466.68 | \n",
" 1.092625e+06 | \n",
" 501832 | \n",
" 6.730961e+06 | \n",
" 456414.62 | \n",
" 2.250349e+06 | \n",
" 71636 | \n",
" 31.413665 | \n",
" 14.747469 | \n",
" 97090 | \n",
" 0.737831 | \n",
" 4.700944 | \n",
" 1943.690095 | \n",
"
\n",
" \n",
" 83 | \n",
" Tama | \n",
" 7242875 | \n",
" 69664.27 | \n",
" 1.046049e+05 | \n",
" 47692 | \n",
" 5.735157e+05 | \n",
" 43573.67 | \n",
" 1.916543e+05 | \n",
" 7655 | \n",
" 25.036489 | \n",
" 13.161979 | \n",
" 17319 | \n",
" 0.442000 | \n",
" 2.515946 | \n",
" 2088.226617 | \n",
"
\n",
" \n",
" 84 | \n",
" Taylor | \n",
" 2073825 | \n",
" 20885.05 | \n",
" 3.135727e+04 | \n",
" 9360 | \n",
" 1.105983e+05 | \n",
" 8433.72 | \n",
" 3.693200e+04 | \n",
" 2304 | \n",
" 16.029514 | \n",
" 13.113828 | \n",
" 6216 | \n",
" 0.370656 | \n",
" 1.356776 | \n",
" 1571.159542 | \n",
"
\n",
" \n",
" 85 | \n",
" Union | \n",
" 9569175 | \n",
" 90467.23 | \n",
" 1.358550e+05 | \n",
" 66536 | \n",
" 9.021116e+05 | \n",
" 68179.74 | \n",
" 3.015172e+05 | \n",
" 9024 | \n",
" 33.412815 | \n",
" 13.231373 | \n",
" 12420 | \n",
" 0.726570 | \n",
" 5.489512 | \n",
" 1292.945614 | \n",
"
\n",
" \n",
" 86 | \n",
" Van Buren | \n",
" 1883075 | \n",
" 18970.16 | \n",
" 2.848695e+04 | \n",
" 12806 | \n",
" 1.742457e+05 | \n",
" 12768.44 | \n",
" 5.823345e+04 | \n",
" 1920 | \n",
" 30.329922 | \n",
" 13.646592 | \n",
" 7271 | \n",
" 0.264063 | \n",
" 1.756078 | \n",
" 1549.129170 | \n",
"
\n",
" \n",
" 87 | \n",
" Wapello | \n",
" 24021278 | \n",
" 251492.94 | \n",
" 3.776542e+05 | \n",
" 186724 | \n",
" 2.292624e+06 | \n",
" 160501.09 | \n",
" 7.658786e+05 | \n",
" 27843 | \n",
" 27.507043 | \n",
" 14.284163 | \n",
" 34982 | \n",
" 0.795924 | \n",
" 4.588105 | \n",
" 1104.862750 | \n",
"
\n",
" \n",
" 88 | \n",
" Warren | \n",
" 19568800 | \n",
" 183359.55 | \n",
" 2.753840e+05 | \n",
" 159019 | \n",
" 1.939043e+06 | \n",
" 146705.53 | \n",
" 6.481145e+05 | \n",
" 19620 | \n",
" 33.033359 | \n",
" 13.217243 | \n",
" 49691 | \n",
" 0.394840 | \n",
" 2.952356 | \n",
" 1617.493665 | \n",
"
\n",
" \n",
" 89 | \n",
" Washington | \n",
" 10984531 | \n",
" 119302.23 | \n",
" 1.791272e+05 | \n",
" 84389 | \n",
" 1.140731e+06 | \n",
" 78241.80 | \n",
" 3.810510e+05 | \n",
" 11745 | \n",
" 32.443678 | \n",
" 14.579561 | \n",
" 22281 | \n",
" 0.527131 | \n",
" 3.511593 | \n",
" 1666.506696 | \n",
"
\n",
" \n",
" 90 | \n",
" Wayne | \n",
" 1344500 | \n",
" 12518.51 | \n",
" 1.880652e+04 | \n",
" 8283 | \n",
" 1.054384e+05 | \n",
" 8001.09 | \n",
" 3.526299e+04 | \n",
" 1268 | \n",
" 27.809929 | \n",
" 13.177998 | \n",
" 6452 | \n",
" 0.196528 | \n",
" 1.240095 | \n",
" 1438.930980 | \n",
"
\n",
" \n",
" 91 | \n",
" Webster | \n",
" 22537959 | \n",
" 231995.40 | \n",
" 3.484066e+05 | \n",
" 228252 | \n",
" 2.705835e+06 | \n",
" 189009.94 | \n",
" 9.042916e+05 | \n",
" 24106 | \n",
" 37.513134 | \n",
" 14.315835 | \n",
" 36769 | \n",
" 0.655607 | \n",
" 5.140470 | \n",
" 1793.295141 | \n",
"
\n",
" \n",
" 92 | \n",
" Winnebago | \n",
" 8009300 | \n",
" 72833.61 | \n",
" 1.094025e+05 | \n",
" 55931 | \n",
" 6.827292e+05 | \n",
" 56888.63 | \n",
" 2.282976e+05 | \n",
" 7667 | \n",
" 29.776648 | \n",
" 12.001155 | \n",
" 10631 | \n",
" 0.721193 | \n",
" 5.351202 | \n",
" 1295.390700 | \n",
"
\n",
" \n",
" 93 | \n",
" Winneshiek | \n",
" 11450225 | \n",
" 107156.18 | \n",
" 1.609449e+05 | \n",
" 86483 | \n",
" 1.189769e+06 | \n",
" 89488.04 | \n",
" 3.977413e+05 | \n",
" 10541 | \n",
" 37.732790 | \n",
" 13.295288 | \n",
" 20561 | \n",
" 0.512670 | \n",
" 4.352319 | \n",
" 1587.428273 | \n",
"
\n",
" \n",
" 94 | \n",
" Woodbury | \n",
" 61274387 | \n",
" 672801.78 | \n",
" 1.010242e+06 | \n",
" 620680 | \n",
" 7.695638e+06 | \n",
" 512631.64 | \n",
" 2.570501e+06 | \n",
" 67732 | \n",
" 37.951055 | \n",
" 15.012023 | \n",
" 102779 | \n",
" 0.659006 | \n",
" 4.987708 | \n",
" 2591.144872 | \n",
"
\n",
" \n",
" 95 | \n",
" Worth | \n",
" 3247475 | \n",
" 28918.72 | \n",
" 4.343867e+04 | \n",
" 20112 | \n",
" 2.466353e+05 | \n",
" 20602.25 | \n",
" 8.250434e+04 | \n",
" 3000 | \n",
" 27.501447 | \n",
" 11.971281 | \n",
" 7572 | \n",
" 0.396197 | \n",
" 2.720847 | \n",
" 925.069678 | \n",
"
\n",
" \n",
" 96 | \n",
" Wright | \n",
" 5575150 | \n",
" 51158.24 | \n",
" 7.684774e+04 | \n",
" 46517 | \n",
" 5.706450e+05 | \n",
" 44906.82 | \n",
" 1.908072e+05 | \n",
" 5461 | \n",
" 34.939967 | \n",
" 12.707313 | \n",
" 12779 | \n",
" 0.427342 | \n",
" 3.514111 | \n",
" 1542.560045 | \n",
"
\n",
" \n",
"
\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",
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"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
" population | \n",
" store_population_ratio | \n",
" consumption_per_capita | \n",
" area | \n",
" stores_per_area | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
" 7092 | \n",
" 0.623237 | \n",
" 4.585780 | \n",
" 1146.149874 | \n",
" 3.856389 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
" 18.706055 | \n",
" 13.328281 | \n",
" 3693 | \n",
" 0.486596 | \n",
" 2.043764 | \n",
" 950.420482 | \n",
" 1.890742 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
" 30.090802 | \n",
" 12.613783 | \n",
" 13884 | \n",
" 0.619058 | \n",
" 4.412976 | \n",
" 1640.663925 | \n",
" 5.238733 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
" 27.708309 | \n",
" 13.375589 | \n",
" 12462 | \n",
" 0.688654 | \n",
" 4.267747 | \n",
" 1439.625361 | \n",
" 5.961273 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
" 26.109389 | \n",
" 12.072829 | \n",
" 5678 | \n",
" 0.360162 | \n",
" 2.327480 | \n",
" 1011.305224 | \n",
" 2.022139 | \n",
"
\n",
" \n",
"
\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 \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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" per_capita_income | \n",
" median_household_income | \n",
" median_family_income | \n",
" number_of_households | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 23497 | \n",
" 45202 | \n",
" 57287 | \n",
" 3292 | \n",
"
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" \n",
" 1 | \n",
" Adams | \n",
" 23549 | \n",
" 40368 | \n",
" 52782 | \n",
" 1715 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 21349 | \n",
" 46623 | \n",
" 55926 | \n",
" 5845 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 20084 | \n",
" 34689 | \n",
" 41250 | \n",
" 5627 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 24207 | \n",
" 42717 | \n",
" 58641 | \n",
" 2617 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
" population | \n",
" store_population_ratio | \n",
" consumption_per_capita | \n",
" area | \n",
" stores_per_area | \n",
" per_capita_income | \n",
" median_household_income | \n",
" median_family_income | \n",
" number_of_households | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
" 7092 | \n",
" 0.623237 | \n",
" 4.585780 | \n",
" 1146.149874 | \n",
" 3.856389 | \n",
" 23497 | \n",
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"
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" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
" 18.706055 | \n",
" 13.328281 | \n",
" 3693 | \n",
" 0.486596 | \n",
" 2.043764 | \n",
" 950.420482 | \n",
" 1.890742 | \n",
" 23549 | \n",
" 40368 | \n",
" 52782 | \n",
" 1715 | \n",
"
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" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
" 30.090802 | \n",
" 12.613783 | \n",
" 13884 | \n",
" 0.619058 | \n",
" 4.412976 | \n",
" 1640.663925 | \n",
" 5.238733 | \n",
" 21349 | \n",
" 46623 | \n",
" 55926 | \n",
" 5845 | \n",
"
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" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
" 27.708309 | \n",
" 13.375589 | \n",
" 12462 | \n",
" 0.688654 | \n",
" 4.267747 | \n",
" 1439.625361 | \n",
" 5.961273 | \n",
" 20084 | \n",
" 34689 | \n",
" 41250 | \n",
" 5627 | \n",
"
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" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
" 26.109389 | \n",
" 12.072829 | \n",
" 5678 | \n",
" 0.360162 | \n",
" 2.327480 | \n",
" 1011.305224 | \n",
" 2.022139 | \n",
" 24207 | \n",
" 42717 | \n",
" 58641 | \n",
" 2617 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
" population | \n",
" store_population_ratio | \n",
" consumption_per_capita | \n",
" area | \n",
" stores_per_area | \n",
" per_capita_income | \n",
" median_household_income | \n",
" median_family_income | \n",
" number_of_households | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 60373.69 | \n",
" 33807 | \n",
" 410805.62 | \n",
" 32522.35 | \n",
" 137466.12 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
" 7092 | \n",
" 0.623237 | \n",
" 4.585780 | \n",
" 1146.149874 | \n",
" 3.856389 | \n",
" 23497 | \n",
" 45202 | \n",
" 57287 | \n",
" 3292 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 27171.39 | \n",
" 8446 | \n",
" 100596.80 | \n",
" 7547.62 | \n",
" 33614.78 | \n",
" 1797 | \n",
" 18.706055 | \n",
" 13.328281 | \n",
" 3693 | \n",
" 0.486596 | \n",
" 2.043764 | \n",
" 950.420482 | \n",
" 1.890742 | \n",
" 23549 | \n",
" 40368 | \n",
" 52782 | \n",
" 1715 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 128238.38 | \n",
" 57833 | \n",
" 772843.46 | \n",
" 61269.76 | \n",
" 258630.44 | \n",
" 8595 | \n",
" 30.090802 | \n",
" 12.613783 | \n",
" 13884 | \n",
" 0.619058 | \n",
" 4.412976 | \n",
" 1640.663925 | \n",
" 5.238733 | \n",
" 21349 | \n",
" 46623 | \n",
" 55926 | \n",
" 5845 | \n",
"
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" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 123839.97 | \n",
" 58261 | \n",
" 711376.15 | \n",
" 53184.66 | \n",
" 237792.71 | \n",
" 8582 | \n",
" 27.708309 | \n",
" 13.375589 | \n",
" 12462 | \n",
" 0.688654 | \n",
" 4.267747 | \n",
" 1439.625361 | \n",
" 5.961273 | \n",
" 20084 | \n",
" 34689 | \n",
" 41250 | \n",
" 5627 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 26656.07 | \n",
" 13978 | \n",
" 159547.63 | \n",
" 13215.43 | \n",
" 53393.70 | \n",
" 2045 | \n",
" 26.109389 | \n",
" 12.072829 | \n",
" 5678 | \n",
" 0.360162 | \n",
" 2.327480 | \n",
" 1011.305224 | \n",
" 2.022139 | \n",
" 24207 | \n",
" 42717 | \n",
" 58641 | \n",
" 2617 | \n",
"
\n",
" \n",
"
\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": 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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" num_stores | \n",
" store_population_ratio | \n",
" consumption_per_capita | \n",
" stores_per_area | \n",
" per_capita_income | \n",
"
\n",
" \n",
" \n",
" \n",
" num_stores | \n",
" 1.000000 | \n",
" 0.366990 | \n",
" 0.382747 | \n",
" 0.955223 | \n",
" 0.345534 | \n",
"
\n",
" \n",
" store_population_ratio | \n",
" 0.366990 | \n",
" 1.000000 | \n",
" 0.891711 | \n",
" 0.395677 | \n",
" 0.214897 | \n",
"
\n",
" \n",
" consumption_per_capita | \n",
" 0.382747 | \n",
" 0.891711 | \n",
" 1.000000 | \n",
" 0.418315 | \n",
" 0.276315 | \n",
"
\n",
" \n",
" stores_per_area | \n",
" 0.955223 | \n",
" 0.395677 | \n",
" 0.418315 | \n",
" 1.000000 | \n",
" 0.358652 | \n",
"
\n",
" \n",
" per_capita_income | \n",
" 0.345534 | \n",
" 0.214897 | \n",
" 0.276315 | \n",
" 0.358652 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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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\ndcSnVs3MCpP0emCJiJhTOhYz6y9O5MzMCqj2xn0GOAJYvRp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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df_final[cols_to_keep].corr())"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sale_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" num_stores | \n",
" 0.995047 | \n",
"
\n",
" \n",
" store_population_ratio | \n",
" 0.344187 | \n",
"
\n",
" \n",
" consumption_per_capita | \n",
" 0.388819 | \n",
"
\n",
" \n",
" stores_per_area | \n",
" 0.961106 | \n",
"
\n",
" \n",
" per_capita_income | \n",
" 0.348343 | \n",
"
\n",
" \n",
" median_household_income | \n",
" 0.190911 | \n",
"
\n",
" \n",
" median_family_income | \n",
" 0.340245 | \n",
"
\n",
" \n",
"
\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": {
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yxA5IYx1vIS1l8y9gCvDN0pjDK4HrgStIwehJ+ZpvBU4HfpePfQS4p3Bc8Tp2IS2z8xhp\npvOVpICynn28lJRVLQdZjbSxfL86syD2x8pKGgMcQwrcHyYth7Mvadzk0rnYjO+zeLykt0nf64rA\nv3O5M3J9ZmZms03VuoxrLS1evm1WtLZWYCeO7UdahqVfnjltc9grd4zyD97MbB7Qd/0N52jk9dON\nhnb578Wvbzut6aLCZuoy7nYRsQCpG7ct5YkWlRIRCwLtze6dImlyO/vNzMzmSr2adD3BrnJA2L6t\nSd24bbmdNON5VrJOzZyxOgA4vp39I0ld2WZmZk2lWbt+u8oBYTskXUkaizen6h9PEy+XIulE0uPy\nzMzMrIk5IDQzMzMrqdosYweEZmZmZiUViwebZ9kZMzMzM5sznCE0MzMzK3GXsZmZmVnF1Sq27Iy7\njM3MzMwqzhlCMzMzsxKvQ2hmZmZWcR5DaGZmZlZxFYsHPYbQzMzMrOocEJqZmZlVnLuMzczMzEo8\nhtDMzMys4qq2DqEDQjMzM7OS7soQRkQf4GxgG2AqcKqk01spNxpYv5UqLpK0ay4zCVi4sK8F+LSk\nKR21wwGhVcp7kzr8b8LMzKw7ZxmfAqwBbAj0Ay6OiOclXVUqtzUwX+HzusCfSMEkEbEMKRhcAZjx\nx66RYBAcEJqZmZn1iIhYCNgV2FTSWGBsRJwM7Ad8LCCU9GbhuN7ACcBJkv6dNw8AXpY0vitt8Sxj\nMzMzs54xkJT1u7uw7S5gnQ6OGwIsCpxU2LYK8HRXG+IMoZmZmVlJNz26bmngdUkfFLa9CvSJiMUl\nTSwfEBE14HDg16Xu4AHAghExCgjgIeCnkp5ppCHOEJqZmZmV9KrVuvzqhAWBd0vb6p8XaOOYwcAy\nwPml7QEsBhwLfJc0QeXWiPhUIw1xhtDMzMyspJsmlUxj5sCv/rmtySDbATdImlTa/i1gvnrWMCJ2\nAF4AtgD+0FFDnCE0MzMzK+mmDOGLwBIRUYzH+gJTWwn46r4F/L28UdL7xS5kSe8CzwGfa6QhDgjN\nzMzMesZY4H1gUGHb14D7WiscEUuQlpW5q7S9FhHPRsRPCtsWAlYCnmqkIe4yNjMzM+sBkqZExMXA\neRGxM/B5YChpFjER0ReYJGlaPuTLwLTy0jKSWiLiOmB4RIwHXieNJXwBuKGRtjggNDMzMyvpxkfX\nHQycC4wCJgFHSap3Cb9ECg4vyZ+XAt4sV5AdRso2XgEsAtwKbCappZFG1FpaGipnNk+YcM31/sGb\nmc0Dltty8zkasZ2y9TFd/ntx6N+OaroHITtDaGZmZlbSq+lCulnjgNDMzMyspJsWpp5reJaxmZmZ\nWcU5IDQzMzOrOHcZm5mZmZW4y9hmWUSsEBGb5vf9ImJ6RCzX0+1qFhExKiKOyu/nj4jde7pNZmZW\nLb1qXX81IweEc8aFwNr5/QTSY2j+23PNaTpbA6fm99sDR/ZgW8zMrIJqtVqXX83IXcZzRi2/kDQd\neK1nm9NcSs9vbM7/sszMrKk1aVzXZT0WEEbEisBvgf8HvAGcKumsiBgAnEF6rt9bwAjgV/mxLEeT\nnss3GfgRMC0fd0qucyBpte+BpJW8R0g6Nu8bDwyTdHH+PBi4TVKviOgHjAO+A5wDLE7K8l0AjARW\nJq0gvr2ktyNiZG7DF4BvkJ4TuK+kMXnf+sD6EbEBsDPp4dL9JE2IiMWAk4AtgT7ANcABkiblNo0E\nTgR+SVpp/K/AbpLea+Cejs7t3BhYHXgQ2F2S8v5lgbNzm18DLsr3dnpEDAF2z9s3BPaW9IcOzvdZ\n4CxgU2AK8HtJP8/7/l++ztWBFuB2YFdJr+Rz7Qb8C9iH9F0dI+nC0nWMBn6ft00H+pFWcf81sDmw\nKOl7O0LS1R3dHzMzM2tdj3QZR0Qf4GZSULU2sC9wfETsANxJ6l5dmxQs7A8cWDh8O1LwsTpwCnBS\nDi4hPdrlQWAVYFfg8PpYPlJQ0tGq44eTgsLdgQNIwdjhwCakAHW3Qtk9gUeB1UjBzg0RsXg+bgyp\ny3MbZs5w/Q1YlRTQbAwMIAWBdUsD2+ZzbpPf79RBu4uOAP4ErAG8mNs1X0TU8vW8kts8hBRUF7tj\nB+VrWof0/XTk76TH6KwPfB/YOSL2iYhFgOuBm0jfxSbAisDPCseuRboP6wJHA+dExMZ5X/27uhv4\nKen3UO92P5P0PwUb57rvBC6ICGe7zczMuqin/ohuAiwB7CzpHeDJiNg/b3sb2CN3tSoifgkMI2WF\nID2w+ZD8bL5TI+IIYE3gP8DypGzjBEnPR8Q3SNm5Rh0r6THgsYj4NXCFpFsBIuKfQBTKPibpyLzv\nYFLG74eSzo6I94C3c9Zv0foBEbEqKXjqL+k/eduO+fpXysXmI2UMnwQej4ibSMHTBQ1eww2SfpPr\n3p30HMRNSNnU5YC18717JiIOJWcJ87EtwHGS3u3oJPla1gW+IOn5vG0vYCFS5nO4pDNy8ecj4q/5\nOuqmAztJeh14ImdT9wBuqReQ9H5ETAY+lPRaPsdo4BRJT+TPp5EC9aVIAbCZmdks61WxPuOeCggD\neDoHgwBIGhkR5wIP5mCwbgzQN2edAMaXHtT8FimIAjgeOAHYMyKuAy6tBxINGld4PxUYX/g8DVig\n8PmuQttbIuIhUtdyewYAk+rBYD5WEfFm3jc5b36mcMxkPrq+jrSU2vV2RDyd655K6gqfHDEjru0F\n9ImIz+TPrzUSDGYBvFEPBvP5rpmxM+LSHCgPJGXyBpK6iOv+k4PBugdJWdeOXAJsnYPPAL5Kuu7e\nDbbbzMysQ7WKDWHvqVnG77exfRozd7HW/9DX29raWLr6BI6TgS+Sxq6tANwWEbvmMuXu4taC4Q9K\nn6e3Uqatsp/ooDyk62tNbwoBjaRy3Z35VZaP7Q18mNv3FCkwq7++AvQH/tdB+1rT1ndIRCxD6noe\nDDxA6vY9jY9fR/n4ejs7cilpqMBE0njPzfHEEzMzm81qta6/mlFPBYRPAytGxCfrGyLiVNKYwa+W\nxoMNImWu3myvwohYICLOBN6TdIakjYDfkcbhQQokFy4cskIn21wOKFcrnLt3/vxIG2XrBCwaEf0L\nx66S26VOtqctxXYtQhq790iufzngdUnjJI0jBc/D22lve54BPhMRny+c74CI+BuwFTBR0paSzpJ0\nVz5X0YoRsVDh85p8dP+KZrQtIhYmLUPzfUnD80SSxfPuJv1P0MzM5ka9arUuv5pRT3UZ/4M0uWFE\nRBxH6vrbkzRh5Py8/VRS9upo0szYdkl6N89sPSsifkYKstYnTeIAuB/YNSJGAUsCQzvZ5vI3PDh3\niV5PmvjSB/hz3vcO0D8iliy18amIuBG4JCL2IwXkZwO3S3oiz9pt7byNBmw14Ef5Gh8AjiV1e4/K\n+58HLouII4HFSAHzzXmWcYOnmHEtj0fEbcCFETGUNP7zcNJ4xDeB5SJio3z+75EC8/sLVXwKOC9/\n/18nffcbFq6j7h1gsTxxaEL+vF1ETCT9bn6by/Xp1AWYmZnZDD2SIZT0IfBd4HPAQ6RlZoZKupa0\nhMmKwL+B3wBnSBqeD+1opvAPSJMa7icFnbeTgiKAX5CWLHkwn+8Xpbo6CrrK574G2Ci3fyCwsaT6\nGMALgG8DN7Zy3E6ksYq3kmbhPkrKqLXVjkZmRxfLXg7sRQoIFwS+LWl6Hpe5Jek7vxf4C3AdaVZ0\nZ89TtyMpQLsnn3eEpHNJs5wvy+e4n9R1PBRYOSLmz8e+ALyc9x8C7CBpTKEtdbeSJgw9Anw5n3M7\n4HHSTO5jSRNnVsPMzMy6pNbS0pXewmqLiIuAFkm79HRbinJmcJSkY3q6Le3J6xAOk/SF7j73hGuu\n9w/ezGwesNyWm8/Rvtnf7XBSl/9e7HH54U3Xb+y127qm27/oPH5uwXaKvJ3/PVvaFhFL0P7vY6Kk\nNieWmJmZNbMmHQrYZQ4Iu6Yr3auz6mTSOn1tKXarzw7/Io3hbE0LabzfHV2suyfun5mZWcOa9ZnE\nXeUuY6sUdxmbmc0b5nSX8e93OrnLfy92ueSwposme2rZGTMzMzObSzggNDMzM6s4jyE0MzMzK6na\nGEIHhGZmZmYlFYsHHRCamZmZlTXrI+i6ymMIzczMzCrOGUIzMzOzkqqNIXSG0MzMzKzinCE0MzMz\nK6lYgtABoZmZmVlZ1bqMHRCamZmZlVQsHvQYQjMzM7Oqc4bQzMzMrMTrEJqZmZlZpThDaJXyg6Fn\n93QTzMxsNhiz5eZztP6KJQgdEJqZmZmVeZaxmZmZWcVVLB70GEIzMzOzqnOG0MzMzKykal3GzhCa\nmZmZVZwzhGZmZmYlFUsQOiA0MzMzK6vawtQOCM3MzMxKKhYPegyhmZmZWdU5Q2hmZmZW4lnGZmZm\nZlYpzhCamZmZlXRXgjAi+gBnA9sAU4FTJZ3eRtmvAOcCawD/AQ6QNLqwf3vgV0Bf4B/A7pImNtIO\nZwjNzMzMSmq1WpdfnXQKKcDbENgHGBYR25YLRcQiwC3AY8CXgb8Cf4uIJfP+tYELgGHAusBiwMhG\nG+GA0MzMzKwHRMRCwK7AgZLGSvo7cDKwXyvFfwJMBvaWNE7S0cAzwFfz/v2AP0q6TNKjwI+BzSJi\n+Uba4oBwDomIjSJi5Z5uh5mZmXVerdb1VycMBOYD7i5suwtYp5Wyg4GrJbXUN0haW9JN+eM6wB2F\nff8FJgCDGmmIA8I555/AZ3u6EWZmZtZ53dRlvDTwuqQPCtteBfpExOKlsl8AXo+I30XEyxExJiLW\nK9X1UumYV4FlGmmIA8I5q1pz1s3MzKwzFgTeLW2rf16gtP3TwBHAi8CmwO3AzRFRD/jaqqtcT6s8\ny3gWRcQBwMHAUqSBngcBl+XdoyLiaEnHRMQg0sDR1YDXgJMkjch1jMzlVyfNDFoPmAicBWwJvA1c\nBRwmaVo+5nhgCLAocC+wr6QnGmjvSNIYhC8A3wCeyseOyfsXbeu8ETGYNED1BuBHwHGSTungfMsA\nZwIbkX6sjwP7S7o7IvoB44Cj8j28TNIBEbE1cBywfL6nh0q6I9e3MPBrYPN87eOAIyRd3dG1m5mZ\nNaqbZhlPY+aArf55Smn7B8C/JQ3Pnx+OiE1IYwVPbKeucj2tcoZwFkTE6qTBn3sDAdwJ/BFYOxfZ\nBjg1IgYAtwGjSUHf0cBpEbFVobodgSOBzSQ9C1xI+r+B9YCtgLWA3+bzbg3sDmwHfAl4BbioE03f\nE3iUFJzeDtxQSE23ed5sOdIPbA3gygbOdRkpU7ou6dr/S5oyX7QeaVDsbyJiICnoPAb4Sj7+xoj4\nYi57JrASsDGwCumeXxAR/p8bMzObbbqpy/hFYImIKMZjfYGpkiaVyr5ESuIUPQ0sW6irb2l/X+Dl\nRhriP6Kzph/QAkyQNCEifgFcC7yR978haUpE7A48KOkXefszOUg8DPh73nafpOsBcvDzXeAzkibn\nbXsAD0XEwfm87wEvSHohIvYH+nei3Y9JOjLXezApG/jDiLipnfMeVDj+JEnjGjzXX4GrJL2U6zsH\nuL5U5teSnsv7LwXOl1QPNs/Kmcm9gUNIQfUp9WxoRJwG7EbK0L7YYJvMzMzmBmOB90kTP+7K274G\n3NdK2XuADUrbBvBRr+Q9wNeBSwAiYllSsHhPIw1xQDhrbiJl2h6NiIeAq0nBzAcRUSw3gNStWzQG\n2KvweXypfC/gxVI9NeCLwBXAvsBzETGGFFRe2GCbW/joR4ekltz2AbkNbZ13xTba2pHzgO3zwNcg\nZQLL//tUrG8A8L2I2LOwbX7SvYb0Q986IvYq1NcC9O5Em8zMzNrVHV3GOWl0MXBeROwMfB4YShoS\nRkT0BSbl4WLnAftHxDDgcmAnUoKoHhCeC4zOccEDpB61ayU930hb3GU8CyRNJU3z3oiUudoZ+HdE\nfK5UdCozB0G9+XgQUxwI+gngf6Tp6MVXf+BJSa8CK5Mye48ChwL3RMQnG2z6B6XPnwA+7Oi89cKS\n3mvkJDkF/k/SuMrxpO71nZj5XkwrvO9NGgtRPP8qpAwhwKWksZgTgXNIYwk9ecfMzGarXrVal1+d\ndDDwIDCKNIb/qLweIaRu4u8DSJoAfAvYgvS3f3Ngc0kv5/33kIaEDSMlfiaS4pKGOEM4C/JEkY0k\nHUeKyn8f5wf5AAAgAElEQVRGmuL9tVJRMXOadxAzjwUoll8EoN41mx9XMxzYOSK+ASwn6VzS+L/h\npDECXwbu76DZNdLYwfo19M6fr+3ovB3U25pVSOnrJeuPzomIfTo4RsAKxS7piDgZUET8GdgeWFvS\ng3nfZoXrMjMzmy2669F1Obk0JL/K+3qVPt8NrNlOXRcDF3elHQ4IZ81U4KiIeAW4lbRo5ELAI8A7\nwFciYiwpk3VgRBxH+qIGkR5Ps29rlUp6Mo/nuzyPD5wOnE9aq+h/OfN2SkS8TBp/sH0+39MNtntw\nHjt4PbA/0Af4s6TJHZy3M/cGYFKuY/uIuJY0QWU4QETM38YxZwB3RsT9pNnMW5AyjBuSMonvANtF\nxERSl3F9wkufzjbOzMzMEncZzwJJY4FdSF22TwKHAztKegr4Dalrc5ikF4DvkNYNeoQ0m/igHMlD\nGgPXUqr+x8BzpEDzllz/D/N5ryUt1XJG3v494LuS/tdAs1uAa0jd3A+RumQ3rk8iae+8heMbkldJ\n35t0Xx7L/96fNIB29dbqk3RvbsM+pCVqdgN+KOlfuat6R9Ls6seBU4FjSSn11TAzM5tNuvFZxnOF\nWktLw3/fbR4QERcBLZJ26em29IRBK23mH7yZ2TxgzDM3zNHI659HnNflvxffPHGvposK3WU8D4mI\nBYGF2ynS2uSWWTnfYrS/Anp9ZpSZmVlTadJEX5c5IJy3HAAc387+i2m9e7qr/gBs0s7+IeT1kMzM\nzJpJrVe1IkIHhPMQSSeSlmzprvNt2l3nMjMz605VyxB6UomZmZlZxTlDaGZmZlbSrLOFu8oZQjMz\nM7OKc4bQzMzMrKRiCUIHhGZmZmZlVesydkBoZmZmVlKxeNBjCM3MzMyqzgGhmZmZWcW5y9jMzMys\nrGJ9xg4IzczMzEo8qcTMzMys4ioWDzogNDMzMyur9apWROhJJWZmZmYV54DQzMzMrOLcZWyV8rfz\nD+npJpiZWRPwGEIzMzOzivMsYzMzM7OKq1g86IDQzMzMrKxqGUJPKjEzMzOrOAeEZmZmZhXnLmMz\nMzOzkor1GDsgNDMzMyur2hhCB4RmZmZmZRUbVOeA0MzMzKykahnCisW/ZmZmZlbmgNDMzMys4txl\nbGZmZlZSsR5jB4RmZmZmZVUbQ+iA0MzMzKykYvGgA8JmFxH9gHFAP0kTImIFoL+km2ZD3SOBFkk7\nz2pdZmZmTaViEaEDwuY3AegLvJ4/XwiMAmY5IAT2nw11mJmZ2VzOAWGTkzQdeK2wqZZfs6Put2ZH\nPWZmZjZ3q3RAWOhu3QE4FVgQuBgYKunDiNgaOA5YHngMOFTSHfnY0cCjwOZAb2AVSe90cL5NgeOB\nAJ4BDpZ0W0TUgJ8BuwHLkLJ9IyQdUzjXKGBjYHXgQWB3SSpcwxeA4cD6wPoRsYGkjSLi/wEn5eNa\ngNuBXSW90sD9GUnuMo6Io4GVgMnAj4BpwKmSTsllPwEcAwzJ9/FmYC9Jb0REn9y27YHPALcC+0r6\nb6H93wHOARYnZTkvAEYCK+dr317S2/lcewJHAEsADwD7S3qso+sxMzNrVK1XtbqMvQ5hchTwPWBr\nYFtgeEQMJAUkxwBfAS4DboyILxaOG0IKjrZqIBj8EnAt8BdgVeAPwNURsRSwE3AgsCsp6DoGODoi\nVitUcQTwJ2AN4EXghoiYv7C/BTgAGEMKbreJiEWA60jdx6sAmwArkoLPRrTkV912wBRScHkKcFJE\nrJj3HZuvYwgwCFgKGJH3nQdsBfw475svX3vxv7bDSUHh7vk6/pq3bZKP2Q0gIrYAhgH7AqsBdwKj\nImLRBq/JzMysQ7Va11/NqNIZwoLDJN0NEBG/JGXUlgPOl3RlLnNWRAwG9gYOyduulXRPg+fYFbhT\n0vH580kRsSCwKPA8METSqLxvREQMA74EjM3bbpD0m9zG3YGXgG8CT9RPIGlyRLwHvC1pUg42j5F0\nRi7yfET8FVirwTbX+HhA+DpwiKQW4NSIOAJYMyKeJQVyB0u6ObdxL+B7EbEYsCOwqaTb874dgBdy\n+5/JdR+bs3yPRcSvgSsk3ZrL/5OUVQU4DDhe0g3581ERsVk+x28bvC4zM7N2edmZarqr8P5BYElg\nPeDzuXuybn4+mqzRAozvxDn657pnkDSs/jYi1omIE0hdpKuTJor0LpzrrsJxb0fE08AACgFhmaRX\nI+LSiDgYGEjKEg4E/tWJdheNz8Fg3VukbN8SpK7gGdcn6UngmIhYh5SJvrew782IUG5/PSAcV6h3\nKh+/t9OABfL7AcDJ+V7VLUDKrJqZmc0WFYsHHRBm7xfe14OwKcCJwCWFfTVSsFI3rZPnaPXnFRG7\nAacD55O6lA8hjZsr+qD0uTcwvb0TRsQypDF29wO3AL8jdcuu24l2F73XyrYaH79/ZW3do958dK9h\n5utr69p6k7rXby21YXI7bTAzM7N2OCBMVgfuyO/XJHXHPgGsIGlG5ioiTgZEmvTQWc/k88wQEXcD\nZwJ7AsMlnZa3L0oag1cPIGuk8XL14xYhjQV8pJXzFDN4WwMTJW1ZOPbATrS5peMikLunX89tfDyf\nZzXSmMkBpGBvEGmiCRGxOCmjpy60Q8Cype/lItKYw2sbrM/MzMwKHBAmZ+Ys3WKk2bBnkTJ0d0bE\n/cANwBbAQcCG+ZjOLu9yHvBERBxECly+RwqW7gB2BjaOiGuAhUkzkefjo25SgB9FxChSxu9YUpfq\nKNJYx6J3gP4RsSRpzN9yEbFRLv89YBtSxrARnbm+3wDHRsSLwP+RAt27c/f2+cBv89jHN0ljNCeQ\nspbLdLIdpwMX5C7zMcAepMkuv+pEW83MzNpXsT5jzzJOrgSuB64gTSQ5UdK9pFmx+5CyXrsBP5RU\nH39XnoHbrpzR2hbYhbRczTbAFpJeJnWBLgw8DFwFPAT8jY8yii3A5cBepIBwQeDbeQ3C+v66C4Bv\nAzeSZiVfRuqGvh8YDAwFVo6I+RpodvEaO7reE0lZuj+Rxig+TwrWIHWB35Kv7V+k7vhvSqp3NXd0\nH2ecW9KfgJ+TguJHSQH6FpKebeB6zMzMGlLrVevyqxnVWloajmnmOeXHvvVwc9qUM4Oj6usSWte9\nMvq26v7gzczmIX0HbzRHI68nzr+yy38vVtn9hw23La/VezYpUTSVtMbv6R0c04+0PvJm9fWR8/ZJ\npARTXQvwaUlTOmqHu4xng7wo8xLtFPlQ0v/Nwilm29NH6vKSNwu3U2SKJE/UMDOzauq+LuNTSGsM\nbwj0Ay6OiOclXdXOMeeSegtnyBNJFwZWIPXEAdBIMAgOCKET3b7tWBO4u53940lfUFd1qnu6QQeQ\nxiq2ZSSpe9vMzMzmgIhYiLRO8aaSxgJj8wTW/UjDrFo7ZgfgU63sGgC8LGl8V9pS6YAw37TeHZVr\noJ57mIPjMSVt2HGpTtd5Imncn5mZmfWMgaRJpMWk0l2ksfIzyat0nER6ilf5ka2rAE93tSGeVGJm\nZmZW0k2PrlsaeF1ScS3eV4E+OfgrOx0YKam1h1IMABaMiFER8VJEXB8RDT+0wQGhmZmZWUk3zTJe\nEHi3tK3+ubj0HBHxTdJT1I5to64gLZ93LPBd0gSVWyOite7lmVS6y9jMzMysNd30LOPio1nr6p9n\nTAaJiE8CI4C9Jb0bEcUHV9R9C5ivPokkjzV8gbSO8h86aogzhGZmZmZltVl4Ne5FYImIKMZjfYGp\nkiYVtq0NfAG4KiLe4qPHtd4YEecASHq/OKNY0rvAc8DnGmmIM4RmZmZmPWMs8D7p8a535W1fA+4r\nlbuX9Mjauhrpkbi7ArfkjOF/gGMkXQwzZjCvBDzVSEMcEJqZmZn1AElTIuJi4LyI2Bn4POmJYkMA\nIqIvMEnSNNKDNGaICIAXJb2eP18HDI+I8aRH1x5L6jK+oZG2OCA0MzMzK+mmMYQAB5MWmh4FTAKO\nkvT3vO8lUnB4SQP1HEbKNl4BLALcSnqSSUPrGFf60XVWPX50nZnZvGFOP7rumUuv6vLfi5V+vG3T\nPdDYGUIzMzOzsopNu63Y5ZqZmZlZmTOEZmZmZiXdOIZwruAMoZmZmVnFOUNoZmZmVlK1DKEDQjMz\nM7OyasWDDgitWj6zxsCeboKZmTWBWq9qRYQeQ2hmZmZWcc4QmpmZmZVVbAyhM4RmZmZmFecMoZmZ\nmVlJxRKEDgjNzMzMyrzsjJmZmVnVeZaxmZmZmVWJM4RmZmZmJVXrMnaG0MzMzKzinCE0MzMzK6tW\ngtABoZmZmVlZ1bqMHRCamZmZlfhZxmZmZmZWKc4QmpmZmZW5y9jMzMys2qo2hnCu6DKOiMERMT2/\n7xcR0yNiuW447+iIGDanz9NZETE+In7SxWOHRMRz7ewfGREXNVjXqIg4qivtMDMzs+YxN2YIJwB9\ngde74Vwt+TW3mZPt6kzdWwPvzaF2mJmZzb2qlSCc+wJCSdOB13q6HfO4hn7mkibN6YaYmZlZz2s4\nIIyIfsA44DvAOcDiwIXABcBIYGVgFLC9pLcjYk/gCGAJ4AFgf0mP5boWBkYAmwMv5zrK5+knaUJE\nrAKcAQwC5gPuB/aQ9FREDM7nPhH4JbAI8FdgN0mNZrY+HxE3AoOB54F9Jd2a27IYcBKwJdAHuAY4\nQNKkfO7bJM3odo+IkUCLpJ0jYtF8fzYiZeSuB/aR9FYu2+b9yb4cEXcDqwNPAjtLejgf+3ngdOAb\nwHTgCuDQ1q45Ir4O/AYI4Nq8eUojNyYiRgOjJA3P1/YGsAzpNzAROFLSZbnsQrlN2+bDr8r36t0G\n7uNIYDhwAjB//ve9wO+AzwF/A4ZIasnn+iWwF7AgcCfpO3uhkWsyMzNrhJed6djhpIBgd+AAUgB2\nOLAJKWjbLSK2AIYB+wKrkf5oj8pBEsB5QH9gfWB/YCitdGNGRC9SEPMsMBBYjxTEnlQotjQpCNkE\n2Ca/36nBa6nlsn8AViEFZpcW9v8NWJUUuG4MDCAFL20pdscOB5bKbd6QdB9+nq+rrfuzSKFdu5EC\no1VJgdh5+dj5gduAT5Lu3/dz+04uNyYilgSuA/5Bun9PAN+j8S7jcvfyvqSA/EukgG9ERHw677sg\nX+sWpHv1NeBXeV9H93FpYCvg68BxpAD/DNJ3sz3wA+C7+Zr2B36Ut68DvArcHBFzXbbbzMyaWK3W\n9VcT6sof0WNzJuuxiPg1cEUho/ZPUqZwG+B4STfkY46KiM2AHSPiUlJQMljS2HzccODsVs7Vh5SN\nPFfSlFz2YuDQQpn5SNmmJ4HHI+ImYC0KWccO/EXSJbnuk4Ef5UBqaVLA1V/Sf/L+HYEnI2KlNuqq\n8VEAtTzwNjBe0tSI2K5Q7jBavz8/Bn6b6zhH0rX5vL8BrsxlNyVlzdaS9L98zfsC10bEz0vt+T7w\nqqQj8ufh+TxdNVbSqblNRwEHAl+KCAHbAd+QNCbv3xMYGBGr0vF9nA8YKuk/EXEOcApwlqT7cvmx\npAxn/d7tLemOvG8v4KV8X66bhWszMzOboWqzjLsSEI4rvJ8KjC99XoCUATo5Ik4o7FsAWImUGewN\njC3se6C1E0maEhEjgJ9ExJqkoGAN4JVS0WcK7yeTAoxGtJCyj8VjIQWiA4BJ9SAmt0cR8WbeN5mZ\nFbNpZwJXA/+XA+W/kLp2of37U1duV5/CsU/nYLBuDOm7XLHUnlWAh0vb7gcWaqXtjZhxnyW9FRGQ\n7vWKpO/0wcL+fwH/iogf0Nh9HJf3Tc31ji+cdyqwQO6WXgb4Y31WetaHj987MzMz64SuBIQflD5P\nb6XMJ0jZo1sL22qkP/79Cp/rWh3vFxGfIgUwr5HGnV1OCiIOKZaTVG5TZ8L6D1vZVgOmtVG+d361\n1u06H/B+btOoiFiW1NW5OWk83LdIWcDetH1/2msXbbSrd/53a0MAytveb6PeRrR2bK2DOju6j8CM\nyURFbf2uIGUjVWrDG+20wczMrHM8hnC2ELCspHH1F/ALYF3gKVIAsXah/Opt1DOY1HW7oaTTJN1G\n6ortjm9JwKIR0b++IU9wWTjvey9v+1ThmBXIgWJEHAR8VdIlkn4A7MJHEy7auj/rNNiu/nmiRt0g\nUqD+bKnso8AaeSxm3erM/iVtxpEC2NXqGyLiuxHxIOn7bu8+NixnRV8Dli7ct/+Supij3YPNzMys\nTbN7IH59DN3pwAUR8TSpO3MPUlbnV7mr8RLgrIjYmTRT9Og26psIfArYOgcX3yRNbGitu7bchkbb\n22pwmWcx3whcEhH7kYLns4HbJT2RZ0pPBX4eEb/L17caKQCC1LW5e77GN/L+f+d9bd6fBtp8CykA\nuzQijgCWBM4CLpc0OXe31v2RNLnlzIj4LWmW7//j493+7Wl0eZrJeWznb/KYvhbgeOC63D3c3n38\nbCfbcTpwXES8Rgoof0kKiJ9s8JrMzMw6VLUxhJ3NEHYUaLWQll35E2lG7bGkLNWGwBaS6hms/YG7\nScHNRaRlUVpK9ZAnKBxDmljyMGnW6b7AkhGxdBtt6szCy62VLX7eiRQ83QrclK9lq9y2yaSZ1tsD\njwFfIU0IqfslcBepq3ssaVbwDvnYju5PW21F0oekwA7S0ix/IM3i3bN8TZLeJE22WCu34RvAxe2c\no9Vzluttw09J39EtwA2ke/aLvK/N+9jKeTpqx6mkCUO/Ax4ClgW+VRpTaWZmNmtqs/BqQrWWlrnx\nQR1mc8Z7kyf6B29mNg+Yf+HF52jo9cro27r896Lv4I2aLiycZ9duy126C7ZT5C1J73RXe+Y2vj9m\nZmZWN6cmlcwNTiatT9fWa2jPNW2u4PtjZmbWll61rr+akLuMrVLcZWxmNm+Y413Gd4zqepfx+hs2\nXVQ4z3YZm5mZmXVV1WYZOyA0MzMzK3NAaGZmZlZtVcsQzsuTSszMzMysAc4QmpmZmZU16WzhrnKG\n0MzMzKzinCE0MzMzK6naGEIHhGZmZmZlDgjNzMzMqq1WsTGEDgjNzMzMekhE9AHOBrYBpgKnSjq9\njbI7AMOAzwMPAT+VdH9h//bAr4C+wD+A3SVNbKQdnlRiZmZm1nNOAdYANgT2AYZFxLblQhHxdeAC\n4GhgFeBu4MaIWCjvXzvvHwasCywGjGy0EQ4IzczMzMpqta6/GpSDuV2BAyWNlfR34GRgv1aKLwUc\nI+kKSeOBY4HPAAPy/v2AP0q6TNKjwI+BzSJi+Uba4i5jMzMzs5JummU8EJiPlO2ruwv4ebmgpL/U\n30fEJ4GDgFeBJ/LmdYATCuX/GxETgEHA8x01xAGhVcqbDz/a000wM7PZYKmvD56zJ+iegHBp4HVJ\nHxS2vQr0iYjFWxv/FxHfAG7OH3eQNKVQ10ul4q8CyzTSEHcZm5mZmZXUetW6/OqEBYF3S9vqnxdo\n45hHgdWBo4CREbFOB3W1Vc/HOENoZmZm1jOmMXPAVv88hVZIeg14DXgkItYF9gLubaeuVuspc4bQ\nzMzMrGe8CCwREcV4rC8wVdKkYsGIWCsiVi8d/ySweKGuvqX9fYGXG2mIA0IzMzOzsm6YZQyMBd4n\nTfyo+xpwXytld6UwaST7KikoBLgH+Hp9R0QsCyybt3fIXcZmZmZmZd0wqUTSlIi4GDgvInYmLTg9\nFBgCEBF9gUmSpgEjgHsj4gDgRmBHYM38b4BzgdERMQZ4ADgTuFZShzOMwRlCMzMzs5nUarUuvzrp\nYOBBYBRwFnBUXo8Q0qzh7wNIegjYmpQpfBjYFPiWpJfz/nuAPUkLU98FTAR2bvh6W1paOttws6b1\n6p2j/YM3M5sHLPX1wXM0hffm4//u8t+Lxb60RtM9CNkZQjMzM7OKc0BoZmZmVnGeVGJmZmZWUqtV\nK2fmgNDMzMysrHseXTfXcEBoZmZmVtKF2cJNzQGhmZmZWVnnnknc9KrVQW5mZmZmM3FAaGZmZlZx\nlQwII2JwREzP7/tFxPSIWK4bzrtERNweEVMj4qLZXPfRETEqvx8SEc/NzjrNzMyqpBufVDJX8BhC\nmAD0BV7vhnPtCKwIDCQ9UmZ2OgX4dRPUaWZmNvdr0sCuqyofEEqaDrzWTadbBHhG0tOzu2JJ7zRD\nnWZmZk3B6xDOHSKiHzAO+A5wDrA4cCFwATASWJn0IOjtJb0dEXsCRwBLAA8A+0t6LNe1MDAC2Bx4\nOddRPk8/SRMiYhXgDGAQMB9wP7CHpKciYnA+94nAL0kB3l+B3SS918H1HA0cld9PBwYDY0kZuM2B\nRXM7jpB0daHc94FjgeWAq4Gf5/uwDulh2D+U9FKufwNJG5bOewvwhKQDC9uuBR6SdFQDbd5A0oYR\nMQQYAtwO7EP67fxe0tBC+YOBA0jf1V3AXpLGR0QNOATYC1gauAc4oPD9NHydufzWwHHA8sBjwKGS\n7mjvWszMzDqj5lnGc53DSUHh7qRg46952yakoG23iNgCGAbsC6wG3AmMiohFcx3nAf2B9YH9gaHA\nTA+tjohewLXAs6Ru3fVIgc9JhWJLA9vm82+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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bkIikj65mDlSZFRl4VI+LDAyq/yWWZAX9LgE/S9K2RUREREREREREpAu9HtPP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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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\n",
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" sale_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 55 | \n",
" Linn | \n",
" 1.806775e+07 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 1.397399e+07 | \n",
"
\n",
" \n",
" 50 | \n",
" Johnson | \n",
" 1.237021e+07 | \n",
"
\n",
" \n",
" 6 | \n",
" Black Hawk | \n",
" 1.192076e+07 | \n",
"
\n",
" \n",
" 94 | \n",
" Woodbury | \n",
" 7.695638e+06 | \n",
"
\n",
" \n",
" 75 | \n",
" Pottawattamie | \n",
" 7.675324e+06 | \n",
"
\n",
" \n",
" 82 | \n",
" Story | \n",
" 6.730961e+06 | \n",
"
\n",
" \n",
" 30 | \n",
" Dubuque | \n",
" 6.621999e+06 | \n",
"
\n",
" \n",
" 16 | \n",
" Cerro Gordo | \n",
" 4.892826e+06 | \n",
"
\n",
" \n",
" 28 | \n",
" Des Moines | \n",
" 3.579586e+06 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" sale_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 56 | \n",
" Louisa | \n",
" 207146.11 | \n",
"
\n",
" \n",
" 26 | \n",
" Decatur | \n",
" 176364.48 | \n",
"
\n",
" \n",
" 86 | \n",
" Van Buren | \n",
" 174245.69 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 159547.63 | \n",
"
\n",
" \n",
" 52 | \n",
" Keokuk | \n",
" 149560.78 | \n",
"
\n",
" \n",
" 77 | \n",
" Ringgold | \n",
" 138680.81 | \n",
"
\n",
" \n",
" 84 | \n",
" Taylor | \n",
" 110598.35 | \n",
"
\n",
" \n",
" 90 | \n",
" Wayne | \n",
" 105438.35 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 100596.80 | \n",
"
\n",
" \n",
" 25 | \n",
" Davis | \n",
" 96185.96 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" feature | \n",
" coefficients | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" population | \n",
" 58.8369 | \n",
"
\n",
" \n",
" 1 | \n",
" store_population_ratio | \n",
" 1.05083e+06 | \n",
"
\n",
" \n",
" 2 | \n",
" stores_per_area | \n",
" 49967.5 | \n",
"
\n",
" \n",
" 3 | \n",
" per_capita_income | \n",
" -71.6356 | \n",
"
\n",
" \n",
"
\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": {
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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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" bottle_volume_ml | \n",
" state_bottle_cost | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
" volume_sold_liters | \n",
" profit | \n",
" num_stores | \n",
" average_store_profit | \n",
" sales_per_litters | \n",
" population | \n",
" store_population_ratio | \n",
" consumption_per_capita | \n",
" area | \n",
" stores_per_area | \n",
" per_capita_income | \n",
" median_household_income | \n",
" median_family_income | \n",
" number_of_households | \n",
" sales_prediction_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 6.037369e+04 | \n",
" 33807 | \n",
" 4.108056e+05 | \n",
" 32522.35 | \n",
" 1.374661e+05 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
" 7092 | \n",
" 0.623237 | \n",
" 4.585780 | \n",
" 1146.149874 | \n",
" 3.856389 | \n",
" 23497 | \n",
" 45202 | \n",
" 57287 | \n",
" 3292 | \n",
" 221482.0 | \n",
"
\n",
" \n",
" 1 | \n",
" Adams | \n",
" 1757403 | \n",
" 18103.82 | \n",
" 2.717139e+04 | \n",
" 8446 | \n",
" 1.005968e+05 | \n",
" 7547.62 | \n",
" 3.361478e+04 | \n",
" 1797 | \n",
" 18.706055 | \n",
" 13.328281 | \n",
" 3693 | \n",
" 0.486596 | \n",
" 2.043764 | \n",
" 950.420482 | \n",
" 1.890742 | \n",
" 23549 | \n",
" 40368 | \n",
" 52782 | \n",
" 1715 | \n",
" -224036.0 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 9079175 | \n",
" 85371.49 | \n",
" 1.282384e+05 | \n",
" 57833 | \n",
" 7.728435e+05 | \n",
" 61269.76 | \n",
" 2.586304e+05 | \n",
" 8595 | \n",
" 30.090802 | \n",
" 12.613783 | \n",
" 13884 | \n",
" 0.619058 | \n",
" 4.412976 | \n",
" 1640.663925 | \n",
" 5.238733 | \n",
" 21349 | \n",
" 46623 | \n",
" 55926 | \n",
" 5845 | \n",
" 839656.0 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 8360175 | \n",
" 82458.44 | \n",
" 1.238400e+05 | \n",
" 58261 | \n",
" 7.113762e+05 | \n",
" 53184.66 | \n",
" 2.377927e+05 | \n",
" 8582 | \n",
" 27.708309 | \n",
" 13.375589 | \n",
" 12462 | \n",
" 0.688654 | \n",
" 4.267747 | \n",
" 1439.625361 | \n",
" 5.961273 | \n",
" 20084 | \n",
" 34689 | \n",
" 41250 | \n",
" 5627 | \n",
" 955846.0 | \n",
"
\n",
" \n",
" 4 | \n",
" Audubon | \n",
" 2043175 | \n",
" 17737.11 | \n",
" 2.665607e+04 | \n",
" 13978 | \n",
" 1.595476e+05 | \n",
" 13215.43 | \n",
" 5.339370e+04 | \n",
" 2045 | \n",
" 26.109389 | \n",
" 12.072829 | \n",
" 5678 | \n",
" 0.360162 | \n",
" 2.327480 | \n",
" 1011.305224 | \n",
" 2.022139 | \n",
" 24207 | \n",
" 42717 | \n",
" 58641 | \n",
" 2617 | \n",
" -280677.0 | \n",
"
\n",
" \n",
" 5 | \n",
" Benton | \n",
" 7790725 | \n",
" 71349.04 | \n",
" 1.071595e+05 | \n",
" 47699 | \n",
" 5.803559e+05 | \n",
" 47612.84 | \n",
" 1.940870e+05 | \n",
" 7731 | \n",
" 25.105032 | \n",
" 12.189062 | \n",
" 25699 | \n",
" 0.300829 | \n",
" 1.852712 | \n",
" 1589.039934 | \n",
" 4.865202 | \n",
" 25111 | \n",
" 54726 | \n",
" 64970 | \n",
" 10302 | \n",
" 912250.0 | \n",
"
\n",
" \n",
" 6 | \n",
" Black Hawk | \n",
" 100801227 | \n",
" 1107541.37 | \n",
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\n",
" \n",
" 7 | \n",
" Boone | \n",
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" \n",
" 8 | \n",
" Bremer | \n",
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\n",
" \n",
" 9 | \n",
" Buchanan | \n",
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\n",
" \n",
" 10 | \n",
" Buena Vista | \n",
" 20832831 | \n",
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\n",
" \n",
" 11 | \n",
" Butler | \n",
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\n",
" \n",
" 12 | \n",
" Calhoun | \n",
" 3787450 | \n",
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\n",
" \n",
" 13 | \n",
" Carroll | \n",
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\n",
" \n",
" 14 | \n",
" Cass | \n",
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"
\n",
" \n",
" 15 | \n",
" Cedar | \n",
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"
\n",
" \n",
" 16 | \n",
" Cerro Gordo | \n",
" 50439956 | \n",
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\n",
" \n",
" 17 | \n",
" Cherokee | \n",
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"
\n",
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" 18 | \n",
" Chickasaw | \n",
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"
\n",
" \n",
" 19 | \n",
" Clarke | \n",
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"
\n",
" \n",
" 20 | \n",
" Clay | \n",
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"
\n",
" \n",
" 21 | \n",
" Clayton | \n",
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\n",
" \n",
" 22 | \n",
" Clinton | \n",
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\n",
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" 23 | \n",
" Crawford | \n",
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"
\n",
" \n",
" 24 | \n",
" Dallas | \n",
" 19782703 | \n",
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"
\n",
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" 25 | \n",
" Davis | \n",
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" 9.618596e+04 | \n",
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\n",
" \n",
" 26 | \n",
" Decatur | \n",
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" 14389 | \n",
" 1.763645e+05 | \n",
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" 5.898266e+04 | \n",
" 1994 | \n",
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" 8141 | \n",
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\n",
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" 27 | \n",
" Delaware | \n",
" 5929650 | \n",
" 56982.70 | \n",
" 8.557961e+04 | \n",
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\n",
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" 28 | \n",
" Des Moines | \n",
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\n",
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" Dickinson | \n",
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\n",
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" 30 | \n",
" Dubuque | \n",
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\n",
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" 31 | \n",
" Emmet | \n",
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" 6071 | \n",
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\n",
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" 32 | \n",
" Fayette | \n",
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\n",
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" 33 | \n",
" Floyd | \n",
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\n",
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" 34 | \n",
" Franklin | \n",
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\n",
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" Greene | \n",
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\n",
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\n",
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" Guthrie | \n",
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\n",
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" 38 | \n",
" Hamilton | \n",
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" 6064 | \n",
" 0.491590 | \n",
" 3.315465 | \n",
" 967.452680 | \n",
" 3.081288 | \n",
" 23063 | \n",
" 43889 | \n",
" 58286 | \n",
" 2682 | \n",
" 15017.0 | \n",
"
\n",
" \n",
" 71 | \n",
" Page | \n",
" 10230328 | \n",
" 103322.96 | \n",
" 1.551729e+05 | \n",
" 81116 | \n",
" 1.057103e+06 | \n",
" 75315.10 | \n",
" 3.532156e+05 | \n",
" 10651 | \n",
" 33.162667 | \n",
" 14.035741 | \n",
" 15391 | \n",
" 0.692028 | \n",
" 4.893451 | \n",
" 1398.118634 | \n",
" 7.618095 | \n",
" 21204 | \n",
" 40778 | \n",
" 52791 | \n",
" 6393 | \n",
" 1134280.0 | \n",
"
\n",
" \n",
" 72 | \n",
" Palo Alto | \n",
" 9396700 | \n",
" 89935.75 | \n",
" 1.350478e+05 | \n",
" 41949 | \n",
" 5.750134e+05 | \n",
" 44627.15 | \n",
" 1.921719e+05 | \n",
" 8667 | \n",
" 22.172831 | \n",
" 12.884834 | \n",
" 9047 | \n",
" 0.957997 | \n",
" 4.932812 | \n",
" 1596.128377 | \n",
" 5.430014 | \n",
" 23071 | \n",
" 42800 | \n",
" 57208 | \n",
" 3994 | \n",
" 797432.0 | \n",
"
\n",
" \n",
" 73 | \n",
" Plymouth | \n",
" 12560609 | \n",
" 132624.74 | \n",
" 1.991814e+05 | \n",
" 94325 | \n",
" 1.286709e+06 | \n",
" 93061.03 | \n",
" 4.300381e+05 | \n",
" 13176 | \n",
" 32.637984 | \n",
" 13.826506 | \n",
" 25200 | \n",
" 0.522857 | \n",
" 3.692898 | \n",
" 2259.953641 | \n",
" 5.830208 | \n",
" 28060 | \n",
" 56379 | \n",
" 69261 | \n",
" 9875 | \n",
" 953171.0 | \n",
"
\n",
" \n",
" 74 | \n",
" Pocahontas | \n",
" 4312750 | \n",
" 37584.77 | \n",
" 5.645243e+04 | \n",
" 24363 | \n",
" 2.971895e+05 | \n",
" 25680.47 | \n",
" 9.933154e+04 | \n",
" 4167 | \n",
" 23.837663 | \n",
" 11.572587 | \n",
" 6886 | \n",
" 0.605141 | \n",
" 3.729374 | \n",
" 1297.431413 | \n",
" 3.211730 | \n",
" 23385 | \n",
" 42105 | \n",
" 56250 | \n",
" 3233 | \n",
" 166156.0 | \n",
"
\n",
" \n",
" 75 | \n",
" Pottawattamie | \n",
" 64280187 | \n",
" 680102.80 | \n",
" 1.021141e+06 | \n",
" 618358 | \n",
" 7.675324e+06 | \n",
" 521376.00 | \n",
" 2.563352e+06 | \n",
" 72472 | \n",
" 35.370233 | \n",
" 14.721285 | \n",
" 93582 | \n",
" 0.774422 | \n",
" 5.571328 | \n",
" 2303.225231 | \n",
" 31.465442 | \n",
" 23782 | \n",
" 48728 | \n",
" 60354 | \n",
" 36775 | \n",
" 6828297.0 | \n",
"
\n",
" \n",
" 76 | \n",
" Poweshiek | \n",
" 16062900 | \n",
" 169084.71 | \n",
" 2.539540e+05 | \n",
" 97474 | \n",
" 1.306022e+06 | \n",
" 91675.42 | \n",
" 4.365483e+05 | \n",
" 17496 | \n",
" 24.951321 | \n",
" 14.246155 | \n",
" 18533 | \n",
" 0.944046 | \n",
" 4.946604 | \n",
" 1580.076857 | \n",
" 11.072879 | \n",
" 25218 | \n",
" 50998 | \n",
" 65744 | \n",
" 7555 | \n",
" 1469057.0 | \n",
"
\n",
" \n",
" 77 | \n",
" Ringgold | \n",
" 1689850 | \n",
" 14072.68 | \n",
" 2.115156e+04 | \n",
" 10061 | \n",
" 1.386808e+05 | \n",
" 11080.29 | \n",
" 4.642018e+04 | \n",
" 1476 | \n",
" 31.449986 | \n",
" 12.515991 | \n",
" 5068 | \n",
" 0.291239 | \n",
" 2.186324 | \n",
" 1198.624350 | \n",
" 1.231412 | \n",
" 21858 | \n",
" 42336 | \n",
" 51269 | \n",
" 2047 | \n",
" -260232.0 | \n",
"
\n",
" \n",
" 78 | \n",
" Sac | \n",
" 7934625 | \n",
" 75245.54 | \n",
" 1.130183e+05 | \n",
" 39571 | \n",
" 5.258452e+05 | \n",
" 42670.28 | \n",
" 1.760915e+05 | \n",
" 7157 | \n",
" 24.604098 | \n",
" 12.323453 | \n",
" 9876 | \n",
" 0.724686 | \n",
" 4.320603 | \n",
" 1412.599164 | \n",
" 5.066547 | \n",
" 23837 | \n",
" 42986 | \n",
" 54304 | \n",
" 4482 | \n",
" 528002.0 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 112482102 | \n",
" 1247750.11 | \n",
" 1.873693e+06 | \n",
" 1237025 | \n",
" 1.397399e+07 | \n",
" 914829.35 | \n",
" 4.667888e+06 | \n",
" 130419 | \n",
" 35.791469 | \n",
" 15.274967 | \n",
" 172474 | \n",
" 0.756166 | \n",
" 5.304158 | \n",
" 1211.649675 | \n",
" 107.637548 | \n",
" 27408 | \n",
" 49964 | \n",
" 64513 | \n",
" 66765 | \n",
" 14997255.0 | \n",
"
\n",
" \n",
" 80 | \n",
" Shelby | \n",
" 5286953 | \n",
" 53350.35 | \n",
" 8.009987e+04 | \n",
" 42963 | \n",
" 5.279510e+05 | \n",
" 38570.77 | \n",
" 1.762560e+05 | \n",
" 5627 | \n",
" 31.323259 | \n",
" 13.687852 | \n",
" 11800 | \n",
" 0.476864 | \n",
" 3.268709 | \n",
" 1299.197014 | \n",
" 4.331137 | \n",
" 22389 | \n",
" 44085 | \n",
" 55523 | \n",
" 5085 | \n",
" 447766.0 | \n",
"
\n",
" \n",
" 81 | \n",
" Sioux | \n",
" 10681100 | \n",
" 102003.82 | \n",
" 1.532028e+05 | \n",
" 77121 | \n",
" 1.069835e+06 | \n",
" 79019.86 | \n",
" 3.575951e+05 | \n",
" 10307 | \n",
" 34.694396 | \n",
" 13.538806 | \n",
" 34898 | \n",
" 0.295346 | \n",
" 2.264309 | \n",
" 2056.178194 | \n",
" 5.012698 | \n",
" 21333 | \n",
" 51557 | \n",
" 60043 | \n",
" 11584 | \n",
" 1725739.0 | \n",
"
\n",
" \n",
" 82 | \n",
" Story | \n",
" 65551096 | \n",
" 727466.68 | \n",
" 1.092625e+06 | \n",
" 501832 | \n",
" 6.730961e+06 | \n",
" 456414.62 | \n",
" 2.250349e+06 | \n",
" 71636 | \n",
" 31.413665 | \n",
" 14.747469 | \n",
" 97090 | \n",
" 0.737831 | \n",
" 4.700944 | \n",
" 1943.690095 | \n",
" 36.855670 | \n",
" 25450 | \n",
" 48248 | \n",
" 74278 | \n",
" 34736 | \n",
" 7146093.0 | \n",
"
\n",
" \n",
" 83 | \n",
" Tama | \n",
" 7242875 | \n",
" 69664.27 | \n",
" 1.046049e+05 | \n",
" 47692 | \n",
" 5.735157e+05 | \n",
" 43573.67 | \n",
" 1.916543e+05 | \n",
" 7655 | \n",
" 25.036489 | \n",
" 13.161979 | \n",
" 17319 | \n",
" 0.442000 | \n",
" 2.515946 | \n",
" 2088.226617 | \n",
" 3.665790 | \n",
" 23041 | \n",
" 46288 | \n",
" 55011 | \n",
" 6947 | \n",
" 655898.0 | \n",
"
\n",
" \n",
" 84 | \n",
" Taylor | \n",
" 2073825 | \n",
" 20885.05 | \n",
" 3.135727e+04 | \n",
" 9360 | \n",
" 1.105983e+05 | \n",
" 8433.72 | \n",
" 3.693200e+04 | \n",
" 2304 | \n",
" 16.029514 | \n",
" 13.113828 | \n",
" 6216 | \n",
" 0.370656 | \n",
" 1.356776 | \n",
" 1571.159542 | \n",
" 1.466433 | \n",
" 21335 | \n",
" 40300 | \n",
" 48156 | \n",
" 2679 | \n",
" -60024.0 | \n",
"
\n",
" \n",
" 85 | \n",
" Union | \n",
" 9569175 | \n",
" 90467.23 | \n",
" 1.358550e+05 | \n",
" 66536 | \n",
" 9.021116e+05 | \n",
" 68179.74 | \n",
" 3.015172e+05 | \n",
" 9024 | \n",
" 33.412815 | \n",
" 13.231373 | \n",
" 12420 | \n",
" 0.726570 | \n",
" 5.489512 | \n",
" 1292.945614 | \n",
" 6.979412 | \n",
" 20435 | \n",
" 40879 | \n",
" 50546 | \n",
" 5271 | \n",
" 1018948.0 | \n",
"
\n",
" \n",
" 86 | \n",
" Van Buren | \n",
" 1883075 | \n",
" 18970.16 | \n",
" 2.848695e+04 | \n",
" 12806 | \n",
" 1.742457e+05 | \n",
" 12768.44 | \n",
" 5.823345e+04 | \n",
" 1920 | \n",
" 30.329922 | \n",
" 13.646592 | \n",
" 7271 | \n",
" 0.264063 | \n",
" 1.756078 | \n",
" 1549.129170 | \n",
" 1.239406 | \n",
" 20209 | \n",
" 40073 | \n",
" 50064 | \n",
" 3108 | \n",
" -40646.0 | \n",
"
\n",
" \n",
" 87 | \n",
" Wapello | \n",
" 24021278 | \n",
" 251492.94 | \n",
" 3.776542e+05 | \n",
" 186724 | \n",
" 2.292624e+06 | \n",
" 160501.09 | \n",
" 7.658786e+05 | \n",
" 27843 | \n",
" 27.507043 | \n",
" 14.284163 | \n",
" 34982 | \n",
" 0.795924 | \n",
" 4.588105 | \n",
" 1104.862750 | \n",
" 25.200415 | \n",
" 22376 | \n",
" 40093 | \n",
" 49309 | \n",
" 14552 | \n",
" 3190720.0 | \n",
"
\n",
" \n",
" 88 | \n",
" Warren | \n",
" 19568800 | \n",
" 183359.55 | \n",
" 2.753840e+05 | \n",
" 159019 | \n",
" 1.939043e+06 | \n",
" 146705.53 | \n",
" 6.481145e+05 | \n",
" 19620 | \n",
" 33.033359 | \n",
" 13.217243 | \n",
" 49691 | \n",
" 0.394840 | \n",
" 2.952356 | \n",
" 1617.493665 | \n",
" 12.129877 | \n",
" 28798 | \n",
" 62034 | \n",
" 74042 | \n",
" 17262 | \n",
" 2521533.0 | \n",
"
\n",
" \n",
" 89 | \n",
" Washington | \n",
" 10984531 | \n",
" 119302.23 | \n",
" 1.791272e+05 | \n",
" 84389 | \n",
" 1.140731e+06 | \n",
" 78241.80 | \n",
" 3.810510e+05 | \n",
" 11745 | \n",
" 32.443678 | \n",
" 14.579561 | \n",
" 22281 | \n",
" 0.527131 | \n",
" 3.511593 | \n",
" 1666.506696 | \n",
" 7.047676 | \n",
" 23979 | \n",
" 50710 | \n",
" 60466 | \n",
" 8741 | \n",
" 1139096.0 | \n",
"
\n",
" \n",
" 90 | \n",
" Wayne | \n",
" 1344500 | \n",
" 12518.51 | \n",
" 1.880652e+04 | \n",
" 8283 | \n",
" 1.054384e+05 | \n",
" 8001.09 | \n",
" 3.526299e+04 | \n",
" 1268 | \n",
" 27.809929 | \n",
" 13.177998 | \n",
" 6452 | \n",
" 0.196528 | \n",
" 1.240095 | \n",
" 1438.930980 | \n",
" 0.881210 | \n",
" 18795 | \n",
" 35425 | \n",
" 44784 | \n",
" 2652 | \n",
" -76407.0 | \n",
"
\n",
" \n",
" 91 | \n",
" Webster | \n",
" 22537959 | \n",
" 231995.40 | \n",
" 3.484066e+05 | \n",
" 228252 | \n",
" 2.705835e+06 | \n",
" 189009.94 | \n",
" 9.042916e+05 | \n",
" 24106 | \n",
" 37.513134 | \n",
" 14.315835 | \n",
" 36769 | \n",
" 0.655607 | \n",
" 5.140470 | \n",
" 1793.295141 | \n",
" 13.442294 | \n",
" 22653 | \n",
" 40806 | \n",
" 54129 | \n",
" 15580 | \n",
" 2541044.0 | \n",
"
\n",
" \n",
" 92 | \n",
" Winnebago | \n",
" 8009300 | \n",
" 72833.61 | \n",
" 1.094025e+05 | \n",
" 55931 | \n",
" 6.827292e+05 | \n",
" 56888.63 | \n",
" 2.282976e+05 | \n",
" 7667 | \n",
" 29.776648 | \n",
" 12.001155 | \n",
" 10631 | \n",
" 0.721193 | \n",
" 5.351202 | \n",
" 1295.390700 | \n",
" 5.918678 | \n",
" 22684 | \n",
" 41871 | \n",
" 58700 | \n",
" 4597 | \n",
" 693928.0 | \n",
"
\n",
" \n",
" 93 | \n",
" Winneshiek | \n",
" 11450225 | \n",
" 107156.18 | \n",
" 1.609449e+05 | \n",
" 86483 | \n",
" 1.189769e+06 | \n",
" 89488.04 | \n",
" 3.977413e+05 | \n",
" 10541 | \n",
" 37.732790 | \n",
" 13.295288 | \n",
" 20561 | \n",
" 0.512670 | \n",
" 4.352319 | \n",
" 1587.428273 | \n",
" 6.640300 | \n",
" 23608 | \n",
" 50693 | \n",
" 61558 | \n",
" 7997 | \n",
" 1028921.0 | \n",
"
\n",
" \n",
" 94 | \n",
" Woodbury | \n",
" 61274387 | \n",
" 672801.78 | \n",
" 1.010242e+06 | \n",
" 620680 | \n",
" 7.695638e+06 | \n",
" 512631.64 | \n",
" 2.570501e+06 | \n",
" 67732 | \n",
" 37.951055 | \n",
" 15.012023 | \n",
" 102779 | \n",
" 0.659006 | \n",
" 4.987708 | \n",
" 2591.144872 | \n",
" 26.139797 | \n",
" 22069 | \n",
" 44343 | \n",
" 55957 | \n",
" 39052 | \n",
" 7104739.0 | \n",
"
\n",
" \n",
" 95 | \n",
" Worth | \n",
" 3247475 | \n",
" 28918.72 | \n",
" 4.343867e+04 | \n",
" 20112 | \n",
" 2.466353e+05 | \n",
" 20602.25 | \n",
" 8.250434e+04 | \n",
" 3000 | \n",
" 27.501447 | \n",
" 11.971281 | \n",
" 7572 | \n",
" 0.396197 | \n",
" 2.720847 | \n",
" 925.069678 | \n",
" 3.242999 | \n",
" 27240 | \n",
" 49673 | \n",
" 56659 | \n",
" 3172 | \n",
" -287641.0 | \n",
"
\n",
" \n",
" 96 | \n",
" Wright | \n",
" 5575150 | \n",
" 51158.24 | \n",
" 7.684774e+04 | \n",
" 46517 | \n",
" 5.706450e+05 | \n",
" 44906.82 | \n",
" 1.908072e+05 | \n",
" 5461 | \n",
" 34.939967 | \n",
" 12.707313 | \n",
" 12779 | \n",
" 0.427342 | \n",
" 3.514111 | \n",
" 1542.560045 | \n",
" 3.540219 | \n",
" 23068 | \n",
" 44035 | \n",
" 53890 | \n",
" 5625 | \n",
" 365167.0 | \n",
"
\n",
" \n",
"
\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",
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"\n",
" median_family_income number_of_households sales_prediction_dollars \n",
"0 57287 3292 221482.0 \n",
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"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",
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"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": {
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" profit | \n",
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" average_store_profit | \n",
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" store_population_ratio | \n",
" consumption_per_capita | \n",
" area | \n",
" stores_per_area | \n",
" per_capita_income | \n",
" median_household_income | \n",
" median_family_income | \n",
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\n",
" \n",
" 64 | \n",
" Mitchell | \n",
" 8198000 | \n",
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" 1.201944e+05 | \n",
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" 4.147482e+05 | \n",
" 34245.82 | \n",
" 1.387916e+05 | \n",
" 8227 | \n",
" 16.870258 | \n",
" 12.110916 | \n",
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" 0.764378 | \n",
" 3.181810 | \n",
" 1086.794650 | \n",
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\n",
" \n",
" 72 | \n",
" Palo Alto | \n",
" 9396700 | \n",
" 89935.75 | \n",
" 1.350478e+05 | \n",
" 41949 | \n",
" 5.750134e+05 | \n",
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" 1.921719e+05 | \n",
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" 22.172831 | \n",
" 12.884834 | \n",
" 9047 | \n",
" 0.957997 | \n",
" 4.932812 | \n",
" 1596.128377 | \n",
" 5.430014 | \n",
" 23071 | \n",
" 42800 | \n",
" 57208 | \n",
" 3994 | \n",
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"
\n",
" \n",
" 46 | \n",
" Iowa | \n",
" 11862800 | \n",
" 102657.53 | \n",
" 1.542814e+05 | \n",
" 74451 | \n",
" 9.711115e+05 | \n",
" 77025.04 | \n",
" 3.251289e+05 | \n",
" 11162 | \n",
" 29.128192 | \n",
" 12.607737 | \n",
" 16311 | \n",
" 0.684323 | \n",
" 4.722276 | \n",
" 1448.032410 | \n",
" 7.708391 | \n",
" 26721 | \n",
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" 64578 | \n",
" 6677 | \n",
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"
\n",
" \n",
" 15 | \n",
" Cedar | \n",
" 7427125 | \n",
" 67426.72 | \n",
" 1.013055e+05 | \n",
" 44550 | \n",
" 4.927014e+05 | \n",
" 38720.95 | \n",
" 1.647957e+05 | \n",
" 8160 | \n",
" 20.195545 | \n",
" 12.724413 | \n",
" 18454 | \n",
" 0.442181 | \n",
" 2.098242 | \n",
" 1232.378198 | \n",
" 6.621344 | \n",
" 24742 | \n",
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"
\n",
" \n",
" 67 | \n",
" Montgomery | \n",
" 6457078 | \n",
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" 1.006981e+05 | \n",
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" 5.570222e+05 | \n",
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" 27.726534 | \n",
" 13.382034 | \n",
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" 0.656528 | \n",
" 4.070868 | \n",
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"
\n",
" \n",
" 92 | \n",
" Winnebago | \n",
" 8009300 | \n",
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" 1.094025e+05 | \n",
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" 6.827292e+05 | \n",
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" 2.282976e+05 | \n",
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" 29.776648 | \n",
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"
\n",
" \n",
" 83 | \n",
" Tama | \n",
" 7242875 | \n",
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" 1.046049e+05 | \n",
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" 5.735157e+05 | \n",
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" 1.916543e+05 | \n",
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" 25.036489 | \n",
" 13.161979 | \n",
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" 0.442000 | \n",
" 2.515946 | \n",
" 2088.226617 | \n",
" 3.665790 | \n",
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"
\n",
" \n",
" 38 | \n",
" Hamilton | \n",
" 7882325 | \n",
" 77319.08 | \n",
" 1.161721e+05 | \n",
" 61011 | \n",
" 7.369458e+05 | \n",
" 56950.35 | \n",
" 2.466456e+05 | \n",
" 8603 | \n",
" 28.669721 | \n",
" 12.940146 | \n",
" 15076 | \n",
" 0.570642 | \n",
" 3.777550 | \n",
" 1425.376163 | \n",
" 6.035600 | \n",
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"
\n",
" \n",
" 41 | \n",
" Harrison | \n",
" 8333900 | \n",
" 82437.45 | \n",
" 1.238054e+05 | \n",
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" 5.046314e+05 | \n",
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" 1.687135e+05 | \n",
" 9131 | \n",
" 18.476999 | \n",
" 13.162064 | \n",
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" 0.645346 | \n",
" 2.709721 | \n",
" 2000.716609 | \n",
" 4.563865 | \n",
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"
\n",
" \n",
" 49 | \n",
" Jefferson | \n",
" 6377025 | \n",
" 66329.72 | \n",
" 9.959432e+04 | \n",
" 56831 | \n",
" 7.276337e+05 | \n",
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" 2.430598e+05 | \n",
" 6446 | \n",
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" 14.068272 | \n",
" 18090 | \n",
" 0.356329 | \n",
" 2.859127 | \n",
" 1228.460352 | \n",
" 5.247219 | \n",
" 23853 | \n",
" 44167 | \n",
" 55352 | \n",
" 6846 | \n",
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"
\n",
" \n",
" 27 | \n",
" Delaware | \n",
" 5929650 | \n",
" 56982.70 | \n",
" 8.557961e+04 | \n",
" 55212 | \n",
" 7.547227e+05 | \n",
" 54845.42 | \n",
" 2.522007e+05 | \n",
" 5749 | \n",
" 43.868622 | \n",
" 13.760907 | \n",
" 17327 | \n",
" 0.331794 | \n",
" 3.165315 | \n",
" 1275.461107 | \n",
" 4.507389 | \n",
" 22578 | \n",
" 47078 | \n",
" 59802 | \n",
" 7062 | \n",
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"
\n",
" \n",
" 78 | \n",
" Sac | \n",
" 7934625 | \n",
" 75245.54 | \n",
" 1.130183e+05 | \n",
" 39571 | \n",
" 5.258452e+05 | \n",
" 42670.28 | \n",
" 1.760915e+05 | \n",
" 7157 | \n",
" 24.604098 | \n",
" 12.323453 | \n",
" 9876 | \n",
" 0.724686 | \n",
" 4.320603 | \n",
" 1412.599164 | \n",
" 5.066547 | \n",
" 23837 | \n",
" 42986 | \n",
" 54304 | \n",
" 4482 | \n",
" 528002.0 | \n",
"
\n",
" \n",
" 59 | \n",
" Madison | \n",
" 7396475 | \n",
" 74647.41 | \n",
" 1.121233e+05 | \n",
" 54411 | \n",
" 6.856734e+05 | \n",
" 51462.23 | \n",
" 2.293326e+05 | \n",
" 7795 | \n",
" 29.420481 | \n",
" 13.323817 | \n",
" 15848 | \n",
" 0.491860 | \n",
" 3.247238 | \n",
" 1409.747269 | \n",
" 5.529360 | \n",
" 25711 | \n",
" 53183 | \n",
" 67099 | \n",
" 6025 | \n",
" 523595.0 | \n",
"
\n",
" \n",
" 17 | \n",
" Cherokee | \n",
" 7638400 | \n",
" 72195.69 | \n",
" 1.084669e+05 | \n",
" 58423 | \n",
" 6.855181e+05 | \n",
" 52491.70 | \n",
" 2.294092e+05 | \n",
" 7408 | \n",
" 30.967770 | \n",
" 13.059553 | \n",
" 11508 | \n",
" 0.643726 | \n",
" 4.561323 | \n",
" 1404.657609 | \n",
" 5.273883 | \n",
" 24507 | \n",
" 44635 | \n",
" 56696 | \n",
" 5207 | \n",
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"
\n",
" \n",
" 34 | \n",
" Franklin | \n",
" 6146325 | \n",
" 60180.12 | \n",
" 9.038277e+04 | \n",
" 36313 | \n",
" 4.522624e+05 | \n",
" 35596.58 | \n",
" 1.512945e+05 | \n",
" 6052 | \n",
" 24.999091 | \n",
" 12.705219 | \n",
" 10170 | \n",
" 0.595084 | \n",
" 3.500155 | \n",
" 1346.541996 | \n",
" 4.494475 | \n",
" 22507 | \n",
" 44863 | \n",
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" 4332 | \n",
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"
\n",
" \n",
" 80 | \n",
" Shelby | \n",
" 5286953 | \n",
" 53350.35 | \n",
" 8.009987e+04 | \n",
" 42963 | \n",
" 5.279510e+05 | \n",
" 38570.77 | \n",
" 1.762560e+05 | \n",
" 5627 | \n",
" 31.323259 | \n",
" 13.687852 | \n",
" 11800 | \n",
" 0.476864 | \n",
" 3.268709 | \n",
" 1299.197014 | \n",
" 4.331137 | \n",
" 22389 | \n",
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" 5085 | \n",
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"
\n",
" \n",
" 19 | \n",
" Clarke | \n",
" 5656450 | \n",
" 54720.08 | \n",
" 8.218209e+04 | \n",
" 37286 | \n",
" 5.035890e+05 | \n",
" 36768.25 | \n",
" 1.683819e+05 | \n",
" 5600 | \n",
" 30.068200 | \n",
" 13.696300 | \n",
" 9309 | \n",
" 0.601568 | \n",
" 3.949753 | \n",
" 954.543677 | \n",
" 5.866678 | \n",
" 23271 | \n",
" 45596 | \n",
" 54707 | \n",
" 3701 | \n",
" 445791.0 | \n",
"
\n",
" \n",
" 31 | \n",
" Emmet | \n",
" 5982525 | \n",
" 60879.97 | \n",
" 9.144739e+04 | \n",
" 38693 | \n",
" 5.019728e+05 | \n",
" 39072.45 | \n",
" 1.678386e+05 | \n",
" 6071 | \n",
" 27.645955 | \n",
" 12.847230 | \n",
" 9658 | \n",
" 0.628598 | \n",
" 4.045605 | \n",
" 1130.769046 | \n",
" 5.368912 | \n",
" 24371 | \n",
" 42286 | \n",
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"
\n",
" \n",
" 57 | \n",
" Lucas | \n",
" 3968675 | \n",
" 40531.53 | \n",
" 6.087117e+04 | \n",
" 29011 | \n",
" 3.761450e+05 | \n",
" 27391.10 | \n",
" 1.257357e+05 | \n",
" 4064 | \n",
" 30.938898 | \n",
" 13.732379 | \n",
" 8647 | \n",
" 0.469990 | \n",
" 3.167700 | \n",
" 1154.264969 | \n",
" 3.520855 | \n",
" 19967 | \n",
" 43005 | \n",
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" 3689 | \n",
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"
\n",
" \n",
" 56 | \n",
" Louisa | \n",
" 3165850 | \n",
" 31041.02 | \n",
" 4.661010e+04 | \n",
" 20602 | \n",
" 2.071461e+05 | \n",
" 14809.68 | \n",
" 6.917951e+04 | \n",
" 3730 | \n",
" 18.546786 | \n",
" 13.987210 | \n",
" 11142 | \n",
" 0.334769 | \n",
" 1.329176 | \n",
" 980.907015 | \n",
" 3.802603 | \n",
" 20367 | \n",
" 50457 | \n",
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" 4346 | \n",
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"
\n",
" \n",
" 96 | \n",
" Wright | \n",
" 5575150 | \n",
" 51158.24 | \n",
" 7.684774e+04 | \n",
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" 5.706450e+05 | \n",
" 44906.82 | \n",
" 1.908072e+05 | \n",
" 5461 | \n",
" 34.939967 | \n",
" 12.707313 | \n",
" 12779 | \n",
" 0.427342 | \n",
" 3.514111 | \n",
" 1542.560045 | \n",
" 3.540219 | \n",
" 23068 | \n",
" 44035 | \n",
" 53890 | \n",
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"
\n",
" \n",
" 43 | \n",
" Howard | \n",
" 5501853 | \n",
" 50667.53 | \n",
" 7.612342e+04 | \n",
" 38946 | \n",
" 5.268240e+05 | \n",
" 42435.81 | \n",
" 1.762160e+05 | \n",
" 5197 | \n",
" 33.907264 | \n",
" 12.414609 | \n",
" 9332 | \n",
" 0.556901 | \n",
" 4.547344 | \n",
" 1553.199575 | \n",
" 3.345996 | \n",
" 22417 | \n",
" 46068 | \n",
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"
\n",
" \n",
" 45 | \n",
" Ida | \n",
" 4907150 | \n",
" 49504.88 | \n",
" 7.433259e+04 | \n",
" 35771 | \n",
" 4.983864e+05 | \n",
" 34949.87 | \n",
" 1.665311e+05 | \n",
" 4777 | \n",
" 34.861015 | \n",
" 14.260037 | \n",
" 6985 | \n",
" 0.683894 | \n",
" 5.003560 | \n",
" 1061.883800 | \n",
" 4.498609 | \n",
" 23841 | \n",
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"
\n",
" \n",
" 35 | \n",
" Greene | \n",
" 5200525 | \n",
" 53392.20 | \n",
" 8.019890e+04 | \n",
" 35511 | \n",
" 4.774988e+05 | \n",
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" 1.596316e+05 | \n",
" 5248 | \n",
" 30.417611 | \n",
" 14.057818 | \n",
" 9011 | \n",
" 0.582399 | \n",
" 3.769480 | \n",
" 1385.511780 | \n",
" 3.787770 | \n",
" 23947 | \n",
" 43286 | \n",
" 60133 | \n",
" 3996 | \n",
" 255811.0 | \n",
"
\n",
" \n",
" 18 | \n",
" Chickasaw | \n",
" 4325975 | \n",
" 37139.09 | \n",
" 5.580234e+04 | \n",
" 27003 | \n",
" 3.508606e+05 | \n",
" 30429.56 | \n",
" 1.173801e+05 | \n",
" 4031 | \n",
" 29.119360 | \n",
" 11.530256 | \n",
" 12023 | \n",
" 0.335274 | \n",
" 2.530946 | \n",
" 1332.800885 | \n",
" 3.024458 | \n",
" 22447 | \n",
" 41372 | \n",
" 50530 | \n",
" 5204 | \n",
" 242653.0 | \n",
"
\n",
" \n",
" 0 | \n",
" Adair | \n",
" 4455875 | \n",
" 40182.62 | \n",
" 6.037369e+04 | \n",
" 33807 | \n",
" 4.108056e+05 | \n",
" 32522.35 | \n",
" 1.374661e+05 | \n",
" 4420 | \n",
" 31.100932 | \n",
" 12.631486 | \n",
" 7092 | \n",
" 0.623237 | \n",
" 4.585780 | \n",
" 1146.149874 | \n",
" 3.856389 | \n",
" 23497 | \n",
" 45202 | \n",
" 57287 | \n",
" 3292 | \n",
" 221482.0 | \n",
"
\n",
" \n",
" 44 | \n",
" Humboldt | \n",
" 4838675 | \n",
" 46974.26 | \n",
" 7.055336e+04 | \n",
" 40013 | \n",
" 5.050238e+05 | \n",
" 38779.60 | \n",
" 1.687570e+05 | \n",
" 4967 | \n",
" 33.975637 | \n",
" 13.022925 | \n",
" 9487 | \n",
" 0.523559 | \n",
" 4.087657 | \n",
" 1325.822767 | \n",
" 3.746353 | \n",
" 24568 | \n",
" 45282 | \n",
" 57063 | \n",
" 4209 | \n",
" 175430.0 | \n",
"
\n",
" \n",
" 74 | \n",
" Pocahontas | \n",
" 4312750 | \n",
" 37584.77 | \n",
" 5.645243e+04 | \n",
" 24363 | \n",
" 2.971895e+05 | \n",
" 25680.47 | \n",
" 9.933154e+04 | \n",
" 4167 | \n",
" 23.837663 | \n",
" 11.572587 | \n",
" 6886 | \n",
" 0.605141 | \n",
" 3.729374 | \n",
" 1297.431413 | \n",
" 3.211730 | \n",
" 23385 | \n",
" 42105 | \n",
" 56250 | \n",
" 3233 | \n",
" 166156.0 | \n",
"
\n",
" \n",
" 63 | \n",
" Mills | \n",
" 4317200 | \n",
" 38409.79 | \n",
" 5.770141e+04 | \n",
" 37547 | \n",
" 4.345716e+05 | \n",
" 34164.64 | \n",
" 1.452974e+05 | \n",
" 3968 | \n",
" 36.617293 | \n",
" 12.719923 | \n",
" 14972 | \n",
" 0.265028 | \n",
" 2.281902 | \n",
" 1220.953340 | \n",
" 3.249919 | \n",
" 25400 | \n",
" 59481 | \n",
" 73532 | \n",
" 5605 | \n",
" 142072.0 | \n",
"
\n",
" \n",
" 26 | \n",
" Decatur | \n",
" 1798250 | \n",
" 17836.60 | \n",
" 2.679681e+04 | \n",
" 14389 | \n",
" 1.763645e+05 | \n",
" 12243.11 | \n",
" 5.898266e+04 | \n",
" 1994 | \n",
" 29.580070 | \n",
" 14.405203 | \n",
" 8141 | \n",
" 0.244933 | \n",
" 1.503883 | \n",
" 1443.427332 | \n",
" 1.381434 | \n",
" 18195 | \n",
" 37138 | \n",
" 48015 | \n",
" 3223 | \n",
" 141811.0 | \n",
"
\n",
" \n",
" 66 | \n",
" Monroe | \n",
" 2891525 | \n",
" 28812.61 | \n",
" 4.327483e+04 | \n",
" 21971 | \n",
" 2.896006e+05 | \n",
" 20937.96 | \n",
" 9.680632e+04 | \n",
" 3000 | \n",
" 32.268773 | \n",
" 13.831367 | \n",
" 7870 | \n",
" 0.381194 | \n",
" 2.660478 | \n",
" 960.814655 | \n",
" 3.122350 | \n",
" 21228 | \n",
" 43245 | \n",
" 53052 | \n",
" 3213 | \n",
" 138773.0 | \n",
"
\n",
" \n",
" 11 | \n",
" Butler | \n",
" 3348800 | \n",
" 27994.24 | \n",
" 4.206102e+04 | \n",
" 24615 | \n",
" 2.786351e+05 | \n",
" 25010.20 | \n",
" 9.331105e+04 | \n",
" 3308 | \n",
" 28.207693 | \n",
" 11.140858 | \n",
" 14791 | \n",
" 0.223650 | \n",
" 1.690907 | \n",
" 1714.432829 | \n",
" 1.929501 | \n",
" 24030 | \n",
" 47702 | \n",
" 59641 | \n",
" 6120 | \n",
" 120103.0 | \n",
"
\n",
" \n",
" 12 | \n",
" Calhoun | \n",
" 3787450 | \n",
" 30301.41 | \n",
" 4.553630e+04 | \n",
" 27214 | \n",
" 3.215042e+05 | \n",
" 27977.64 | \n",
" 1.075994e+05 | \n",
" 3625 | \n",
" 29.682604 | \n",
" 11.491468 | \n",
" 9846 | \n",
" 0.368170 | \n",
" 2.841523 | \n",
" 1593.242060 | \n",
" 2.275235 | \n",
" 23049 | \n",
" 41611 | \n",
" 50037 | \n",
" 4242 | \n",
" 68571.0 | \n",
"
\n",
" \n",
" 39 | \n",
" Hancock | \n",
" 3509425 | \n",
" 28221.14 | \n",
" 4.241179e+04 | \n",
" 26820 | \n",
" 3.061684e+05 | \n",
" 28072.83 | \n",
" 1.025907e+05 | \n",
" 3101 | \n",
" 33.083089 | \n",
" 10.906219 | \n",
" 10835 | \n",
" 0.286202 | \n",
" 2.590940 | \n",
" 1435.986414 | \n",
" 2.159491 | \n",
" 22713 | \n",
" 47318 | \n",
" 55922 | \n",
" 4741 | \n",
" 58912.0 | \n",
"
\n",
" \n",
" 52 | \n",
" Keokuk | \n",
" 2720900 | \n",
" 23814.27 | \n",
" 3.576359e+04 | \n",
" 13554 | \n",
" 1.495608e+05 | \n",
" 13026.40 | \n",
" 4.999794e+04 | \n",
" 2797 | \n",
" 17.875560 | \n",
" 11.481359 | \n",
" 10119 | \n",
" 0.276411 | \n",
" 1.287321 | \n",
" 1568.990538 | \n",
" 1.782675 | \n",
" 22088 | \n",
" 42698 | \n",
" 53456 | \n",
" 4408 | \n",
" 32440.0 | \n",
"
\n",
" \n",
" 70 | \n",
" Osceola | \n",
" 3107475 | \n",
" 29141.20 | \n",
" 4.377315e+04 | \n",
" 19244 | \n",
" 2.562448e+05 | \n",
" 20104.98 | \n",
" 8.574306e+04 | \n",
" 2981 | \n",
" 28.763187 | \n",
" 12.745341 | \n",
" 6064 | \n",
" 0.491590 | \n",
" 3.315465 | \n",
" 967.452680 | \n",
" 3.081288 | \n",
" 23063 | \n",
" 43889 | \n",
" 58286 | \n",
" 2682 | \n",
" 15017.0 | \n",
"
\n",
" \n",
" 36 | \n",
" Grundy | \n",
" 4369250 | \n",
" 36670.31 | \n",
" 5.510175e+04 | \n",
" 26210 | \n",
" 3.018362e+05 | \n",
" 26267.96 | \n",
" 1.009891e+05 | \n",
" 4341 | \n",
" 23.264027 | \n",
" 11.490660 | \n",
" 12313 | \n",
" 0.352554 | \n",
" 2.133352 | \n",
" 1097.912646 | \n",
" 3.953866 | \n",
" 26916 | \n",
" 56184 | \n",
" 68151 | \n",
" 5131 | \n",
" 4175.0 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" sales_per_litters | \n",
" population | \n",
" stores_per_area | \n",
" sales_prediction_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 55 | \n",
" Linn | \n",
" 14.794975 | \n",
" 221661 | \n",
" 85.036195 | \n",
" 16780528.0 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 15.274967 | \n",
" 172474 | \n",
" 107.637548 | \n",
" 14997255.0 | \n",
"
\n",
" \n",
" 50 | \n",
" Johnson | \n",
" 15.770199 | \n",
" 146547 | \n",
" 77.091908 | \n",
" 11867678.0 | \n",
"
\n",
" \n",
" 6 | \n",
" Black Hawk | \n",
" 15.024919 | \n",
" 132904 | \n",
" 71.288290 | \n",
" 11283189.0 | \n",
"
\n",
" \n",
"
\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",
" county | \n",
" sales_per_litters | \n",
" population | \n",
" stores_per_area | \n",
" sales_prediction_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 55 | \n",
" Linn | \n",
" 14.794975 | \n",
" 221661 | \n",
" 85.036195 | \n",
" 16780528.0 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 15.274967 | \n",
" 172474 | \n",
" 107.637548 | \n",
" 14997255.0 | \n",
"
\n",
" \n",
" 50 | \n",
" Johnson | \n",
" 15.770199 | \n",
" 146547 | \n",
" 77.091908 | \n",
" 11867678.0 | \n",
"
\n",
" \n",
" 6 | \n",
" Black Hawk | \n",
" 15.024919 | \n",
" 132904 | \n",
" 71.288290 | \n",
" 11283189.0 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" county | \n",
" sales_per_litters | \n",
" population | \n",
" stores_per_area | \n",
" sales_prediction_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 50 | \n",
" Johnson | \n",
" 15.770199 | \n",
" 146547 | \n",
" 77.091908 | \n",
" 11867678.0 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 15.274967 | \n",
" 172474 | \n",
" 107.637548 | \n",
" 14997255.0 | \n",
"
\n",
" \n",
" 28 | \n",
" Des Moines | \n",
" 15.174117 | \n",
" 39739 | \n",
" 34.976332 | \n",
" 3968601.0 | \n",
"
\n",
" \n",
" 24 | \n",
" Dallas | \n",
" 15.116572 | \n",
" 84516 | \n",
" 12.321483 | \n",
" 4125161.0 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" county | \n",
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" population | \n",
" stores_per_area | \n",
" sales_prediction_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 26 | \n",
" Decatur | \n",
" 14.405203 | \n",
" 8141 | \n",
" 1.381434 | \n",
" 141811.0 | \n",
"
\n",
" \n",
" 52 | \n",
" Keokuk | \n",
" 11.481359 | \n",
" 10119 | \n",
" 1.782675 | \n",
" 32440.0 | \n",
"
\n",
" \n",
" 11 | \n",
" Butler | \n",
" 11.140858 | \n",
" 14791 | \n",
" 1.929501 | \n",
" 120103.0 | \n",
"
\n",
" \n",
" 39 | \n",
" Hancock | \n",
" 10.906219 | \n",
" 10835 | \n",
" 2.159491 | \n",
" 58912.0 | \n",
"
\n",
" \n",
" 12 | \n",
" Calhoun | \n",
" 11.491468 | \n",
" 9846 | \n",
" 2.275235 | \n",
" 68571.0 | \n",
"
\n",
" \n",
" 18 | \n",
" Chickasaw | \n",
" 11.530256 | \n",
" 12023 | \n",
" 3.024458 | \n",
" 242653.0 | \n",
"
\n",
" \n",
" 70 | \n",
" Osceola | \n",
" 12.745341 | \n",
" 6064 | \n",
" 3.081288 | \n",
" 15017.0 | \n",
"
\n",
" \n",
" 66 | \n",
" Monroe | \n",
" 13.831367 | \n",
" 7870 | \n",
" 3.122350 | \n",
" 138773.0 | \n",
"
\n",
" \n",
" 74 | \n",
" Pocahontas | \n",
" 11.572587 | \n",
" 6886 | \n",
" 3.211730 | \n",
" 166156.0 | \n",
"
\n",
" \n",
" 63 | \n",
" Mills | \n",
" 12.719923 | \n",
" 14972 | \n",
" 3.249919 | \n",
" 142072.0 | \n",
"
\n",
" \n",
" 43 | \n",
" Howard | \n",
" 12.414609 | \n",
" 9332 | \n",
" 3.345996 | \n",
" 335431.0 | \n",
"
\n",
" \n",
" 57 | \n",
" Lucas | \n",
" 13.732379 | \n",
" 8647 | \n",
" 3.520855 | \n",
" 388043.0 | \n",
"
\n",
" \n",
" 96 | \n",
" Wright | \n",
" 12.707313 | \n",
" 12779 | \n",
" 3.540219 | \n",
" 365167.0 | \n",
"
\n",
" \n",
" 83 | \n",
" Tama | \n",
" 13.161979 | \n",
" 17319 | \n",
" 3.665790 | \n",
" 655898.0 | \n",
"
\n",
" \n",
" 44 | \n",
" Humboldt | \n",
" 13.022925 | \n",
" 9487 | \n",
" 3.746353 | \n",
" 175430.0 | \n",
"
\n",
" \n",
" 35 | \n",
" Greene | \n",
" 14.057818 | \n",
" 9011 | \n",
" 3.787770 | \n",
" 255811.0 | \n",
"
\n",
" \n",
" 56 | \n",
" Louisa | \n",
" 13.987210 | \n",
" 11142 | \n",
" 3.802603 | \n",
" 378171.0 | \n",
"
\n",
" \n",
" 0 | \n",
" Adair | \n",
" 12.631486 | \n",
" 7092 | \n",
" 3.856389 | \n",
" 221482.0 | \n",
"
\n",
" \n",
" 36 | \n",
" Grundy | \n",
" 11.490660 | \n",
" 12313 | \n",
" 3.953866 | \n",
" 4175.0 | \n",
"
\n",
" \n",
" 80 | \n",
" Shelby | \n",
" 13.687852 | \n",
" 11800 | \n",
" 4.331137 | \n",
" 447766.0 | \n",
"
\n",
" \n",
" 32 | \n",
" Fayette | \n",
" 12.617454 | \n",
" 20054 | \n",
" 4.463878 | \n",
" 955344.0 | \n",
"
\n",
" \n",
" 34 | \n",
" Franklin | \n",
" 12.705219 | \n",
" 10170 | \n",
" 4.494475 | \n",
" 475800.0 | \n",
"
\n",
" \n",
" 45 | \n",
" Ida | \n",
" 14.260037 | \n",
" 6985 | \n",
" 4.498609 | \n",
" 286374.0 | \n",
"
\n",
" \n",
" 27 | \n",
" Delaware | \n",
" 13.760907 | \n",
" 17327 | \n",
" 4.507389 | \n",
" 615781.0 | \n",
"
\n",
" \n",
" 41 | \n",
" Harrison | \n",
" 13.162064 | \n",
" 14149 | \n",
" 4.563865 | \n",
" 643413.0 | \n",
"
\n",
" \n",
" 5 | \n",
" Benton | \n",
" 12.189062 | \n",
" 25699 | \n",
" 4.865202 | \n",
" 912250.0 | \n",
"
\n",
" \n",
" 81 | \n",
" Sioux | \n",
" 13.538806 | \n",
" 34898 | \n",
" 5.012698 | \n",
" 1725739.0 | \n",
"
\n",
" \n",
" 21 | \n",
" Clayton | \n",
" 13.195825 | \n",
" 17590 | \n",
" 5.023060 | \n",
" 954141.0 | \n",
"
\n",
" \n",
" 78 | \n",
" Sac | \n",
" 12.323453 | \n",
" 9876 | \n",
" 5.066547 | \n",
" 528002.0 | \n",
"
\n",
" \n",
" 23 | \n",
" Crawford | \n",
" 13.822321 | \n",
" 16940 | \n",
" 5.215863 | \n",
" 960452.0 | \n",
"
\n",
" \n",
" 2 | \n",
" Allamakee | \n",
" 12.613783 | \n",
" 13884 | \n",
" 5.238733 | \n",
" 839656.0 | \n",
"
\n",
" \n",
" 49 | \n",
" Jefferson | \n",
" 14.068272 | \n",
" 18090 | \n",
" 5.247219 | \n",
" 632088.0 | \n",
"
\n",
" \n",
" 17 | \n",
" Cherokee | \n",
" 13.059553 | \n",
" 11508 | \n",
" 5.273883 | \n",
" 501313.0 | \n",
"
\n",
" \n",
" 31 | \n",
" Emmet | \n",
" 12.847230 | \n",
" 9658 | \n",
" 5.368912 | \n",
" 391058.0 | \n",
"
\n",
" \n",
" 72 | \n",
" Palo Alto | \n",
" 12.884834 | \n",
" 9047 | \n",
" 5.430014 | \n",
" 797432.0 | \n",
"
\n",
" \n",
" 59 | \n",
" Madison | \n",
" 13.323817 | \n",
" 15848 | \n",
" 5.529360 | \n",
" 523595.0 | \n",
"
\n",
" \n",
" 14 | \n",
" Cass | \n",
" 13.252889 | \n",
" 13157 | \n",
" 5.622151 | \n",
" 931866.0 | \n",
"
\n",
" \n",
" 73 | \n",
" Plymouth | \n",
" 13.826506 | \n",
" 25200 | \n",
" 5.830208 | \n",
" 953171.0 | \n",
"
\n",
" \n",
" 19 | \n",
" Clarke | \n",
" 13.696300 | \n",
" 9309 | \n",
" 5.866678 | \n",
" 445791.0 | \n",
"
\n",
" \n",
" 67 | \n",
" Montgomery | \n",
" 13.382034 | \n",
" 10225 | \n",
" 5.886740 | \n",
" 699564.0 | \n",
"
\n",
" \n",
" 92 | \n",
" Winnebago | \n",
" 12.001155 | \n",
" 10631 | \n",
" 5.918678 | \n",
" 693928.0 | \n",
"
\n",
" \n",
" 3 | \n",
" Appanoose | \n",
" 13.375589 | \n",
" 12462 | \n",
" 5.961273 | \n",
" 955846.0 | \n",
"
\n",
" \n",
" 38 | \n",
" Hamilton | \n",
" 12.940146 | \n",
" 15076 | \n",
" 6.035600 | \n",
" 654023.0 | \n",
"
\n",
" \n",
" 65 | \n",
" Monona | \n",
" 13.660539 | \n",
" 8898 | \n",
" 6.049634 | \n",
" 1017900.0 | \n",
"
\n",
" \n",
" 47 | \n",
" Jackson | \n",
" 12.838469 | \n",
" 19472 | \n",
" 6.067410 | \n",
" 1120072.0 | \n",
"
\n",
" \n",
" 53 | \n",
" Kossuth | \n",
" 13.700965 | \n",
" 15114 | \n",
" 6.318563 | \n",
" 834826.0 | \n",
"
\n",
" \n",
" 58 | \n",
" Lyon | \n",
" 13.718707 | \n",
" 11754 | \n",
" 6.344742 | \n",
" 962622.0 | \n",
"
\n",
" \n",
" 42 | \n",
" Henry | \n",
" 14.271205 | \n",
" 19773 | \n",
" 6.566097 | \n",
" 959348.0 | \n",
"
\n",
" \n",
" 33 | \n",
" Floyd | \n",
" 13.846056 | \n",
" 15873 | \n",
" 6.601745 | \n",
" 913846.0 | \n",
"
\n",
" \n",
" 15 | \n",
" Cedar | \n",
" 12.724413 | \n",
" 18454 | \n",
" 6.621344 | \n",
" 748698.0 | \n",
"
\n",
" \n",
" 93 | \n",
" Winneshiek | \n",
" 13.295288 | \n",
" 20561 | \n",
" 6.640300 | \n",
" 1028921.0 | \n",
"
\n",
" \n",
" 85 | \n",
" Union | \n",
" 13.231373 | \n",
" 12420 | \n",
" 6.979412 | \n",
" 1018948.0 | \n",
"
\n",
" \n",
" 89 | \n",
" Washington | \n",
" 14.579561 | \n",
" 22281 | \n",
" 7.047676 | \n",
" 1139096.0 | \n",
"
\n",
" \n",
" 64 | \n",
" Mitchell | \n",
" 12.110916 | \n",
" 10763 | \n",
" 7.569967 | \n",
" 819843.0 | \n",
"
\n",
" \n",
" 71 | \n",
" Page | \n",
" 14.035741 | \n",
" 15391 | \n",
" 7.618095 | \n",
" 1134280.0 | \n",
"
\n",
" \n",
" 46 | \n",
" Iowa | \n",
" 12.607737 | \n",
" 16311 | \n",
" 7.708391 | \n",
" 789613.0 | \n",
"
\n",
" \n",
" 40 | \n",
" Hardin | \n",
" 13.009862 | \n",
" 17226 | \n",
" 7.769505 | \n",
" 1140430.0 | \n",
"
\n",
" \n",
" 60 | \n",
" Mahaska | \n",
" 13.342219 | \n",
" 22181 | \n",
" 7.949059 | \n",
" 1255159.0 | \n",
"
\n",
" \n",
" 13 | \n",
" Carroll | \n",
" 13.476729 | \n",
" 20437 | \n",
" 8.136696 | \n",
" 1200276.0 | \n",
"
\n",
" \n",
" 69 | \n",
" O'Brien | \n",
" 12.506521 | \n",
" 14020 | \n",
" 8.611285 | \n",
" 1166399.0 | \n",
"
\n",
" \n",
" 9 | \n",
" Buchanan | \n",
" 13.504469 | \n",
" 20992 | \n",
" 8.803275 | \n",
" 1269271.0 | \n",
"
\n",
" \n",
" 51 | \n",
" Jones | \n",
" 13.074420 | \n",
" 20439 | \n",
" 9.691983 | \n",
" 1487368.0 | \n",
"
\n",
" \n",
" 76 | \n",
" Poweshiek | \n",
" 14.246155 | \n",
" 18533 | \n",
" 11.072879 | \n",
" 1469057.0 | \n",
"
\n",
" \n",
" 20 | \n",
" Clay | \n",
" 13.073807 | \n",
" 16333 | \n",
" 11.702255 | \n",
" 1348963.0 | \n",
"
\n",
" \n",
" 88 | \n",
" Warren | \n",
" 13.217243 | \n",
" 49691 | \n",
" 12.129877 | \n",
" 2521533.0 | \n",
"
\n",
" \n",
" 24 | \n",
" Dallas | \n",
" 15.116572 | \n",
" 84516 | \n",
" 12.321483 | \n",
" 4125161.0 | \n",
"
\n",
" \n",
" 10 | \n",
" Buena Vista | \n",
" 14.543544 | \n",
" 20332 | \n",
" 13.155325 | \n",
" 2087010.0 | \n",
"
\n",
" \n",
" 91 | \n",
" Webster | \n",
" 14.315835 | \n",
" 36769 | \n",
" 13.442294 | \n",
" 2541044.0 | \n",
"
\n",
" \n",
" 61 | \n",
" Marion | \n",
" 14.093066 | \n",
" 33189 | \n",
" 13.708075 | \n",
" 2177923.0 | \n",
"
\n",
" \n",
" 48 | \n",
" Jasper | \n",
" 12.996996 | \n",
" 36708 | \n",
" 13.740373 | \n",
" 2486830.0 | \n",
"
\n",
" \n",
" 7 | \n",
" Boone | \n",
" 13.277107 | \n",
" 26532 | \n",
" 14.083864 | \n",
" 1696331.0 | \n",
"
\n",
" \n",
" 8 | \n",
" Bremer | \n",
" 13.042060 | \n",
" 24798 | \n",
" 14.144601 | \n",
" 1657708.0 | \n",
"
\n",
" \n",
" 62 | \n",
" Marshall | \n",
" 14.875434 | \n",
" 40312 | \n",
" 15.464035 | \n",
" 2791982.0 | \n",
"
\n",
" \n",
" 22 | \n",
" Clinton | \n",
" 13.627250 | \n",
" 47309 | \n",
" 16.674016 | \n",
" 3197853.0 | \n",
"
\n",
" \n",
" 68 | \n",
" Muscatine | \n",
" 13.923365 | \n",
" 42940 | \n",
" 18.572652 | \n",
" 3138265.0 | \n",
"
\n",
" \n",
" 54 | \n",
" Lee | \n",
" 14.395224 | \n",
" 34615 | \n",
" 22.574731 | \n",
" 3104487.0 | \n",
"
\n",
" \n",
" 87 | \n",
" Wapello | \n",
" 14.284163 | \n",
" 34982 | \n",
" 25.200415 | \n",
" 3190720.0 | \n",
"
\n",
" \n",
" 94 | \n",
" Woodbury | \n",
" 15.012023 | \n",
" 102779 | \n",
" 26.139797 | \n",
" 7104739.0 | \n",
"
\n",
" \n",
" 29 | \n",
" Dickinson | \n",
" 14.023690 | \n",
" 17243 | \n",
" 27.448097 | \n",
" 2628643.0 | \n",
"
\n",
" \n",
" 75 | \n",
" Pottawattamie | \n",
" 14.721285 | \n",
" 93582 | \n",
" 31.465442 | \n",
" 6828297.0 | \n",
"
\n",
" \n",
" 16 | \n",
" Cerro Gordo | \n",
" 13.808162 | \n",
" 43070 | \n",
" 34.428514 | \n",
" 4328297.0 | \n",
"
\n",
" \n",
" 28 | \n",
" Des Moines | \n",
" 15.174117 | \n",
" 39739 | \n",
" 34.976332 | \n",
" 3968601.0 | \n",
"
\n",
" \n",
" 82 | \n",
" Story | \n",
" 14.747469 | \n",
" 97090 | \n",
" 36.855670 | \n",
" 7146093.0 | \n",
"
\n",
" \n",
" 30 | \n",
" Dubuque | \n",
" 14.393144 | \n",
" 97003 | \n",
" 42.015240 | \n",
" 7310597.0 | \n",
"
\n",
" \n",
" 6 | \n",
" Black Hawk | \n",
" 15.024919 | \n",
" 132904 | \n",
" 71.288290 | \n",
" 11283189.0 | \n",
"
\n",
" \n",
" 50 | \n",
" Johnson | \n",
" 15.770199 | \n",
" 146547 | \n",
" 77.091908 | \n",
" 11867678.0 | \n",
"
\n",
" \n",
" 55 | \n",
" Linn | \n",
" 14.794975 | \n",
" 221661 | \n",
" 85.036195 | \n",
" 16780528.0 | \n",
"
\n",
" \n",
" 79 | \n",
" Scott | \n",
" 15.274967 | \n",
" 172474 | \n",
" 107.637548 | \n",
" 14997255.0 | \n",
"
\n",
" \n",
"
\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",
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]
},
"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": {
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" | \n",
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" 0.645346 | \n",
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" 6 | \n",
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" 20 | \n",
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" 18533 | \n",
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" \n",
" 69 | \n",
" O'Brien | \n",
" 12.506521 | \n",
" 14020 | \n",
" 8.611285 | \n",
" 1166399.0 | \n",
" 0.995292 | \n",
"
\n",
" \n",
" 10 | \n",
" Buena Vista | \n",
" 14.543544 | \n",
" 20332 | \n",
" 13.155325 | \n",
" 2087010.0 | \n",
" 1.062266 | \n",
"
\n",
" \n",
" 65 | \n",
" Monona | \n",
" 13.660539 | \n",
" 8898 | \n",
" 6.049634 | \n",
" 1017900.0 | \n",
" 1.126433 | \n",
"
\n",
" \n",
" 16 | \n",
" Cerro Gordo | \n",
" 13.808162 | \n",
" 43070 | \n",
" 34.428514 | \n",
" 4328297.0 | \n",
" 1.197260 | \n",
"
\n",
" \n",
" 29 | \n",
" Dickinson | \n",
" 14.023690 | \n",
" 17243 | \n",
" 27.448097 | \n",
" 2628643.0 | \n",
" 1.630227 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" State Bottle Cost | \n",
" State Bottle Retail | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" $18.09 | \n",
" $27.14 | \n",
"
\n",
" \n",
" 1 | \n",
" $18.09 | \n",
" $27.14 | \n",
"
\n",
" \n",
" 2 | \n",
" $6.40 | \n",
" $9.60 | \n",
"
\n",
" \n",
" 3 | \n",
" $35.55 | \n",
" $53.34 | \n",
"
\n",
" \n",
" 4 | \n",
" $6.40 | \n",
" $9.60 | \n",
"
\n",
" \n",
"
\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."
]
}
],
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