{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><img style=\"padding: 0 15px; float: left;\" src=\"Graphs/FTLogo300.jpg\" alt=\"FT Crusader Logo\" Width='140' Height= '250'/></p> \n",
    "<p> <h2> Saint Paul City Council Election Voting Workbook; 07/31/19</h2> <a name=\"tc\"></a>\n",
    "<p> <h3> By Frogtown Crusader (Abu Nayeem)</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Disclaimer:*** Technically, the Minnesota voting data is not publicly accessible, but is publicly available. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set-up/ Load Data\n",
    "\n",
    "Note: I cannot provide the raw Election and Voting Records Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import requests # library to handle requests\n",
    "import folium\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats #used to get correlation coefficient\n",
    "\n",
    "# Load Election Data\n",
    "df_election1 = pd.read_csv('Election01.txt')\n",
    "df_election2 = pd.read_csv('Election02.txt')\n",
    "df_election3 = pd.read_csv('Election03.txt')\n",
    "df_election4 = pd.read_csv('Election04.txt')\n",
    "df_election5 = pd.read_csv('Election05.txt')\n",
    "df_election6 = pd.read_csv('Election06.txt')\n",
    "df_election7 = pd.read_csv('Election07.txt')\n",
    "df_election8 = pd.read_csv('Election08.txt')\n",
    "\n",
    "#Combine Election the data\n",
    "df_election_hal1= pd.concat([df_election1,df_election2,df_election3,df_election4])\n",
    "df_election_hal2= pd.concat([df_election5,df_election6,df_election7,df_election8])\n",
    "\n",
    "#Load Voter Data\n",
    "#df_voter1 = pd.read_csv('Voter01.txt')\n",
    "#df_voter2 = pd.read_csv('Voter02.txt')\n",
    "#df_voter3 = pd.read_csv('Voter03.txt') -bugged\n",
    "df_voter4 = pd.read_csv('Voter04.txt') #had desired county code: Saint Paul/ \n",
    "df_voter5 = pd.read_csv('Voter05.txt') #had desired county code: Saint Paul/ Minneapolis\n",
    "#df_voter6 = pd.read_csv('Voter06.txt') -bugged\n",
    "#df_voter7 = pd.read_csv('Voter07.txt')\n",
    "#df_voter8 = pd.read_csv('Voter08.txt') -bugged\n",
    "\n",
    "#Combine Voter Data\n",
    "df_voter_ward1= pd.concat([df_voter4, df_voter5])\n",
    "\n",
    "#Saint Paul Data\n",
    "sp_voter= df_voter_ward1.query(\"City== 'ST PAUL'\") #Only city of Saint Paul\n",
    "\n",
    "#Minneapolis data\n",
    "minn_voter= df_voter5.query(\"City== 'MINNEAPOLIS'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VoterId', 'CountyCode', 'FirstName', 'MiddleName', 'LastName',\n",
       "       'NameSuffix', 'HouseNumber', 'StreetName', 'UnitType', 'UnitNumber',\n",
       "       'Address2', 'City', 'State', 'ZipCode', 'MailAddress', 'MailCity',\n",
       "       'MailState', 'MailZipCode', 'PhoneNumber', 'RegistrationDate',\n",
       "       'DOBYear', 'StateMcdCode', 'McdName', 'PrecinctCode', 'PrecinctName',\n",
       "       'WardCode', 'School', 'SchSub', 'Judicial', 'Legislative', 'StateSen',\n",
       "       'Congressional', 'Commissioner', 'Park', 'SoilWater', 'Hospital',\n",
       "       'LegacyId', 'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View Columns\n",
    "minn_voter.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Registered Voter Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create an Age column\n",
    "sp_voter['Age'] = 2019 - sp_voter['DOBYear'] \n",
    "minn_voter['Age'] = 2019 - minn_voter['DOBYear'] \n",
    "\n",
    "\n",
    "#Graphing using seaborn\n",
    "sns.set_style(\"darkgrid\") #white, white-grid, ticks\n",
    "ax= sns.kdeplot(minn_voter.query('Age<101').Age, label='Minneapolis')\n",
    "sns.kdeplot(sp_voter.query('Age<101').Age, label='St. Paul')\n",
    "\n",
    "ax.set_title('Twin Cities Registered Voters Age Distribution: 06/24/19')\n",
    "\n",
    "#Save Graph\n",
    "figure = ax.get_figure()    \n",
    "figure.savefig('RegisterVoterDistribution.png', dpi=400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Total Registered Voters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\17189\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:2369: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the `bins` kwarg\n",
    "#axs[0].hist(com_Ward1['Age'], bins=20)\n",
    "axs[0].set_ylabel('Registered Voters: 06/24/19')\n",
    "\n",
    "axs[0].hist(sp_voter.query('Age<101').Age, bins=20)\n",
    "axs[1].hist(minn_voter.query('Age<101').Age, bins=20)\n",
    "\n",
    "axs[0].set_title('Saint Paul')\n",
    "axs[1].set_title('Minneapolis')\n",
    "\n",
    "figure = fig.get_figure()    \n",
    "fig.savefig('RegisterVotersCount.png', dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Ward 3 Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "\n",
    "ax=sns.kdeplot(sp_voter.query('Age<101').Age, label='SaintPaul_All')\n",
    "sns.kdeplot(sp_voter.query('WardCode==3 and Age<101').Age, label='Ward 3')\n",
    "\n",
    "ax.set_title('Registered Voter Age Distribution Comparison Ward 3: 06/24/19')\n",
    "\n",
    "#Save Graph\n",
    "figure = ax.get_figure()    \n",
    "figure.savefig('AgeDistributionSP3.png', dpi=400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ward/ Voter Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    29145\n",
       "2.0    26735\n",
       "4.0    26475\n",
       "1.0    21699\n",
       "7.0    20584\n",
       "5.0    19933\n",
       "6.0    19431\n",
       "Name: WardCode, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find total current registered Voters\n",
    "sp_voter['WardCode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    28144\n",
       "2.0    26279\n",
       "4.0    25400\n",
       "1.0    21118\n",
       "7.0    20041\n",
       "5.0    19418\n",
       "6.0    18856\n",
       "Name: WardCode, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find Adjusted Total in 2016 Election\n",
    "sp_voter.query('Age>20').WardCode.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    27612\n",
       "2.0    26001\n",
       "4.0    24715\n",
       "1.0    20737\n",
       "7.0    19693\n",
       "5.0    19074\n",
       "6.0    18509\n",
       "Name: WardCode, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find Adjusted Total in 2015 Election\n",
    "sp_voter.query('Age>21').WardCode.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VoterId', 'CountyCode', 'FirstName', 'MiddleName', 'LastName',\n",
       "       'NameSuffix', 'HouseNumber', 'StreetName', 'UnitType', 'UnitNumber',\n",
       "       'Address2', 'City', 'State', 'ZipCode', 'MailAddress', 'MailCity',\n",
       "       'MailState', 'MailZipCode', 'PhoneNumber', 'RegistrationDate',\n",
       "       'DOBYear', 'StateMcdCode', 'McdName', 'PrecinctCode', 'PrecinctName',\n",
       "       'WardCode', 'School', 'SchSub', 'Judicial', 'Legislative', 'StateSen',\n",
       "       'Congressional', 'Commissioner', 'Park', 'SoilWater', 'Hospital',\n",
       "       'LegacyId'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voter Info COlumns\n",
    "df_voter_ward1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prep Election Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VoterId', 'ElectionDate', 'ElectionDescription', 'VotingMethod'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Election Info Columns\n",
    "df_election_hal2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I choose to work with smaller column dataset first computation will increase for larger dataset and election dataset needs to be condensed to a single line to be more easily accessible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the \"Year\" variable from election date. \n",
    "#NOTE: My normal method to converting datetime was too computational for my computer to handle. I commented it out\n",
    "\n",
    "#df_election_all['DateTime']= pd.to_datetime(df_election_all['ElectionDate']) # Create new column called DateTime\n",
    "#df_election_all['Year']= df_election_all['DateTime'].dt.year #create year column\n",
    "\n",
    "df_election_hal1['Year'] = df_election_hal1['ElectionDate'].str.strip().str[6:]\n",
    "df_election_hal1.Year = df_election_hal1.Year.astype(int) #convert to integer\n",
    "df_election_hal1= df_election_hal1.query('Year in (2011,2015,2016,2017,2018)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_election_hal2['Year'] = df_election_hal2['ElectionDate'].str.strip().str[6:]\n",
    "df_election_hal2.Year = df_election_hal2.Year.astype(int) #convert to integer\n",
    "df_election_hal2= df_election_hal2.query('Year in (2011,2015,2016,2017,2018)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#COmbine\n",
    "df_election_all= pd.concat([df_election_hal1, df_election_hal2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select primary key from Voter table\n",
    "Voter_ID= sp_voter.iloc[:,[0,1]]\n",
    "\n",
    "#Specify Primary Key and keep only the matches\n",
    "df_SP_raw=df_election_all.set_index('VoterId').join(Voter_ID.set_index('VoterId'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing Event Values\n",
    "#Ward1_15= df_election_ward1.query(\"Year==2015\")\n",
    "#Ward1_15['ElectionDescription'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I already selected the events I'm interested\n",
    "df_SP= df_SP_raw.query('ElectionDescription in (\"11/07/2017 - MUNICIPAL GENERAL\",\"11/08/2016 - STATE GENERAL\",\"11/03/2015 - MUNICIPAL GENERAL\",\"11/06/2018 - STATE GENERAL\")')\n",
    "\n",
    "#Create a Count Variable (participation from 2015 outward)\n",
    "df_SP['Count'] = df_SP['Year'].apply(lambda x: 0 if x==2011  else 1)\n",
    "\n",
    "df_SP['SP_Vote15'] = df_SP['ElectionDescription'].apply(lambda x: 1 if (x=='11/03/2015 - MUNICIPAL GENERAL')  else 0)\n",
    "df_SP['SP_Vote16'] = df_SP['ElectionDescription'].apply(lambda x: 1 if (x=='11/08/2016 - STATE GENERAL')  else 0)\n",
    "df_SP['SP_Vote17'] = df_SP['ElectionDescription'].apply(lambda x: 1 if (x=='11/07/2017 - MUNICIPAL GENERAL')  else 0)\n",
    "df_SP['SP_Vote18'] = df_SP['ElectionDescription'].apply(lambda x: 1 if (x=='11/06/2018 - STATE GENERAL')  else 0)\n",
    "\n",
    "#Aggregate the data\n",
    "Features= ['SP_Vote15','SP_Vote16','SP_Vote17','SP_Vote18']\n",
    "df_SP= df_SP[Features].groupby(['VoterId']).sum()\n",
    "\n",
    "#Combine with voter data\n",
    "df_SP=df_SP.reset_index()\n",
    "com_SP=sp_voter.set_index('VoterId').join(df_SP.set_index('VoterId'))\n",
    "\n",
    "#Correct the Precinct Name File so connect with JSON file for Mapping\n",
    "com_SP.PrecinctName=com_SP.PrecinctName.str.replace('ST. PAUL', 'Saint Paul')\n",
    "com_SP.PrecinctName=com_SP.PrecinctName.str.replace('-0', '-')\n",
    "\n",
    "\n",
    "\n",
    "#Repeat Steps with Minneapolis\n",
    "#select primary key from Voter table\n",
    "MVoter_ID= minn_voter.iloc[:,[0,1]]\n",
    "\n",
    "#Specify Primary Key and keep only the matches\n",
    "df_minn=df_election_all.set_index('VoterId').join(MVoter_ID.set_index('VoterId'))\n",
    "\n",
    "# I already selected the events I'm interested\n",
    "df_M1= df_minn.query('ElectionDescription in (\"11/07/2017 - MUNICIPAL GENERAL\")')\n",
    "\n",
    "#Create a Count Variable (participation from 2015 outward)\n",
    "df_M1['Count'] = df_M1['Year'].apply(lambda x: 0 if x==2011  else 1)\n",
    "\n",
    "#set Value for 2015 election\n",
    "df_M1['Mpls_Vote17'] = df_M1['ElectionDescription'].apply(lambda x: 1 if (x=='11/07/2017 - MUNICIPAL GENERAL')  else 0)\n",
    "\n",
    "Features= ['Mpls_Vote17','Count']\n",
    "df_M1= df_M1[Features].groupby(['VoterId']).sum()\n",
    "\n",
    "df_M1=df_M1.reset_index()\n",
    "com_Mpls=minn_voter.set_index('VoterId').join(df_M1.set_index('VoterId'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Voting Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "#com_SP['Age'] = 2019 - com_SP['DOBYear'] \n",
    "#com_Mpls['Age'] = 2019 - com_Mpls['DOBYear'] \n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, sharey=True, tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the `bins` kwarg\n",
    "#axs[0].hist(com_Ward1['Age'], bins=20)\n",
    "axs[0].set_ylabel('Votes')\n",
    "\n",
    "axs[0].hist(com_SP.query('SP_Vote18>0 and Age<101').Age, bins=10)\n",
    "axs[1].hist(com_SP.query('SP_Vote17>0 and Age<101').Age, bins=10)\n",
    "axs[2].hist(com_SP.query('SP_Vote15>0 and Age<101').Age, bins=10)\n",
    "axs[3].hist(com_Mpls.query('Mpls_Vote17>0 and Age<101').Age, bins=10)\n",
    "\n",
    "\n",
    "axs[0].set_title('SP_2018Sen')\n",
    "axs[1].set_title('SP_2017Mayor')\n",
    "axs[2].set_title('SP_2015City')\n",
    "axs[3].set_title('Mpls_2017City')\n",
    "\n",
    "\n",
    "figure = fig.get_figure()    \n",
    "fig.savefig('ElectionVoteCount.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import savefig\n",
    "\n",
    "#Correcting for Age\n",
    "Features= ['Age','Mpls_Vote17']\n",
    "Features1= ['Age','SP_Vote15']\n",
    "\n",
    "m=com_Mpls[Features]\n",
    "m['Age']= m['Age'] -2\n",
    "s=com_SP[Features1]\n",
    "s['Age']= s['Age'] -4\n",
    "\n",
    "#Graphing\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "\n",
    "bg= sns.distplot(m.query('Mpls_Vote17>0 and Age<101').Age, bins=10, ax=axs[0], axlabel=False)\n",
    "sns.distplot(s.query('SP_Vote15>0 and Age<101').Age, bins=10, ax=axs[1], axlabel=False)\n",
    "#axs[3].hist(com_Mpls.query('Mpls_Vote17>0 and Age<101').Age, bins=20)\n",
    "\n",
    "#bg= sns.distplot(com_Mpls.query('Mpls_Vote17>0').Age, bins=10, ax=ax1, axlabel=False)#, kde=False)\n",
    "\n",
    "axs[0].set_title('Mpls 2017 Council Voter_Age Dist')\n",
    "axs[1].set_title('SP 2015 Council Voter_Age Dist')\n",
    "\n",
    "#com_Mpls.Age.value_counts()\n",
    "\n",
    "figure = bg.get_figure()    \n",
    "figure.savefig('Twin_Cities_Voter_Age_Distribution.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create Intro Graphs\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import savefig\n",
    "\n",
    "#Correcting for Age\n",
    "Features1= ['Age','SP_Vote15']\n",
    "s=com_SP[Features1]\n",
    "s['Age']= s['Age'] -4\n",
    "\n",
    "\n",
    "#Graphing\n",
    "\n",
    "ax=sns.kdeplot(sp_voter.query('Age<101').Age, label='Registered Voters')\n",
    "sns.distplot(s.query('SP_Vote15>0 and Age<101').Age, bins=10, label='Actual Voters', hist=False)\n",
    "\n",
    "ax.set_title('2015 City Council Voter Age Distribution: 06/24/19')\n",
    "ax.set(xlabel='')\n",
    "\n",
    "figure = ax.get_figure()    \n",
    "figure.savefig('CityCouncilAgeDistribution.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "com_SP['Counting']=1\n",
    "com_Mpls['Counting']=1\n",
    "\n",
    "Features= ['Age','SP_Vote15','Counting']\n",
    "SP_15= com_SP[Features].groupby(['Age']).sum()\n",
    "SP_15['2015_SP_Council'] = SP_15.SP_Vote15 / SP_15.Counting\n",
    "SP_15=SP_15.reset_index()\n",
    "SP_15['Age']= SP_15['Age'] -4 \n",
    "SP_15= SP_15.query('Age<86 and Age>17')\n",
    "f= ['Age','2015_SP_Council']\n",
    "SP_15= SP_15[f]\n",
    "\n",
    "Features= ['Age','SP_Vote16','Counting']\n",
    "SP_16= com_SP[Features].groupby(['Age']).sum()\n",
    "SP_16['2016_SP_Pres'] = SP_16.SP_Vote16 / SP_16.Counting\n",
    "SP_16=SP_16.reset_index()\n",
    "SP_16['Age']= SP_16['Age'] -3 \n",
    "SP_16= SP_16.query('Age<86 and Age>17')\n",
    "f= ['Age','2016_SP_Pres']\n",
    "SP_16= SP_16[f]\n",
    "\n",
    "Features= ['Age','SP_Vote17','Counting']\n",
    "SP_17= com_SP[Features].groupby(['Age']).sum()\n",
    "SP_17['2017_SP_Mayor'] = SP_17.SP_Vote17 / SP_17.Counting\n",
    "SP_17=SP_17.reset_index()\n",
    "SP_17['Age']= SP_17['Age'] -2 \n",
    "SP_17= SP_17.query('Age<86 and Age>17')\n",
    "f= ['Age','2017_SP_Mayor']\n",
    "SP_17= SP_17[f]\n",
    "\n",
    "Features= ['Age','SP_Vote18','Counting']\n",
    "SP_18= com_SP[Features].groupby(['Age']).sum()\n",
    "SP_18['2018_SP_Senate'] = SP_18.SP_Vote18 / SP_18.Counting\n",
    "SP_18=SP_18.reset_index()\n",
    "SP_18['Age']= SP_18['Age'] -1\n",
    "SP_18= SP_18.query('Age<86 and Age>17')\n",
    "f= ['Age','2018_SP_Senate']\n",
    "SP_18= SP_18[f]\n",
    "\n",
    "#Minneapolis\n",
    "Features= ['Age','Mpls_Vote17','Counting']\n",
    "Mpls_17= com_Mpls[Features].groupby(['Age']).sum()\n",
    "Mpls_17['2017_Mpls_Council'] = Mpls_17.Mpls_Vote17 / Mpls_17.Counting\n",
    "Mpls_17=Mpls_17.reset_index()\n",
    "Mpls_17['Age']= Mpls_17['Age'] -2 \n",
    "Mpls_17= Mpls_17.query('Age<86 and Age>17')\n",
    "f= ['Age','2017_Mpls_Council']\n",
    "Mpls_17= Mpls_17[f]\n",
    "\n",
    "# Create Single Table\n",
    "a=SP_15.set_index('Age').join(SP_16.set_index('Age'))\n",
    "a=a.reset_index()\n",
    "b=a.set_index('Age').join(SP_17.set_index('Age'))\n",
    "b=b.reset_index()\n",
    "c=b.set_index('Age').join(Mpls_17.set_index('Age'))\n",
    "c=c.reset_index()\n",
    "d=c.set_index('Age').join(SP_18.set_index('Age'))\n",
    "\n",
    "\n",
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Voter Turnout Percentage by Age; 06/24/19\")\n",
    "\n",
    "col= ['2015_SP_Council', '2016_SP_Pres', '2017_Mpls_Council',  '2017_SP_Mayor','2018_SP_Senate']\n",
    "\n",
    "bg=sns.lineplot(data=d[col])\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"\")\n",
    "\n",
    "figure = bg.get_figure() \n",
    "figure.savefig('VoterTurnoutPercentage.png', dpi=400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ward/Young Voter Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Votes</th>\n",
       "      <th>Total_Registered</th>\n",
       "      <th>Total_Voting%</th>\n",
       "      <th>Youth_Votes</th>\n",
       "      <th>Youth_Registered</th>\n",
       "      <th>Youth_Voting%</th>\n",
       "      <th>YouthVote%_by_Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WardCode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2637.0</td>\n",
       "      <td>20737</td>\n",
       "      <td>12.72</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1231</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4884.0</td>\n",
       "      <td>26001</td>\n",
       "      <td>18.78</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1232</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4939.0</td>\n",
       "      <td>27612</td>\n",
       "      <td>17.89</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1547</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4905.0</td>\n",
       "      <td>24715</td>\n",
       "      <td>19.85</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2160</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>3396.0</td>\n",
       "      <td>19074</td>\n",
       "      <td>17.80</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>1931.0</td>\n",
       "      <td>18509</td>\n",
       "      <td>10.43</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>2.27</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>19693</td>\n",
       "      <td>9.04</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>24472.0</td>\n",
       "      <td>156341</td>\n",
       "      <td>15.65</td>\n",
       "      <td>248.0</td>\n",
       "      <td>9520</td>\n",
       "      <td>2.61</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total_Votes  Total_Registered  Total_Voting%  Youth_Votes  \\\n",
       "WardCode                                                              \n",
       "1.0            2637.0             20737          12.72         19.0   \n",
       "2.0            4884.0             26001          18.78         37.0   \n",
       "3.0            4939.0             27612          17.89         67.0   \n",
       "4.0            4905.0             24715          19.85         47.0   \n",
       "5.0            3396.0             19074          17.80         34.0   \n",
       "6.0            1931.0             18509          10.43         25.0   \n",
       "7.0            1780.0             19693           9.04         19.0   \n",
       "All           24472.0            156341          15.65        248.0   \n",
       "\n",
       "          Youth_Registered  Youth_Voting%  YouthVote%_by_Total  \n",
       "WardCode                                                        \n",
       "1.0                   1231           1.54                 0.72  \n",
       "2.0                   1232           3.00                 0.76  \n",
       "3.0                   1547           4.33                 1.36  \n",
       "4.0                   2160           2.18                 0.96  \n",
       "5.0                   1100           3.09                 1.00  \n",
       "6.0                   1100           2.27                 1.29  \n",
       "7.0                   1150           1.65                 1.07  \n",
       "All                   9520           2.61                 1.01  "
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2015 Saint Paul City Council Numbers Setup\n",
    "\n",
    "com_SP['Counting']=1\n",
    "\n",
    "#Setup For all Age\n",
    "Features= ['Age','WardCode','SP_Vote15','Counting']\n",
    "E_15= com_SP[Features].groupby(['WardCode','Age']).sum()\n",
    "E_15=E_15.reset_index()\n",
    "E_15['Age']= E_15['Age'] -4 \n",
    "E_15= E_15.query('Age>17')\n",
    "E_15_All= E_15.groupby(['WardCode']).sum()\n",
    "E_15_All['Vote%'] = round(E_15_All.SP_Vote15 / E_15_All.Counting, 4) *100\n",
    "E_15_All.drop(['Age'], axis=1, inplace=True)\n",
    "E_15_All.columns= ['Total_Votes','Total_Registered','Total_Voting%']\n",
    "E_15_All=E_15_All.reset_index()\n",
    "\n",
    "\n",
    "#Setup for youth [18-20]\n",
    "E_15_Young= E_15.query('Age<21')\n",
    "E_15_Young= E_15_Young.groupby(['WardCode']).sum()\n",
    "E_15_Young['YouthVote%'] = round(E_15_Young.SP_Vote15 / E_15_Young.Counting, 4) *100\n",
    "E_15_Young.drop(['Age'], axis=1, inplace=True)\n",
    "E_15_Young.columns= ['Youth_Votes','Youth_Registered','Youth_Voting%']\n",
    "E_15_Young=E_15_Young.reset_index()\n",
    "E_15_Young['YouthVote%_by_Total']= round(E_15_Young.Youth_Votes/ E_15_All.Total_Votes, 4) *100 \n",
    "\n",
    "E_15_All=E_15_All.set_index('WardCode').join(E_15_Young.set_index('WardCode'))\n",
    "\n",
    "#Total Column\n",
    "\n",
    "Features= ['Age','SP_Vote15','Counting']\n",
    "El= com_SP[Features].groupby(['Age']).sum()\n",
    "El=El.reset_index()\n",
    "El['Age']= El['Age'] -4 \n",
    "El= El.query('Age>17')\n",
    "El['WardCode']= 'All'\n",
    "Ela= El.groupby(['WardCode']).sum()\n",
    "Ela['Vote%'] = round(Ela.SP_Vote15 / Ela.Counting, 4) *100\n",
    "Ela.drop(['Age'], axis=1, inplace=True)\n",
    "Ela.columns= ['Total_Votes','Total_Registered','Total_Voting%']\n",
    "Ela=Ela.reset_index()\n",
    "Ela\n",
    "\n",
    "Ely= El.query('Age<21')\n",
    "Ely= Ely.groupby(['WardCode']).sum()\n",
    "Ely['Vote%'] = round(Ely.SP_Vote15 / Ely.Counting, 4) *100\n",
    "Ely.drop(['Age'], axis=1, inplace=True)\n",
    "Ely.columns= ['Youth_Votes','Youth_Registered','Youth_Voting%']\n",
    "Ely=Ely.reset_index()\n",
    "Ely['YouthVote%_by_Total']= round(Ely.Youth_Votes/ Ela.Total_Votes, 4) *100 \n",
    "Ely\n",
    "\n",
    "b=Ela.set_index('WardCode').join(Ely.set_index('WardCode'))\n",
    "\n",
    "#Final Table\n",
    "Fin=E_15_All.append(b)\n",
    "E_15_All= E_15_All.reset_index()\n",
    "\n",
    "Fin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fin= Fin.reset_index()\n",
    "\n",
    "#save table\n",
    "Fin.to_csv(r'Data/SaintPaul_2015_Ward_YouthVoting_06-24-19.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Votes</th>\n",
       "      <th>Total_Registered</th>\n",
       "      <th>Total_Voting%</th>\n",
       "      <th>Youth_Votes</th>\n",
       "      <th>Youth_Registered</th>\n",
       "      <th>Youth_Voting%</th>\n",
       "      <th>YouthVote%_by_Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WardCode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>16324.0</td>\n",
       "      <td>21118</td>\n",
       "      <td>77.30</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1158</td>\n",
       "      <td>59.33</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>21190.0</td>\n",
       "      <td>26279</td>\n",
       "      <td>80.63</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1024</td>\n",
       "      <td>59.96</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>24678.0</td>\n",
       "      <td>28144</td>\n",
       "      <td>87.68</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>1558</td>\n",
       "      <td>64.25</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>21323.0</td>\n",
       "      <td>25400</td>\n",
       "      <td>83.95</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>2153</td>\n",
       "      <td>71.02</td>\n",
       "      <td>7.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>14971.0</td>\n",
       "      <td>19418</td>\n",
       "      <td>77.10</td>\n",
       "      <td>645.0</td>\n",
       "      <td>1084</td>\n",
       "      <td>59.50</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>14415.0</td>\n",
       "      <td>18856</td>\n",
       "      <td>76.45</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1069</td>\n",
       "      <td>58.56</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>15606.0</td>\n",
       "      <td>20041</td>\n",
       "      <td>77.87</td>\n",
       "      <td>637.0</td>\n",
       "      <td>1105</td>\n",
       "      <td>57.65</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>128507.0</td>\n",
       "      <td>159256</td>\n",
       "      <td>80.69</td>\n",
       "      <td>5739.0</td>\n",
       "      <td>9151</td>\n",
       "      <td>62.71</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total_Votes  Total_Registered  Total_Voting%  Youth_Votes  \\\n",
       "WardCode                                                              \n",
       "1.0           16324.0             21118          77.30        687.0   \n",
       "2.0           21190.0             26279          80.63        614.0   \n",
       "3.0           24678.0             28144          87.68       1001.0   \n",
       "4.0           21323.0             25400          83.95       1529.0   \n",
       "5.0           14971.0             19418          77.10        645.0   \n",
       "6.0           14415.0             18856          76.45        626.0   \n",
       "7.0           15606.0             20041          77.87        637.0   \n",
       "All          128507.0            159256          80.69       5739.0   \n",
       "\n",
       "          Youth_Registered  Youth_Voting%  YouthVote%_by_Total  \n",
       "WardCode                                                        \n",
       "1.0                   1158          59.33                 4.21  \n",
       "2.0                   1024          59.96                 2.90  \n",
       "3.0                   1558          64.25                 4.06  \n",
       "4.0                   2153          71.02                 7.17  \n",
       "5.0                   1084          59.50                 4.31  \n",
       "6.0                   1069          58.56                 4.34  \n",
       "7.0                   1105          57.65                 4.08  \n",
       "All                   9151          62.71                 4.47  "
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2016 Saint Paul Presidential Numbers Setup\n",
    "\n",
    "com_SP['Counting']=1\n",
    "\n",
    "#Setup For all Age\n",
    "Features= ['Age','WardCode','SP_Vote16','Counting']\n",
    "E_16= com_SP[Features].groupby(['WardCode','Age']).sum()\n",
    "E_16=E_16.reset_index()\n",
    "E_16['Age']= E_16['Age'] -3\n",
    "E_16= E_16.query('Age>17')\n",
    "E_16_All= E_16.groupby(['WardCode']).sum()\n",
    "E_16_All['Vote%'] = round(E_16_All.SP_Vote16 / E_16_All.Counting, 4) *100\n",
    "E_16_All.drop(['Age'], axis=1, inplace=True)\n",
    "E_16_All.columns= ['Total_Votes','Total_Registered','Total_Voting%']\n",
    "E_16_All=E_16_All.reset_index()\n",
    "\n",
    "\n",
    "#Setup for youth [18-20]\n",
    "E_16_Young= E_16.query('Age<21')\n",
    "E_16_Young= E_16_Young.groupby(['WardCode']).sum()\n",
    "E_16_Young['YouthVote%'] = round(E_16_Young.SP_Vote16 / E_16_Young.Counting, 4) *100\n",
    "E_16_Young.drop(['Age'], axis=1, inplace=True)\n",
    "E_16_Young.columns= ['Youth_Votes','Youth_Registered','Youth_Voting%']\n",
    "E_16_Young=E_16_Young.reset_index()\n",
    "E_16_Young['YouthVote%_by_Total']= round(E_16_Young.Youth_Votes/ E_16_All.Total_Votes, 4) *100 \n",
    "\n",
    "\n",
    "E_16_All=E_16_All.set_index('WardCode').join(E_16_Young.set_index('WardCode'))\n",
    "\n",
    "#Total Column\n",
    "\n",
    "Features= ['Age','SP_Vote16','Counting']\n",
    "El= com_SP[Features].groupby(['Age']).sum()\n",
    "El=El.reset_index()\n",
    "El['Age']= El['Age'] -3 \n",
    "El= El.query('Age>17')\n",
    "El['WardCode']= 'All'\n",
    "Ela= El.groupby(['WardCode']).sum()\n",
    "Ela['Vote%'] = round(Ela.SP_Vote16 / Ela.Counting, 4) *100\n",
    "Ela.drop(['Age'], axis=1, inplace=True)\n",
    "Ela.columns= ['Total_Votes','Total_Registered','Total_Voting%']\n",
    "Ela=Ela.reset_index()\n",
    "Ela\n",
    "\n",
    "Ely= El.query('Age<21')\n",
    "Ely= Ely.groupby(['WardCode']).sum()\n",
    "Ely['Vote%'] = round(Ely.SP_Vote16 / Ely.Counting, 4) *100\n",
    "Ely.drop(['Age'], axis=1, inplace=True)\n",
    "Ely.columns= ['Youth_Votes','Youth_Registered','Youth_Voting%']\n",
    "Ely=Ely.reset_index()\n",
    "Ely['YouthVote%_by_Total']= round(Ely.Youth_Votes/ Ela.Total_Votes, 4) *100 \n",
    "Ely\n",
    "\n",
    "b=Ela.set_index('WardCode').join(Ely.set_index('WardCode'))\n",
    "\n",
    "#Final Table\n",
    "Tin=E_16_All.append(b)\n",
    "\n",
    "Tin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tin= Tin.reset_index()\n",
    "\n",
    "Tin.to_csv(r'Data/SaintPaul_2016_Ward_YouthVoting_06-24-19.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precinct Fragmentation\n",
    "\n",
    "This code can be used to determine fragmentation in other legislative districts with a few tweeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Ward Aggregate Info\n",
    "Features= ['WardCode','Total_Votes','Total_Registered']\n",
    "PW_15=E_15_All[Features]\n",
    "PW_15.columns= ['WardCode','Total_WardVotes','Total_WardRegistered']\n",
    "\n",
    "\n",
    "#By Precinct\n",
    "\n",
    "com_SP['Counting']=1\n",
    "\n",
    "#Setup For all Age\n",
    "Features= ['Age','WardCode','PrecinctName','SP_Vote15','Counting']\n",
    "P_15= com_SP[Features].query('Age>21').groupby(['WardCode','PrecinctName']).sum()\n",
    "P_15=P_15.reset_index()\n",
    "P_15['Voting%'] = round(P_15.SP_Vote15 / P_15.Counting, 4) *100\n",
    "\n",
    "#Join the Ward Table\n",
    "P_15=P_15.set_index('WardCode').join(PW_15.set_index('WardCode'))\n",
    "\n",
    "#Determine Weight Variables/other Cleaning\n",
    "P_15['Projected_Weight']= round(P_15.Counting / P_15.Total_WardRegistered, 4) *100\n",
    "P_15['Actual_Weight']= round(P_15.SP_Vote15 / P_15.Total_WardVotes, 4) *100\n",
    "P_15['Representation']= P_15.Actual_Weight - P_15.Projected_Weight\n",
    "#P_15['StateMcdCode']= 140\n",
    "P_15.drop(['Age'], axis=1, inplace=True)\n",
    "P_15=P_15.reset_index()\n",
    "\n",
    "#Rename columns\n",
    "P_15.columns= ['WardCode','PrecinctName', 'Precinct_Vote', 'Precinct_Register', 'Voting%','Total_WardVotes', 'Total_WardRegistered', 'Projected_Weight','Actual_Weight', 'Representation']\n",
    "\n",
    "#Save file as it can be used as datasource and people can have access\n",
    "P_15.to_csv(r'Data/SaintPaul_PrecinctAnalysis_06-24-19.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up Mapping\n",
    "\n",
    "import folium\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import shapely as sh\n",
    "import numpy as np\n",
    "\n",
    "#Note the shape file was editted to contain only saint paul precincts\n",
    "\n",
    "\n",
    "def map_2015_SP_CityCouncilRepresentation_byPrecinct(Ward=0):\n",
    "    #setup\n",
    "    sp_geo = r'Shapefiles/SaintPaul_VotingPrecincts.json'\n",
    "    \n",
    "    if Ward==0:\n",
    "        B=P_15 \n",
    "        A='City Council'\n",
    "        zo= 12\n",
    "    else: \n",
    "        B= P_15[(P_15['WardCode'] == Ward)]\n",
    "        A= 'Ward ' + str(Ward) +' '\n",
    "        zo=13\n",
    "    \n",
    "    #Set up Variable Zoom Locations\n",
    "    data = [[0, [44.953930, -93.096058]], [1, [44.958326, -93.122926]], [2, [44.933159, -93.115535]], [3, [44.918270, -93.176131]], [4, [44.961494, -93.176991]], [5, [44.978117, -93.106602]], [6, [44.978945, -93.047281]], [7, [44.942040, -93.033178]]] \n",
    "  \n",
    "    # Create the pandas DataFrame \n",
    "    Loc = pd.DataFrame(data, columns = ['Ward', 'Location']) \n",
    "    Loc.iloc[Ward,1:]\n",
    "    \n",
    "    \n",
    "    # generate a new map\n",
    "    SP_map = folium.Map(location=Loc.iloc[Ward,1], zoom_start=zo,tiles=\"OpenStreetMap\")\n",
    "\n",
    "    SP_map.choropleth(\n",
    "        geo_data=sp_geo,\n",
    "        data=B,\n",
    "        columns=['PrecinctName','Representation'],\n",
    "        key_on=\"feature.properties.Precinct\",\n",
    "        fill_color='YlOrRd', \n",
    "        fill_opacity=0.7, \n",
    "        line_opacity=0.2,\n",
    "        legend_name='2015 Saint Paul ' + A +' Precinct Representation: 06/24/19',\n",
    "        highlight= True\n",
    "    )\n",
    "    folium.LayerControl().add_to(SP_map)\n",
    "                             \n",
    "    # display map\n",
    "    return SP_map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WardCode</th>\n",
       "      <th>PrecinctName</th>\n",
       "      <th>Precinct_Vote</th>\n",
       "      <th>Precinct_Register</th>\n",
       "      <th>Voting%</th>\n",
       "      <th>Projected_Weight</th>\n",
       "      <th>Actual_Weight</th>\n",
       "      <th>Representation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-1</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1126</td>\n",
       "      <td>24.78</td>\n",
       "      <td>5.43</td>\n",
       "      <td>10.58</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-10</td>\n",
       "      <td>35.0</td>\n",
       "      <td>442</td>\n",
       "      <td>7.92</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-11</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1067</td>\n",
       "      <td>11.53</td>\n",
       "      <td>5.15</td>\n",
       "      <td>4.66</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-12</td>\n",
       "      <td>66.0</td>\n",
       "      <td>328</td>\n",
       "      <td>20.12</td>\n",
       "      <td>1.58</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-13</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1135</td>\n",
       "      <td>15.42</td>\n",
       "      <td>5.47</td>\n",
       "      <td>6.64</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-14</td>\n",
       "      <td>126.0</td>\n",
       "      <td>925</td>\n",
       "      <td>13.62</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4.78</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-15</td>\n",
       "      <td>37.0</td>\n",
       "      <td>700</td>\n",
       "      <td>5.29</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-16</td>\n",
       "      <td>31.0</td>\n",
       "      <td>235</td>\n",
       "      <td>13.19</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-2</td>\n",
       "      <td>268.0</td>\n",
       "      <td>2046</td>\n",
       "      <td>13.10</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.16</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-3</td>\n",
       "      <td>174.0</td>\n",
       "      <td>1792</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.64</td>\n",
       "      <td>6.60</td>\n",
       "      <td>-2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-4</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1296</td>\n",
       "      <td>10.57</td>\n",
       "      <td>6.25</td>\n",
       "      <td>5.20</td>\n",
       "      <td>-1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-5</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1275</td>\n",
       "      <td>11.29</td>\n",
       "      <td>6.15</td>\n",
       "      <td>5.46</td>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-6</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2645</td>\n",
       "      <td>12.51</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.55</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-7</td>\n",
       "      <td>264.0</td>\n",
       "      <td>1444</td>\n",
       "      <td>18.28</td>\n",
       "      <td>6.96</td>\n",
       "      <td>10.01</td>\n",
       "      <td>3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-8</td>\n",
       "      <td>269.0</td>\n",
       "      <td>2104</td>\n",
       "      <td>12.79</td>\n",
       "      <td>10.15</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saint Paul W-1 P-9</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2177</td>\n",
       "      <td>8.18</td>\n",
       "      <td>10.50</td>\n",
       "      <td>6.75</td>\n",
       "      <td>-3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-1</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1571</td>\n",
       "      <td>27.63</td>\n",
       "      <td>6.04</td>\n",
       "      <td>8.89</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-11</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2841</td>\n",
       "      <td>12.35</td>\n",
       "      <td>10.93</td>\n",
       "      <td>7.19</td>\n",
       "      <td>-3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-12</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1683</td>\n",
       "      <td>25.79</td>\n",
       "      <td>6.47</td>\n",
       "      <td>8.89</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-13</td>\n",
       "      <td>347.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>23.13</td>\n",
       "      <td>5.77</td>\n",
       "      <td>7.10</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-14</td>\n",
       "      <td>304.0</td>\n",
       "      <td>1669</td>\n",
       "      <td>18.21</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.22</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-15</td>\n",
       "      <td>290.0</td>\n",
       "      <td>2574</td>\n",
       "      <td>11.27</td>\n",
       "      <td>9.90</td>\n",
       "      <td>5.94</td>\n",
       "      <td>-3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-2</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1653</td>\n",
       "      <td>27.22</td>\n",
       "      <td>6.36</td>\n",
       "      <td>9.21</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-3</td>\n",
       "      <td>446.0</td>\n",
       "      <td>2571</td>\n",
       "      <td>17.35</td>\n",
       "      <td>9.89</td>\n",
       "      <td>9.13</td>\n",
       "      <td>-0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-4</td>\n",
       "      <td>298.0</td>\n",
       "      <td>1519</td>\n",
       "      <td>19.62</td>\n",
       "      <td>5.84</td>\n",
       "      <td>6.10</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-5</td>\n",
       "      <td>524.0</td>\n",
       "      <td>2323</td>\n",
       "      <td>22.56</td>\n",
       "      <td>8.93</td>\n",
       "      <td>10.73</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-6</td>\n",
       "      <td>104.0</td>\n",
       "      <td>603</td>\n",
       "      <td>17.25</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.13</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2194</td>\n",
       "      <td>17.09</td>\n",
       "      <td>8.44</td>\n",
       "      <td>7.68</td>\n",
       "      <td>-0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Saint Paul W-2 P-8</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1211</td>\n",
       "      <td>12.96</td>\n",
       "      <td>4.66</td>\n",
       "      <td>3.21</td>\n",
       "      <td>-1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Saint Paul W-5 P-5</td>\n",
       "      <td>181.0</td>\n",
       "      <td>1775</td>\n",
       "      <td>10.20</td>\n",
       "      <td>9.31</td>\n",
       "      <td>5.33</td>\n",
       "      <td>-3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Saint Paul W-5 P-6</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1783</td>\n",
       "      <td>10.94</td>\n",
       "      <td>9.35</td>\n",
       "      <td>5.74</td>\n",
       "      <td>-3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Saint Paul W-5 P-7</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1119</td>\n",
       "      <td>10.01</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.30</td>\n",
       "      <td>-2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Saint Paul W-5 P-8</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2297</td>\n",
       "      <td>9.53</td>\n",
       "      <td>12.04</td>\n",
       "      <td>6.45</td>\n",
       "      <td>-5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Saint Paul W-5 P-9</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1652</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.66</td>\n",
       "      <td>4.36</td>\n",
       "      <td>-4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-1</td>\n",
       "      <td>165.0</td>\n",
       "      <td>1370</td>\n",
       "      <td>12.04</td>\n",
       "      <td>7.40</td>\n",
       "      <td>8.54</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-10</td>\n",
       "      <td>115.0</td>\n",
       "      <td>977</td>\n",
       "      <td>11.77</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.96</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-11</td>\n",
       "      <td>192.0</td>\n",
       "      <td>1627</td>\n",
       "      <td>11.80</td>\n",
       "      <td>8.79</td>\n",
       "      <td>9.94</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-12</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1123</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.33</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-2</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1236</td>\n",
       "      <td>19.74</td>\n",
       "      <td>6.68</td>\n",
       "      <td>12.64</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1493</td>\n",
       "      <td>13.06</td>\n",
       "      <td>8.07</td>\n",
       "      <td>10.10</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-4</td>\n",
       "      <td>169.0</td>\n",
       "      <td>1867</td>\n",
       "      <td>9.05</td>\n",
       "      <td>10.09</td>\n",
       "      <td>8.75</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-5</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2052</td>\n",
       "      <td>9.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.36</td>\n",
       "      <td>-0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-6</td>\n",
       "      <td>186.0</td>\n",
       "      <td>2391</td>\n",
       "      <td>7.78</td>\n",
       "      <td>12.92</td>\n",
       "      <td>9.63</td>\n",
       "      <td>-3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-7</td>\n",
       "      <td>70.0</td>\n",
       "      <td>623</td>\n",
       "      <td>11.24</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-8</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1511</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.16</td>\n",
       "      <td>6.68</td>\n",
       "      <td>-1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Saint Paul W-6 P-9</td>\n",
       "      <td>163.0</td>\n",
       "      <td>2239</td>\n",
       "      <td>7.28</td>\n",
       "      <td>12.10</td>\n",
       "      <td>8.44</td>\n",
       "      <td>-3.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-1</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2050</td>\n",
       "      <td>9.32</td>\n",
       "      <td>10.41</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-10</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1376</td>\n",
       "      <td>7.70</td>\n",
       "      <td>6.99</td>\n",
       "      <td>5.96</td>\n",
       "      <td>-1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-11</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1205</td>\n",
       "      <td>5.23</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.54</td>\n",
       "      <td>-2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-12</td>\n",
       "      <td>266.0</td>\n",
       "      <td>2565</td>\n",
       "      <td>10.37</td>\n",
       "      <td>13.02</td>\n",
       "      <td>14.94</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-13</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1862</td>\n",
       "      <td>13.10</td>\n",
       "      <td>9.46</td>\n",
       "      <td>13.71</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1247</td>\n",
       "      <td>5.69</td>\n",
       "      <td>6.33</td>\n",
       "      <td>3.99</td>\n",
       "      <td>-2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1420</td>\n",
       "      <td>6.76</td>\n",
       "      <td>7.21</td>\n",
       "      <td>5.39</td>\n",
       "      <td>-1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>894</td>\n",
       "      <td>8.39</td>\n",
       "      <td>4.54</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1348</td>\n",
       "      <td>18.77</td>\n",
       "      <td>6.85</td>\n",
       "      <td>14.21</td>\n",
       "      <td>7.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1356</td>\n",
       "      <td>6.05</td>\n",
       "      <td>6.89</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-7</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1190</td>\n",
       "      <td>7.82</td>\n",
       "      <td>6.04</td>\n",
       "      <td>5.22</td>\n",
       "      <td>-0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-8</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1672</td>\n",
       "      <td>8.19</td>\n",
       "      <td>8.49</td>\n",
       "      <td>7.70</td>\n",
       "      <td>-0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Saint Paul W-7 P-9</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1508</td>\n",
       "      <td>6.83</td>\n",
       "      <td>7.66</td>\n",
       "      <td>5.79</td>\n",
       "      <td>-1.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    WardCode         PrecinctName  Precinct_Vote  Precinct_Register  Voting%  \\\n",
       "0        1.0   Saint Paul W-1 P-1          279.0               1126    24.78   \n",
       "1        1.0  Saint Paul W-1 P-10           35.0                442     7.92   \n",
       "2        1.0  Saint Paul W-1 P-11          123.0               1067    11.53   \n",
       "3        1.0  Saint Paul W-1 P-12           66.0                328    20.12   \n",
       "4        1.0  Saint Paul W-1 P-13          175.0               1135    15.42   \n",
       "5        1.0  Saint Paul W-1 P-14          126.0                925    13.62   \n",
       "6        1.0  Saint Paul W-1 P-15           37.0                700     5.29   \n",
       "7        1.0  Saint Paul W-1 P-16           31.0                235    13.19   \n",
       "8        1.0   Saint Paul W-1 P-2          268.0               2046    13.10   \n",
       "9        1.0   Saint Paul W-1 P-3          174.0               1792     9.71   \n",
       "10       1.0   Saint Paul W-1 P-4          137.0               1296    10.57   \n",
       "11       1.0   Saint Paul W-1 P-5          144.0               1275    11.29   \n",
       "12       1.0   Saint Paul W-1 P-6          331.0               2645    12.51   \n",
       "13       1.0   Saint Paul W-1 P-7          264.0               1444    18.28   \n",
       "14       1.0   Saint Paul W-1 P-8          269.0               2104    12.79   \n",
       "15       1.0   Saint Paul W-1 P-9          178.0               2177     8.18   \n",
       "16       2.0   Saint Paul W-2 P-1          434.0               1571    27.63   \n",
       "17       2.0  Saint Paul W-2 P-10            3.0                 91     3.30   \n",
       "18       2.0  Saint Paul W-2 P-11          351.0               2841    12.35   \n",
       "19       2.0  Saint Paul W-2 P-12          434.0               1683    25.79   \n",
       "20       2.0  Saint Paul W-2 P-13          347.0               1500    23.13   \n",
       "21       2.0  Saint Paul W-2 P-14          304.0               1669    18.21   \n",
       "22       2.0  Saint Paul W-2 P-15          290.0               2574    11.27   \n",
       "23       2.0   Saint Paul W-2 P-2          450.0               1653    27.22   \n",
       "24       2.0   Saint Paul W-2 P-3          446.0               2571    17.35   \n",
       "25       2.0   Saint Paul W-2 P-4          298.0               1519    19.62   \n",
       "26       2.0   Saint Paul W-2 P-5          524.0               2323    22.56   \n",
       "27       2.0   Saint Paul W-2 P-6          104.0                603    17.25   \n",
       "28       2.0   Saint Paul W-2 P-7          375.0               2194    17.09   \n",
       "29       2.0   Saint Paul W-2 P-8          157.0               1211    12.96   \n",
       "..       ...                  ...            ...                ...      ...   \n",
       "65       5.0   Saint Paul W-5 P-5          181.0               1775    10.20   \n",
       "66       5.0   Saint Paul W-5 P-6          195.0               1783    10.94   \n",
       "67       5.0   Saint Paul W-5 P-7          112.0               1119    10.01   \n",
       "68       5.0   Saint Paul W-5 P-8          219.0               2297     9.53   \n",
       "69       5.0   Saint Paul W-5 P-9          148.0               1652     8.96   \n",
       "70       6.0   Saint Paul W-6 P-1          165.0               1370    12.04   \n",
       "71       6.0  Saint Paul W-6 P-10          115.0                977    11.77   \n",
       "72       6.0  Saint Paul W-6 P-11          192.0               1627    11.80   \n",
       "73       6.0  Saint Paul W-6 P-12          103.0               1123     9.17   \n",
       "74       6.0   Saint Paul W-6 P-2          244.0               1236    19.74   \n",
       "75       6.0   Saint Paul W-6 P-3          195.0               1493    13.06   \n",
       "76       6.0   Saint Paul W-6 P-4          169.0               1867     9.05   \n",
       "77       6.0   Saint Paul W-6 P-5          200.0               2052     9.75   \n",
       "78       6.0   Saint Paul W-6 P-6          186.0               2391     7.78   \n",
       "79       6.0   Saint Paul W-6 P-7           70.0                623    11.24   \n",
       "80       6.0   Saint Paul W-6 P-8          129.0               1511     8.54   \n",
       "81       6.0   Saint Paul W-6 P-9          163.0               2239     7.28   \n",
       "82       7.0   Saint Paul W-7 P-1          191.0               2050     9.32   \n",
       "83       7.0  Saint Paul W-7 P-10          106.0               1376     7.70   \n",
       "84       7.0  Saint Paul W-7 P-11           63.0               1205     5.23   \n",
       "85       7.0  Saint Paul W-7 P-12          266.0               2565    10.37   \n",
       "86       7.0  Saint Paul W-7 P-13          244.0               1862    13.10   \n",
       "87       7.0   Saint Paul W-7 P-2           71.0               1247     5.69   \n",
       "88       7.0   Saint Paul W-7 P-3           96.0               1420     6.76   \n",
       "89       7.0   Saint Paul W-7 P-4           75.0                894     8.39   \n",
       "90       7.0   Saint Paul W-7 P-5          253.0               1348    18.77   \n",
       "91       7.0   Saint Paul W-7 P-6           82.0               1356     6.05   \n",
       "92       7.0   Saint Paul W-7 P-7           93.0               1190     7.82   \n",
       "93       7.0   Saint Paul W-7 P-8          137.0               1672     8.19   \n",
       "94       7.0   Saint Paul W-7 P-9          103.0               1508     6.83   \n",
       "\n",
       "    Projected_Weight  Actual_Weight  Representation  \n",
       "0               5.43          10.58            5.15  \n",
       "1               2.13           1.33           -0.80  \n",
       "2               5.15           4.66           -0.49  \n",
       "3               1.58           2.50            0.92  \n",
       "4               5.47           6.64            1.17  \n",
       "5               4.46           4.78            0.32  \n",
       "6               3.38           1.40           -1.98  \n",
       "7               1.13           1.18            0.05  \n",
       "8               9.87          10.16            0.29  \n",
       "9               8.64           6.60           -2.04  \n",
       "10              6.25           5.20           -1.05  \n",
       "11              6.15           5.46           -0.69  \n",
       "12             12.75          12.55           -0.20  \n",
       "13              6.96          10.01            3.05  \n",
       "14             10.15          10.20            0.05  \n",
       "15             10.50           6.75           -3.75  \n",
       "16              6.04           8.89            2.85  \n",
       "17              0.35           0.06           -0.29  \n",
       "18             10.93           7.19           -3.74  \n",
       "19              6.47           8.89            2.42  \n",
       "20              5.77           7.10            1.33  \n",
       "21              6.42           6.22           -0.20  \n",
       "22              9.90           5.94           -3.96  \n",
       "23              6.36           9.21            2.85  \n",
       "24              9.89           9.13           -0.76  \n",
       "25              5.84           6.10            0.26  \n",
       "26              8.93          10.73            1.80  \n",
       "27              2.32           2.13           -0.19  \n",
       "28              8.44           7.68           -0.76  \n",
       "29              4.66           3.21           -1.45  \n",
       "..               ...            ...             ...  \n",
       "65              9.31           5.33           -3.98  \n",
       "66              9.35           5.74           -3.61  \n",
       "67              5.87           3.30           -2.57  \n",
       "68             12.04           6.45           -5.59  \n",
       "69              8.66           4.36           -4.30  \n",
       "70              7.40           8.54            1.14  \n",
       "71              5.28           5.96            0.68  \n",
       "72              8.79           9.94            1.15  \n",
       "73              6.07           5.33           -0.74  \n",
       "74              6.68          12.64            5.96  \n",
       "75              8.07          10.10            2.03  \n",
       "76             10.09           8.75           -1.34  \n",
       "77             11.09          10.36           -0.73  \n",
       "78             12.92           9.63           -3.29  \n",
       "79              3.37           3.63            0.26  \n",
       "80              8.16           6.68           -1.48  \n",
       "81             12.10           8.44           -3.66  \n",
       "82             10.41          10.73            0.32  \n",
       "83              6.99           5.96           -1.03  \n",
       "84              6.12           3.54           -2.58  \n",
       "85             13.02          14.94            1.92  \n",
       "86              9.46          13.71            4.25  \n",
       "87              6.33           3.99           -2.34  \n",
       "88              7.21           5.39           -1.82  \n",
       "89              4.54           4.21           -0.33  \n",
       "90              6.85          14.21            7.36  \n",
       "91              6.89           4.61           -2.28  \n",
       "92              6.04           5.22           -0.82  \n",
       "93              8.49           7.70           -0.79  \n",
       "94              7.66           5.79           -1.87  \n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features= ['WardCode','PrecinctName', 'Precinct_Vote', 'Precinct_Register', 'Voting%', 'Projected_Weight','Actual_Weight', 'Representation']\n",
    "P_15[Features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'P_15' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-384918f7102c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Sample Map; Select the ward number in paranthesis to specify it; Default is all\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmap_2015_SP_CityCouncilRepresentation_byPrecinct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-2-536cb12ca1ed>\u001b[0m in \u001b[0;36mmap_2015_SP_CityCouncilRepresentation_byPrecinct\u001b[1;34m(Ward)\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mWard\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mB\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mP_15\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m         \u001b[0mA\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'City Council'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[0mzo\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'P_15' is not defined"
     ]
    }
   ],
   "source": [
    "#Sample Map; Select the ward number in paranthesis to specify it; Default is all\n",
    "\n",
    "map_2015_SP_CityCouncilRepresentation_byPrecinct()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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