EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Accuracy Type</th>\n",
       "      <th>Number</th>\n",
       "      <th>Street</th>\n",
       "      <th>City.1</th>\n",
       "      <th>State.1</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Congressional District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>41.747452</td>\n",
       "      <td>-88.238622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>IL</td>\n",
       "      <td>DuPage County</td>\n",
       "      <td>60504.0</td>\n",
       "      <td>US</td>\n",
       "      <td>TIGER/Line® dataset from the US Census Bureau</td>\n",
       "      <td>IL11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carbondale</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>37.739004</td>\n",
       "      <td>-89.209109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carbondale</td>\n",
       "      <td>IL</td>\n",
       "      <td>Jackson County</td>\n",
       "      <td>62901.0</td>\n",
       "      <td>US</td>\n",
       "      <td>TIGER/Line® dataset from the US Census Bureau</td>\n",
       "      <td>IL12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Champaign</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>40.107890</td>\n",
       "      <td>-88.244266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champaign</td>\n",
       "      <td>IL</td>\n",
       "      <td>Champaign County</td>\n",
       "      <td>61820.0</td>\n",
       "      <td>US</td>\n",
       "      <td>TIGER/Line® dataset from the US Census Bureau</td>\n",
       "      <td>IL13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City     State   Latitude  Longitude  Accuracy Score Accuracy Type  \\\n",
       "0      Aurora  Illinois  41.747452 -88.238622             1.0         place   \n",
       "1  Carbondale  Illinois  37.739004 -89.209109             1.0         place   \n",
       "2   Champaign  Illinois  40.107890 -88.244266             1.0         place   \n",
       "\n",
       "   Number  Street      City.1 State.1            County      Zip Country  \\\n",
       "0     NaN     NaN      Aurora      IL     DuPage County  60504.0      US   \n",
       "1     NaN     NaN  Carbondale      IL    Jackson County  62901.0      US   \n",
       "2     NaN     NaN   Champaign      IL  Champaign County  61820.0      US   \n",
       "\n",
       "                                          Source Congressional District  \n",
       "0  TIGER/Line® dataset from the US Census Bureau                   IL11  \n",
       "1  TIGER/Line® dataset from the US Census Bureau                   IL12  \n",
       "2  TIGER/Line® dataset from the US Census Bureau                   IL13  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('city_info_geocodio_2.csv')\n",
    "df.shape\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Accuracy Type</th>\n",
       "      <th>Number</th>\n",
       "      <th>Street</th>\n",
       "      <th>City.1</th>\n",
       "      <th>State.1</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Congressional District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Blue River</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Rangley</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Polk County</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Hamilton County Parks</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      City     State  Latitude  Longitude  Accuracy Score  \\\n",
       "63              Blue River  Colorado       0.0        0.0             0.0   \n",
       "85                 Rangley  Colorado       0.0        0.0             0.0   \n",
       "98             Polk County   Florida       0.0        0.0             0.0   \n",
       "172                 Bexley      Ohio       0.0        0.0             0.0   \n",
       "191  Hamilton County Parks      Ohio       0.0        0.0             0.0   \n",
       "\n",
       "    Accuracy Type  Number  Street City.1 State.1 County  Zip Country Source  \\\n",
       "63            NaN     NaN     NaN    NaN     NaN    NaN  NaN     NaN    NaN   \n",
       "85            NaN     NaN     NaN    NaN     NaN    NaN  NaN     NaN    NaN   \n",
       "98            NaN     NaN     NaN    NaN     NaN    NaN  NaN     NaN    NaN   \n",
       "172           NaN     NaN     NaN    NaN     NaN    NaN  NaN     NaN    NaN   \n",
       "191           NaN     NaN     NaN    NaN     NaN    NaN  NaN     NaN    NaN   \n",
       "\n",
       "    Congressional District  \n",
       "63                     NaN  \n",
       "85                     NaN  \n",
       "98                     NaN  \n",
       "172                    NaN  \n",
       "191                    NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Geocodio told me that five values weren't succesfful mapped, but for the sake of best practice, I checked anyway\n",
    "# I suspect that these either weren't named correctly or for some reason weren't in geocodio's database \n",
    "# Because there are only five missing values, I'll fix this manually, although that obviously isn't a scalable \n",
    "# solution. \n",
    "\n",
    "df.loc[df['Latitude'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geocodio tells you the likely accuracy of the lat/long it's provided with an accuracy score. Some of the scores do not have high accuracy scores, which is worth mentioning. For now, we'll see if any mapping errors actually occur. If they do, I'll go back and engineer a solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(239, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We have 239 cities, so we want to make sure there's 239 rows. \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because I'm only missing five values, I'm going to manually find their lat/lon coordinates and then add them in. I'll do this here because I want them mapped in all future data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manual updating of relevent values \n",
    "\n",
    "new_data = [(40.0874759, -108.8048292, 'CO3', 'CO'), (39.446649, -106.03757, 'CO2', 'CO'), \n",
    "            (28.022243, -81.732857, 'FL9', 'FL'), (39.9689532, -82.9376804, 'OH3', 'OH'), \n",
    "            (39.103119, -84.512016, 'OH1', 'OH')]\n",
    "\n",
    "indexes = (63, 85, 98, 172, 191)\n",
    "\n",
    "for i in range(5): \n",
    "    df.loc[indexes[i],'Latitude'] = new_data[i][0]\n",
    "    df.loc[indexes[i], 'Longitude'] = new_data[i][1]\n",
    "    df.loc[indexes[i], 'Congressional District'] = new_data[i][2]\n",
    "    df.loc[indexes[i], 'State.1'] = new_data[i][3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Accuracy Type</th>\n",
       "      <th>Number</th>\n",
       "      <th>Street</th>\n",
       "      <th>City.1</th>\n",
       "      <th>State.1</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Congressional District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [City, State, Latitude, Longitude, Accuracy Score, Accuracy Type, Number, Street, City.1, State.1, County, Zip, Country, Source, Congressional District]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see we've fixed all lat/lon problems \n",
    "\n",
    "df.loc[df['Latitude'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Accuracy Type</th>\n",
       "      <th>State.1</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Country</th>\n",
       "      <th>Congressional District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>41.747452</td>\n",
       "      <td>-88.238622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>IL</td>\n",
       "      <td>DuPage County</td>\n",
       "      <td>60504.0</td>\n",
       "      <td>US</td>\n",
       "      <td>IL11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     City     State   Latitude  Longitude  Accuracy Score Accuracy Type  \\\n",
       "0  Aurora  Illinois  41.747452 -88.238622             1.0         place   \n",
       "\n",
       "  State.1         County      Zip Country Congressional District  \n",
       "0      IL  DuPage County  60504.0      US                   IL11  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping unnecessary columns added by Geocodio for the sake of data tidiness \n",
    "\n",
    "df = df.drop(columns = ['Number', 'Street', 'City.1', 'Source'])\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better. The campaign wants to keep track of the status of the anti-panhandling statutes/ordinances in each city, and they want to be able to update those values to reflect varying degrees of success as the campaign goes on. I'm going to create a 'status' value for each city set it's default to \"active.\" Then I'm going to map \"status text\" and \"marker color\" right onto the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding in a \"status\" column\n",
    "\n",
    "df['status'] = 0\n",
    "\n",
    "\n",
    "# Creating a conditional column explaining ordinance status\n",
    "\n",
    "d_text = {0: 'Ordinance Active - With No Response', \n",
    "          1: 'Ordinance Active - With Response Indicating No Immediate Repeal',\n",
    "          2: 'Ordinance Active - With Committment To Review', \n",
    "          3: 'Ordinance Halted - With Committment to Review',\n",
    "          4: 'Ordinance Repealed'}\n",
    "\n",
    "df['statusText'] = df['status'].map(d_text)\n",
    "\n",
    "# Setting point color conditionally based upon status \n",
    "\n",
    "d_color = {0: 'rgb(255, 0, 0)',\n",
    "           1: 'rgb(255, 192, 203)',\n",
    "           2: 'rgb(255, 165, 0)', \n",
    "           3: 'rgb(255, 255, 0)', \n",
    "           4: 'rgb(127, 255, 0)'}\n",
    "\n",
    "df['color'] = df['status'].map(d_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Accuracy Type</th>\n",
       "      <th>State.1</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Country</th>\n",
       "      <th>Congressional District</th>\n",
       "      <th>status</th>\n",
       "      <th>statusText</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>41.747452</td>\n",
       "      <td>-88.238622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>IL</td>\n",
       "      <td>DuPage County</td>\n",
       "      <td>60504.0</td>\n",
       "      <td>US</td>\n",
       "      <td>IL11</td>\n",
       "      <td>0</td>\n",
       "      <td>Ordinance Active - With No Response</td>\n",
       "      <td>rgb(255, 0, 0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carbondale</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>37.739004</td>\n",
       "      <td>-89.209109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>IL</td>\n",
       "      <td>Jackson County</td>\n",
       "      <td>62901.0</td>\n",
       "      <td>US</td>\n",
       "      <td>IL12</td>\n",
       "      <td>0</td>\n",
       "      <td>Ordinance Active - With No Response</td>\n",
       "      <td>rgb(255, 0, 0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Champaign</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>40.107890</td>\n",
       "      <td>-88.244266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>place</td>\n",
       "      <td>IL</td>\n",
       "      <td>Champaign County</td>\n",
       "      <td>61820.0</td>\n",
       "      <td>US</td>\n",
       "      <td>IL13</td>\n",
       "      <td>0</td>\n",
       "      <td>Ordinance Active - With No Response</td>\n",
       "      <td>rgb(255, 0, 0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City     State   Latitude  Longitude  Accuracy Score Accuracy Type  \\\n",
       "0      Aurora  Illinois  41.747452 -88.238622             1.0         place   \n",
       "1  Carbondale  Illinois  37.739004 -89.209109             1.0         place   \n",
       "2   Champaign  Illinois  40.107890 -88.244266             1.0         place   \n",
       "\n",
       "  State.1            County      Zip Country Congressional District  status  \\\n",
       "0      IL     DuPage County  60504.0      US                   IL11       0   \n",
       "1      IL    Jackson County  62901.0      US                   IL12       0   \n",
       "2      IL  Champaign County  61820.0      US                   IL13       0   \n",
       "\n",
       "                            statusText           color  \n",
       "0  Ordinance Active - With No Response  rgb(255, 0, 0)  \n",
       "1  Ordinance Active - With No Response  rgb(255, 0, 0)  \n",
       "2  Ordinance Active - With No Response  rgb(255, 0, 0)  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(df.dtypes)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm going to persist my changes. I'll make updates periodically, but I'll use a seperate notebook for that so that I don't have to continously repeat this project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_data.csv', mode ='w+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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