David Hoebbel_MatPlotLib_Homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Your objective is to build a Bubble Plot that showcases the relationship between four key variables:\n",
    "\n",
    "##Average Fare ($) Per City\n",
    "##Total Number of Rides Per City\n",
    "##Total Number of Drivers Per City\n",
    "##City Type (Urban, Suburban, Rural)\n",
    "\n",
    "#In addition, you will be expected to produce the following three pie charts:\n",
    "\n",
    "##% of Total Fares by City Type\n",
    "##% of Total Rides by City Type\n",
    "##% of Total Drivers by City Type\n",
    "\n",
    "#As final considerations:\n",
    "\n",
    "##You must use the Pandas Library and the Jupyter Notebook.\n",
    "##You must use the Matplotlib libraries.\n",
    "##You must include a written description of three observable trends based on the data.\n",
    "##You must use proper labeling of your plots, including aspects like: Plot Titles, Axes Labels, Legend ##Labels, Wedge Percentages, and Wedge Labels.\n",
    "##Remember when making your plots to consider aesthetics!\n",
    "###You must stick to the Pyber color scheme (Gold, Light Sky Blue, and Light Coral) in producing your ###plot and pie charts.\n",
    "###When making your Bubble Plot, experiment with effects like alpha, edgecolor, and linewidths.\n",
    "###When making your Pie Chart, experiment with effects like shadow, startangle, and explosion.\n",
    "\n",
    "#You must include an exported markdown version of your Notebook called  README.md in your GitHub ##repository.\n",
    "#See Example Solution for a reference on expected format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "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 Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.plotly as py\n",
    "import plotly as pl\n",
    "import cufflinks as cf\n",
    "import plotly.tools as tls\n",
    "from plotly.graph_objs import Scatter, Layout\n",
    "from plotly.offline import plot\n",
    "import plotly.graph_objs as go\n",
    "pl.offline.init_notebook_mode(connected=True)\n",
    "pl.tools.set_credentials_file(username='davidhoebbel', api_key='obcfaofD1NR2Oa9kiS3D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read CSV\n",
    "city_data = pd.read_csv(\"Resources/city_data.csv\")\n",
    "ride_data = pd.read_csv(\"Resources/ride_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nguyenbury</td>\n",
       "      <td>8</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Douglas</td>\n",
       "      <td>12</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Dawnfurt</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rodriguezburgh</td>\n",
       "      <td>52</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city  driver_count   type\n",
       "0      Kelseyland            63  Urban\n",
       "1      Nguyenbury             8  Urban\n",
       "2    East Douglas            12  Urban\n",
       "3   West Dawnfurt            34  Urban\n",
       "4  Rodriguezburgh            52  Urban"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print Abridged CSV Data Tables\n",
    "city_data.set_index('city')\n",
    "city_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sarabury</td>\n",
       "      <td>2016-01-16 13:49:27</td>\n",
       "      <td>38.35</td>\n",
       "      <td>5403689035038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Roy</td>\n",
       "      <td>2016-01-02 18:42:34</td>\n",
       "      <td>17.49</td>\n",
       "      <td>4036272335942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wiseborough</td>\n",
       "      <td>2016-01-21 17:35:29</td>\n",
       "      <td>44.18</td>\n",
       "      <td>3645042422587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spencertown</td>\n",
       "      <td>2016-07-31 14:53:22</td>\n",
       "      <td>6.87</td>\n",
       "      <td>2242596575892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nguyenbury</td>\n",
       "      <td>2016-07-09 04:42:44</td>\n",
       "      <td>6.28</td>\n",
       "      <td>1543057793673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city                 date   fare        ride_id\n",
       "0     Sarabury  2016-01-16 13:49:27  38.35  5403689035038\n",
       "1    South Roy  2016-01-02 18:42:34  17.49  4036272335942\n",
       "2  Wiseborough  2016-01-21 17:35:29  44.18  3645042422587\n",
       "3  Spencertown  2016-07-31 14:53:22   6.87  2242596575892\n",
       "4   Nguyenbury  2016-07-09 04:42:44   6.28  1543057793673"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print abridged CSV data tables\n",
    "ride_data.set_index('city')\n",
    "ride_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>driver_count</th>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2016-08-19 04:27:52</td>\n",
       "      <td>5.51</td>\n",
       "      <td>6246006544795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2016-04-17 06:59:50</td>\n",
       "      <td>5.54</td>\n",
       "      <td>7466473222333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2016-05-04 15:06:07</td>\n",
       "      <td>30.54</td>\n",
       "      <td>2140501382736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2016-01-25 20:44:56</td>\n",
       "      <td>12.08</td>\n",
       "      <td>1896987891309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kelseyland</td>\n",
       "      <td>63</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2016-08-09 18:19:47</td>\n",
       "      <td>17.91</td>\n",
       "      <td>8784212854829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  driver_count   type                 date   fare        ride_id\n",
       "0  Kelseyland            63  Urban  2016-08-19 04:27:52   5.51  6246006544795\n",
       "1  Kelseyland            63  Urban  2016-04-17 06:59:50   5.54  7466473222333\n",
       "2  Kelseyland            63  Urban  2016-05-04 15:06:07  30.54  2140501382736\n",
       "3  Kelseyland            63  Urban  2016-01-25 20:44:56  12.08  1896987891309\n",
       "4  Kelseyland            63  Urban  2016-08-09 18:19:47  17.91  8784212854829"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge data tables on city\n",
    "#pd.join(other, lsuffix='city', rsuffix='city')\n",
    "#Need to get into jupyter from earlier folder\n",
    "uber_merge = pd.merge(city_data, ride_data, on=\"city\")\n",
    "uber_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alvarezhaven</td>\n",
       "      <td>23.928710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alyssaberg</td>\n",
       "      <td>20.609615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anitamouth</td>\n",
       "      <td>37.315556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antoniomouth</td>\n",
       "      <td>23.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aprilchester</td>\n",
       "      <td>21.981579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arnoldview</td>\n",
       "      <td>25.106452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Campbellport</td>\n",
       "      <td>33.711333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carrollbury</td>\n",
       "      <td>36.606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carrollfort</td>\n",
       "      <td>25.395517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Clarkstad</td>\n",
       "      <td>31.051667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Conwaymouth</td>\n",
       "      <td>34.591818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Davidtown</td>\n",
       "      <td>22.978095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Davistown</td>\n",
       "      <td>21.497200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>East Cherylfurt</td>\n",
       "      <td>31.416154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>East Douglas</td>\n",
       "      <td>26.169091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>East Erin</td>\n",
       "      <td>24.478214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>East Jenniferchester</td>\n",
       "      <td>32.599474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>East Leslie</td>\n",
       "      <td>33.660909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>East Stephen</td>\n",
       "      <td>39.053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>East Troybury</td>\n",
       "      <td>33.244286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Edwardsbury</td>\n",
       "      <td>26.876667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Erikport</td>\n",
       "      <td>30.043750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Eriktown</td>\n",
       "      <td>25.478947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Floresberg</td>\n",
       "      <td>32.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fosterside</td>\n",
       "      <td>23.034583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hernandezshire</td>\n",
       "      <td>32.002222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Horneland</td>\n",
       "      <td>21.482500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jacksonfort</td>\n",
       "      <td>32.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Jacobfort</td>\n",
       "      <td>24.779355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jasonfort</td>\n",
       "      <td>27.831667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>South Roy</td>\n",
       "      <td>26.031364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>South Shannonborough</td>\n",
       "      <td>26.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Spencertown</td>\n",
       "      <td>23.681154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Stevensport</td>\n",
       "      <td>31.948000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Stewartview</td>\n",
       "      <td>21.614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Swansonbury</td>\n",
       "      <td>27.464706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Thomastown</td>\n",
       "      <td>30.308333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Tiffanyton</td>\n",
       "      <td>28.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Torresshire</td>\n",
       "      <td>24.207308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Travisville</td>\n",
       "      <td>27.220870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Vickimouth</td>\n",
       "      <td>21.474667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Webstertown</td>\n",
       "      <td>29.721250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>West Alexis</td>\n",
       "      <td>19.523000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>West Brandy</td>\n",
       "      <td>24.157667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>West Brittanyton</td>\n",
       "      <td>25.436250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>West Dawnfurt</td>\n",
       "      <td>22.330345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>West Evan</td>\n",
       "      <td>27.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>West Jefferyfurt</td>\n",
       "      <td>21.072857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>West Kevintown</td>\n",
       "      <td>21.528571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>West Oscar</td>\n",
       "      <td>24.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>West Pamelaborough</td>\n",
       "      <td>33.799286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>West Paulport</td>\n",
       "      <td>33.278235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>West Peter</td>\n",
       "      <td>24.875484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>West Sydneyhaven</td>\n",
       "      <td>22.368333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>West Tony</td>\n",
       "      <td>29.609474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Williamchester</td>\n",
       "      <td>34.278182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Williamshire</td>\n",
       "      <td>26.990323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Wiseborough</td>\n",
       "      <td>22.676842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Yolandafurt</td>\n",
       "      <td>27.205500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Zimmermanmouth</td>\n",
       "      <td>28.301667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     city       fare\n",
       "0            Alvarezhaven  23.928710\n",
       "1              Alyssaberg  20.609615\n",
       "2              Anitamouth  37.315556\n",
       "3            Antoniomouth  23.625000\n",
       "4            Aprilchester  21.981579\n",
       "5              Arnoldview  25.106452\n",
       "6            Campbellport  33.711333\n",
       "7             Carrollbury  36.606000\n",
       "8             Carrollfort  25.395517\n",
       "9               Clarkstad  31.051667\n",
       "10            Conwaymouth  34.591818\n",
       "11              Davidtown  22.978095\n",
       "12              Davistown  21.497200\n",
       "13        East Cherylfurt  31.416154\n",
       "14           East Douglas  26.169091\n",
       "15              East Erin  24.478214\n",
       "16   East Jenniferchester  32.599474\n",
       "17            East Leslie  33.660909\n",
       "18           East Stephen  39.053000\n",
       "19          East Troybury  33.244286\n",
       "20            Edwardsbury  26.876667\n",
       "21               Erikport  30.043750\n",
       "22               Eriktown  25.478947\n",
       "23             Floresberg  32.310000\n",
       "24             Fosterside  23.034583\n",
       "25         Hernandezshire  32.002222\n",
       "26              Horneland  21.482500\n",
       "27            Jacksonfort  32.006667\n",
       "28              Jacobfort  24.779355\n",
       "29              Jasonfort  27.831667\n",
       "..                    ...        ...\n",
       "95              South Roy  26.031364\n",
       "96   South Shannonborough  26.516667\n",
       "97            Spencertown  23.681154\n",
       "98            Stevensport  31.948000\n",
       "99            Stewartview  21.614000\n",
       "100           Swansonbury  27.464706\n",
       "101            Thomastown  30.308333\n",
       "102            Tiffanyton  28.510000\n",
       "103           Torresshire  24.207308\n",
       "104           Travisville  27.220870\n",
       "105            Vickimouth  21.474667\n",
       "106           Webstertown  29.721250\n",
       "107           West Alexis  19.523000\n",
       "108           West Brandy  24.157667\n",
       "109      West Brittanyton  25.436250\n",
       "110         West Dawnfurt  22.330345\n",
       "111             West Evan  27.013333\n",
       "112      West Jefferyfurt  21.072857\n",
       "113        West Kevintown  21.528571\n",
       "114            West Oscar  24.280000\n",
       "115    West Pamelaborough  33.799286\n",
       "116         West Paulport  33.278235\n",
       "117            West Peter  24.875484\n",
       "118      West Sydneyhaven  22.368333\n",
       "119             West Tony  29.609474\n",
       "120        Williamchester  34.278182\n",
       "121          Williamshire  26.990323\n",
       "122           Wiseborough  22.676842\n",
       "123           Yolandafurt  27.205500\n",
       "124        Zimmermanmouth  28.301667\n",
       "\n",
       "[125 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Groupby to find average per city\n",
    "##Average Fare ($) Per City\n",
    "city_fare = uber_merge.groupby(['city'], as_index=False)\n",
    "city_fare[\"fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alvarezhaven</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alyssaberg</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anitamouth</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antoniomouth</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aprilchester</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arnoldview</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Campbellport</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carrollbury</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carrollfort</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Clarkstad</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Conwaymouth</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Davidtown</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Davistown</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>East Cherylfurt</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>East Douglas</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>East Erin</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>East Jenniferchester</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>East Leslie</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>East Stephen</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>East Troybury</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Edwardsbury</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Erikport</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Eriktown</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Floresberg</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fosterside</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hernandezshire</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Horneland</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jacksonfort</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Jacobfort</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jasonfort</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>South Roy</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>South Shannonborough</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Spencertown</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Stevensport</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Stewartview</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Swansonbury</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Thomastown</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Tiffanyton</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Torresshire</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Travisville</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Vickimouth</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Webstertown</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>West Alexis</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>West Brandy</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>West Brittanyton</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>West Dawnfurt</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>West Evan</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>West Jefferyfurt</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>West Kevintown</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>West Oscar</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>West Pamelaborough</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>West Paulport</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>West Peter</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>West Sydneyhaven</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>West Tony</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Williamchester</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Williamshire</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Wiseborough</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Yolandafurt</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Zimmermanmouth</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     city  ride_id\n",
       "0            Alvarezhaven       31\n",
       "1              Alyssaberg       26\n",
       "2              Anitamouth        9\n",
       "3            Antoniomouth       22\n",
       "4            Aprilchester       19\n",
       "5              Arnoldview       31\n",
       "6            Campbellport       15\n",
       "7             Carrollbury       10\n",
       "8             Carrollfort       29\n",
       "9               Clarkstad       12\n",
       "10            Conwaymouth       11\n",
       "11              Davidtown       21\n",
       "12              Davistown       25\n",
       "13        East Cherylfurt       13\n",
       "14           East Douglas       22\n",
       "15              East Erin       28\n",
       "16   East Jenniferchester       19\n",
       "17            East Leslie       11\n",
       "18           East Stephen       10\n",
       "19          East Troybury        7\n",
       "20            Edwardsbury       27\n",
       "21               Erikport        8\n",
       "22               Eriktown       19\n",
       "23             Floresberg       10\n",
       "24             Fosterside       24\n",
       "25         Hernandezshire        9\n",
       "26              Horneland        4\n",
       "27            Jacksonfort        6\n",
       "28              Jacobfort       31\n",
       "29              Jasonfort       12\n",
       "..                    ...      ...\n",
       "95              South Roy       22\n",
       "96   South Shannonborough       15\n",
       "97            Spencertown       26\n",
       "98            Stevensport        5\n",
       "99            Stewartview       30\n",
       "100           Swansonbury       34\n",
       "101            Thomastown       24\n",
       "102            Tiffanyton       13\n",
       "103           Torresshire       26\n",
       "104           Travisville       23\n",
       "105            Vickimouth       15\n",
       "106           Webstertown       16\n",
       "107           West Alexis       20\n",
       "108           West Brandy       30\n",
       "109      West Brittanyton       24\n",
       "110         West Dawnfurt       29\n",
       "111             West Evan       12\n",
       "112      West Jefferyfurt       21\n",
       "113        West Kevintown        7\n",
       "114            West Oscar       29\n",
       "115    West Pamelaborough       14\n",
       "116         West Paulport       17\n",
       "117            West Peter       31\n",
       "118      West Sydneyhaven       18\n",
       "119             West Tony       19\n",
       "120        Williamchester       11\n",
       "121          Williamshire       31\n",
       "122           Wiseborough       19\n",
       "123           Yolandafurt       20\n",
       "124        Zimmermanmouth       24\n",
       "\n",
       "[125 rows x 2 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Total Number of Rides Per City\n",
    "ride_count = uber_merge.groupby(['city'], as_index=False)\n",
    "ride_count[\"ride_id\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alvarezhaven</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alyssaberg</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anitamouth</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antoniomouth</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aprilchester</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arnoldview</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Campbellport</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carrollbury</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carrollfort</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Clarkstad</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Conwaymouth</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Davidtown</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Davistown</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>East Cherylfurt</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>East Douglas</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>East Erin</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>East Jenniferchester</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>East Leslie</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>East Stephen</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>East Troybury</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Edwardsbury</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Erikport</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Eriktown</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Floresberg</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fosterside</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hernandezshire</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Horneland</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jacksonfort</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Jacobfort</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jasonfort</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>South Roy</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>South Shannonborough</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Spencertown</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Stevensport</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Stewartview</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Swansonbury</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Thomastown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Tiffanyton</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Torresshire</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Travisville</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Vickimouth</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Webstertown</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>West Alexis</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>West Brandy</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>West Brittanyton</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>West Dawnfurt</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>West Evan</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>West Jefferyfurt</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>West Kevintown</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>West Oscar</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>West Pamelaborough</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>West Paulport</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>West Peter</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>West Sydneyhaven</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>West Tony</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Williamchester</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Williamshire</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Wiseborough</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Yolandafurt</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Zimmermanmouth</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     city  driver_count\n",
       "0            Alvarezhaven            21\n",
       "1              Alyssaberg            67\n",
       "2              Anitamouth            16\n",
       "3            Antoniomouth            21\n",
       "4            Aprilchester            49\n",
       "5              Arnoldview            41\n",
       "6            Campbellport            26\n",
       "7             Carrollbury             4\n",
       "8             Carrollfort            55\n",
       "9               Clarkstad            21\n",
       "10            Conwaymouth            18\n",
       "11              Davidtown            73\n",
       "12              Davistown            25\n",
       "13        East Cherylfurt             9\n",
       "14           East Douglas            12\n",
       "15              East Erin            43\n",
       "16   East Jenniferchester            22\n",
       "17            East Leslie             9\n",
       "18           East Stephen             6\n",
       "19          East Troybury             3\n",
       "20            Edwardsbury            11\n",
       "21               Erikport             3\n",
       "22               Eriktown            15\n",
       "23             Floresberg             7\n",
       "24             Fosterside            69\n",
       "25         Hernandezshire            10\n",
       "26              Horneland             8\n",
       "27            Jacksonfort             6\n",
       "28              Jacobfort            52\n",
       "29              Jasonfort            25\n",
       "..                    ...           ...\n",
       "95              South Roy            35\n",
       "96   South Shannonborough             9\n",
       "97            Spencertown            68\n",
       "98            Stevensport             6\n",
       "99            Stewartview            49\n",
       "100           Swansonbury            64\n",
       "101            Thomastown             1\n",
       "102            Tiffanyton            21\n",
       "103           Torresshire            70\n",
       "104           Travisville            37\n",
       "105            Vickimouth            13\n",
       "106           Webstertown            26\n",
       "107           West Alexis            47\n",
       "108           West Brandy            12\n",
       "109      West Brittanyton             9\n",
       "110         West Dawnfurt            34\n",
       "111             West Evan             4\n",
       "112      West Jefferyfurt            65\n",
       "113        West Kevintown             5\n",
       "114            West Oscar            11\n",
       "115    West Pamelaborough            27\n",
       "116         West Paulport             5\n",
       "117            West Peter            61\n",
       "118      West Sydneyhaven            70\n",
       "119             West Tony            17\n",
       "120        Williamchester            26\n",
       "121          Williamshire            70\n",
       "122           Wiseborough            55\n",
       "123           Yolandafurt             7\n",
       "124        Zimmermanmouth            45\n",
       "\n",
       "[125 rows x 2 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Total Number of Drivers Per City\n",
    "city_drivers = uber_merge.groupby(['city'], as_index=False)\n",
    "city_drivers[\"driver_count\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uber_merge\n",
    "#city_fare\n",
    "#ride_count\n",
    "#city_drivers\n",
    "#merge into single table\n",
    "#pd.merge(uber_merge, city_fare, ride_count, city_drivers, on=[\"Index\"], how='inner',suffixes=('_fare','_count','_drivers'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alvarezhaven</th>\n",
       "      <td>23.928710</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alyssaberg</th>\n",
       "      <td>20.609615</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anitamouth</th>\n",
       "      <td>37.315556</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antoniomouth</th>\n",
       "      <td>23.625000</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aprilchester</th>\n",
       "      <td>21.981579</td>\n",
       "      <td>19</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fare ride_id driver_count\n",
       "                   mean   count         mean\n",
       "city                                        \n",
       "Alvarezhaven  23.928710      31           21\n",
       "Alyssaberg    20.609615      26           67\n",
       "Anitamouth    37.315556       9           16\n",
       "Antoniomouth  23.625000      22           21\n",
       "Aprilchester  21.981579      19           49"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Consolidate groupby values into single table for charting purposes\n",
    "\n",
    "f = {'fare':['mean'], 'ride_id':['count'], 'driver_count':['mean']}\n",
    "\n",
    "merged_df = uber_merge.groupby('city').agg(f)\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~davidhoebbel/0 or inside your plot.ly account where it is named 'bubblechart'\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGGdJREFUeJzt3XmUXGWdxvHvYwIKYUmABgNBQIxs\nMyRAD4K4IAEGECEygCxi1GjUoyOMehQcj9swCuqA4DaTQ4AcBQSikIgeJQRwXEawAwEJAQMYSUxI\nGkhkUyDwmz/et6Roq7qqurvSXS/P55w6VXer+7u3bj391ntvVSsiMDOzsrxsuAswM7Oh53A3MyuQ\nw93MrEAOdzOzAjnczcwK5HA3MyuQw71DSVos6eDhrmOwJJ0q6fp+pt8s6X0bsqaXGklvlHTvcNdh\nQ8vh3oQcMGslvbyN6whJT0p6QtKfJJ0naVS9+SNir4i4uV31DBVJl0p6Jm/Xo5LmS9q9Mj0iLouI\nw9u07o0lfV7S0rxvl0m6WNLO7Vhf1XoPlrSineuos97PS3pW0uP59ntJ35Q0vr/lIuIXEbHbBqzz\ntZKulvSwpD9LulPSx/o73odovZdKOrud6xhJHO4N5CB4IxDAMW1e3aSI2AyYApwCvL9GPaPbXEM7\nfCVv1w7An4BZG2i9c0iv2SnAlsAkYCFp/5bqyojYHNgKeDvwSmBhvYBv5/FU67kl7QrcAiwH/jEi\ntgROALqBzdtVy0tSRPjWzw34LPAr4DzguqrxBwAPAaOqxr0duDM/3gSYDawFlgCfBFb0s54AXlM1\nfDXwzfx4GfAp4E7gaWB0Hndonj4K+DRwP/A4KcB2zNN2B+YDjwL3AifWWf9JQE+fcf8GzMuPjwLu\nzs//J+ATTe6/S4Gzq4aPAp6sGn438Muq4cOAe4A/A98Efg68r2r6e/P+XAv8DNipznoPBf5S2Q91\n5tkemJf3zX3A+/up++Dq1y/v/0/k1+TPwJXAK4Axeb3PA0/k2/Z91tvo2Nkf6AEeA1YD5zW5rz8P\nfK/PuFHAHcDXqrcjH08PAd+t3jbgTGBOn+e4ALgwP96S9Md5VT4Ozq5sR34tfwWcn/fp2TVq/B7w\n4wbbcQywGFgH3Azs0c/75G+vU9W2fRxYk2t8T542A3gWeCa/Jj/aUBkyXDe33Bt7F3BZvv2zpO0A\nIuI3wJPAIVXzngJcnh9/DtgZeDUpsN7Z7Aol7Un6tHB71eiTgbcCYyNifZ9FPpanHwVsQQrApySN\nIQX75cC2eZ5vS9qrxmrnAbtJmlhne2YBH4jUKvwH4MZmt6dqu8bkGu6rM30b4AfAZ4BtSH+sDqqa\nPpX0R+w4oAv4BXBFndUdCtwaEcv7KekKUhhsDxwPfElSK636E4EjgF2AvYF3R8STwJHAyojYLN9W\nVi/UxLFzAXBBRGwB7Apc1UJNLxIRzwFzScdTxStJLfudSKFX7QrgKElbAOSukhOrapsNrAdeA+wD\nHA5UnxN5HfAA6Xj7zxolHUr6RFWTpNfmGs4gvcY/AX4kaeMGm1q9bVuSPiVOB74laVxEzCS9h7+S\nX5O3Nfl8Hcvh3g9JbyC9Aa6KiIWksDmlapYrSGGFpM1J4VoJmxOBL0XE2ohYAVzYxCpvk7QW+BFw\nEXBJ1bQLI2J5RPylxnLvAz4TEfdGckdEPAIcDSyLiEsiYn1E3EYKz+P7PkFEPEUKgcr2TCS1+ufl\nWZ4F9pS0Rd6m25rYnopPSFpHavW/ATitznxHAXdHxJyIeBb4Oql1WfEB4MsRsST/gfsSMFnSTjWe\na2tSy60mSTvmWj4VEX+NiEWkfV6vtloujIiVEfEo6TWb3MKy/R07zwKvkbRNRDyR/xgMxkpSmFc8\nD3wuIp7uezxFxB+B24CpedQhwFMR8ZvcsDkSOCMinoyINaRW+knV64qIb+Tjrdax2u/rAryD1LKf\nn4+Br5E+Bb++yW19FvhiRDwbET8htdI32PmEkcTh3r9pwPUR8XAevjyPo2r4uHyi9TjgtvzmgNQa\nrG419teCrNg3IsZFxK4R8ZmIeL7J5Xck/eHpayfgdZLWVW7AqaTWTS2XkwOH9Efs2hz6AP9CCqA/\nSvq5pAOb2J6Kr0XEWNInmb9Q/832on0W6fN09XbvBFxQtS2PAiK10vp6BOjvROL2wKMR8XjVuD/W\nea56qv/wPAVs1sKy/R0704HXAvdI+q2ko1t43lp2IO2rit6I+GuD2qqPg0qrfSdgI2BV1WvwP6RW\nekWj47yZ16WyH8jvgeU0/7o80ueTbauvSzEc7nVI2oTU+n6zpIckPUTqg54kaRJARNxNOhCP5MVv\nAkitkwlVwzsOsqT+fr5zOenje63xP4+IsVW3zSLiQ3We53pgG0mTSW/uv21PRPw2Io4lvZGvZQBd\nBRHxIHA6KaA3qTHLKqr2kyTx4v22nNQ1VL09m0TEr2s81w3A/pIm1JgGuTWbW80VryL1I0PqNtm0\nalq9P4i1NPyp1f6OnYhYGhEnk/b1ucCc3KXVMkkvA95G6sJqtr6rgYPzvnt7VW3LSed8tqna/1tE\nRHU3X6PnvoHUUKhnJemPSKX+yjFQeV2eoo2vS0kc7vVNBZ4D9iR93J4M7EF6k7yrar7LgY8CbyK9\nKSquAs6SNE7SDsBH2ljrRcB/SJqoZG9JWwPXAa+VdJqkjfLtnyTtUetJcotnDvBV0sf4+fC3SwpP\nlbRl/qj8GGnftCwi5pPewH37egF+DOwl6bh8pcVHefGb979J+3SvXNeWkk6os54bcv3XSNpP0mhJ\nm0v6oKT35r74XwNflvQKSXuTWsyX5adYROp73krSK0l9wM1aDWwtacsG89U8diS9U1JXbrWuy6Nb\n2t/5td6D1NXzStIFAU2JiF7SicxLgD9ExJI8fhWpAfBfkraQ9DJJu0p6cwulfQ54vaSv5v2KpNdI\n+p6ksaT3zVslTZG0Eenk6NOk1wrS63KKpFGSjgBaWfdq0jmwlwSHe33TgEsi4sGIeKhyI13BcWrV\nZV5XkM7S31jVfQPwRdLJuj+QWitzSAdpO5xHelNcTwreWcAmucvhcFKf6EpSN8K5QH/X619OOul1\ndZ+Pt6cByyQ9BnyQfIJY0quUrmF/VQv1fhX4pPp8byDvvxOAc0gf3yeSrr6oTL8m1//9XMddpJZv\nPceTTshdSbqi5S7SJXc35Oknk7qKVgLXkPqh5+dp3yVdZbKMtF+vbHbjIuIe0nHxQO6+2L7OrPWO\nnSOAxZKeIJ1cPanSjZL39Rv/7ple8I683DrS+ZJHgP36ntRtQuU4uLzP+HcBG5OunFpLOq77vY6+\nWkTcDxxI2u+LJf2ZdB6oB3g8Iu4lHVvfAB4mfep4W0Q8k5/i9Dyu0sV4bQvbNIt03midpFaW60hK\n3ZrWbpI+RHqTttLSMDMbELfc20TSeEkH5Y+uu5E+Xl4z3HWZ2UtDw3CXtJukRVW3xySdkfsi5yt9\ntXu+pHEbouAOsjHpSoLHSdeEzwW+PawVmdlLRkvdMvkLDX8ifVHhw6RLyc6RdCYwLiI+1Z4yzcys\nFa12y0wB7s/X4x5L+rYa+X5q3aXMzGyDavVHg07ihW/RbZcvjSIiVknattYCkmaQL3sbM2bMfrvv\nvnut2czMrI6FCxc+HBFdrSzTdLdM/m2HlcBeEbFa0rr8rcPK9LUR0W+/e3d3d/T09LRSn5nZS56k\nhRHR3coyrXTLHEn6ivTqPLxa+WdE8/2aVlZsZmbt00q4n8yLf4FvHi/8zso00tUgZmY2AjQV7pI2\nJf1s7Q+rRp8DHCZpaZ52ztCXZ2ZmA9HUCdX8y4Bb9xn3CGX/Rxszs47lb6iamRXI4W5mViCHu5lZ\ngRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYFcribmRXI4W5m\nViCHu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYFcribmRXI4W5mVqCmwl3SWElzJN0jaYmk\nAyVtJWm+pKX5fly7izUzs+Y023K/APhpROwOTAKWAGcCCyJiIrAgD5uZ2QjQMNwlbQG8CZgFEBHP\nRMQ64Fhgdp5tNjC1XUWamVlrmmm5vxroBS6RdLukiySNAbaLiFUA+X7bWgtLmiGpR1JPb2/vkBVu\nZmb1NRPuo4F9ge9ExD7Ak7TQBRMRMyOiOyK6u7q6BlimmZm1oplwXwGsiIhb8vAcUtivljQeIN+v\naU+JZmbWqobhHhEPAcsl7ZZHTQHuBuYB0/K4acDctlRoZmYtG93kfP8KXCZpY+AB4D2kPwxXSZoO\nPAic0J4SzcysVU2Fe0QsArprTJoytOWYmdlQ8DdUzcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3\nMyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnc\nzcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyvQ6GZmkrQMeBx4DlgfEd2StgKuBHYGlgEnRsTa\n9pRpZmataKXl/paImBwR3Xn4TGBBREwEFuRhMzMbAQbTLXMsMDs/ng1MHXw5ZmY2FJoN9wCul7RQ\n0ow8bruIWAWQ77ettaCkGZJ6JPX09vYOvmIzM2uoqT534KCIWClpW2C+pHuaXUFEzARmAnR3d8cA\najQzsxY11XKPiJX5fg1wDbA/sFrSeIB8v6ZdRZqZWWsahrukMZI2rzwGDgfuAuYB0/Js04C57SrS\nzMxa00y3zHbANZIq818eET+V9FvgKknTgQeBE9pXppmZtaJhuEfEA8CkGuMfAaa0oygzMxscf0PV\nzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRw\nNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI53M3MCuRwNzMrkMPdzKxADnczswI1\nHe6SRkm6XdJ1eXgXSbdIWirpSkkbt69MMzNrRSst99OBJVXD5wLnR8REYC0wfSgLMzOzgWsq3CVN\nAN4KXJSHBRwCzMmzzAamtqNAMzNrXbMt968DnwSez8NbA+siYn0eXgHsMMS1mZnZADUMd0lHA2si\nYmH16BqzRp3lZ0jqkdTT29s7wDLNzKwVzbTcDwKOkbQM+D6pO+brwFhJo/M8E4CVtRaOiJkR0R0R\n3V1dXUNQspmZNdIw3CPirIiYEBE7AycBN0bEqcBNwPF5tmnA3LZVaWZmLRnMde6fAj4m6T5SH/ys\noSnJzMwGa3TjWV4QETcDN+fHDwD7D31JZmY2WP6GqplZgRzuZmYFcribmRXI4W5mViCHu5lZgRzu\nZmYFcribmRXI4W5mViCHu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYFcribmRXI4W5mViCH\nu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYFahjukl4h6VZJd0haLOkLefwukm6RtFTSlZI2\nbn+5ZmbWjGZa7k8Dh0TEJGAycISkA4BzgfMjYiKwFpjevjLNzKwVDcM9kify4Eb5FsAhwJw8fjYw\ntS0VmplZy5rqc5c0StIiYA0wH7gfWBcR6/MsK4Ad2lOimZm1qqlwj4jnImIyMAHYH9ij1my1lpU0\nQ1KPpJ7e3t6BV2pmZk1r6WqZiFgH3AwcAIyVNDpPmgCsrLPMzIjojojurq6uwdRqZmZNauZqmS5J\nY/PjTYBDgSXATcDxebZpwNx2FWlmZq0Z3XgWxgOzJY0i/TG4KiKuk3Q38H1JZwO3A7PaWKeZmbWg\nYbhHxJ3APjXGP0DqfzczsxHG31A1MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnc\nzcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAO\ndzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjUMd0k7SrpJ0hJJiyWdnsdvJWm+pKX5flz7yzUzs2Y0\n03JfD3w8IvYADgA+LGlP4ExgQURMBBbkYTMzGwEahntErIqI2/Ljx4ElwA7AscDsPNtsYGq7ijQz\ns9a01OcuaWdgH+AWYLuIWAXpDwCwbZ1lZkjqkdTT29s7uGrNzKwpTYe7pM2AHwBnRMRjzS4XETMj\nojsiuru6ugZSo5mZtaipcJe0ESnYL4uIH+bRqyWNz9PHA2vaU6KZmbWqmatlBMwClkTEeVWT5gHT\n8uNpwNyhL8/MzAZidBPzHAScBvxO0qI87tPAOcBVkqYDDwIntKdEMzNrVcNwj4hfAqozecrQlmNm\nZkPB31A1MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAO\ndzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQ\nw93MrEANw13SxZLWSLqratxWkuZLWprvx7W3TDMza0UzLfdLgSP6jDsTWBARE4EFedjMzEaIhuEe\nEf8LPNpn9LHA7Px4NjB1iOsyM7NBGGif+3YRsQog329bb0ZJMyT1SOrp7e0d4OrMzKwVbT+hGhEz\nI6I7Irq7urravTozM2Pg4b5a0niAfL9m6EoyM7PBGmi4zwOm5cfTgLlDU46ZmQ2FZi6FvAL4P2A3\nSSskTQfOAQ6TtBQ4LA+bmdkIMbrRDBFxcp1JU4a4FjMzGyL+hqqZWYEc7mZmBXK4m5kVyOFuZlYg\nh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kV\nyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBRpUuEs6QtK9ku6TdOZQFWVmZoMz\n4HCXNAr4FnAksCdwsqQ9h6owMzMbuMG03PcH7ouIByLiGeD7wLFDU5aZmQ3G6EEsuwOwvGp4BfC6\nvjNJmgHMyINPS7prEOscbtsADw93EQPUybWD6x9urn947dbqAoMJd9UYF383ImImMBNAUk9EdA9i\nncOqk+vv5NrB9Q831z+8JPW0usxgumVWADtWDU8AVg7i+czMbIgMJtx/C0yUtIukjYGTgHlDU5aZ\nmQ3GgLtlImK9pI8APwNGARdHxOIGi80c6PpGiE6uv5NrB9c/3Fz/8Gq5fkX8XTe5mZl1OH9D1cys\nQA53M7MCbZBw77SfKZB0saQ11dfkS9pK0nxJS/P9uOGssT+SdpR0k6QlkhZLOj2P74htkPQKSbdK\nuiPX/4U8fhdJt+T6r8wn8kckSaMk3S7pujzcSbUvk/Q7SYsql+B1yrEDIGmspDmS7snvgQM7pX5J\nu+X9Xrk9JumMgdTf9nDv0J8puBQ4os+4M4EFETERWJCHR6r1wMcjYg/gAODDeZ93yjY8DRwSEZOA\nycARkg4AzgXOz/WvBaYPY42NnA4sqRrupNoB3hIRk6uuDe+UYwfgAuCnEbE7MIn0OnRE/RFxb97v\nk4H9gKeAaxhI/RHR1htwIPCzquGzgLPavd4hqHtn4K6q4XuB8fnxeODe4a6xhW2ZCxzWidsAbArc\nRvr288PA6Dz+RcfVSLqRvvOxADgEuI70hb+OqD3XtwzYps+4jjh2gC2AP5AvFum0+vvUfDjwq4HW\nvyG6ZWr9TMEOG2C9Q227iFgFkO+3HeZ6miJpZ2Af4BY6aBtyt8YiYA0wH7gfWBcR6/MsI/k4+jrw\nSeD5PLw1nVM7pG+aXy9pYf75EOicY+fVQC9wSe4Wu0jSGDqn/monAVfkxy3XvyHCvamfKbChJ2kz\n4AfAGRHx2HDX04qIeC7SR9MJpB+p26PWbBu2qsYkHQ2siYiF1aNrzDriaq9yUETsS+pK/bCkNw13\nQS0YDewLfCci9gGeZIR2wfQnn5M5Brh6oM+xIcK9lJ8pWC1pPEC+XzPM9fRL0kakYL8sIn6YR3fU\nNgBExDrgZtK5g7GSKl+8G6nH0UHAMZKWkX4p9RBSS74TagcgIlbm+zWk/t796ZxjZwWwIiJuycNz\nSGHfKfVXHAncFhGr83DL9W+IcC/lZwrmAdPy42mkfuwRSZKAWcCSiDivalJHbIOkLklj8+NNgENJ\nJ8VuAo7Ps43I+iPirIiYEBE7k471GyPiVDqgdgBJYyRtXnlM6ve9iw45diLiIWC5pMqvKE4B7qZD\n6q9yMi90ycBA6t9AJwaOAn5P6jf99+E+UdFEvVcAq4BnSS2B6aR+0wXA0ny/1XDX2U/9byB97L8T\nWJRvR3XKNgB7A7fn+u8CPpvHvxq4FbiP9HH15cNda4PtOBi4rpNqz3XekW+LK+/XTjl2cq2TgZ58\n/FwLjOuw+jcFHgG2rBrXcv3++QEzswL5G6pmZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjh\nbmZWoP8H3yRulhMgY14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2ff550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "merged_df.iplot(kind='bubble', x='fare', y='ride_id', size='driver_count', xTitle='Avg Fare', yTitle='Rider Count',\n",
    "             filename='bubblechart')\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Avg Price vs. Ride Count vs. Driver Count\")\n",
    "\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([0,70])\n",
    "axes.set_ylim([0,70])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>4255.09</td>\n",
       "      <td>125</td>\n",
       "      <td>5.816000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>20335.69</td>\n",
       "      <td>657</td>\n",
       "      <td>14.809741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>40078.34</td>\n",
       "      <td>1625</td>\n",
       "      <td>39.692923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare ride_id driver_count\n",
       "               sum   count         mean\n",
       "type                                   \n",
       "Rural      4255.09     125     5.816000\n",
       "Suburban  20335.69     657    14.809741\n",
       "Urban     40078.34    1625    39.692923"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Consolidate groupby values into single table for charting purposes\n",
    "\n",
    "m = {'fare':['sum'], 'ride_id':['count'], 'driver_count':['mean']}\n",
    "\n",
    "merged_df2 = uber_merge.groupby(['type']).agg(m)\n",
    "merged_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>fare %</th>\n",
       "      <th>ride %</th>\n",
       "      <th>driver %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</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>Rural</th>\n",
       "      <td>4255.09</td>\n",
       "      <td>125</td>\n",
       "      <td>5.816000</td>\n",
       "      <td>0.065798</td>\n",
       "      <td>0.051932</td>\n",
       "      <td>0.096421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>20335.69</td>\n",
       "      <td>657</td>\n",
       "      <td>14.809741</td>\n",
       "      <td>0.314458</td>\n",
       "      <td>0.272954</td>\n",
       "      <td>0.245525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>40078.34</td>\n",
       "      <td>1625</td>\n",
       "      <td>39.692923</td>\n",
       "      <td>0.619745</td>\n",
       "      <td>0.675114</td>\n",
       "      <td>0.658054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare ride_id driver_count    fare %    ride %  driver %\n",
       "               sum   count         mean                              \n",
       "type                                                                 \n",
       "Rural      4255.09     125     5.816000  0.065798  0.051932  0.096421\n",
       "Suburban  20335.69     657    14.809741  0.314458  0.272954  0.245525\n",
       "Urban     40078.34    1625    39.692923  0.619745  0.675114  0.658054"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##City Type (Urban, Suburban, Rural)\n",
    "#In addition, you will be expected to produce the following three pie charts:\n",
    "##% of Total Fares by City Type\n",
    "##% of Total Rides by City Type\n",
    "##% of Total Drivers by City Type\n",
    "merged_df2['fare %'] = (merged_df2['fare'])/(merged_df2['fare'].sum())\n",
    "merged_df2['ride %'] = (merged_df2['ride_id'])/(merged_df2['ride_id'].sum())\n",
    "merged_df2['driver %'] = (merged_df2['driver_count'])/(merged_df2['driver_count'].sum())\n",
    "merged_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG+wDovwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2da470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fare % by city type\n",
    "plt.pie(merged_df2['fare %'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc38ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ride % by city type\n",
    "plt.pie(merged_df2['ride %'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGp1rPSZXogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6dcd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# driver % by city type\n",
    "plt.pie(merged_df2['driver %'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Observable Trends\n",
    "#1. Higher average fares associated with cities that have smaller amounts of Uber drivers\n",
    "#2. Rider count and driver count are positively correlated\n",
    "#3. Urban cities have more riders, drivers and a higher share of the overall fares. This is followed by Suburban then Rural cities."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}