analysis/chargepoint_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chargepoint Sessions Data Analysis\n",
    "\n",
    "This notebook performs some analysis on the electric vehicle chargpeoint sessions data from Dundee City Council.\n",
    "\n",
    "The anonymous data contains details of all charging sessions on the public network hosted by the Council. We can use Pandas to answer some questions about how people use the chargers such as;\n",
    "\n",
    "* Which chargepoint sites are most popular?\n",
    "* How much energy has been delivered to vehicles?\n",
    "* What times of day are most popular for charging your car?\n",
    "\n",
    "This notebook is written in python only.\n",
    "\n",
    "## Preparing the Environment\n",
    "\n",
    "Before we can get started install and import the python modules that we'll be using in this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ckanapi\n",
      "  Downloading ckanapi-4.1.tar.gz\n",
      "Requirement already satisfied: setuptools in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from ckanapi)\n",
      "Collecting docopt (from ckanapi)\n",
      "  Downloading docopt-0.6.2.tar.gz\n",
      "Requirement already satisfied: requests in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from ckanapi)\n",
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      "Requirement already satisfied: idna<2.7,>=2.5 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from requests->ckanapi)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from requests->ckanapi)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from requests->ckanapi)\n",
      "Building wheels for collected packages: ckanapi, docopt\n",
      "  Running setup.py bdist_wheel for ckanapi ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/nbuser/.cache/pip/wheels/33/59/4e/bc1f33e8861c8725b34d1228e7fc6dbd40cd267733c841325f\n",
      "  Running setup.py bdist_wheel for docopt ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/nbuser/.cache/pip/wheels/b2/16/5f/c33a2bb5f2dce71205f8e65cbfd05647d79d441282be31fd82\n",
      "Successfully built ckanapi docopt\n",
      "Installing collected packages: docopt, ckanapi\n",
      "Successfully installed ckanapi-4.1 docopt-0.6.2\n"
     ]
    }
   ],
   "source": [
    "# Install the CKAN API\n",
    "!pip install ckanapi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import modules\n",
    "from pandas import *\n",
    "from datetime import datetime\n",
    "import calendar\n",
    "import requests, ckanapi\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure the [CKAN API](https://github.com/ckan/ckanapi) to connect to [data.dundeecity.gov.uk](https://data.dundeecity.gov.uk), Dundee's CKAN instance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd = ckanapi.RemoteCKAN('https://data.dundeecity.gov.uk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data\n",
    "\n",
    "We'll get the charging sessoin data from the Dundee datastore using the [datastore API](http://docs.ckan.org/en/latest/maintaining/datastore.html#the-datastore-api), and then load it into a [DataFrame](https://pandas.pydata.org/pandas-docs/), which lets us analyse the dataset in pandas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Download sessions using the CKAN Datastore API\n",
    "charge_sessions_ds = dd.action.datastore_search(resource_id='d748579a-f05f-47e6-a76b-e76d1e2e9589', limit=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>CP ID</th>\n",
       "      <th>Charging event</th>\n",
       "      <th>Connector</th>\n",
       "      <th>Cost</th>\n",
       "      <th>End Date</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Group</th>\n",
       "      <th>Model</th>\n",
       "      <th>Site</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Total kWh</th>\n",
       "      <th>_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4032912</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td></td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50269.0</td>\n",
       "      <td>4032900</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Housing Office East, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50523.0</td>\n",
       "      <td>4032891</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; Dundee City Council; Charge...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Marchbanks, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50338.0</td>\n",
       "      <td>4032879</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT Triple Rapid Charger</td>\n",
       "      <td>Social Work Building, Jack Martin Way, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50240.0</td>\n",
       "      <td>4032877</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Gellatly Street Car Park, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>11:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>51201.0</td>\n",
       "      <td>4032866</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Single Outlet</td>\n",
       "      <td>Dundee House, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>11:56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50675.0</td>\n",
       "      <td>4032809</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Olympia Multi-Storey Car Park, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>11:42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50267.0</td>\n",
       "      <td>4032762</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; Dundee City Council; Charge...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Social Work Building, Jack Martin Way, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>11:28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50692.0</td>\n",
       "      <td>4032755</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT Triple Rapid Charger</td>\n",
       "      <td>Dundee Ice Arena, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>11:25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CP ID  Charging event  Connector  Cost End Date End Time  \\\n",
       "1      NaN         4032912          2   NaN     None            \n",
       "2  50269.0         4032900          2   NaN     None            \n",
       "3  50523.0         4032891          1   NaN     None            \n",
       "4  50338.0         4032879          2   NaN     None            \n",
       "5  50240.0         4032877          2   NaN     None            \n",
       "6  51201.0         4032866          2   NaN     None            \n",
       "7  50675.0         4032809          1   NaN     None            \n",
       "8  50267.0         4032762          2   NaN     None            \n",
       "9  50692.0         4032755          2   NaN     None            \n",
       "\n",
       "                                               Group  \\\n",
       "1  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "2  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "3  APT Controls; CYC; Dundee City Council; Charge...   \n",
       "4  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "5  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "6  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "7  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "8  APT Controls; CYC; Dundee City Council; Charge...   \n",
       "9  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "\n",
       "                      Model                                           Site  \\\n",
       "1       APT 7kW Dual Outlet                                                  \n",
       "2       APT 7kW Dual Outlet                    Housing Office East, Dundee   \n",
       "3       APT 7kW Dual Outlet                             Marchbanks, Dundee   \n",
       "4  APT Triple Rapid Charger  Social Work Building, Jack Martin Way, Dundee   \n",
       "5       APT 7kW Dual Outlet               Gellatly Street Car Park, Dundee   \n",
       "6     APT 7kW Single Outlet                           Dundee House, Dundee   \n",
       "7       APT 7kW Dual Outlet          Olympia Multi-Storey Car Park, Dundee   \n",
       "8       APT 7kW Dual Outlet  Social Work Building, Jack Martin Way, Dundee   \n",
       "9  APT Triple Rapid Charger                       Dundee Ice Arena, Dundee   \n",
       "\n",
       "            Start Date Start Time  Total kWh  _id  \n",
       "1  2017-01-12T00:00:00      12:09        NaN    2  \n",
       "2  2017-01-12T00:00:00      12:07        NaN    3  \n",
       "3  2017-01-12T00:00:00      12:03        NaN    4  \n",
       "4  2017-01-12T00:00:00      12:00        NaN    5  \n",
       "5  2017-01-12T00:00:00      11:59        NaN    6  \n",
       "6  2017-01-12T00:00:00      11:56        NaN    7  \n",
       "7  2017-01-12T00:00:00      11:42        NaN    8  \n",
       "8  2017-01-12T00:00:00      11:28        NaN    9  \n",
       "9  2017-01-12T00:00:00      11:25        NaN   10  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to Pandas Dataframe\n",
    "df = pandas.DataFrame(charge_sessions_ds['records'])\n",
    "df[1:10] # Print the first 10 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which are the busiest charging sites?\n",
    "\n",
    "Where are the most popular chargepoints?\n",
    "\n",
    ">Note this data includes both public chargepoints (like the Queen Street Car Park), and private chargepoints used by the Council such as the public works department on Clepington Road.\n",
    "\n",
    "### By Number of Charging Sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Site\n",
       "Public Works Dept, Clepington Rd. Dundee         3332\n",
       "Queen Street Car Park, Broughty Ferry, Dundee    2600\n",
       "Dundee Ice Arena, Dundee                         2102\n",
       "Greenmarket Multi Car Park, Dundee               1126\n",
       "Dundee House, Dundee                              866\n",
       "Gellatly Street Car Park, Dundee                  788\n",
       "Social Work Building, Jack Martin Way, Dundee     553\n",
       "Whitfield Centre, Dundee                          466\n",
       "Janet Brougham House, Dundee                      378\n",
       "Housing Office East, Dundee                       337\n",
       "Olympia Multi-Storey Car Park, Dundee             316\n",
       "South Tay Street                                  292\n",
       "Marchbanks, Dundee                                272\n",
       "Dock Street, Dundee                               271\n",
       "Brington Place Sheltered Housing, Dundee          257\n",
       "Trades Lane                                       240\n",
       "Sinclair Street                                   229\n",
       "Nethergate, Dundee                                189\n",
       "Housing Office West, Dundee                       181\n",
       "Balunie Drive, Dundee                             172\n",
       "Menziehill House, Dundee                          158\n",
       "DCC Environment, 34 Harefield Road                142\n",
       "University of Dundee, Nethergate, Dundee           74\n",
       "Earn Cresent, Dundee                               53\n",
       "Oakland Day Centre, Dundee                         42\n",
       "Turriff House Rannoch Road, Dundee                 34\n",
       "Mitchell Street, Dundee                            18\n",
       "Ardler Complex, Dundee                             12\n",
       "                                                    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Site']).size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By total power consumption\n",
    "\n",
    "Sometimes if a session doesn't end correctly there's no power consumption recorded.  To calculate the total we have to filter out those values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Site\n",
       "Public Works Dept, Clepington Rd. Dundee         24092.007\n",
       "Dundee Ice Arena, Dundee                         22466.537\n",
       "Queen Street Car Park, Broughty Ferry, Dundee    18715.366\n",
       "Greenmarket Multi Car Park, Dundee               12527.712\n",
       "Gellatly Street Car Park, Dundee                  5252.037\n",
       "Social Work Building, Jack Martin Way, Dundee     4076.366\n",
       "Dundee House, Dundee                              3404.880\n",
       "South Tay Street                                  2921.838\n",
       "Olympia Multi-Storey Car Park, Dundee             2597.180\n",
       "Janet Brougham House, Dundee                      2419.990\n",
       "Balunie Drive, Dundee                             2213.070\n",
       "Whitfield Centre, Dundee                          2194.090\n",
       "Sinclair Street                                   2131.513\n",
       "Housing Office East, Dundee                       2117.770\n",
       "Marchbanks, Dundee                                2091.560\n",
       "Nethergate, Dundee                                1662.660\n",
       "Dock Street, Dundee                               1628.077\n",
       "Trades Lane                                       1516.479\n",
       "Housing Office West, Dundee                       1448.400\n",
       "DCC Environment, 34 Harefield Road                1314.710\n",
       "Brington Place Sheltered Housing, Dundee          1186.720\n",
       "Menziehill House, Dundee                           886.850\n",
       "University of Dundee, Nethergate, Dundee           770.898\n",
       "Earn Cresent, Dundee                               634.460\n",
       "Oakland Day Centre, Dundee                         595.240\n",
       "Turriff House Rannoch Road, Dundee                 311.180\n",
       "Mitchell Street, Dundee                            183.560\n",
       "Ardler Complex, Dundee                              51.400\n",
       "Name: Total kWh, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total power usage - group by session. \n",
    "sessions_by_site=df[df['Total kWh'].notnull()].groupby(['Site'])['Total kWh'].sum()\n",
    "sessions_by_site.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f90e9f7bfd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90e9f7bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sessions_by_site.sort_values(ascending=False).plot(x='Site',y='Total kWh', kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session Dates and Times\n",
    "\n",
    "What about days and times;\n",
    "\n",
    "* What times are busiest for charging? \n",
    "* Does the busy time vary by location?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is delivered with a separate date and time column, but we can use the `to_datetime` function to convert this into a datetime object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>CP ID</th>\n",
       "      <th>Charging event</th>\n",
       "      <th>Connector</th>\n",
       "      <th>Cost</th>\n",
       "      <th>End Date</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Group</th>\n",
       "      <th>Model</th>\n",
       "      <th>Site</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Total kWh</th>\n",
       "      <th>_id</th>\n",
       "      <th>StartDateTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-12 12:09:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4032911</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT Triple Rapid Charger</td>\n",
       "      <td></td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-12 12:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12 12:09:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4032912</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td></td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-12 12:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12 12:07:00</th>\n",
       "      <td>50269.0</td>\n",
       "      <td>4032900</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Housing Office East, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-01-12 12:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12 12:03:00</th>\n",
       "      <td>50523.0</td>\n",
       "      <td>4032891</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; Dundee City Council; Charge...</td>\n",
       "      <td>APT 7kW Dual Outlet</td>\n",
       "      <td>Marchbanks, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-12 12:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12 12:00:00</th>\n",
       "      <td>50338.0</td>\n",
       "      <td>4032879</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>APT Controls; CYC; ChargePlace Scotland; Dunde...</td>\n",
       "      <td>APT Triple Rapid Charger</td>\n",
       "      <td>Social Work Building, Jack Martin Way, Dundee</td>\n",
       "      <td>2017-01-12T00:00:00</td>\n",
       "      <td>12:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-01-12 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CP ID  Charging event  Connector  Cost End Date  \\\n",
       "StartDateTime                                                            \n",
       "2017-01-12 12:09:00      NaN         4032911          2   NaN     None   \n",
       "2017-01-12 12:09:00      NaN         4032912          2   NaN     None   \n",
       "2017-01-12 12:07:00  50269.0         4032900          2   NaN     None   \n",
       "2017-01-12 12:03:00  50523.0         4032891          1   NaN     None   \n",
       "2017-01-12 12:00:00  50338.0         4032879          2   NaN     None   \n",
       "\n",
       "                    End Time  \\\n",
       "StartDateTime                  \n",
       "2017-01-12 12:09:00            \n",
       "2017-01-12 12:09:00            \n",
       "2017-01-12 12:07:00            \n",
       "2017-01-12 12:03:00            \n",
       "2017-01-12 12:00:00            \n",
       "\n",
       "                                                                 Group  \\\n",
       "StartDateTime                                                            \n",
       "2017-01-12 12:09:00  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "2017-01-12 12:09:00  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "2017-01-12 12:07:00  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "2017-01-12 12:03:00  APT Controls; CYC; Dundee City Council; Charge...   \n",
       "2017-01-12 12:00:00  APT Controls; CYC; ChargePlace Scotland; Dunde...   \n",
       "\n",
       "                                        Model  \\\n",
       "StartDateTime                                   \n",
       "2017-01-12 12:09:00  APT Triple Rapid Charger   \n",
       "2017-01-12 12:09:00       APT 7kW Dual Outlet   \n",
       "2017-01-12 12:07:00       APT 7kW Dual Outlet   \n",
       "2017-01-12 12:03:00       APT 7kW Dual Outlet   \n",
       "2017-01-12 12:00:00  APT Triple Rapid Charger   \n",
       "\n",
       "                                                              Site  \\\n",
       "StartDateTime                                                        \n",
       "2017-01-12 12:09:00                                                  \n",
       "2017-01-12 12:09:00                                                  \n",
       "2017-01-12 12:07:00                    Housing Office East, Dundee   \n",
       "2017-01-12 12:03:00                             Marchbanks, Dundee   \n",
       "2017-01-12 12:00:00  Social Work Building, Jack Martin Way, Dundee   \n",
       "\n",
       "                              Start Date Start Time  Total kWh  _id  \\\n",
       "StartDateTime                                                         \n",
       "2017-01-12 12:09:00  2017-01-12T00:00:00      12:09        NaN    1   \n",
       "2017-01-12 12:09:00  2017-01-12T00:00:00      12:09        NaN    2   \n",
       "2017-01-12 12:07:00  2017-01-12T00:00:00      12:07        NaN    3   \n",
       "2017-01-12 12:03:00  2017-01-12T00:00:00      12:03        NaN    4   \n",
       "2017-01-12 12:00:00  2017-01-12T00:00:00      12:00        NaN    5   \n",
       "\n",
       "                          StartDateTime  \n",
       "StartDateTime                            \n",
       "2017-01-12 12:09:00 2017-01-12 12:09:00  \n",
       "2017-01-12 12:09:00 2017-01-12 12:09:00  \n",
       "2017-01-12 12:07:00 2017-01-12 12:07:00  \n",
       "2017-01-12 12:03:00 2017-01-12 12:03:00  \n",
       "2017-01-12 12:00:00 2017-01-12 12:00:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a datetime column for start of session (join the start date and start time string columns)\n",
    "df['StartDateTime'] = pandas.to_datetime(df['Start Date'].str.slice(0,10) + ' '+ df['Start Time'],format=\"%Y-%m-%d %H:%M\")\n",
    "df = df.set_index(pandas.DatetimeIndex(df['StartDateTime']))\n",
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StartDateTime\n",
       "2017-01-15     587\n",
       "2017-01-22       0\n",
       "2017-01-29       0\n",
       "2017-02-05       0\n",
       "2017-02-12     463\n",
       "2017-02-19       0\n",
       "2017-02-26       0\n",
       "2017-03-05       0\n",
       "2017-03-12     477\n",
       "2017-03-19       0\n",
       "2017-03-26       0\n",
       "2017-04-02       0\n",
       "2017-04-09     159\n",
       "2017-04-16     321\n",
       "2017-04-23       0\n",
       "2017-04-30       0\n",
       "2017-05-07       0\n",
       "2017-05-14     460\n",
       "2017-05-21       0\n",
       "2017-05-28       0\n",
       "2017-06-04       0\n",
       "2017-06-11     573\n",
       "2017-06-18       0\n",
       "2017-06-25       0\n",
       "2017-07-02       0\n",
       "2017-07-09     191\n",
       "2017-07-16     347\n",
       "2017-07-23       0\n",
       "2017-07-30       0\n",
       "2017-08-06       0\n",
       "2017-08-13     520\n",
       "2017-08-20       0\n",
       "2017-08-27       0\n",
       "2017-09-03       0\n",
       "2017-09-10     329\n",
       "2017-09-17     986\n",
       "2017-09-24    1156\n",
       "2017-10-01    1017\n",
       "2017-10-08       0\n",
       "2017-10-15     983\n",
       "2017-10-22    1168\n",
       "2017-10-29    1176\n",
       "2017-11-05     345\n",
       "2017-11-12     503\n",
       "2017-11-19    1253\n",
       "2017-11-26    1222\n",
       "2017-12-03     780\n",
       "2017-12-10     329\n",
       "2017-12-17     157\n",
       "Freq: W-SUN, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('W').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When resampling to show the count of sessions per week above you can see there's a problem with some of the dates.  This data should be for September to December 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StartDateTime\n",
       "0      276\n",
       "1      318\n",
       "2      223\n",
       "3      182\n",
       "4      146\n",
       "5      132\n",
       "6      192\n",
       "7      452\n",
       "8      618\n",
       "9      867\n",
       "10     965\n",
       "11    1155\n",
       "12    1361\n",
       "13    1206\n",
       "14    1113\n",
       "15    1124\n",
       "16    1021\n",
       "17     704\n",
       "18     532\n",
       "19     539\n",
       "20     524\n",
       "21     890\n",
       "22     470\n",
       "23     492\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([df.index.hour]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add columns to the data frame for the day of week the session was started and also the hour of the day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['DayofWeek']=df.index.dayofweek\n",
    "df['StrDayofWeek']= df.index.weekday_name\n",
    "df['StartHour'] = df.index.hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charting the time of day to see when most sessions are started. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f90e9f53358>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90e9bd06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['StartHour']).size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there patterns of busy times? Does this vary by day of week and by chargepoint type (e.g. Rapid Chargers/Fast Chargers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>StartHour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StrDayofWeek</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "StartHour      0    1    2    3    4    5    6     7     8     9   ...   \\\n",
       "StrDayofWeek                                                       ...    \n",
       "Monday        8.0  3.0  4.0  5.0  2.0  2.0  7.0  17.0  51.0  76.0  ...    \n",
       "Tuesday       6.0  4.0  1.0  1.0  2.0  NaN  5.0  20.0  53.0  70.0  ...    \n",
       "Wednesday     3.0  NaN  1.0  NaN  3.0  NaN  1.0  24.0  53.0  80.0  ...    \n",
       "Thursday      4.0  2.0  3.0  1.0  4.0  NaN  5.0  21.0  48.0  59.0  ...    \n",
       "Friday        4.0  1.0  1.0  2.0  NaN  NaN  1.0  20.0  65.0  73.0  ...    \n",
       "Saturday      5.0  3.0  3.0  6.0  2.0  6.0  7.0  16.0  25.0  55.0  ...    \n",
       "Sunday        2.0  7.0  NaN  5.0  1.0  3.0  3.0  14.0  23.0  38.0  ...    \n",
       "\n",
       "StartHour        14     15     16    17    18    19    20    21    22    23  \n",
       "StrDayofWeek                                                                 \n",
       "Monday         86.0  113.0  105.0  42.0  32.0  19.0  15.0  71.0   8.0  26.0  \n",
       "Tuesday       108.0  105.0   96.0  45.0  22.0  21.0  15.0  79.0   6.0  20.0  \n",
       "Wednesday     104.0  115.0   76.0  47.0  15.0  21.0  12.0  72.0   6.0  15.0  \n",
       "Thursday       71.0  114.0   87.0  47.0  24.0  18.0  17.0  62.0   8.0  18.0  \n",
       "Friday         97.0   99.0   74.0  58.0  21.0  23.0  11.0  64.0   7.0  16.0  \n",
       "Saturday       65.0   65.0   47.0  33.0  25.0  19.0  20.0  68.0   9.0  16.0  \n",
       "Sunday         56.0   52.0   52.0  34.0  16.0  16.0  16.0  56.0  10.0  25.0  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rapid_sessions = df[df['Model'].str.contains(\"Rapid\")].pivot_table(index=['StrDayofWeek'],columns='StartHour',values='Charging event',aggfunc='count')\n",
    "rapid_sessions = rapid_sessions.reindex_axis(calendar.day_name, axis=0)\n",
    "fast_sessions = df[~df['Model'].str.contains(\"Rapid\")].pivot_table(index=['StrDayofWeek'],columns='StartHour',values='Charging event',aggfunc='count')\n",
    "\n",
    "fast_sessions = fast_sessions.reindex_axis(calendar.day_name, axis=0)\n",
    "\n",
    "fast_sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90e9a49d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(rapid_sessions, ax=ax,xticklabels=2 , cmap=\"YlGnBu\")\n",
    "ax.set_title(\"Rapid Charge Sessions\")\n",
    "ax.set_xlabel(\"Start Time\")\n",
    "ax.set_ylabel(\"Day\")\n",
    "fig.savefig(\"SessionHeatMap_rapid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90e9abe4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(fast_sessions, ax=ax,xticklabels=2,cmap='YlGnBu' )\n",
    "fig.savefig(\"SessionHeatMap_fast.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![data.dundeecity.gov.uk](https://data.dundeecity.gov.uk/uploads/admin/2017-09-21-173807.593429Footer3EuroScot.png) ![Smart Cities Scotland](https://data.dundeecity.gov.uk/uploads/admin/2017-09-21-173808.054196Footer2SmartCities.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "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
}