Python-Visualisation/UK_CarbonEmission.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carbon Emission Intesity in United Kingdom\n",
    "    .. Using boxplots to identify carbon intensity for different hours of the data in the United Kingdom. \n",
    "    .. Data Source : https://api.carbonintensity.org.uk/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computational Packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualisation Packages\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Utility Packages\n",
    "from urllib2 import urlopen\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Fetching data from URL = ', 'https://api.carbonintensity.org.uk/intensity/2018-01-01T00:00Z/2018-01-31T23:59Z')\n",
      "('Fetching data from URL = ', 'https://api.carbonintensity.org.uk/intensity/2018-02-01T00:00Z/2018-02-28T23:59Z')\n",
      "('Fetching data from URL = ', 'https://api.carbonintensity.org.uk/intensity/2018-03-01T00:00Z/2018-03-31T23:59Z')\n",
      "('Fetching data from URL = ', 'https://api.carbonintensity.org.uk/intensity/2018-04-01T00:00Z/2018-04-30T23:59Z')\n",
      "('Fetching data from URL = ', 'https://api.carbonintensity.org.uk/intensity/2018-05-01T00:00Z/2018-05-31T23:59Z')\n"
     ]
    }
   ],
   "source": [
    "# Ingesting data using CI API for all data between 01-Jan-2018 and 31-May-2018\n",
    "\n",
    "days_in_month = {1:31, 2:28, 3:31, 4:30, 5:31}\n",
    "start_template = '2018-key-01T00:00Z'\n",
    "end_template = '2018-key-valT23:59Z'\n",
    "base_url = 'https://api.carbonintensity.org.uk/intensity/%s/%s'\n",
    "ci_pdFrame = pd.DataFrame()\n",
    "\n",
    "for key in days_in_month.keys():\n",
    "    period = (start_template.replace('key', '0%s' % key), \\\n",
    "    end_template.replace('key-val', '0%s-%s' % (key, days_in_month.get(key))))\n",
    "    appended_url = base_url % period\n",
    "    print (\"Fetching data from URL = \", appended_url)\n",
    "    fetchedResponse = urlopen(appended_url)\n",
    "    temp_data = json.loads(fetchedResponse.read())['data']\n",
    "    \n",
    "    # Pandas Dataframe\n",
    "    ci_frame_local = pd.DataFrame()\n",
    "    ci_frame_local['intensity'] = [intensity['intensity']['actual'] for intensity in temp_data]\n",
    "    ci_frame_local['timestamp'] = [pd.to_datetime(timestamp['from']) for timestamp in temp_data]\n",
    "    ci_frame_local['classification'] = [timestamp['intensity']['index'] for classification in temp_data]\n",
    "    ci_pdFrame = pd.concat([ci_pdFrame, ci_frame_local], axis=0) \n",
    "\n",
    "ci_pdFrame.set_index('timestamp', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -- Summary statistics of the ingested data --\n",
      "\n",
      "         intensity\n",
      "count  7243.000000\n",
      "mean    261.521055\n",
      "std      72.426902\n",
      "min      99.000000\n",
      "25%     210.000000\n",
      "50%     256.000000\n",
      "75%     312.000000\n",
      "max     476.000000\n",
      "\n",
      " -- Information about the pandas frame --\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7248 entries, 2017-12-31 23:30:00 to 2018-05-31 23:00:00\n",
      "Data columns (total 2 columns):\n",
      "intensity         7243 non-null float64\n",
      "classification    7248 non-null object\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 169.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print (\"\\n -- Summary statistics of the ingested data --\\n\")\n",
    "print (ci_pdFrame.describe())\n",
    "print (\"\\n -- Information about the pandas frame --\\n\")\n",
    "print (ci_pdFrame.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Different Intensity levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_intensity = ci_pdFrame [ci_pdFrame.classification == 'low']\n",
    "moderate_intensity = ci_pdFrame [ci_pdFrame.classification == 'moderate']\n",
    "high_intensity = ci_pdFrame [ci_pdFrame.classification == 'high']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>intensity</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-31 23:30:00</th>\n",
       "      <td>155.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>157.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:30:00</th>\n",
       "      <td>164.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:00:00</th>\n",
       "      <td>171.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:30:00</th>\n",
       "      <td>172.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:00:00</th>\n",
       "      <td>172.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:30:00</th>\n",
       "      <td>170.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:00:00</th>\n",
       "      <td>172.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:30:00</th>\n",
       "      <td>166.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:00:00</th>\n",
       "      <td>157.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     intensity classification\n",
       "timestamp                                    \n",
       "2017-12-31 23:30:00      155.0            low\n",
       "2018-01-01 00:00:00      157.0            low\n",
       "2018-01-01 00:30:00      164.0            low\n",
       "2018-01-01 01:00:00      171.0            low\n",
       "2018-01-01 01:30:00      172.0            low\n",
       "2018-01-01 02:00:00      172.0            low\n",
       "2018-01-01 02:30:00      170.0            low\n",
       "2018-01-01 03:00:00      172.0            low\n",
       "2018-01-01 03:30:00      166.0            low\n",
       "2018-01-01 04:00:00      157.0            low"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Low Intensity\n",
    "low_intensity.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>intensity</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-28 23:30:00</th>\n",
       "      <td>339.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:00:00</th>\n",
       "      <td>348.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 00:30:00</th>\n",
       "      <td>354.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 01:00:00</th>\n",
       "      <td>362.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 01:30:00</th>\n",
       "      <td>350.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 02:00:00</th>\n",
       "      <td>350.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 02:30:00</th>\n",
       "      <td>347.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 03:00:00</th>\n",
       "      <td>347.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 03:30:00</th>\n",
       "      <td>339.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01 04:00:00</th>\n",
       "      <td>350.0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     intensity classification\n",
       "timestamp                                    \n",
       "2018-02-28 23:30:00      339.0       moderate\n",
       "2018-03-01 00:00:00      348.0       moderate\n",
       "2018-03-01 00:30:00      354.0       moderate\n",
       "2018-03-01 01:00:00      362.0       moderate\n",
       "2018-03-01 01:30:00      350.0       moderate\n",
       "2018-03-01 02:00:00      350.0       moderate\n",
       "2018-03-01 02:30:00      347.0       moderate\n",
       "2018-03-01 03:00:00      347.0       moderate\n",
       "2018-03-01 03:30:00      339.0       moderate\n",
       "2018-03-01 04:00:00      350.0       moderate"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moderate Intensity\n",
    "moderate_intensity.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>intensity</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-31 23:30:00</th>\n",
       "      <td>137.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 00:00:00</th>\n",
       "      <td>140.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 00:30:00</th>\n",
       "      <td>147.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 01:00:00</th>\n",
       "      <td>149.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 01:30:00</th>\n",
       "      <td>148.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 02:00:00</th>\n",
       "      <td>151.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 02:30:00</th>\n",
       "      <td>152.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 03:00:00</th>\n",
       "      <td>152.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 03:30:00</th>\n",
       "      <td>149.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 04:00:00</th>\n",
       "      <td>146.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     intensity classification\n",
       "timestamp                                    \n",
       "2018-01-31 23:30:00      137.0           high\n",
       "2018-02-01 00:00:00      140.0           high\n",
       "2018-02-01 00:30:00      147.0           high\n",
       "2018-02-01 01:00:00      149.0           high\n",
       "2018-02-01 01:30:00      148.0           high\n",
       "2018-02-01 02:00:00      151.0           high\n",
       "2018-02-01 02:30:00      152.0           high\n",
       "2018-02-01 03:00:00      152.0           high\n",
       "2018-02-01 03:30:00      149.0           high\n",
       "2018-02-01 04:00:00      146.0           high"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# High Intensity\n",
    "high_intensity.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low = 121.0, High = 99.0, Moderate=102.0\n"
     ]
    }
   ],
   "source": [
    "# Calculating the high, low, and moderate threshold value of the carbon intensity\n",
    "\n",
    "ci_pdFrame_grouped = ci_pdFrame.groupby(by='classification').min()\n",
    "\n",
    "extreme_threshold_val = ci_pdFrame_grouped[ci_pdFrame_grouped.index == 'high'].values[0][0]\n",
    "low_threshold_val = ci_pdFrame_grouped[ci_pdFrame_grouped.index=='low'].values[0][0]\n",
    "moderate_threshold_val = ci_pdFrame_grouped[ci_pdFrame_grouped.index == 'moderate'].values[0][0]\n",
    "\n",
    "print (\"Low = %s, High = %s, Moderate=%s\" % \\\n",
    "       (str(low_threshold_val), str(extreme_threshold_val), str(moderate_threshold_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAHwCAYAAAAWztarAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzSquIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c13bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure (figsize=(15, 8))\n",
    "ci_pdFrame_grouped_hourly = ci_pdFrame.groupby(ci_pdFrame.index.hour)\n",
    "\n",
    "# Box plot visualisation\n",
    "plt.boxplot([hg.intensity for _, hg in ci_pdFrame_grouped_hourly], medianprops = dict(color='blue'))\n",
    "y_min, y_max = plt.ylim()\n",
    "\n",
    "\n",
    "plt.fill_between (x=np.arange(26), y1=np.ones(26)*extreme_threshold_val, y2=np.ones(26)*y_max, \n",
    "                  color='red', alpha=.4, label='Extreme')\n",
    "plt.fill_between (x=np.arange(26), y1=np.ones(26)*moderate_threshold_val, y2=np.ones(26)*extreme_threshold_val, \n",
    "                  color='orange', alpha=.5, label='Moderate')\n",
    "plt.fill_between (x=np.arange(26), y1=np.ones(26)*moderate_threshold_val, y2=np.ones(26)*y_min, \n",
    "                  color='green', alpha=.4, label='Safe')\n",
    "\n",
    "# Visualisation aesthetics\n",
    "plt.ylim (y_min, y_max)\n",
    "plt.title ('Air Carbon Intensity from Jan 1 to May 31, 2018')\n",
    "plt.xlabel ('Day Hours')\n",
    "plt.ylabel (' Carbon Intensity (in gCO_2/kWH)')\n",
    "plt.legend (loc='best', ncol=3, shadow=True, fancybox=True)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}