Jaipur_Data Formation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "from collections import namedtuple\n",
    "import pandas as pd\n",
    "import requests\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "API_KEY = 'Insert API Key Here'\n",
    "BASE_URL = \"http://api.wunderground.com/api/{}/history_{}/q/26.9124,75.7873.json\" #latitude and longitude of jaipur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_date = datetime(2018, 3, 12)\n",
    "features = [\"date\", \"meantempm\", \"meandewptm\", \"meanpressurem\", \"maxhumidity\", \"minhumidity\", \"maxtempm\",\n",
    "            \"mintempm\", \"maxdewptm\", \"mindewptm\", \"maxpressurem\", \"minpressurem\", \"precipm\"]\n",
    "DailySummary = namedtuple(\"DailySummary\", features) # date to be change after the getting the exact 500 dataset to increase further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_weather_data(url, api_key, target_date, days):\n",
    "    records = []\n",
    "    for _ in range(days):\n",
    "        request = BASE_URL.format(API_KEY, target_date.strftime('%Y%m%d'))\n",
    "        response = requests.get(request)\n",
    "        if response.status_code == 200:\n",
    "            data = response.json()['history']['dailysummary'][0]\n",
    "            records.append(DailySummary(\n",
    "                date=target_date,\n",
    "                meantempm=data['meantempm'],\n",
    "                meandewptm=data['meandewptm'],\n",
    "                meanpressurem=data['meanpressurem'],\n",
    "                maxhumidity=data['maxhumidity'],\n",
    "                minhumidity=data['minhumidity'],\n",
    "                maxtempm=data['maxtempm'],\n",
    "                mintempm=data['mintempm'],\n",
    "                maxdewptm=data['maxdewptm'],\n",
    "                mindewptm=data['mindewptm'],\n",
    "                maxpressurem=data['maxpressurem'],\n",
    "                minpressurem=data['minpressurem'],\n",
    "                precipm=data['precipm']))\n",
    "        time.sleep(6)\n",
    "        target_date += timedelta(days=1)\n",
    "    return records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'history'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-46dc948db4fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrecords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_weather_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBASE_URL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAPI_KEY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-4-adb05c4c1911>\u001b[0m in \u001b[0;36mextract_weather_data\u001b[1;34m(url, api_key, target_date, days)\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'history'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'dailysummary'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m             records.append(DailySummary(\n\u001b[0;32m      9\u001b[0m                 \u001b[0mdate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtarget_date\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'history'"
     ]
    }
   ],
   "source": [
    "records = extract_weather_data(BASE_URL, API_KEY, target_date, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "records += extract_weather_data(BASE_URL, API_KEY, target_date, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(records, columns=features).set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantempm</th>\n",
       "      <th>meandewptm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-12</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-13</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-15</th>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-17</th>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-18</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-19</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           meantempm meandewptm\n",
       "date                           \n",
       "2018-03-12        27          3\n",
       "2018-03-13        28          1\n",
       "2018-03-14        27          2\n",
       "2018-03-15        26          5\n",
       "2018-03-16        26          4\n",
       "2018-03-17        24          2\n",
       "2018-03-18        25          1\n",
       "2018-03-19        26          3\n",
       "2018-03-20        27          7\n",
       "2018-03-21        26          2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[['meantempm', 'meandewptm']].tail(10)\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantempm</th>\n",
       "      <th>meandewptm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-12</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-13</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-15</th>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-17</th>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-18</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-19</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           meantempm meandewptm\n",
       "date                           \n",
       "2018-03-12        27          3\n",
       "2018-03-13        28          1\n",
       "2018-03-14        27          2\n",
       "2018-03-15        26          5\n",
       "2018-03-16        26          4\n",
       "2018-03-17        24          2\n",
       "2018-03-18        25          1\n",
       "2018-03-19        26          3\n",
       "2018-03-20        27          7\n",
       "2018-03-21        26          2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[['meantempm', 'meandewptm']].head(10)\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('JaipurRawData3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('JaipurRawData3.csv').set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantempm</th>\n",
       "      <th>meandewptm</th>\n",
       "    </tr>\n",
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       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>34</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-02</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-03</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-04</th>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-05</th>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-06</th>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-07</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-08</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            meantempm  meandewptm\n",
       "date                             \n",
       "2016-05-01         34          -1\n",
       "2016-05-02         36           4\n",
       "2016-05-03         35           6\n",
       "2016-05-04         34           7\n",
       "2016-05-05         31          11\n",
       "2016-05-06         28          13\n",
       "2016-05-07         30          10\n",
       "2016-05-08         34           8\n",
       "2016-05-09         34          11\n",
       "2016-05-10         34          16"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[['meantempm', 'meandewptm']].head(10)  \n",
    "tmp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>meantempm</th>\n",
       "      <th>meandewptm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-02</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-03</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-04</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-06</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-07</th>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-09</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-10</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            meantempm  meandewptm\n",
       "date                             \n",
       "2018-03-02         26           6\n",
       "2018-03-03         26           4\n",
       "2018-03-04         25           8\n",
       "2018-03-05         23           7\n",
       "2018-03-06         22           4\n",
       "2018-03-07         24           2\n",
       "2018-03-08         24           1\n",
       "2018-03-09         26           3\n",
       "2018-03-10         26           4\n",
       "2018-03-11         26           3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[['meantempm', 'meandewptm']].tail(10)  \n",
    "tmp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantempm</th>\n",
       "      <th>meandewptm</th>\n",
       "      <th>meantempm_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>34</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-02</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-03</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-04</th>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-05</th>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-06</th>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-07</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-08</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            meantempm  meandewptm  meantempm_1\n",
       "date                                          \n",
       "2016-05-01         34          -1          NaN\n",
       "2016-05-02         36           4         34.0\n",
       "2016-05-03         35           6         36.0\n",
       "2016-05-04         34           7         35.0\n",
       "2016-05-05         31          11         34.0\n",
       "2016-05-06         28          13         31.0\n",
       "2016-05-07         30          10         28.0\n",
       "2016-05-08         34           8         30.0\n",
       "2016-05-09         34          11         34.0\n",
       "2016-05-10         34          16         34.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 day prior\n",
    "N = 1\n",
    "\n",
    "# target measurement of mean temperature\n",
    "feature = 'meantempm'\n",
    "\n",
    "# total number of rows\n",
    "rows = tmp.shape[0]\n",
    "\n",
    "# a list representing Nth prior measurements of feature\n",
    "# notice that the front of the list needs to be padded with N\n",
    "# None values to maintain the constistent rows length for each N\n",
    "nth_prior_measurements = [None]*N + [tmp[feature][i-N] for i in range(N, rows)]\n",
    "\n",
    "# make a new column name of feature_N and add to DataFrame\n",
    "col_name = \"{}_{}\".format(feature, N)\n",
    "tmp[col_name] = nth_prior_measurements\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def derive_nth_day_feature(df, feature, N):\n",
    "    rows = df.shape[0]\n",
    "    nth_prior_meassurements = [None]*N + [df[feature][i-N] for i in range(N, rows)]\n",
    "    col_name = \"{}_{}\".format(feature, N)\n",
    "    df[col_name] = nth_prior_meassurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in features:\n",
    "    if feature != 'date':\n",
    "        for N in range(1, 4):\n",
    "            derive_nth_day_feature(df, feature, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['meantempm', 'meandewptm', 'meanpressurem', 'maxhumidity',\n",
       "       'minhumidity', 'maxtempm', 'mintempm', 'maxdewptm', 'mindewptm',\n",
       "       'maxpressurem', 'minpressurem', 'precipm', 'meantempm_1', 'meantempm_2',\n",
       "       'meantempm_3', 'meandewptm_1', 'meandewptm_2', 'meandewptm_3',\n",
       "       'meanpressurem_1', 'meanpressurem_2', 'meanpressurem_3',\n",
       "       'maxhumidity_1', 'maxhumidity_2', 'maxhumidity_3', 'minhumidity_1',\n",
       "       'minhumidity_2', 'minhumidity_3', 'maxtempm_1', 'maxtempm_2',\n",
       "       'maxtempm_3', 'mintempm_1', 'mintempm_2', 'mintempm_3', 'maxdewptm_1',\n",
       "       'maxdewptm_2', 'maxdewptm_3', 'mindewptm_1', 'mindewptm_2',\n",
       "       'mindewptm_3', 'maxpressurem_1', 'maxpressurem_2', 'maxpressurem_3',\n",
       "       'minpressurem_1', 'minpressurem_2', 'minpressurem_3', 'precipm_1',\n",
       "       'precipm_2', 'precipm_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['meantempm', 'maxtempm', 'mintempm', 'meantempm_1', 'meantempm_2',\n",
       "       'meantempm_3', 'meandewptm_1', 'meandewptm_2', 'meandewptm_3',\n",
       "       'meanpressurem_1', 'meanpressurem_2', 'meanpressurem_3',\n",
       "       'maxhumidity_1', 'maxhumidity_2', 'maxhumidity_3', 'minhumidity_1',\n",
       "       'minhumidity_2', 'minhumidity_3', 'maxtempm_1', 'maxtempm_2',\n",
       "       'maxtempm_3', 'mintempm_1', 'mintempm_2', 'mintempm_3', 'maxdewptm_1',\n",
       "       'maxdewptm_2', 'maxdewptm_3', 'mindewptm_1', 'mindewptm_2',\n",
       "       'mindewptm_3', 'maxpressurem_1', 'maxpressurem_2', 'maxpressurem_3',\n",
       "       'minpressurem_1', 'minpressurem_2', 'minpressurem_3', 'precipm_1',\n",
       "       'precipm_2', 'precipm_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make list of original features without meantempm, mintempm, and maxtempm\n",
    "to_remove = [feature \n",
    "             for feature in features \n",
    "             if feature not in ['meantempm', 'mintempm', 'maxtempm']]\n",
    "\n",
    "# make a list of columns to keep\n",
    "to_keep = [col for col in df.columns if col not in to_remove]\n",
    "\n",
    "# select only the columns in to_keep and assign to df\n",
    "df = df[to_keep]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 679 entries, 2016-05-01 to 2018-03-11\n",
      "Data columns (total 39 columns):\n",
      "meantempm          679 non-null int64\n",
      "maxtempm           679 non-null int64\n",
      "mintempm           679 non-null int64\n",
      "meantempm_1        678 non-null float64\n",
      "meantempm_2        677 non-null float64\n",
      "meantempm_3        676 non-null float64\n",
      "meandewptm_1       678 non-null float64\n",
      "meandewptm_2       677 non-null float64\n",
      "meandewptm_3       676 non-null float64\n",
      "meanpressurem_1    678 non-null float64\n",
      "meanpressurem_2    677 non-null float64\n",
      "meanpressurem_3    676 non-null float64\n",
      "maxhumidity_1      678 non-null float64\n",
      "maxhumidity_2      677 non-null float64\n",
      "maxhumidity_3      676 non-null float64\n",
      "minhumidity_1      678 non-null float64\n",
      "minhumidity_2      677 non-null float64\n",
      "minhumidity_3      676 non-null float64\n",
      "maxtempm_1         678 non-null float64\n",
      "maxtempm_2         677 non-null float64\n",
      "maxtempm_3         676 non-null float64\n",
      "mintempm_1         678 non-null float64\n",
      "mintempm_2         677 non-null float64\n",
      "mintempm_3         676 non-null float64\n",
      "maxdewptm_1        678 non-null float64\n",
      "maxdewptm_2        677 non-null float64\n",
      "maxdewptm_3        676 non-null float64\n",
      "mindewptm_1        678 non-null float64\n",
      "mindewptm_2        677 non-null float64\n",
      "mindewptm_3        676 non-null float64\n",
      "maxpressurem_1     678 non-null float64\n",
      "maxpressurem_2     677 non-null float64\n",
      "maxpressurem_3     676 non-null float64\n",
      "minpressurem_1     678 non-null float64\n",
      "minpressurem_2     677 non-null float64\n",
      "minpressurem_3     676 non-null float64\n",
      "precipm_1          678 non-null float64\n",
      "precipm_2          677 non-null float64\n",
      "precipm_3          676 non-null float64\n",
      "dtypes: float64(36), int64(3)\n",
      "memory usage: 232.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 679 entries, 2016-05-01 to 2018-03-11\n",
      "Data columns (total 39 columns):\n",
      "meantempm          679 non-null int64\n",
      "maxtempm           679 non-null int64\n",
      "mintempm           679 non-null int64\n",
      "meantempm_1        678 non-null float64\n",
      "meantempm_2        677 non-null float64\n",
      "meantempm_3        676 non-null float64\n",
      "meandewptm_1       678 non-null float64\n",
      "meandewptm_2       677 non-null float64\n",
      "meandewptm_3       676 non-null float64\n",
      "meanpressurem_1    678 non-null float64\n",
      "meanpressurem_2    677 non-null float64\n",
      "meanpressurem_3    676 non-null float64\n",
      "maxhumidity_1      678 non-null float64\n",
      "maxhumidity_2      677 non-null float64\n",
      "maxhumidity_3      676 non-null float64\n",
      "minhumidity_1      678 non-null float64\n",
      "minhumidity_2      677 non-null float64\n",
      "minhumidity_3      676 non-null float64\n",
      "maxtempm_1         678 non-null float64\n",
      "maxtempm_2         677 non-null float64\n",
      "maxtempm_3         676 non-null float64\n",
      "mintempm_1         678 non-null float64\n",
      "mintempm_2         677 non-null float64\n",
      "mintempm_3         676 non-null float64\n",
      "maxdewptm_1        678 non-null float64\n",
      "maxdewptm_2        677 non-null float64\n",
      "maxdewptm_3        676 non-null float64\n",
      "mindewptm_1        678 non-null float64\n",
      "mindewptm_2        677 non-null float64\n",
      "mindewptm_3        676 non-null float64\n",
      "maxpressurem_1     678 non-null float64\n",
      "maxpressurem_2     677 non-null float64\n",
      "maxpressurem_3     676 non-null float64\n",
      "minpressurem_1     678 non-null float64\n",
      "minpressurem_2     677 non-null float64\n",
      "minpressurem_3     676 non-null float64\n",
      "precipm_1          678 non-null float64\n",
      "precipm_2          677 non-null float64\n",
      "precipm_3          676 non-null float64\n",
      "dtypes: float64(36), int64(3)\n",
      "memory usage: 232.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.apply(pd.to_numeric, errors='coerce')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:12: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>outliers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mindewptm_1</th>\n",
       "      <td>678.0</td>\n",
       "      <td>7.454277</td>\n",
       "      <td>11.696310</td>\n",
       "      <td>-94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mindewptm_2</th>\n",
       "      <td>677.0</td>\n",
       "      <td>7.465288</td>\n",
       "      <td>11.701441</td>\n",
       "      <td>-94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mindewptm_3</th>\n",
       "      <td>676.0</td>\n",
       "      <td>7.477811</td>\n",
       "      <td>11.705565</td>\n",
       "      <td>-94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipm_1</th>\n",
       "      <td>678.0</td>\n",
       "      <td>1.246903</td>\n",
       "      <td>5.428048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipm_2</th>\n",
       "      <td>677.0</td>\n",
       "      <td>1.248744</td>\n",
       "      <td>5.431849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipm_3</th>\n",
       "      <td>676.0</td>\n",
       "      <td>1.250592</td>\n",
       "      <td>5.435659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count      mean        std   min  25%  50%   75%   max  outliers\n",
       "mindewptm_1  678.0  7.454277  11.696310 -94.0  0.0  6.0  18.0  25.0      True\n",
       "mindewptm_2  677.0  7.465288  11.701441 -94.0  0.0  6.0  18.0  25.0      True\n",
       "mindewptm_3  676.0  7.477811  11.705565 -94.0  0.0  6.0  18.0  25.0      True\n",
       "precipm_1    678.0  1.246903   5.428048   0.0  0.0  0.0   0.0  57.0      True\n",
       "precipm_2    677.0  1.248744   5.431849   0.0  0.0  0.0   0.0  57.0      True\n",
       "precipm_3    676.0  1.250592   5.435659   0.0  0.0  0.0   0.0  57.0      True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call describe on df and transpose it due to the large number of columns\n",
    "spread = df.describe().T\n",
    "\n",
    "# precalculate interquartile range for ease of use in next calculation\n",
    "IQR = spread['75%'] - spread['25%']\n",
    "\n",
    "# create an outliers column which is either 3 IQRs below the first quartile or\n",
    "# 3 IQRs above the third quartile\n",
    "spread['outliers'] = (spread['min']<(spread['25%']-(3*IQR)))|(spread['max'] > (spread['75%']+3*IQR))\n",
    "\n",
    "# just display the features containing extreme outliers\n",
    "spread.ix[spread.outliers,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [14, 8]\n",
    "df.maxhumidity_1.hist()\n",
    "plt.title('Distribution of maxhumidity_1')\n",
    "plt.xlabel('maxhumidity_1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.minpressurem_1.hist()\n",
    "plt.title('Distribution of minpressurem_1')\n",
    "plt.xlabel('minpressurem_1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# iterate over the precip columns\n",
    "for precip_col in ['precipm_1', 'precipm_2', 'precipm_3']:\n",
    "    # create a boolean array of values representing nans\n",
    "    missing_vals = pd.isnull(df[precip_col])\n",
    "    df[precip_col][missing_vals] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 676 entries, 2016-05-04 to 2018-03-11\n",
      "Data columns (total 39 columns):\n",
      "meantempm          676 non-null int64\n",
      "maxtempm           676 non-null int64\n",
      "mintempm           676 non-null int64\n",
      "meantempm_1        676 non-null float64\n",
      "meantempm_2        676 non-null float64\n",
      "meantempm_3        676 non-null float64\n",
      "meandewptm_1       676 non-null float64\n",
      "meandewptm_2       676 non-null float64\n",
      "meandewptm_3       676 non-null float64\n",
      "meanpressurem_1    676 non-null float64\n",
      "meanpressurem_2    676 non-null float64\n",
      "meanpressurem_3    676 non-null float64\n",
      "maxhumidity_1      676 non-null float64\n",
      "maxhumidity_2      676 non-null float64\n",
      "maxhumidity_3      676 non-null float64\n",
      "minhumidity_1      676 non-null float64\n",
      "minhumidity_2      676 non-null float64\n",
      "minhumidity_3      676 non-null float64\n",
      "maxtempm_1         676 non-null float64\n",
      "maxtempm_2         676 non-null float64\n",
      "maxtempm_3         676 non-null float64\n",
      "mintempm_1         676 non-null float64\n",
      "mintempm_2         676 non-null float64\n",
      "mintempm_3         676 non-null float64\n",
      "maxdewptm_1        676 non-null float64\n",
      "maxdewptm_2        676 non-null float64\n",
      "maxdewptm_3        676 non-null float64\n",
      "mindewptm_1        676 non-null float64\n",
      "mindewptm_2        676 non-null float64\n",
      "mindewptm_3        676 non-null float64\n",
      "maxpressurem_1     676 non-null float64\n",
      "maxpressurem_2     676 non-null float64\n",
      "maxpressurem_3     676 non-null float64\n",
      "minpressurem_1     676 non-null float64\n",
      "minpressurem_2     676 non-null float64\n",
      "minpressurem_3     676 non-null float64\n",
      "precipm_1          676 non-null float64\n",
      "precipm_2          676 non-null float64\n",
      "precipm_3          676 non-null float64\n",
      "dtypes: float64(36), int64(3)\n",
      "memory usage: 211.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('JaipurFinalCleanData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}