data_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "\n",
    "prepare data for: \n",
    "\n",
    "ad_viz_plotval_data_ozone_Bronx.csv \n",
    "\n",
    "all_states_data_sorted.csv\n",
    "\n",
    "station number for NY: 725030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "# import my own module\n",
    "from utility import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_oz = pd.read_csv('ad_viz_plotval_data_ozone_Bronx.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_oz.head(2)\n",
    "df_oz = df_oz[ ['Date', 'Daily Max 8-hour Ozone Concentration', 'DAILY_AQI_VALUE'] ]\n",
    "# convert Date to pandas datetime object\n",
    "df_oz.Date = pd.to_datetime(df_oz.Date)\n",
    "# set Date as index column\n",
    "df_oz = df_oz.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Daily Max 8-hour Ozone Concentration</th>\n",
       "      <th>DAILY_AQI_VALUE</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>2012-08-14</th>\n",
       "      <td>0.04</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily Max 8-hour Ozone Concentration  DAILY_AQI_VALUE\n",
       "Date                                                             \n",
       "2012-08-14                                  0.04               37"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oz.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Daily Max 8-hour Ozone Concentration</th>\n",
       "      <th>DAILY_AQI_VALUE</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>2020-05-25</th>\n",
       "      <td>0.029</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily Max 8-hour Ozone Concentration  DAILY_AQI_VALUE\n",
       "Date                                                             \n",
       "2020-05-25                                 0.029               27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oz.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset date range: 2012-08-14  to  2020-05-25\n",
      "Number of missing dates in the dataset : 97 / 2745 \n"
     ]
    }
   ],
   "source": [
    "missing_dates = pd.date_range(df_oz.index.min(), df_oz.index.max()).difference(df_oz.index)\n",
    "print ('Dataset date range:', df_oz.index.min().strftime('%Y-%m-%d'), ' to ' ,df_oz.index.max().strftime('%Y-%m-%d') )    \n",
    "print ('Number of missing dates in the dataset : %d / %d ' % ( len(missing_dates), df_oz.shape[0] ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After filling the missing dates: the # of missing dates: 0\n"
     ]
    }
   ],
   "source": [
    "# work on the missing dates\n",
    "df_oz = df_oz.reindex(  pd.date_range(df_oz.index.min(), df_oz.index.max())  ) \n",
    "# fill those missing dates with the last known date\n",
    "df_oz.ffill(axis=0, inplace=True)\n",
    "# Now move the Date index as one of the columns, easier for later analysis\n",
    "df_oz = df_oz.rename_axis('Date').reset_index()\n",
    "print ( 'After filling the missing dates: the # of missing dates:', df_oz.isnull().any().sum() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicate data\n",
    "print ( df_oz.duplicated(subset='Date', keep='first').sum() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Although there are 0 duplicate data let's remove the duplicate anyway\n",
    "df_oz = df_oz.drop_duplicates(subset='Date', keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Date</th>\n",
       "      <th>Daily Max 8-hour Ozone Concentration</th>\n",
       "      <th>DAILY_AQI_VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-14</td>\n",
       "      <td>0.040</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-08-15</td>\n",
       "      <td>0.045</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Daily Max 8-hour Ozone Concentration  DAILY_AQI_VALUE\n",
       "0 2012-08-14                                 0.040             37.0\n",
       "1 2012-08-15                                 0.045             42.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oz.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:102: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "plt.plot(df_oz.Date, df_oz['Daily Max 8-hour Ozone Concentration'])\n",
    "plt.xticks(fontsize=14, rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_oz.to_csv('bronx_processed_ozone.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('all_states_data_sorted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stn</th>\n",
       "      <th>wban</th>\n",
       "      <th>year</th>\n",
       "      <th>mo</th>\n",
       "      <th>da</th>\n",
       "      <th>temp</th>\n",
       "      <th>count_temp</th>\n",
       "      <th>dewp</th>\n",
       "      <th>count_dewp</th>\n",
       "      <th>slp</th>\n",
       "      <th>...</th>\n",
       "      <th>flag_min</th>\n",
       "      <th>prcp</th>\n",
       "      <th>flag_prcp</th>\n",
       "      <th>sndp</th>\n",
       "      <th>fog</th>\n",
       "      <th>rain_drizzle</th>\n",
       "      <th>snow_ice_pellets</th>\n",
       "      <th>hail</th>\n",
       "      <th>thunder</th>\n",
       "      <th>tornado_funnel_cloud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>42.7</td>\n",
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       "      <td>1022.8</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>51.7</td>\n",
       "      <td>24</td>\n",
       "      <td>48.6</td>\n",
       "      <td>24</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>99.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      stn   wban  year  mo  da  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "0  722950  23174  1944   1   1  59.0           6  42.7           6  1022.8   \n",
       "1  722950  23174  1944   1   2  51.7          24  48.6          24  1022.3   \n",
       "\n",
       "   ...  flag_min   prcp  flag_prcp   sndp  fog  rain_drizzle  \\\n",
       "0  ...         *   0.00          I  999.9    0             0   \n",
       "1  ...         *  99.99        NaN  999.9    1             1   \n",
       "\n",
       "   snow_ice_pellets  hail  thunder  tornado_funnel_cloud  \n",
       "0                 0     0        0                     0  \n",
       "1                 0     0        0                     0  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>wban</th>\n",
       "      <th>year</th>\n",
       "      <th>mo</th>\n",
       "      <th>da</th>\n",
       "      <th>temp</th>\n",
       "      <th>count_temp</th>\n",
       "      <th>dewp</th>\n",
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       "      <th>prcp</th>\n",
       "      <th>flag_prcp</th>\n",
       "      <th>sndp</th>\n",
       "      <th>fog</th>\n",
       "      <th>rain_drizzle</th>\n",
       "      <th>snow_ice_pellets</th>\n",
       "      <th>hail</th>\n",
       "      <th>thunder</th>\n",
       "      <th>tornado_funnel_cloud</th>\n",
       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>722950</td>\n",
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       "      <td>1944</td>\n",
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       "      <td>6</td>\n",
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       "      <td>6</td>\n",
       "      <td>1022.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>I</td>\n",
       "      <td>999.9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1944-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>51.7</td>\n",
       "      <td>24</td>\n",
       "      <td>48.6</td>\n",
       "      <td>24</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>...</td>\n",
       "      <td>99.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1944-01-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      stn   wban  year  mo  da  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "0  722950  23174  1944   1   1  59.0           6  42.7           6  1022.8   \n",
       "1  722950  23174  1944   1   2  51.7          24  48.6          24  1022.3   \n",
       "\n",
       "   ...   prcp  flag_prcp   sndp  fog  rain_drizzle  snow_ice_pellets  hail  \\\n",
       "0  ...   0.00          I  999.9    0             0                 0     0   \n",
       "1  ...  99.99        NaN  999.9    1             1                 0     0   \n",
       "\n",
       "   thunder  tornado_funnel_cloud       Date  \n",
       "0        0                     0 1944-01-01  \n",
       "1        0                     0 1944-01-02  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df[['year', 'mo', 'da']].astype(str).agg('-'.join, axis=1))\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>temp</th>\n",
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       "      <th>thunder</th>\n",
       "      <th>tornado_funnel_cloud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1944-01-01</th>\n",
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       "      <td>23174</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
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       "      <td>6</td>\n",
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       "      <td>6</td>\n",
       "      <td>9999.9</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1944-01-02</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>51.7</td>\n",
       "      <td>24</td>\n",
       "      <td>48.6</td>\n",
       "      <td>24</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>24</td>\n",
       "      <td>9999.9</td>\n",
       "      <td>0</td>\n",
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       "      <td>99.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               stn   wban  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "Date                                                                    \n",
       "1944-01-01  722950  23174  59.0           6  42.7           6  1022.8   \n",
       "1944-01-02  722950  23174  51.7          24  48.6          24  1022.3   \n",
       "\n",
       "            count_slp     stp  count_stp  ...  flag_min   prcp  flag_prcp  \\\n",
       "Date                                      ...                               \n",
       "1944-01-01          6  9999.9          0  ...         *   0.00          I   \n",
       "1944-01-02         24  9999.9          0  ...         *  99.99        NaN   \n",
       "\n",
       "             sndp  fog  rain_drizzle  snow_ice_pellets hail  thunder  \\\n",
       "Date                                                                   \n",
       "1944-01-01  999.9    0             0                 0    0        0   \n",
       "1944-01-02  999.9    1             1                 0    0        0   \n",
       "\n",
       "           tornado_funnel_cloud  \n",
       "Date                             \n",
       "1944-01-01                    0  \n",
       "1944-01-02                    0  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Date')\n",
    "#drop the individual Y, M, D columns and sort as dates\n",
    "df = df.drop(['year', 'mo', 'da'], axis=1).sort_index()\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset date range: 1944-01-01  to  2020-05-18\n",
      "Number of missing dates in the dataset : 10308 / 60473 \n"
     ]
    }
   ],
   "source": [
    "#Check for missing dates\n",
    "missing_dates = pd.date_range(df.index.min(), df.index.max()).difference(df.index)\n",
    "print ('Dataset date range:', df.index.min().strftime('%Y-%m-%d'), ' to ' ,df.index.max().strftime('%Y-%m-%d') )    \n",
    "print ('Number of missing dates in the dataset : %d / %d ' % ( len(missing_dates), df.shape[0] ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the entire date range there is a huge number of missing dates.\n",
    "let's check only last 20 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex([], dtype='datetime64[ns]', freq=None) 0\n"
     ]
    }
   ],
   "source": [
    "md2 = pd.date_range(datetime(1973, 1, 1), df.index.max()).difference(df.index)\n",
    "print (md2, len(md2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing dates since 1973 (last 47 years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>tornado_funnel_cloud</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>1973-01-01</th>\n",
       "      <td>725020</td>\n",
       "      <td>14734</td>\n",
       "      <td>58.9</td>\n",
       "      <td>24</td>\n",
       "      <td>46.7</td>\n",
       "      <td>24</td>\n",
       "      <td>1015.8</td>\n",
       "      <td>24</td>\n",
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       "      <td>20</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>58.6</td>\n",
       "      <td>24</td>\n",
       "      <td>12.9</td>\n",
       "      <td>24</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>24</td>\n",
       "      <td>1015.1</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               stn   wban  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "Date                                                                    \n",
       "1973-01-01  725020  14734  58.9          24  46.7          24  1015.8   \n",
       "1973-01-01  722950  23174  58.6          24  12.9          24  1018.7   \n",
       "\n",
       "            count_slp     stp  count_stp  ...  flag_min  prcp  flag_prcp  \\\n",
       "Date                                      ...                              \n",
       "1973-01-01         24  1014.5         20  ...         *   0.0          D   \n",
       "1973-01-01         24  1015.1         24  ...         *   0.0          A   \n",
       "\n",
       "             sndp  fog  rain_drizzle  snow_ice_pellets hail  thunder  \\\n",
       "Date                                                                   \n",
       "1973-01-01  999.9    0             1                 0    0        0   \n",
       "1973-01-01  999.9    0             0                 0    0        0   \n",
       "\n",
       "           tornado_funnel_cloud  \n",
       "Date                             \n",
       "1973-01-01                    0  \n",
       "1973-01-01                    0  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[df.index>=datetime(1973, 1, 1)]\n",
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset date range: 1973-01-01  to  2020-05-18\n",
      "Number of missing dates in the dataset : 0 / 60188 \n"
     ]
    }
   ],
   "source": [
    "missing_dates = pd.date_range(df2.index.min(), df2.index.max()).difference(df2.index)\n",
    "print ('Dataset date range:', df2.index.min().strftime('%Y-%m-%d'), ' to ' ,df2.index.max().strftime('%Y-%m-%d') )    \n",
    "print ('Number of missing dates in the dataset : %d / %d ' % ( len(missing_dates), df2.shape[0] ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stn</th>\n",
       "      <th>wban</th>\n",
       "      <th>temp</th>\n",
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       "      <th>thunder</th>\n",
       "      <th>tornado_funnel_cloud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>725020</td>\n",
       "      <td>14734</td>\n",
       "      <td>60.9</td>\n",
       "      <td>24</td>\n",
       "      <td>46.4</td>\n",
       "      <td>24</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>24</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>725030</td>\n",
       "      <td>14732</td>\n",
       "      <td>61.0</td>\n",
       "      <td>24</td>\n",
       "      <td>43.9</td>\n",
       "      <td>24</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>722950</td>\n",
       "      <td>23174</td>\n",
       "      <td>63.8</td>\n",
       "      <td>6</td>\n",
       "      <td>58.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1012.3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>725030</td>\n",
       "      <td>14732</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8</td>\n",
       "      <td>44.2</td>\n",
       "      <td>8</td>\n",
       "      <td>1022.4</td>\n",
       "      <td>8</td>\n",
       "      <td>21.4</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>725020</td>\n",
       "      <td>14734</td>\n",
       "      <td>55.6</td>\n",
       "      <td>8</td>\n",
       "      <td>47.6</td>\n",
       "      <td>8</td>\n",
       "      <td>1022.5</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               stn   wban  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "Date                                                                    \n",
       "2020-05-17  725020  14734  60.9          24  46.4          24  1024.0   \n",
       "2020-05-17  725030  14732  61.0          24  43.9          24  1024.1   \n",
       "2020-05-18  722950  23174  63.8           6  58.1           6  1012.3   \n",
       "2020-05-18  725030  14732  57.0           8  44.2           8  1022.4   \n",
       "2020-05-18  725020  14734  55.6           8  47.6           8  1022.5   \n",
       "\n",
       "            count_slp   stp  count_stp  ...  flag_min  prcp  flag_prcp   sndp  \\\n",
       "Date                                    ...                                     \n",
       "2020-05-17         24  23.0         24  ...         *   0.0          G  999.9   \n",
       "2020-05-17         23  23.0         24  ...         *   0.0          G  999.9   \n",
       "2020-05-18          5   0.7          6  ...         *   0.0          G  999.9   \n",
       "2020-05-18          8  21.4          8  ...         *   0.0          G  999.9   \n",
       "2020-05-18          8  21.5          8  ...         *   0.0          G  999.9   \n",
       "\n",
       "            fog  rain_drizzle  snow_ice_pellets hail  thunder  \\\n",
       "Date                                                            \n",
       "2020-05-17    0             0                 0    0        0   \n",
       "2020-05-17    0             0                 0    0        0   \n",
       "2020-05-18    0             0                 0    0        0   \n",
       "2020-05-18    0             0                 0    0        0   \n",
       "2020-05-18    1             0                 0    0        0   \n",
       "\n",
       "           tornado_funnel_cloud  \n",
       "Date                             \n",
       "2020-05-17                    0  \n",
       "2020-05-17                    0  \n",
       "2020-05-18                    0  \n",
       "2020-05-18                    0  \n",
       "2020-05-18                10000  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking with duplicates \n",
    "# lot's of duplicates\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2= df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Date</th>\n",
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       "      <th>0</th>\n",
       "      <td>1973-01-01</td>\n",
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       "      <td>1973-01-01</td>\n",
       "      <td>722950</td>\n",
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       "      <td>58.6</td>\n",
       "      <td>24</td>\n",
       "      <td>12.9</td>\n",
       "      <td>24</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>24</td>\n",
       "      <td>1015.1</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     stn   wban  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "0 1973-01-01  725020  14734  58.9          24  46.7          24  1015.8   \n",
       "1 1973-01-01  722950  23174  58.6          24  12.9          24  1018.7   \n",
       "\n",
       "   count_slp     stp  ...  flag_min  prcp  flag_prcp   sndp  fog  \\\n",
       "0         24  1014.5  ...         *   0.0          D  999.9    0   \n",
       "1         24  1015.1  ...         *   0.0          A  999.9    0   \n",
       "\n",
       "   rain_drizzle  snow_ice_pellets  hail thunder  tornado_funnel_cloud  \n",
       "0             1                 0     0       0                     0  \n",
       "1             0                 0     0       0                     0  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are there any duplicates? True\n"
     ]
    }
   ],
   "source": [
    "# check for the duplicates\n",
    "print ('Are there any duplicates?', df2['Date'].duplicated().any() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4        False\n",
       "         ...  \n",
       "60183     True\n",
       "60184     True\n",
       "60185    False\n",
       "60186     True\n",
       "60187     True\n",
       "Name: Date, Length: 60188, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Date.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.drop_duplicates(subset='Date', keep='first').set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are there any duplicates? False\n"
     ]
    }
   ],
   "source": [
    "print ('Are there any duplicates?', df2.index.duplicated().any() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1973-01-01</th>\n",
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       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-02</th>\n",
       "      <td>725020</td>\n",
       "      <td>14734</td>\n",
       "      <td>41.0</td>\n",
       "      <td>24</td>\n",
       "      <td>23.9</td>\n",
       "      <td>24</td>\n",
       "      <td>1020.6</td>\n",
       "      <td>24</td>\n",
       "      <td>1019.6</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               stn   wban  temp  count_temp  dewp  count_dewp     slp  \\\n",
       "Date                                                                    \n",
       "1973-01-01  725020  14734  58.9          24  46.7          24  1015.8   \n",
       "1973-01-02  725020  14734  41.0          24  23.9          24  1020.6   \n",
       "\n",
       "            count_slp     stp  count_stp  ...  flag_min  prcp  flag_prcp  \\\n",
       "Date                                      ...                              \n",
       "1973-01-01         24  1014.5         20  ...         *   0.0          D   \n",
       "1973-01-02         24  1019.6         24  ...         *   0.0          D   \n",
       "\n",
       "             sndp  fog  rain_drizzle  snow_ice_pellets hail  thunder  \\\n",
       "Date                                                                   \n",
       "1973-01-01  999.9    0             1                 0    0        0   \n",
       "1973-01-02  999.9    0             0                 0    0        0   \n",
       "\n",
       "           tornado_funnel_cloud  \n",
       "Date                             \n",
       "1973-01-01                    0  \n",
       "1973-01-02                    0  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "plt.plot(df2.index, df2['temp'])\n",
    "plt.xticks(fontsize=14, rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('bronx_processed_temp.csv', index=True)"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}