CombineFlight_WeatherData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BYNlbp71M7NA",
    "outputId": "a368ad84-a5c6-44b3-d6c5-66e26c79b055"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "#Garbage Collector\n",
    "import gc\n",
    "import pickle\n",
    "\n",
    "from google.colab import drive\n",
    "\n",
    "drive.mount('/content/gdrive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ny5j9wt_NJRQ"
   },
   "outputs": [],
   "source": [
    "flightDataPath = 'gdrive/My Drive/FlightDelayPrediction/FlightData/'\n",
    "weatherDataPath = 'gdrive/My Drive/FlightDelayPrediction/WeatherData/'\n",
    "valid_airports = ('ATL','CLT','DEN','1DFW','EWR','IAH','JFK','LAS','LAX','MCO','MIA','ORD','PHX','SEA','SFO')\n",
    "years=['2016','2017']\n",
    "reqd_flight_columns=set(('FlightDate','Quarter','Year','Month','DayofMonth','DepTime','DepDelay15','CRSDepTime','Origin','Dest',\\\n",
    "                     'DepDelayMinutes','ArrTime','CRSArrTime','ArrDel15','ArrDelayMinutes'))\n",
    "master_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GAEGhBF1izUo"
   },
   "source": [
    "# Generating Weather DataFrame\n",
    "\n",
    "the corresponding airport id like 'JFK' has been appended to the weather data for each airport for each month in order to make the join between flight data and weather data easier. The same weather dataframe has been **saved as a pickle file** inorder to save computation during future runs of the program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kDJ-aU02RC2_"
   },
   "outputs": [],
   "source": [
    "weather_df = pd.DataFrame()\n",
    "\n",
    "f = None\n",
    "jsonOb = None\n",
    "\n",
    "\n",
    "#Checks to see if previously generated pickle file is present, else generate new weather dictionary\n",
    "try:\n",
    "  pkl_file = open(weatherDataPath+'WeatherData.pkl','rb')\n",
    "  weather_df = pickle.load(pkl_file)\n",
    "  pkl_file.close()\n",
    "\n",
    "except FileNotFoundError:\n",
    "\n",
    "  pkl_file = open(weatherDataPath+'WeatherData.pkl','wb')\n",
    "\n",
    "  i=False\n",
    "  print('Computing!')\n",
    "\n",
    "  for airport in valid_airports:\n",
    "    for year in years:\n",
    "      for month in range(1,12+1):\n",
    "        windspeedKmph=[]\n",
    "        FeelsLikeF=[]\n",
    "        winddir16Point=[]\n",
    "        FeelsLikeC=[]\n",
    "        DewPointC=[]\n",
    "        windspeedMiles=[]\n",
    "        DewPointF=[]\n",
    "        HeatIndexF=[]\n",
    "        cloudcover=[]\n",
    "        HeatIndexC=[]\n",
    "        precipMM=[]\n",
    "        weatherIconUrl=[]\n",
    "        WindGustMiles=[]\n",
    "        pressure=[]\n",
    "        WindGustKmph=[]\n",
    "        weatherDesc=[]\n",
    "        visibility=[]\n",
    "        weatherCode=[]\n",
    "        tempC=[]\n",
    "        tempF=[]\n",
    "        WindChillF=[]\n",
    "        WindChillC=[]\n",
    "        winddirDegree=[]\n",
    "        humidity=[]\n",
    "        time=[]\n",
    "\n",
    "        f = open(weatherDataPath + airport + '/' + year + '-' + str(month)+'.json')\n",
    "        jsonOb = json.load(f)\n",
    "        temp_df = pd.DataFrame(jsonOb['data']['weather'])\n",
    "        hourly_data = temp_df['hourly']\n",
    "        temp_df = temp_df.drop(columns=['hourly','astronomy'])\n",
    "\n",
    "        temp_df = pd.concat([temp_df]*24,ignore_index=True)\n",
    "        index =temp_df.columns.get_loc('date')\n",
    "        temp_df = pd.DataFrame(sorted(temp_df.values,key=lambda x: x[index].split('-')[::-1]),columns=temp_df.columns)\n",
    "        \n",
    "        for i in range(len(hourly_data)):\n",
    "          for j in range(len(hourly_data[0])):\n",
    "            windspeedKmph.append(hourly_data[i][j]['windspeedKmph'])\n",
    "            cloudcover.append(hourly_data[i][j]['cloudcover'])\n",
    "            precipMM.append(hourly_data[i][j]['precipMM'])\n",
    "            pressure.append(hourly_data[i][j]['pressure'])\n",
    "            WindGustKmph.append(hourly_data[i][j]['WindGustKmph'])\n",
    "            visibility.append(hourly_data[i][j]['visibility'])\n",
    "            tempF.append(hourly_data[i][j]['tempF'])\n",
    "            WindChillF.append(hourly_data[i][j]['WindChillF'])\n",
    "            winddirDegree.append(hourly_data[i][j]['winddirDegree'])\n",
    "            humidity.append(hourly_data[i][j]['humidity'])\n",
    "            time.append(hourly_data[i][j]['time'])\n",
    "            weatherCode.append(hourly_data[i][j]['weatherCode'])\n",
    "            DewPointF.append(hourly_data[i][j]['DewPointF'])\n",
    "\n",
    "        compiled_hourly = pd.DataFrame()\n",
    "        compiled_hourly['windspeedKmph']=windspeedKmph\n",
    "        compiled_hourly['DewPointF']=DewPointF\n",
    "        compiled_hourly['cloudcover']=cloudcover\n",
    "        compiled_hourly['precipMM']=precipMM\n",
    "        compiled_hourly['pressure']=pressure\n",
    "        compiled_hourly['WindGustKmph']=WindGustKmph\n",
    "        compiled_hourly['visibility']=visibility\n",
    "        compiled_hourly['weatherCode']=weatherCode\n",
    "        compiled_hourly['tempF']=tempF\n",
    "        compiled_hourly['WindChillF']=WindChillF\n",
    "        compiled_hourly['winddirDegree']=winddirDegree\n",
    "        compiled_hourly['humidity']=humidity\n",
    "        compiled_hourly['time']=time\n",
    "        temp_df = pd.concat([temp_df,compiled_hourly],axis=1)\n",
    "        airportList = [airport for i in range(temp_df.shape[0])]\n",
    "        temp_df['Airport']=airportList\n",
    "        \n",
    "        if i:\n",
    "          weather_df = pd.concat([weather_df,temp_df],ignore_index=True)\n",
    "        else:\n",
    "          weather_df = temp_df\n",
    "        i = True\n",
    "        f.close()\n",
    "\n",
    "  weather_df.drop(columns=['mintempC','maxtempF','mintempF','maxtempC','sunHour','uvIndex'],inplace=True)\n",
    "  pickle.dump(weather_df,pkl_file)\n",
    "  pkl_file.close()\n",
    "      \n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-CXNNV2bWdFq",
    "outputId": "13ab42d3-7bb2-4eb8-b315-2e899194c332"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263160, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "id": "dNTdlAKzp6t1",
    "outputId": "7bc225b3-cd9b-4f08-bf5c-2ebbfb8a73c4"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalSnow_cm</th>\n",
       "      <th>date</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>pressure</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>visibility</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>humidity</th>\n",
       "      <th>time</th>\n",
       "      <th>Airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
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       "      <td>ATL</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>63</td>\n",
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       "      <td>1023</td>\n",
       "      <td>15</td>\n",
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       "      <td>303</td>\n",
       "      <td>59</td>\n",
       "      <td>1700</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>73</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1800</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>119</td>\n",
       "      <td>45</td>\n",
       "      <td>41</td>\n",
       "      <td>302</td>\n",
       "      <td>64</td>\n",
       "      <td>1900</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>116</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>301</td>\n",
       "      <td>68</td>\n",
       "      <td>2000</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>9</td>\n",
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       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>299</td>\n",
       "      <td>71</td>\n",
       "      <td>2100</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>9</td>\n",
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       "      <td>297</td>\n",
       "      <td>73</td>\n",
       "      <td>2200</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
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       "      <td>2016-01-02</td>\n",
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       "      <td>34</td>\n",
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       "      <td>2300</td>\n",
       "      <td>ATL</td>\n",
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       "    <tr>\n",
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       "      <td>2016-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10</td>\n",
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       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>293</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>ATL</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>116</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>297</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "      <td>301</td>\n",
       "      <td>77</td>\n",
       "      <td>200</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalSnow_cm        date windspeedKmph  ... humidity  time Airport\n",
       "40          0.0  2016-01-02            12  ...       58  1600     ATL\n",
       "41          0.0  2016-01-02            11  ...       59  1700     ATL\n",
       "42          0.0  2016-01-02            11  ...       60  1800     ATL\n",
       "43          0.0  2016-01-02            10  ...       64  1900     ATL\n",
       "44          0.0  2016-01-02             9  ...       68  2000     ATL\n",
       "45          0.0  2016-01-02             9  ...       71  2100     ATL\n",
       "46          0.0  2016-01-02             9  ...       73  2200     ATL\n",
       "47          0.0  2016-01-02             9  ...       74  2300     ATL\n",
       "48          0.0  2016-01-03            10  ...       75     0     ATL\n",
       "49          0.0  2016-01-03            10  ...       76   100     ATL\n",
       "50          0.0  2016-01-03            10  ...       77   200     ATL\n",
       "\n",
       "[11 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.loc[40:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ll5AAelylLXX"
   },
   "source": [
    "# Joining Flight Data of All Flights with the weather data of the departure airport of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pzlbiJFTiZqk",
    "outputId": "8f08ad19-39cc-410f-ad9c-acfbd09e9de9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating File!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "  joined_pkl = open(flightDataPath + 'JoinedData.pkl','rb')\n",
    "  print('Loaded from File!')\n",
    "  joined_df = pickle.load(joined_pkl)\n",
    "  joined_pkl.close()\n",
    "\n",
    "except FileNotFoundError:    \n",
    "  print('Generating File!')\n",
    "  joined_df = pd.DataFrame()\n",
    "  temp_df = pd.DataFrame()\n",
    "\n",
    "  i=False\n",
    "\n",
    "  for year in years:\n",
    "    for months in range(1,12+1):\n",
    "      nearest_hour=[]\n",
    "      temp_df = pd.read_csv(flightDataPath + year +'/' + str(months) + '.csv',low_memory=False)\n",
    "      \n",
    "\n",
    "      temp_df.dropna(subset=['DepTime','FlightDate','Origin'],inplace=True)\n",
    "      temp_df.drop(columns=list(set(temp_df.columns)-reqd_flight_columns),inplace=True)\n",
    "      \n",
    "      \n",
    "      for i in range(len(temp_df)):\n",
    "        time = temp_df.iloc[i]['DepTime']\n",
    "        hr = time // 100\n",
    "        mm = time % 100\n",
    "        if mm >= 30:\n",
    "          hr += 1\n",
    "          if hr == 24:\n",
    "            hr  = 23\n",
    "        time = hr * 100\n",
    "        if time == 0:\n",
    "          nearest_hour.append('0')\n",
    "        else:\n",
    "          nearest_hour.append(str(int(time)))\n",
    "      temp_df['NearestDepTime']=nearest_hour         \n",
    "      temp_df = pd.merge(temp_df,weather_df,left_on=['FlightDate','Origin','NearestDepTime'],right_on=['date','Airport','time'],sort=False)\n",
    "      if i:\n",
    "        joined_df = pd.concat([joined_df,temp_df],ignore_index=True, sort=False)\n",
    "      else:\n",
    "        joined_df = temp_df\n",
    "      i=True\n",
    "  joined_df.drop(columns=['Airport'],inplace=True)\n",
    "  joined_pkl = open(flightDataPath + 'JoinedData.pkl','wb')\n",
    "  pickle.dump(joined_df,joined_pkl)\n",
    "  joined_pkl.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "xlcUA8fYlrvk",
    "outputId": "71f6316e-4a53-44ad-f53d-2bdf75e9087c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Quarter', 'Month', 'DayofMonth', 'FlightDate', 'Origin',\n",
       "       'Dest', 'CRSDepTime', 'DepTime', 'DepDelayMinutes', 'CRSArrTime',\n",
       "       'ArrTime', 'ArrDelayMinutes', 'ArrDel15', 'NearestDepTime',\n",
       "       'totalSnow_cm', 'date', 'windspeedKmph', 'DewPointF', 'cloudcover',\n",
       "       'precipMM', 'pressure', 'WindGustKmph', 'visibility', 'weatherCode',\n",
       "       'tempF', 'WindChillF', 'winddirDegree', 'humidity', 'time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "c1KLy-SzLxVC",
    "outputId": "1507e143-929d-4dad-b963-25e9bc120322"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1855994, 30)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "pHCSLWRTnUvk",
    "outputId": "f9885eb6-b8d9-4150-f337-a45cc6cd50ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1610.0\n",
       "1          1237.0\n",
       "2          1010.0\n",
       "3          1138.0\n",
       "4          1401.0\n",
       "            ...  \n",
       "1855989     134.0\n",
       "1855990     144.0\n",
       "1855991     759.0\n",
       "1855992    1831.0\n",
       "1855993    2007.0\n",
       "Name: ArrTime, Length: 1855994, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df['ArrTime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "h5CFdPa8-mQj",
    "outputId": "17b58ba5-df54-42f6-989d-0394ae36eefb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SEA', 'JFK', 'PHX', 'MCO', 'DEN', 'MIA', 'DFW', 'IAH', 'LAX',\n",
       "       'LAS', 'SFO', 'ORD', 'ATL', 'CLT', 'EWR'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df['Origin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "colab_type": "code",
    "id": "7WgH4QXhP6ff",
    "outputId": "e1f1a08c-9402-4586-d5e3-2d288f3ebee5"
   },
   "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>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>DepDelayMinutes</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>ArrTime</th>\n",
       "      <th>ArrDelayMinutes</th>\n",
       "      <th>ArrDel15</th>\n",
       "      <th>NearestDepTime</th>\n",
       "      <th>totalSnow_cm</th>\n",
       "      <th>date</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>pressure</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>visibility</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>humidity</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
       "      <td>JFK</td>\n",
       "      <td>745.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
       "      <td>PHX</td>\n",
       "      <td>820.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>724.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>759.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1143.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>738.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1855989</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>ORD</td>\n",
       "      <td>SFO</td>\n",
       "      <td>2322.0</td>\n",
       "      <td>2312.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>122</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>219</td>\n",
       "      <td>90</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855990</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>ORD</td>\n",
       "      <td>EWR</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>2306.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>122</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>219</td>\n",
       "      <td>90</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855991</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>SEA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>17.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1023</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
       "      <td>177</td>\n",
       "      <td>89</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855992</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>IAH</td>\n",
       "      <td>SFO</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1831.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>113</td>\n",
       "      <td>78</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855993</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>IAH</td>\n",
       "      <td>MCO</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>113</td>\n",
       "      <td>78</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1855994 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Year  Quarter  Month  ...  winddirDegree humidity  time\n",
       "0        2016.0      1.0    1.0  ...            108       65   800\n",
       "1        2016.0      1.0    1.0  ...            108       65   800\n",
       "2        2016.0      1.0    1.0  ...            108       65   800\n",
       "3        2016.0      1.0    1.0  ...            108       65   800\n",
       "4        2016.0      1.0    1.0  ...            108       65   800\n",
       "...         ...      ...    ...  ...            ...      ...   ...\n",
       "1855989  2017.0      4.0   12.0  ...            219       90  2300\n",
       "1855990  2017.0      4.0   12.0  ...            219       90  2300\n",
       "1855991  2017.0      4.0   12.0  ...            177       89   200\n",
       "1855992  2017.0      4.0   12.0  ...            113       78  1700\n",
       "1855993  2017.0      4.0   12.0  ...            113       78  1700\n",
       "\n",
       "[1855994 rows x 30 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df"
   ]
  }
 ],
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