03_Weather_Data_Processing.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "governmental-paint",
   "metadata": {},
   "source": [
    "# Flight Delay Prediction\n",
    "\n",
    "## Weather Data Processing\n",
    "- Exploring <strong>2016_1.json</strong> weather Data to generalize\n",
    "- Loading the json file into a Data Frame\n",
    "- Isolating the weather data and examining its column structure\n",
    "- Flattening the nested structure based on the required columns\n",
    "- Filtering the required data columns\n",
    "- Combining all the weather data after data handling into a df\n",
    "- Ensuring by visualizing that the data has no missing values\n",
    "- Storing df as <strong>weather_data.csv</strong> in the <strong>Data</strong> directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "checked-forwarding",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "preceding-guyana",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the required modules\n",
    "import pandas as pd\n",
    "# import glob\n",
    "import missingno as msno\n",
    "%matplotlib inline\n",
    "\n",
    "# Data for filtering\n",
    "airports = (\"ATL\", \"CLT\", \"DEN\", \"DFW\", \"EWR\", \"IAH\", \"JFK\", \"LAS\", \"LAX\", \"MCO\", \"MIA\", \"ORD\", \"PHX\", \"SEA\", \"SFO\")\n",
    "fields = (\"windspeedKmph\",\n",
    "          \"winddirDegree\", \n",
    "          \"weatherCode\",\n",
    "          \"precipMM\",\n",
    "          \"visibility\",\n",
    "          \"pressure\",\n",
    "          \"cloudcover\",\n",
    "          \"DewPointF\",\n",
    "          \"WindGustKmph\",\n",
    "          \"tempF\",\n",
    "          \"WindChillF\",\n",
    "          \"humidity\",\n",
    "          \"date\",\n",
    "          \"time\")\n",
    "years = (\"2016\", \"2017\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rocky-ownership",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elementary-sample",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "miniature-feedback",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>request</th>\n",
       "      <td>[{'query': 'ATL, Hartsfield-Jackson Atlanta In...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>[{'mintempC': '5', 'maxtempF': '51', 'sunHour'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                      data\n",
       "request  [{'query': 'ATL, Hartsfield-Jackson Atlanta In...\n",
       "weather  [{'mintempC': '5', 'maxtempF': '51', 'sunHour'..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading one json file to analyze its structure\n",
    "data = pd.read_json(\"Data/Weather_Data/ATL/2016_1.json\")\n",
    "print(type(data))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "confused-tactics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "\n",
      "Index(['mintempC', 'maxtempF', 'sunHour', 'mintempF', 'maxtempC', 'hourly',\n",
      "       'totalSnow_cm', 'date', 'astronomy', 'uvIndex'],\n",
      "      dtype='object')\n",
      "\n",
      "(31, 10)\n",
      "\n",
      "RangeIndex(start=0, stop=31, step=1)\n",
      "\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mintempC</th>\n",
       "      <th>maxtempF</th>\n",
       "      <th>sunHour</th>\n",
       "      <th>mintempF</th>\n",
       "      <th>maxtempC</th>\n",
       "      <th>hourly</th>\n",
       "      <th>totalSnow_cm</th>\n",
       "      <th>date</th>\n",
       "      <th>astronomy</th>\n",
       "      <th>uvIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>7.5</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>[{'windspeedKmph': '11', 'FeelsLikeF': '46', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>[{'moon_phase': 'Waning Gibbous', 'moonrise': ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>[{'windspeedKmph': '15', 'FeelsLikeF': '34', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>7.5</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>[{'windspeedKmph': '10', 'FeelsLikeF': '37', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>46</td>\n",
       "      <td>9.5</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>[{'windspeedKmph': '13', 'FeelsLikeF': '37', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>[{'windspeedKmph': '12', 'FeelsLikeF': '28', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>[{'moon_phase': 'Waning Crescent', 'moonrise':...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  mintempC maxtempF sunHour mintempF maxtempC  \\\n",
       "0        5       51     7.5       42       11   \n",
       "1        2       48     9.0       35        9   \n",
       "2        3       51     7.5       38       11   \n",
       "3       -1       46     9.5       31        8   \n",
       "4        1       43    10.0       33        6   \n",
       "\n",
       "                                              hourly totalSnow_cm        date  \\\n",
       "0  [{'windspeedKmph': '11', 'FeelsLikeF': '46', '...          0.0  2016-01-01   \n",
       "1  [{'windspeedKmph': '15', 'FeelsLikeF': '34', '...          0.0  2016-01-02   \n",
       "2  [{'windspeedKmph': '10', 'FeelsLikeF': '37', '...          0.0  2016-01-03   \n",
       "3  [{'windspeedKmph': '13', 'FeelsLikeF': '37', '...          0.0  2016-01-04   \n",
       "4  [{'windspeedKmph': '12', 'FeelsLikeF': '28', '...          0.0  2016-01-05   \n",
       "\n",
       "                                           astronomy uvIndex  \n",
       "0  [{'moon_phase': 'Waning Gibbous', 'moonrise': ...       0  \n",
       "1  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "2  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "3  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "4  [{'moon_phase': 'Waning Crescent', 'moonrise':...       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "# Isolating the weather data and examining its columns\n",
    "wd = data[\"data\"][\"weather\"]\n",
    "print(type(wd), end=\"\\n\\n\")\n",
    "wd_df = (pd.DataFrame(wd))\n",
    "print(wd_df.columns, end=\"\\n\\n\")\n",
    "print(wd_df.shape, end=\"\\n\\n\")\n",
    "print(wd_df.index, end=\"\\n\\n\")\n",
    "wd_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-flavor",
   "metadata": {},
   "source": [
    "### Extracting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vulnerable-exposure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['mintempC', 'maxtempF', 'sunHour', 'mintempF', 'maxtempC', 'hourly',\n",
      "       'totalSnow_cm', 'date', 'astronomy', 'uvIndex'],\n",
      "      dtype='object')\n",
      "\n",
      "(31, 10)\n",
      "\n",
      "RangeIndex(start=0, stop=31, step=1)\n",
      "\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>mintempC</th>\n",
       "      <th>maxtempF</th>\n",
       "      <th>sunHour</th>\n",
       "      <th>mintempF</th>\n",
       "      <th>maxtempC</th>\n",
       "      <th>hourly</th>\n",
       "      <th>totalSnow_cm</th>\n",
       "      <th>date</th>\n",
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       "      <td>11</td>\n",
       "      <td>[{'windspeedKmph': '11', 'FeelsLikeF': '46', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>[{'moon_phase': 'Waning Gibbous', 'moonrise': ...</td>\n",
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       "      <td>2016-01-02</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
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       "      <td>11</td>\n",
       "      <td>[{'windspeedKmph': '10', 'FeelsLikeF': '37', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>46</td>\n",
       "      <td>9.5</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>[{'windspeedKmph': '13', 'FeelsLikeF': '37', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>[{'moon_phase': 'Last Quarter', 'moonrise': '0...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>[{'windspeedKmph': '12', 'FeelsLikeF': '28', '...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>[{'moon_phase': 'Waning Crescent', 'moonrise':...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  mintempC maxtempF sunHour mintempF maxtempC  \\\n",
       "0        5       51     7.5       42       11   \n",
       "1        2       48     9.0       35        9   \n",
       "2        3       51     7.5       38       11   \n",
       "3       -1       46     9.5       31        8   \n",
       "4        1       43    10.0       33        6   \n",
       "\n",
       "                                              hourly totalSnow_cm        date  \\\n",
       "0  [{'windspeedKmph': '11', 'FeelsLikeF': '46', '...          0.0  2016-01-01   \n",
       "1  [{'windspeedKmph': '15', 'FeelsLikeF': '34', '...          0.0  2016-01-02   \n",
       "2  [{'windspeedKmph': '10', 'FeelsLikeF': '37', '...          0.0  2016-01-03   \n",
       "3  [{'windspeedKmph': '13', 'FeelsLikeF': '37', '...          0.0  2016-01-04   \n",
       "4  [{'windspeedKmph': '12', 'FeelsLikeF': '28', '...          0.0  2016-01-05   \n",
       "\n",
       "                                           astronomy uvIndex  \n",
       "0  [{'moon_phase': 'Waning Gibbous', 'moonrise': ...       0  \n",
       "1  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "2  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "3  [{'moon_phase': 'Last Quarter', 'moonrise': '0...       0  \n",
       "4  [{'moon_phase': 'Waning Crescent', 'moonrise':...       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns hourly has a lot of useful nested fields that are of interest\n",
    "# so the nested data needs flattening\n",
    "wd_df = pd.json_normalize(data=wd)\n",
    "print(wd_df.columns, end=\"\\n\\n\")\n",
    "print(wd_df.shape, end=\"\\n\\n\")\n",
    "print(wd_df.index, end=\"\\n\\n\")\n",
    "# print(wd_df.iloc[0][\"hourly\"])\n",
    "wd_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-pressing",
   "metadata": {},
   "source": [
    "### Flattening and Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "normal-future",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tempF', 'weatherCode', 'pressure', 'time', 'cloudcover', 'WindChillF', 'windspeedKmph', 'precipMM', 'WindGustKmph', 'visibility', 'date', 'humidity', 'winddirDegree', 'DewPointF'}\n"
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       "      <th>windspeedKmph</th>\n",
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       "      <th>precipMM</th>\n",
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       "      <td>2016-01-01</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>314</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>314</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>400</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>13</td>\n",
       "      <td>169</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1015</td>\n",
       "      <td>96</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>82</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>13</td>\n",
       "      <td>165</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1015</td>\n",
       "      <td>95</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>82</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>12</td>\n",
       "      <td>162</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1015</td>\n",
       "      <td>95</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>83</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>13</td>\n",
       "      <td>164</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1015</td>\n",
       "      <td>92</td>\n",
       "      <td>54</td>\n",
       "      <td>21</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>83</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>14</td>\n",
       "      <td>167</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1015</td>\n",
       "      <td>89</td>\n",
       "      <td>53</td>\n",
       "      <td>23</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>84</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>744 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    windspeedKmph winddirDegree weatherCode precipMM visibility pressure  \\\n",
       "0              11           318         176      0.1         10     1023   \n",
       "1              13           317         176      0.0         10     1023   \n",
       "2              14           315         122      0.0         10     1023   \n",
       "3              16           314         122      0.0         10     1023   \n",
       "4              17           314         122      0.0         10     1023   \n",
       "..            ...           ...         ...      ...        ...      ...   \n",
       "739            13           169         122      0.0         10     1015   \n",
       "740            13           165         122      0.0         10     1015   \n",
       "741            12           162         122      0.0         10     1015   \n",
       "742            13           164         122      0.0         10     1015   \n",
       "743            14           167         122      0.0         10     1015   \n",
       "\n",
       "    cloudcover DewPointF WindGustKmph tempF WindChillF humidity        date  \\\n",
       "0          100        47           17    49         46       91  2016-01-01   \n",
       "1          100        44           22    46         42       92  2016-01-01   \n",
       "2          100        41           26    43         38       92  2016-01-01   \n",
       "3          100        38           30    40         33       93  2016-01-01   \n",
       "4          100        38           30    40         33       93  2016-01-01   \n",
       "..         ...       ...          ...   ...        ...      ...         ...   \n",
       "739         96        54           20    60         60       82  2016-01-31   \n",
       "740         95        54           20    60         59       82  2016-01-31   \n",
       "741         95        54           20    59         59       83  2016-01-31   \n",
       "742         92        54           21    59         58       83  2016-01-31   \n",
       "743         89        53           23    58         57       84  2016-01-31   \n",
       "\n",
       "     time  \n",
       "0       0  \n",
       "1     100  \n",
       "2     200  \n",
       "3     300  \n",
       "4     400  \n",
       "..    ...  \n",
       "739  1900  \n",
       "740  2000  \n",
       "741  2100  \n",
       "742  2200  \n",
       "743  2300  \n",
       "\n",
       "[744 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flattening the data by one more column namely, hourly and, retaining the date column\n",
    "wd_df = pd.json_normalize(data=wd, record_path=\"hourly\", meta=\"date\")\n",
    "col1 = set(wd_df.columns)\n",
    "wd_df = wd_df[list(fields)]\n",
    "col2 = wd_df.columns\n",
    "print(col1.intersection(col2))\n",
    "wd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premium-bunch",
   "metadata": {},
   "source": [
    "## Loading All the Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "metallic-roller",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data/Weather_Data/ATL/2016_1.json\n",
      "Data/Weather_Data/ATL/2016_2.json\n",
      "Data/Weather_Data/ATL/2016_3.json\n",
      "Data/Weather_Data/ATL/2016_4.json\n",
      "Data/Weather_Data/ATL/2016_5.json\n",
      "Data/Weather_Data/ATL/2016_6.json\n",
      "Data/Weather_Data/ATL/2016_7.json\n",
      "Data/Weather_Data/ATL/2016_8.json\n",
      "Data/Weather_Data/ATL/2016_9.json\n",
      "Data/Weather_Data/ATL/2016_10.json\n",
      "Data/Weather_Data/ATL/2016_11.json\n",
      "Data/Weather_Data/ATL/2016_12.json\n",
      "Data/Weather_Data/ATL/2017_1.json\n",
      "Data/Weather_Data/ATL/2017_2.json\n",
      "Data/Weather_Data/ATL/2017_3.json\n",
      "Data/Weather_Data/ATL/2017_4.json\n",
      "Data/Weather_Data/ATL/2017_5.json\n",
      "Data/Weather_Data/ATL/2017_6.json\n",
      "Data/Weather_Data/ATL/2017_7.json\n",
      "Data/Weather_Data/ATL/2017_8.json\n",
      "Data/Weather_Data/ATL/2017_9.json\n",
      "Data/Weather_Data/ATL/2017_10.json\n",
      "Data/Weather_Data/ATL/2017_11.json\n",
      "Data/Weather_Data/ATL/2017_12.json\n",
      "Data/Weather_Data/CLT/2016_1.json\n",
      "Data/Weather_Data/CLT/2016_2.json\n",
      "Data/Weather_Data/CLT/2016_3.json\n",
      "Data/Weather_Data/CLT/2016_4.json\n",
      "Data/Weather_Data/CLT/2016_5.json\n",
      "Data/Weather_Data/CLT/2016_6.json\n",
      "Data/Weather_Data/CLT/2016_7.json\n",
      "Data/Weather_Data/CLT/2016_8.json\n",
      "Data/Weather_Data/CLT/2016_9.json\n",
      "Data/Weather_Data/CLT/2016_10.json\n",
      "Data/Weather_Data/CLT/2016_11.json\n",
      "Data/Weather_Data/CLT/2016_12.json\n",
      "Data/Weather_Data/CLT/2017_1.json\n",
      "Data/Weather_Data/CLT/2017_2.json\n",
      "Data/Weather_Data/CLT/2017_3.json\n",
      "Data/Weather_Data/CLT/2017_4.json\n",
      "Data/Weather_Data/CLT/2017_5.json\n",
      "Data/Weather_Data/CLT/2017_6.json\n",
      "Data/Weather_Data/CLT/2017_7.json\n",
      "Data/Weather_Data/CLT/2017_8.json\n",
      "Data/Weather_Data/CLT/2017_9.json\n",
      "Data/Weather_Data/CLT/2017_10.json\n",
      "Data/Weather_Data/CLT/2017_11.json\n",
      "Data/Weather_Data/CLT/2017_12.json\n",
      "Data/Weather_Data/DEN/2016_1.json\n",
      "Data/Weather_Data/DEN/2016_2.json\n",
      "Data/Weather_Data/DEN/2016_3.json\n",
      "Data/Weather_Data/DEN/2016_4.json\n",
      "Data/Weather_Data/DEN/2016_5.json\n",
      "Data/Weather_Data/DEN/2016_6.json\n",
      "Data/Weather_Data/DEN/2016_7.json\n",
      "Data/Weather_Data/DEN/2016_8.json\n",
      "Data/Weather_Data/DEN/2016_9.json\n",
      "Data/Weather_Data/DEN/2016_10.json\n",
      "Data/Weather_Data/DEN/2016_11.json\n",
      "Data/Weather_Data/DEN/2016_12.json\n",
      "Data/Weather_Data/DEN/2017_1.json\n",
      "Data/Weather_Data/DEN/2017_2.json\n",
      "Data/Weather_Data/DEN/2017_3.json\n",
      "Data/Weather_Data/DEN/2017_4.json\n",
      "Data/Weather_Data/DEN/2017_5.json\n",
      "Data/Weather_Data/DEN/2017_6.json\n",
      "Data/Weather_Data/DEN/2017_7.json\n",
      "Data/Weather_Data/DEN/2017_8.json\n",
      "Data/Weather_Data/DEN/2017_9.json\n",
      "Data/Weather_Data/DEN/2017_10.json\n",
      "Data/Weather_Data/DEN/2017_11.json\n",
      "Data/Weather_Data/DEN/2017_12.json\n",
      "Data/Weather_Data/DFW/2016_1.json\n",
      "Data/Weather_Data/DFW/2016_2.json\n",
      "Data/Weather_Data/DFW/2016_3.json\n",
      "Data/Weather_Data/DFW/2016_4.json\n",
      "Data/Weather_Data/DFW/2016_5.json\n",
      "Data/Weather_Data/DFW/2016_6.json\n",
      "Data/Weather_Data/DFW/2016_7.json\n",
      "Data/Weather_Data/DFW/2016_8.json\n",
      "Data/Weather_Data/DFW/2016_9.json\n",
      "Data/Weather_Data/DFW/2016_10.json\n",
      "Data/Weather_Data/DFW/2016_11.json\n",
      "Data/Weather_Data/DFW/2016_12.json\n",
      "Data/Weather_Data/DFW/2017_1.json\n",
      "Data/Weather_Data/DFW/2017_2.json\n",
      "Data/Weather_Data/DFW/2017_3.json\n",
      "Data/Weather_Data/DFW/2017_4.json\n",
      "Data/Weather_Data/DFW/2017_5.json\n",
      "Data/Weather_Data/DFW/2017_6.json\n",
      "Data/Weather_Data/DFW/2017_7.json\n",
      "Data/Weather_Data/DFW/2017_8.json\n",
      "Data/Weather_Data/DFW/2017_9.json\n",
      "Data/Weather_Data/DFW/2017_10.json\n",
      "Data/Weather_Data/DFW/2017_11.json\n",
      "Data/Weather_Data/DFW/2017_12.json\n",
      "Data/Weather_Data/EWR/2016_1.json\n",
      "Data/Weather_Data/EWR/2016_2.json\n",
      "Data/Weather_Data/EWR/2016_3.json\n",
      "Data/Weather_Data/EWR/2016_4.json\n",
      "Data/Weather_Data/EWR/2016_5.json\n",
      "Data/Weather_Data/EWR/2016_6.json\n",
      "Data/Weather_Data/EWR/2016_7.json\n",
      "Data/Weather_Data/EWR/2016_8.json\n",
      "Data/Weather_Data/EWR/2016_9.json\n",
      "Data/Weather_Data/EWR/2016_10.json\n",
      "Data/Weather_Data/EWR/2016_11.json\n",
      "Data/Weather_Data/EWR/2016_12.json\n",
      "Data/Weather_Data/EWR/2017_1.json\n",
      "Data/Weather_Data/EWR/2017_2.json\n",
      "Data/Weather_Data/EWR/2017_3.json\n",
      "Data/Weather_Data/EWR/2017_4.json\n",
      "Data/Weather_Data/EWR/2017_5.json\n",
      "Data/Weather_Data/EWR/2017_6.json\n",
      "Data/Weather_Data/EWR/2017_7.json\n",
      "Data/Weather_Data/EWR/2017_8.json\n",
      "Data/Weather_Data/EWR/2017_9.json\n",
      "Data/Weather_Data/EWR/2017_10.json\n",
      "Data/Weather_Data/EWR/2017_11.json\n",
      "Data/Weather_Data/EWR/2017_12.json\n",
      "Data/Weather_Data/IAH/2016_1.json\n",
      "Data/Weather_Data/IAH/2016_2.json\n",
      "Data/Weather_Data/IAH/2016_3.json\n",
      "Data/Weather_Data/IAH/2016_4.json\n",
      "Data/Weather_Data/IAH/2016_5.json\n",
      "Data/Weather_Data/IAH/2016_6.json\n",
      "Data/Weather_Data/IAH/2016_7.json\n",
      "Data/Weather_Data/IAH/2016_8.json\n",
      "Data/Weather_Data/IAH/2016_9.json\n",
      "Data/Weather_Data/IAH/2016_10.json\n",
      "Data/Weather_Data/IAH/2016_11.json\n",
      "Data/Weather_Data/IAH/2016_12.json\n",
      "Data/Weather_Data/IAH/2017_1.json\n",
      "Data/Weather_Data/IAH/2017_2.json\n",
      "Data/Weather_Data/IAH/2017_3.json\n",
      "Data/Weather_Data/IAH/2017_4.json\n",
      "Data/Weather_Data/IAH/2017_5.json\n",
      "Data/Weather_Data/IAH/2017_6.json\n",
      "Data/Weather_Data/IAH/2017_7.json\n",
      "Data/Weather_Data/IAH/2017_8.json\n",
      "Data/Weather_Data/IAH/2017_9.json\n",
      "Data/Weather_Data/IAH/2017_10.json\n",
      "Data/Weather_Data/IAH/2017_11.json\n",
      "Data/Weather_Data/IAH/2017_12.json\n",
      "Data/Weather_Data/JFK/2016_1.json\n",
      "Data/Weather_Data/JFK/2016_2.json\n",
      "Data/Weather_Data/JFK/2016_3.json\n",
      "Data/Weather_Data/JFK/2016_4.json\n",
      "Data/Weather_Data/JFK/2016_5.json\n",
      "Data/Weather_Data/JFK/2016_6.json\n",
      "Data/Weather_Data/JFK/2016_7.json\n",
      "Data/Weather_Data/JFK/2016_8.json\n",
      "Data/Weather_Data/JFK/2016_9.json\n",
      "Data/Weather_Data/JFK/2016_10.json\n",
      "Data/Weather_Data/JFK/2016_11.json\n",
      "Data/Weather_Data/JFK/2016_12.json\n",
      "Data/Weather_Data/JFK/2017_1.json\n",
      "Data/Weather_Data/JFK/2017_2.json\n",
      "Data/Weather_Data/JFK/2017_3.json\n",
      "Data/Weather_Data/JFK/2017_4.json\n",
      "Data/Weather_Data/JFK/2017_5.json\n",
      "Data/Weather_Data/JFK/2017_6.json\n",
      "Data/Weather_Data/JFK/2017_7.json\n",
      "Data/Weather_Data/JFK/2017_8.json\n",
      "Data/Weather_Data/JFK/2017_9.json\n",
      "Data/Weather_Data/JFK/2017_10.json\n",
      "Data/Weather_Data/JFK/2017_11.json\n",
      "Data/Weather_Data/JFK/2017_12.json\n",
      "Data/Weather_Data/LAS/2016_1.json\n",
      "Data/Weather_Data/LAS/2016_2.json\n",
      "Data/Weather_Data/LAS/2016_3.json\n",
      "Data/Weather_Data/LAS/2016_4.json\n",
      "Data/Weather_Data/LAS/2016_5.json\n",
      "Data/Weather_Data/LAS/2016_6.json\n",
      "Data/Weather_Data/LAS/2016_7.json\n",
      "Data/Weather_Data/LAS/2016_8.json\n",
      "Data/Weather_Data/LAS/2016_9.json\n",
      "Data/Weather_Data/LAS/2016_10.json\n",
      "Data/Weather_Data/LAS/2016_11.json\n",
      "Data/Weather_Data/LAS/2016_12.json\n",
      "Data/Weather_Data/LAS/2017_1.json\n",
      "Data/Weather_Data/LAS/2017_2.json\n",
      "Data/Weather_Data/LAS/2017_3.json\n",
      "Data/Weather_Data/LAS/2017_4.json\n",
      "Data/Weather_Data/LAS/2017_5.json\n",
      "Data/Weather_Data/LAS/2017_6.json\n",
      "Data/Weather_Data/LAS/2017_7.json\n",
      "Data/Weather_Data/LAS/2017_8.json\n",
      "Data/Weather_Data/LAS/2017_9.json\n",
      "Data/Weather_Data/LAS/2017_10.json\n",
      "Data/Weather_Data/LAS/2017_11.json\n",
      "Data/Weather_Data/LAS/2017_12.json\n",
      "Data/Weather_Data/LAX/2016_1.json\n",
      "Data/Weather_Data/LAX/2016_2.json\n",
      "Data/Weather_Data/LAX/2016_3.json\n",
      "Data/Weather_Data/LAX/2016_4.json\n",
      "Data/Weather_Data/LAX/2016_5.json\n",
      "Data/Weather_Data/LAX/2016_6.json\n",
      "Data/Weather_Data/LAX/2016_7.json\n",
      "Data/Weather_Data/LAX/2016_8.json\n",
      "Data/Weather_Data/LAX/2016_9.json\n",
      "Data/Weather_Data/LAX/2016_10.json\n",
      "Data/Weather_Data/LAX/2016_11.json\n",
      "Data/Weather_Data/LAX/2016_12.json\n",
      "Data/Weather_Data/LAX/2017_1.json\n",
      "Data/Weather_Data/LAX/2017_2.json\n",
      "Data/Weather_Data/LAX/2017_3.json\n",
      "Data/Weather_Data/LAX/2017_4.json\n",
      "Data/Weather_Data/LAX/2017_5.json\n",
      "Data/Weather_Data/LAX/2017_6.json\n",
      "Data/Weather_Data/LAX/2017_7.json\n",
      "Data/Weather_Data/LAX/2017_8.json\n",
      "Data/Weather_Data/LAX/2017_9.json\n",
      "Data/Weather_Data/LAX/2017_10.json\n",
      "Data/Weather_Data/LAX/2017_11.json\n",
      "Data/Weather_Data/LAX/2017_12.json\n",
      "Data/Weather_Data/MCO/2016_1.json\n",
      "Data/Weather_Data/MCO/2016_2.json\n",
      "Data/Weather_Data/MCO/2016_3.json\n",
      "Data/Weather_Data/MCO/2016_4.json\n",
      "Data/Weather_Data/MCO/2016_5.json\n",
      "Data/Weather_Data/MCO/2016_6.json\n",
      "Data/Weather_Data/MCO/2016_7.json\n",
      "Data/Weather_Data/MCO/2016_8.json\n",
      "Data/Weather_Data/MCO/2016_9.json\n",
      "Data/Weather_Data/MCO/2016_10.json\n",
      "Data/Weather_Data/MCO/2016_11.json\n",
      "Data/Weather_Data/MCO/2016_12.json\n",
      "Data/Weather_Data/MCO/2017_1.json\n",
      "Data/Weather_Data/MCO/2017_2.json\n",
      "Data/Weather_Data/MCO/2017_3.json\n",
      "Data/Weather_Data/MCO/2017_4.json\n",
      "Data/Weather_Data/MCO/2017_5.json\n",
      "Data/Weather_Data/MCO/2017_6.json\n",
      "Data/Weather_Data/MCO/2017_7.json\n",
      "Data/Weather_Data/MCO/2017_8.json\n",
      "Data/Weather_Data/MCO/2017_9.json\n",
      "Data/Weather_Data/MCO/2017_10.json\n",
      "Data/Weather_Data/MCO/2017_11.json\n",
      "Data/Weather_Data/MCO/2017_12.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data/Weather_Data/MIA/2016_1.json\n",
      "Data/Weather_Data/MIA/2016_2.json\n",
      "Data/Weather_Data/MIA/2016_3.json\n",
      "Data/Weather_Data/MIA/2016_4.json\n",
      "Data/Weather_Data/MIA/2016_5.json\n",
      "Data/Weather_Data/MIA/2016_6.json\n",
      "Data/Weather_Data/MIA/2016_7.json\n",
      "Data/Weather_Data/MIA/2016_8.json\n",
      "Data/Weather_Data/MIA/2016_9.json\n",
      "Data/Weather_Data/MIA/2016_10.json\n",
      "Data/Weather_Data/MIA/2016_11.json\n",
      "Data/Weather_Data/MIA/2016_12.json\n",
      "Data/Weather_Data/MIA/2017_1.json\n",
      "Data/Weather_Data/MIA/2017_2.json\n",
      "Data/Weather_Data/MIA/2017_3.json\n",
      "Data/Weather_Data/MIA/2017_4.json\n",
      "Data/Weather_Data/MIA/2017_5.json\n",
      "Data/Weather_Data/MIA/2017_6.json\n",
      "Data/Weather_Data/MIA/2017_7.json\n",
      "Data/Weather_Data/MIA/2017_8.json\n",
      "Data/Weather_Data/MIA/2017_9.json\n",
      "Data/Weather_Data/MIA/2017_10.json\n",
      "Data/Weather_Data/MIA/2017_11.json\n",
      "Data/Weather_Data/MIA/2017_12.json\n",
      "Data/Weather_Data/ORD/2016_1.json\n",
      "Data/Weather_Data/ORD/2016_2.json\n",
      "Data/Weather_Data/ORD/2016_3.json\n",
      "Data/Weather_Data/ORD/2016_4.json\n",
      "Data/Weather_Data/ORD/2016_5.json\n",
      "Data/Weather_Data/ORD/2016_6.json\n",
      "Data/Weather_Data/ORD/2016_7.json\n",
      "Data/Weather_Data/ORD/2016_8.json\n",
      "Data/Weather_Data/ORD/2016_9.json\n",
      "Data/Weather_Data/ORD/2016_10.json\n",
      "Data/Weather_Data/ORD/2016_11.json\n",
      "Data/Weather_Data/ORD/2016_12.json\n",
      "Data/Weather_Data/ORD/2017_1.json\n",
      "Data/Weather_Data/ORD/2017_2.json\n",
      "Data/Weather_Data/ORD/2017_3.json\n",
      "Data/Weather_Data/ORD/2017_4.json\n",
      "Data/Weather_Data/ORD/2017_5.json\n",
      "Data/Weather_Data/ORD/2017_6.json\n",
      "Data/Weather_Data/ORD/2017_7.json\n",
      "Data/Weather_Data/ORD/2017_8.json\n",
      "Data/Weather_Data/ORD/2017_9.json\n",
      "Data/Weather_Data/ORD/2017_10.json\n",
      "Data/Weather_Data/ORD/2017_11.json\n",
      "Data/Weather_Data/ORD/2017_12.json\n",
      "Data/Weather_Data/PHX/2016_1.json\n",
      "Data/Weather_Data/PHX/2016_2.json\n",
      "Data/Weather_Data/PHX/2016_3.json\n",
      "Data/Weather_Data/PHX/2016_4.json\n",
      "Data/Weather_Data/PHX/2016_5.json\n",
      "Data/Weather_Data/PHX/2016_6.json\n",
      "Data/Weather_Data/PHX/2016_7.json\n",
      "Data/Weather_Data/PHX/2016_8.json\n",
      "Data/Weather_Data/PHX/2016_9.json\n",
      "Data/Weather_Data/PHX/2016_10.json\n",
      "Data/Weather_Data/PHX/2016_11.json\n",
      "Data/Weather_Data/PHX/2016_12.json\n",
      "Data/Weather_Data/PHX/2017_1.json\n",
      "Data/Weather_Data/PHX/2017_2.json\n",
      "Data/Weather_Data/PHX/2017_3.json\n",
      "Data/Weather_Data/PHX/2017_4.json\n",
      "Data/Weather_Data/PHX/2017_5.json\n",
      "Data/Weather_Data/PHX/2017_6.json\n",
      "Data/Weather_Data/PHX/2017_7.json\n",
      "Data/Weather_Data/PHX/2017_8.json\n",
      "Data/Weather_Data/PHX/2017_9.json\n",
      "Data/Weather_Data/PHX/2017_10.json\n",
      "Data/Weather_Data/PHX/2017_11.json\n",
      "Data/Weather_Data/PHX/2017_12.json\n",
      "Data/Weather_Data/SEA/2016_1.json\n",
      "Data/Weather_Data/SEA/2016_2.json\n",
      "Data/Weather_Data/SEA/2016_3.json\n",
      "Data/Weather_Data/SEA/2016_4.json\n",
      "Data/Weather_Data/SEA/2016_5.json\n",
      "Data/Weather_Data/SEA/2016_6.json\n",
      "Data/Weather_Data/SEA/2016_7.json\n",
      "Data/Weather_Data/SEA/2016_8.json\n",
      "Data/Weather_Data/SEA/2016_9.json\n",
      "Data/Weather_Data/SEA/2016_10.json\n",
      "Data/Weather_Data/SEA/2016_11.json\n",
      "Data/Weather_Data/SEA/2016_12.json\n",
      "Data/Weather_Data/SEA/2017_1.json\n",
      "Data/Weather_Data/SEA/2017_2.json\n",
      "Data/Weather_Data/SEA/2017_3.json\n",
      "Data/Weather_Data/SEA/2017_4.json\n",
      "Data/Weather_Data/SEA/2017_5.json\n",
      "Data/Weather_Data/SEA/2017_6.json\n",
      "Data/Weather_Data/SEA/2017_7.json\n",
      "Data/Weather_Data/SEA/2017_8.json\n",
      "Data/Weather_Data/SEA/2017_9.json\n",
      "Data/Weather_Data/SEA/2017_10.json\n",
      "Data/Weather_Data/SEA/2017_11.json\n",
      "Data/Weather_Data/SEA/2017_12.json\n",
      "Data/Weather_Data/SFO/2016_1.json\n",
      "Data/Weather_Data/SFO/2016_2.json\n",
      "Data/Weather_Data/SFO/2016_3.json\n",
      "Data/Weather_Data/SFO/2016_4.json\n",
      "Data/Weather_Data/SFO/2016_5.json\n",
      "Data/Weather_Data/SFO/2016_6.json\n",
      "Data/Weather_Data/SFO/2016_7.json\n",
      "Data/Weather_Data/SFO/2016_8.json\n",
      "Data/Weather_Data/SFO/2016_9.json\n",
      "Data/Weather_Data/SFO/2016_10.json\n",
      "Data/Weather_Data/SFO/2016_11.json\n",
      "Data/Weather_Data/SFO/2016_12.json\n",
      "Data/Weather_Data/SFO/2017_1.json\n",
      "Data/Weather_Data/SFO/2017_2.json\n",
      "Data/Weather_Data/SFO/2017_3.json\n",
      "Data/Weather_Data/SFO/2017_4.json\n",
      "Data/Weather_Data/SFO/2017_5.json\n",
      "Data/Weather_Data/SFO/2017_6.json\n",
      "Data/Weather_Data/SFO/2017_7.json\n",
      "Data/Weather_Data/SFO/2017_8.json\n",
      "Data/Weather_Data/SFO/2017_9.json\n",
      "Data/Weather_Data/SFO/2017_10.json\n",
      "Data/Weather_Data/SFO/2017_11.json\n",
      "Data/Weather_Data/SFO/2017_12.json\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "NOT REQUIRED\n",
    "# Loading all the csv files from the Data dir into a list with their relative path names\n",
    "weather_data_path = \"Data/Weather_Data/\"\n",
    "weather_data_files = [file for file in glob.iglob(weather_data_path + '**/*.json', recursive=True)]\n",
    "print(weather_data_files)\n",
    "\"\"\"\n",
    "\n",
    "weather_data = list()\n",
    "# Iterating over the files and performing data handling\n",
    "for city in airports:\n",
    "    for year in years:\n",
    "        for month in range(1, 12+1):\n",
    "            file = f\"Data/Weather_Data/{city}/{year}_{month}.json\"\n",
    "            df = pd.read_json(file)\n",
    "            wd = df[\"data\"][\"weather\"] # list\n",
    "            wd_df = pd.json_normalize(data=wd)\n",
    "            wd_df = pd.json_normalize(data=wd, record_path=\"hourly\", meta=\"date\")\n",
    "            wd_df = wd_df[list(fields)]\n",
    "            wd_df[\"airport\"] = city\n",
    "            weather_data.append(wd_df)\n",
    "            print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescription-disclosure",
   "metadata": {},
   "source": [
    "## Combining All the Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vulnerable-convenience",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 263160 entries, 0 to 263159\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   windspeedKmph  263160 non-null  object\n",
      " 1   winddirDegree  263160 non-null  object\n",
      " 2   weatherCode    263160 non-null  object\n",
      " 3   precipMM       263160 non-null  object\n",
      " 4   visibility     263160 non-null  object\n",
      " 5   pressure       263160 non-null  object\n",
      " 6   cloudcover     263160 non-null  object\n",
      " 7   DewPointF      263160 non-null  object\n",
      " 8   WindGustKmph   263160 non-null  object\n",
      " 9   tempF          263160 non-null  object\n",
      " 10  WindChillF     263160 non-null  object\n",
      " 11  humidity       263160 non-null  object\n",
      " 12  date           263160 non-null  object\n",
      " 13  time           263160 non-null  object\n",
      " 14  airport        263160 non-null  object\n",
      "dtypes: object(15)\n",
      "memory usage: 30.1+ MB\n",
      "None\n",
      "\n",
      "Index(['windspeedKmph', 'winddirDegree', 'weatherCode', 'precipMM',\n",
      "       'visibility', 'pressure', 'cloudcover', 'DewPointF', 'WindGustKmph',\n",
      "       'tempF', 'WindChillF', 'humidity', 'date', 'time', 'airport'],\n",
      "      dtype='object')\n",
      "\n",
      "(263160, 15)\n",
      "\n",
      "RangeIndex(start=0, stop=263160, step=1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Concatenating the list of weather Data Frames\n",
    "weather_data_df = pd.concat(weather_data)\n",
    "weather_data_df = weather_data_df.reset_index(drop=True)\n",
    "print(weather_data_df.info(), end=\"\\n\\n\")\n",
    "print(weather_data_df.columns, end=\"\\n\\n\")\n",
    "print(weather_data_df.shape, end=\"\\n\\n\")\n",
    "print(weather_data_df.index, end=\"\\n\\n\")\n",
    "# print(len(weather_data))\n",
    "# Storing the combined data as a csv file\n",
    "weather_data_df.to_csv(\"Data/weather_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superior-porcelain",
   "metadata": {},
   "source": [
    "## Checking for Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sealed-james",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.698618x0.755)\n",
      "windspeedKmph    0\n",
      "winddirDegree    0\n",
      "weatherCode      0\n",
      "precipMM         0\n",
      "visibility       0\n",
      "pressure         0\n",
      "cloudcover       0\n",
      "DewPointF        0\n",
      "WindGustKmph     0\n",
      "tempF            0\n",
      "WindChillF       0\n",
      "humidity         0\n",
      "date             0\n",
      "time             0\n",
      "airport          0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(msno.matrix(weather_data_df))\n",
    "print(weather_data_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "anonymous-commodity",
   "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>windspeedKmph</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>visibility</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>humidity</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>318</td>\n",
       "      <td>176</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>91</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>317</td>\n",
       "      <td>176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>92</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>100</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>315</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>26</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>92</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>200</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>314</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>300</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>314</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1023</td>\n",
       "      <td>100</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>400</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  windspeedKmph winddirDegree weatherCode precipMM visibility pressure  \\\n",
       "0            11           318         176      0.1         10     1023   \n",
       "1            13           317         176      0.0         10     1023   \n",
       "2            14           315         122      0.0         10     1023   \n",
       "3            16           314         122      0.0         10     1023   \n",
       "4            17           314         122      0.0         10     1023   \n",
       "\n",
       "  cloudcover DewPointF WindGustKmph tempF WindChillF humidity        date  \\\n",
       "0        100        47           17    49         46       91  2016-01-01   \n",
       "1        100        44           22    46         42       92  2016-01-01   \n",
       "2        100        41           26    43         38       92  2016-01-01   \n",
       "3        100        38           30    40         33       93  2016-01-01   \n",
       "4        100        38           30    40         33       93  2016-01-01   \n",
       "\n",
       "  time airport  \n",
       "0    0     ATL  \n",
       "1  100     ATL  \n",
       "2  200     ATL  \n",
       "3  300     ATL  \n",
       "4  400     ATL  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "measured-consensus",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>windspeedKmph</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>visibility</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>humidity</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>263155</th>\n",
       "      <td>2</td>\n",
       "      <td>279</td>\n",
       "      <td>116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1019</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>92</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1900</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263156</th>\n",
       "      <td>2</td>\n",
       "      <td>255</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1019</td>\n",
       "      <td>44</td>\n",
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       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>92</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2000</td>\n",
       "      <td>SFO</td>\n",
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       "    <tr>\n",
       "      <th>263157</th>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>38</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>93</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2100</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263158</th>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>95</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2200</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263159</th>\n",
       "      <td>2</td>\n",
       "      <td>194</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1020</td>\n",
       "      <td>79</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>97</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2300</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       windspeedKmph winddirDegree weatherCode precipMM visibility pressure  \\\n",
       "263155             2           279         116      0.0         10     1019   \n",
       "263156             2           255         248      0.0         10     1019   \n",
       "263157             1           230         248      0.0         10     1020   \n",
       "263158             2           212         248      0.0         10     1020   \n",
       "263159             2           194         122      0.0         10     1020   \n",
       "\n",
       "       cloudcover DewPointF WindGustKmph tempF WindChillF humidity  \\\n",
       "263155         51        52            5    55         55       92   \n",
       "263156         44        53            5    55         55       92   \n",
       "263157         38        53            5    55         55       93   \n",
       "263158         58        52            4    53         53       95   \n",
       "263159         79        51            4    52         52       97   \n",
       "\n",
       "              date  time airport  \n",
       "263155  2017-12-31  1900     SFO  \n",
       "263156  2017-12-31  2000     SFO  \n",
       "263157  2017-12-31  2100     SFO  \n",
       "263158  2017-12-31  2200     SFO  \n",
       "263159  2017-12-31  2300     SFO  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "gentle-beach",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263160, 15)\n",
      "\n",
      "Index(['windspeedKmph', 'winddirDegree', 'weatherCode', 'precipMM',\n",
      "       'visibility', 'pressure', 'cloudcover', 'DewPointF', 'WindGustKmph',\n",
      "       'tempF', 'WindChillF', 'humidity', 'date', 'time', 'airport'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(weather_data_df.shape, end=\"\\n\\n\")\n",
    "print(weather_data_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-television",
   "metadata": {},
   "source": [
    "## Checking for Duplicates\n",
    "There won't duplicates in this data since the weather data was collected hourly (<strong>time</strong> column) from jan to dec for 2016 and 2017. <br>\n",
    "In other words, (date, time) is the PRIMARY KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "certified-tuition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263160, 15)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weather_data_df.drop_duplicates()\n",
    "print(weather_data_df.shape, end=\"\\n\\n\")"
   ]
  }
 ],
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