weather_data_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'read_csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m# Import Weather Datset\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m weather_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\\\u001b[39m\u001b[39minput\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mwundergound.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m weather_data\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'read_csv'"
     ]
    }
   ],
   "source": [
    "# Import Weather Datset\n",
    "weather_data = pd.read_csv('data\\input\\wundergound.csv')\n",
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10194 entries, 0 to 10193\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Date         10194 non-null  object \n",
      " 1   Time         10194 non-null  object \n",
      " 2   Temperature  10194 non-null  int64  \n",
      " 3   Dew Point    10194 non-null  int64  \n",
      " 4   Humidity     10194 non-null  int64  \n",
      " 5   Wind         10188 non-null  object \n",
      " 6   Wind Speed   10188 non-null  float64\n",
      " 7   Wind Gust    2732 non-null   float64\n",
      " 8   Pressure     10194 non-null  float64\n",
      " 9   Precip       10194 non-null  float64\n",
      " 10  Condition    10194 non-null  object \n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 876.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#attribute types\n",
    "weather_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Date      Time  Temperature  Dew Point  Humidity  Wind  \\\n",
      "0      2022-01-01  11:15:00           33         28        82   NNE   \n",
      "1      2022-01-01  11:29:00           32         27        82   NNE   \n",
      "2      2022-01-01  11:53:00           32         27        82   NNE   \n",
      "3      2022-01-01  12:00:00           32         28        85   NNE   \n",
      "4      2022-01-01  12:10:00           32         28        85   NNE   \n",
      "...           ...       ...          ...        ...       ...   ...   \n",
      "10189  2022-12-31  13:53:00           37         30        76    SE   \n",
      "10190  2022-12-31  14:53:00           37         31        79    SE   \n",
      "10191  2022-12-31  15:53:00           37         32        82  CALM   \n",
      "10192  2022-12-31  16:53:00           37         32        82    SE   \n",
      "10193  2022-12-31  17:53:00           37         32        82   ESE   \n",
      "\n",
      "       Wind Speed  Wind Gust  Pressure  Precip           Condition  \\\n",
      "0            21.0       31.0     29.17     0.0  Light Snow / Windy   \n",
      "1            22.0       31.0     29.17     0.0  Light Snow / Windy   \n",
      "2            21.0       33.0     29.18     0.0  Light Snow / Windy   \n",
      "3            22.0       31.0     29.18     0.0  Light Snow / Windy   \n",
      "4            21.0       29.0     29.17     0.0  Light Snow / Windy   \n",
      "...           ...        ...       ...     ...                 ...   \n",
      "10189         9.0       18.0     29.23     0.0              Cloudy   \n",
      "10190        10.0        NaN     29.21     0.0              Cloudy   \n",
      "10191         0.0        NaN     29.22     0.0              Cloudy   \n",
      "10192         6.0        NaN     29.22     0.0              Cloudy   \n",
      "10193         7.0        NaN     29.19     0.0              Cloudy   \n",
      "\n",
      "                 DateTime  \n",
      "0     2022-01-01 11:15:00  \n",
      "1     2022-01-01 11:29:00  \n",
      "2     2022-01-01 11:53:00  \n",
      "3     2022-01-01 12:00:00  \n",
      "4     2022-01-01 12:10:00  \n",
      "...                   ...  \n",
      "10189 2022-12-31 13:53:00  \n",
      "10190 2022-12-31 14:53:00  \n",
      "10191 2022-12-31 15:53:00  \n",
      "10192 2022-12-31 16:53:00  \n",
      "10193 2022-12-31 17:53:00  \n",
      "\n",
      "[10194 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# Kombiniere \"Date\" und \"Time\" und speichere das Ergebnis in der Spalte \"DateTime\"\n",
    "weather_data['DateTime'] = pd.to_datetime(weather_data['Date'] + ' ' + weather_data['Time'], format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "# Setze den Index des DataFrames auf die Spalte \"DateTime\"\n",
    "weather_data_indexed_by_date = weather_data.set_index('DateTime')\n",
    "\n",
    "# Ausgabe des aktualisierten DataFrame mit der neuen Spalte\n",
    "print(weather_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10194 entries, 0 to 10193\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Date         10194 non-null  object        \n",
      " 1   Time         10194 non-null  object        \n",
      " 2   Temperature  10194 non-null  int64         \n",
      " 3   Dew Point    10194 non-null  int64         \n",
      " 4   Humidity     10194 non-null  int64         \n",
      " 5   Wind         10188 non-null  object        \n",
      " 6   Wind Speed   10188 non-null  float64       \n",
      " 7   Wind Gust    2732 non-null   float64       \n",
      " 8   Pressure     10194 non-null  float64       \n",
      " 9   Precip       10194 non-null  float64       \n",
      " 10  Condition    10194 non-null  object        \n",
      " 11  DateTime     10194 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(4)\n",
      "memory usage: 955.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#attribute types\n",
    "weather_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              0\n",
       "Time              0\n",
       "Temperature       0\n",
       "Dew Point         0\n",
       "Humidity          0\n",
       "Wind              6\n",
       "Wind Speed        6\n",
       "Wind Gust      7462\n",
       "Pressure          0\n",
       "Precip            0\n",
       "Condition         0\n",
       "DateTime          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "weather_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete \"Time\", \"Date\", \"Dew Point\", \"Humidity\", \"Wind\", \"Wind Gust\", \"Pressure\" columns\n",
    "weather_data.drop(['Time', 'Date', 'Dew Point', 'Humidity', 'Wind', 'Wind Gust', 'Pressure'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperature    0\n",
       "Wind Speed     6\n",
       "Precip         0\n",
       "Condition      0\n",
       "DateTime       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "weather_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 6 nan values for Wind Speed\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Condition</th>\n",
       "      <th>DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5225</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rain with Thunder</td>\n",
       "      <td>2022-07-04 18:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5965</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>2022-07-31 03:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6098</th>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>2022-08-05 01:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7950</th>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>2022-10-15 02:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8609</th>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>2022-11-09 03:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>2022-11-19 09:53:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Temperature  Wind Speed  Precip                Condition  \\\n",
       "5225           81         NaN     0.0  Light Rain with Thunder   \n",
       "5965           81         NaN     0.0            Partly Cloudy   \n",
       "6098           78         NaN     0.0                     Fair   \n",
       "7950           40         NaN     0.0                     Fair   \n",
       "8609           56         NaN     0.0                   Cloudy   \n",
       "8947           26         NaN     0.0            Mostly Cloudy   \n",
       "\n",
       "                DateTime  \n",
       "5225 2022-07-04 18:50:00  \n",
       "5965 2022-07-31 03:53:00  \n",
       "6098 2022-08-05 01:53:00  \n",
       "7950 2022-10-15 02:53:00  \n",
       "8609 2022-11-09 03:53:00  \n",
       "8947 2022-11-19 09:53:00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print nan Values for columns \"Wind Speed\"\n",
    "nan_values_for_Wind_Speed = weather_data.loc[np.isnan(weather_data['Wind Speed'])]\n",
    "print(\"There are\", len(nan_values_for_Wind_Speed), \"nan values for Wind Speed\")\n",
    "nan_values_for_Wind_Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Delete nan Values rows from Wind Speed \n",
    "weather_data = weather_data.dropna(axis=0, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperature    0\n",
       "Wind Speed     0\n",
       "Precip         0\n",
       "Condition      0\n",
       "DateTime       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "weather_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10188 entries, 0 to 10193\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Temperature  10188 non-null  int64         \n",
      " 1   Wind Speed   10188 non-null  float64       \n",
      " 2   Precip       10188 non-null  float64       \n",
      " 3   Condition    10188 non-null  object        \n",
      " 4   DateTime     10188 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(1)\n",
      "memory usage: 477.6+ KB\n"
     ]
    }
   ],
   "source": [
    "weather_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8336   2022-10-30 02:53:00\n",
      "8337   2022-10-30 02:53:00\n",
      "Name: DateTime, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicate entries in the \"DateTime\" column\n",
    "duplicates = weather_data[weather_data['DateTime'].duplicated(keep=False)]\n",
    "\n",
    "# Output of the duplicate entries of the \"DateTime\" column\n",
    "print(duplicates['DateTime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\werwie\\AppData\\Local\\Temp\\ipykernel_17080\\2532675772.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  weather_data.drop_duplicates(subset='DateTime', keep='first', inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Delete duplicates \n",
    "weather_data.drop_duplicates(subset='DateTime', keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Condition</th>\n",
       "      <th>DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Temperature, Wind Speed, Precip, Condition, DateTime]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there are duplicate entries\n",
    "weather_data[weather_data.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Earliest date entry: 2022-01-01 11:15:00\n",
      "Latest date entry: 2022-12-31 17:53:00\n"
     ]
    }
   ],
   "source": [
    "#check earliest DateTime entry and latest DateTime entry \n",
    "start = weather_data['DateTime'].min()\n",
    "print(\"Earliest date entry:\", start)\n",
    "end = weather_data['DateTime'].max()\n",
    "print(\"Latest date entry:\", end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8707"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_range = pd.date_range(start, end, freq = 'H')\n",
    "\n",
    "#for faster lookup\n",
    "def get_missing_dates():\n",
    "    out = []\n",
    "    weather_data_indexed_by_date = weather_data.set_index('DateTime')\n",
    "\n",
    "    for date in date_range:\n",
    "        try:\n",
    "           weather_data_indexed_by_date.loc[date.strftime('%Y-%m-%d %H:%M:%S'),]\n",
    "        except KeyError:\n",
    "            out.append(date)\n",
    "    return out\n",
    "missing_dates = get_missing_dates()\n",
    "len(missing_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = {'January': 0, 'February': 0, 'March': 0, 'April': 0, \n",
    "          'May': 0, 'June': 0, 'July': 0, 'August': 0, \n",
    "          'September': 0, 'October': 0, 'November': 0, 'December': 0}\n",
    "\n",
    "for date in missing_dates:\n",
    "    months[date.month_name()] = months[date.month_name()] + 1\n",
    "\n",
    "Fig = plt.figure()\n",
    "ax = Fig.add_axes([0.5, 0.5, 1.5, 0.5])\n",
    "ax.plot(months.keys(), months.values())\n",
    "ax.set_xlabel('Months')\n",
    "ax.set_ylabel('Number of missing dates')\n",
    "ax.set_title('Missing Data for every month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minium temperature: -9 Fahrenheit\n",
      "Max temperature: 101 Fahrenheit\n"
     ]
    }
   ],
   "source": [
    "#check min and max temperature \n",
    "min_temperature = weather_data[\"Temperature\"].min()\n",
    "print(\"Minium temperature:\", min_temperature, \"Fahrenheit\")\n",
    "\n",
    "max_temperature = weather_data[\"Temperature\"].max()\n",
    "print(\"Max temperature:\", max_temperature, \"Fahrenheit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minium Wind Speed: 0.0 mph\n",
      "Max Wind Speed: 37.0 mph\n"
     ]
    }
   ],
   "source": [
    "#check min and max Wind Speed\n",
    "min_Wind_Speed = weather_data[\"Wind Speed\"].min()\n",
    "print(\"Minium Wind Speed:\", min_Wind_Speed, \"mph\")\n",
    "\n",
    "max_Wind_Speed = weather_data[\"Wind Speed\"].max()\n",
    "print(\"Max Wind Speed:\", max_Wind_Speed, \"mph\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Weather Data into 15min steps according Taxi Data and fill missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit data into minute steps of 00,15,30,45\n",
    "index = 0\n",
    "time = timedelta(minutes=15)\n",
    "last_index = weather_data.last_valid_index()\n",
    "fit_weather_data = pd.DataFrame({'date_time' : [], 'temperature' : [], 'wind_speed' : [], 'precip' : [], 'condition' : []})\n",
    "current_date = datetime(2022, 1, 1, 0, 0, 0)\n",
    "end_date = datetime(2023, 1, 1, 0, 0, 0)\n",
    "\n",
    "#loops to calculate last fitting weather data entry to compute current step\n",
    "while current_date < end_date:\n",
    "\n",
    "    if last_index != index:\n",
    "        next_index = index\n",
    "        search = True\n",
    "\n",
    "        while search:\n",
    "\n",
    "          if next_index + 1 >= last_index:\n",
    "             break\n",
    "          \n",
    "          while next_index + 1 not in weather_data.index:\n",
    "            next_index += 1\n",
    "\n",
    "          if weather_data.loc[next_index + 1, 'DateTime'] < current_date:\n",
    "            next_index += 1\n",
    "            index = next_index\n",
    "\n",
    "          else:\n",
    "             search = False\n",
    "             break\n",
    "\n",
    "    #new row with time, temperature, wind speed, precip, condition\n",
    "    new_row = {'date_time' : current_date, 'temperature' : weather_data.loc[index, 'Temperature'], 'wind_speed' : weather_data.loc[index, 'Wind Speed'],\n",
    "                'precip' : weather_data.loc[index, 'Precip'], 'condition' : weather_data.loc[index, 'Condition']}\n",
    "    #add to new dataframe\n",
    "    fit_weather_data.loc[len(fit_weather_data)] = new_row\n",
    "    #next 15min date\n",
    "    current_date += time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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_time</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>precip</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01 00:00:00</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Snow / Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01 00:15:00</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Snow / Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01 00:30:00</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Snow / Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01 00:45:00</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Snow / Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01 01:00:00</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Snow / Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35035</th>\n",
       "      <td>2022-12-31 22:45:00</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35036</th>\n",
       "      <td>2022-12-31 23:00:00</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35037</th>\n",
       "      <td>2022-12-31 23:15:00</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35038</th>\n",
       "      <td>2022-12-31 23:30:00</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35039</th>\n",
       "      <td>2022-12-31 23:45:00</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35040 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_time  temperature  wind_speed  precip           condition\n",
       "0     2022-01-01 00:00:00           33        21.0     0.0  Light Snow / Windy\n",
       "1     2022-01-01 00:15:00           33        21.0     0.0  Light Snow / Windy\n",
       "2     2022-01-01 00:30:00           33        21.0     0.0  Light Snow / Windy\n",
       "3     2022-01-01 00:45:00           33        21.0     0.0  Light Snow / Windy\n",
       "4     2022-01-01 01:00:00           33        21.0     0.0  Light Snow / Windy\n",
       "...                   ...          ...         ...     ...                 ...\n",
       "35035 2022-12-31 22:45:00           37         6.0     0.0              Cloudy\n",
       "35036 2022-12-31 23:00:00           37         6.0     0.0              Cloudy\n",
       "35037 2022-12-31 23:15:00           37         6.0     0.0              Cloudy\n",
       "35038 2022-12-31 23:30:00           37         6.0     0.0              Cloudy\n",
       "35039 2022-12-31 23:45:00           37         6.0     0.0              Cloudy\n",
       "\n",
       "[35040 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export CSV Data \n",
    "fit_weather_data.to_csv('data\\prepared\\weather_data_2022.csv', index=False)"
   ]
  }
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