src/modules/Exploratory_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Use this notebook to get familiar with the datasets we have. There is 10 questions we need to answer during the EDA.\n",
    "\n",
    "\n",
    "We shouldn't limit our EDA to these 10 questions. Let's be creative :)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='darkgrid', context='talk')\n",
    "from prepare_data_function import prepare_data_flights\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import data to work in Tasks\n",
    "df_flights_sample = pd.read_csv(\"../../data/raw/flights_sample.csv\")\n",
    "df = pd.read_csv('../../data/processed/dec_2019_delay_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 1**: Test the hypothesis that the delay is from Normal distribution. and that **mean** of the delay is 0. Be careful about the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>dep_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673124</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673125</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673126</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673127</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673128</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673129 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           fl_date  dep_delay\n",
       "0       2019-12-01         39\n",
       "1       2019-12-01         22\n",
       "2       2019-12-01         -3\n",
       "3       2019-12-01          2\n",
       "4       2019-12-01         18\n",
       "...            ...        ...\n",
       "673124  2019-12-31         15\n",
       "673125  2019-12-31         48\n",
       "673126  2019-12-31         -5\n",
       "673127  2019-12-31          1\n",
       "673128  2019-12-02         61\n",
       "\n",
       "[673129 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>dep_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>673129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.465559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51.565875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2710.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dep_delay\n",
       "count  673129.000000\n",
       "mean       12.465559\n",
       "std        51.565875\n",
       "min       -49.000000\n",
       "25%        -5.000000\n",
       "50%        -2.000000\n",
       "75%         9.000000\n",
       "max      2710.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dep_delay'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fl_date      0\n",
       "dep_delay    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>fl_date</th>\n",
       "      <th>dep_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fl_date  dep_delay\n",
       "0  2019-12-01         39\n",
       "1  2019-12-01         22\n",
       "2  2019-12-01         -3\n",
       "3  2019-12-01          2\n",
       "4  2019-12-01         18"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_delay = df.drop(df[df['dep_delay']>400].index)\n",
    "dep_delay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(dep_delay['dep_delay'], kde=True)\n",
    "\n",
    "# Set the axis limits and labels\n",
    "plt.xlim(-50, 400)\n",
    "plt.ylim(0, 15000)\n",
    "plt.xlabel(\"Departure Delay\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptive statistics of the scaled data:\n",
      "                   0\n",
      "count  6.715430e+05\n",
      "mean   4.012220e-17\n",
      "std    1.000001e+00\n",
      "min   -1.610176e+00\n",
      "25%   -4.263454e-01\n",
      "50%   -3.456297e-01\n",
      "75%   -4.967210e-02\n",
      "max    1.047028e+01\n",
      "Median of the scaled data:\n",
      " 0   -0.34563\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScaler object\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Transform the data\n",
    "scaled_delay = scaler.fit_transform(dep_delay['dep_delay'].values.reshape(-1, 1))\n",
    "\n",
    "# Convert the scaled data to a DataFrame\n",
    "scaled_delay = pd.DataFrame(scaled_delay)\n",
    "\n",
    "\n",
    "# Get the descriptive statistics of the scaled data\n",
    "desc_stats = scaled_delay.describe()\n",
    "median = scaled_delay.median()\n",
    "print(\"Descriptive statistics of the scaled data:\\n\", desc_stats)\n",
    "print(\"Median of the scaled data:\\n\", median)\n",
    "\n",
    "# Create a histogram of the scaled data\n",
    "sns.histplot(scaled_delay, kde=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 2**: Is average/median monthly delay different during the year? If yes, which are months with the biggest delays and what could be the reason?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>avg_departure_delay</th>\n",
       "      <th>avg_arrival_delay</th>\n",
       "      <th>flights_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>9.66</td>\n",
       "      <td>3.19</td>\n",
       "      <td>600720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>9.77</td>\n",
       "      <td>4.15</td>\n",
       "      <td>556050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>7.66</td>\n",
       "      <td>2.13</td>\n",
       "      <td>646045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>8.35</td>\n",
       "      <td>3.61</td>\n",
       "      <td>641490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>10.72</td>\n",
       "      <td>5.99</td>\n",
       "      <td>663029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  avg_departure_delay  avg_arrival_delay  flights_per_month\n",
       "0  2018      1                 9.66               3.19             600720\n",
       "1  2018      2                 9.77               4.15             556050\n",
       "2  2018      3                 7.66               2.13             646045\n",
       "3  2018      4                 8.35               3.61             641490\n",
       "4  2018      5                10.72               5.99             663029"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/monthly_average_delay.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>avg_departure_delay</th>\n",
       "      <th>avg_arrival_delay</th>\n",
       "      <th>flights_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>12.70</td>\n",
       "      <td>7.75</td>\n",
       "      <td>705249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>14.23</td>\n",
       "      <td>8.97</td>\n",
       "      <td>703717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>13.44</td>\n",
       "      <td>9.00</td>\n",
       "      <td>689108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>13.96</td>\n",
       "      <td>10.10</td>\n",
       "      <td>687153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>7.99</td>\n",
       "      <td>2.65</td>\n",
       "      <td>687120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>11.99</td>\n",
       "      <td>7.03</td>\n",
       "      <td>680766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>16.00</td>\n",
       "      <td>11.99</td>\n",
       "      <td>680459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>9.15</td>\n",
       "      <td>3.49</td>\n",
       "      <td>675943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>12.47</td>\n",
       "      <td>6.44</td>\n",
       "      <td>673129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>13.25</td>\n",
       "      <td>8.83</td>\n",
       "      <td>669405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  avg_departure_delay  avg_arrival_delay  flights_per_month\n",
       "19  2019      8                12.70               7.75             705249\n",
       "18  2019      7                14.23               8.97             703717\n",
       "6   2018      7                13.44               9.00             689108\n",
       "7   2018      8                13.96              10.10             687153\n",
       "21  2019     10                 7.99               2.65             687120\n",
       "16  2019      5                11.99               7.03             680766\n",
       "17  2019      6                16.00              11.99             680459\n",
       "14  2019      3                 9.15               3.49             675943\n",
       "23  2019     12                12.47               6.44             673129\n",
       "5   2018      6                13.25               8.83             669405"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['flights_per_month'],ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = df['avg_departure_delay']\n",
    "x2 = df['avg_arrival_delay']\n",
    "y = df['flights_per_month']\n",
    "\n",
    "# First Scatter plot\n",
    "plt.scatter(x1, y, c =\"r\", marker = 'D', label = 'departure')\n",
    "\n",
    "#Second Scatter plot\n",
    "plt.scatter(x2, y, c =\"b\", marker = 'o', label = 'arrival')\n",
    "\n",
    "plt.title('Average Monthly Flight Delay (2018-2019)')\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.xlabel('Average Flight Delay (mins)')\n",
    "plt.ylabel('Flights per Month')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Flight Departure Delay per Month')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='month', y='avg_departure_delay', hue='year')\n",
    " \n",
    "# labels for x & y axis\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Average Departure Delay')\n",
    " \n",
    "# title of plot\n",
    "plt.title('Flight Departure Delay per Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Flight Arrival Delay per Month')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='month', y='avg_arrival_delay', hue='year')\n",
    " \n",
    "# labels for x & y axis\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Average Arrival Delay')\n",
    " \n",
    "# title of plot\n",
    "plt.title('Flight Arrival Delay per Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 3**: Does the weather affect the delay? \n",
    "Use the API to pull the weather information for flights. There is no need to get weather for ALL flights. We can choose the right representative sample. Let's focus on four weather types:\n",
    "- sunny\n",
    "- cloudy\n",
    "- rainy\n",
    "- snow.\n",
    "Test the hypothesis that these 4 delays are from the same distribution. If they are not, which ones are significantly different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import all the airports from the flight table\n",
    "df_airports = pd.read_csv(\"../../data/raw/airportsFlightsTable.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 376 entries, 0 to 375\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   airport_id  376 non-null    int64 \n",
      " 1   iata_code   376 non-null    object\n",
      " 2   city        376 non-null    object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 8.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_airports.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>airport_id</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10135</td>\n",
       "      <td>ABE</td>\n",
       "      <td>Allentown/Bethlehem/Easton, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10136</td>\n",
       "      <td>ABI</td>\n",
       "      <td>Abilene, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10140</td>\n",
       "      <td>ABQ</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10141</td>\n",
       "      <td>ABR</td>\n",
       "      <td>Aberdeen, SD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10146</td>\n",
       "      <td>ABY</td>\n",
       "      <td>Albany, GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>15991</td>\n",
       "      <td>YAK</td>\n",
       "      <td>Yakutat, AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>16101</td>\n",
       "      <td>YKM</td>\n",
       "      <td>Yakima, WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>16133</td>\n",
       "      <td>YNG</td>\n",
       "      <td>Youngstown/Warren, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>16218</td>\n",
       "      <td>YUM</td>\n",
       "      <td>Yuma, AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>16869</td>\n",
       "      <td>XWA</td>\n",
       "      <td>Williston, ND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     airport_id iata_code                            city\n",
       "0         10135       ABE  Allentown/Bethlehem/Easton, PA\n",
       "1         10136       ABI                     Abilene, TX\n",
       "2         10140       ABQ                 Albuquerque, NM\n",
       "3         10141       ABR                    Aberdeen, SD\n",
       "4         10146       ABY                      Albany, GA\n",
       "..          ...       ...                             ...\n",
       "371       15991       YAK                     Yakutat, AK\n",
       "372       16101       YKM                      Yakima, WA\n",
       "373       16133       YNG           Youngstown/Warren, OH\n",
       "374       16218       YUM                        Yuma, AZ\n",
       "375       16869       XWA                   Williston, ND\n",
       "\n",
       "[376 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import geolocation for all the airports (got this information from https://ourairports.com/airports.html)\n",
    "global_airports = pd.read_csv(\"../../data/raw/worldAirports.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35489 entries, 0 to 35488\n",
      "Data columns (total 23 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 35489 non-null  int64  \n",
      " 1   ident              35489 non-null  object \n",
      " 2   type               35489 non-null  object \n",
      " 3   name               35489 non-null  object \n",
      " 4   latitude_deg       35489 non-null  float64\n",
      " 5   longitude_deg      35489 non-null  float64\n",
      " 6   elevation_ft       30858 non-null  float64\n",
      " 7   continent          19757 non-null  object \n",
      " 8   country_name       35489 non-null  object \n",
      " 9   iso_country        35287 non-null  object \n",
      " 10  region_name        35489 non-null  object \n",
      " 11  iso_region         35489 non-null  object \n",
      " 12  local_region       35485 non-null  object \n",
      " 13  municipality       31489 non-null  object \n",
      " 14  scheduled_service  35489 non-null  int64  \n",
      " 15  gps_code           23908 non-null  object \n",
      " 16  iata_code          8029 non-null   object \n",
      " 17  local_code         15682 non-null  object \n",
      " 18  home_link          3054 non-null   object \n",
      " 19  wikipedia_link     9179 non-null   object \n",
      " 20  keywords           8328 non-null   object \n",
      " 21  score              35489 non-null  int64  \n",
      " 22  last_updated       35489 non-null  object \n",
      "dtypes: float64(3), int64(3), object(17)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "global_airports.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ident</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>latitude_deg</th>\n",
       "      <th>longitude_deg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EGLL</td>\n",
       "      <td>LHR</td>\n",
       "      <td>51.470600</td>\n",
       "      <td>-0.461941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KLAX</td>\n",
       "      <td>LAX</td>\n",
       "      <td>33.942501</td>\n",
       "      <td>-118.407997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KORD</td>\n",
       "      <td>ORD</td>\n",
       "      <td>41.978600</td>\n",
       "      <td>-87.904800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KJFK</td>\n",
       "      <td>JFK</td>\n",
       "      <td>40.639447</td>\n",
       "      <td>-73.779317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KATL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>33.636700</td>\n",
       "      <td>-84.428101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35484</th>\n",
       "      <td>CMB7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.253000</td>\n",
       "      <td>-74.807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35485</th>\n",
       "      <td>12CN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.268306</td>\n",
       "      <td>-122.116689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35486</th>\n",
       "      <td>8CL6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.288502</td>\n",
       "      <td>-122.189003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35487</th>\n",
       "      <td>0ID2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.442902</td>\n",
       "      <td>-113.773003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35488</th>\n",
       "      <td>0U8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.610199</td>\n",
       "      <td>-113.896004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35489 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ident iata_code  latitude_deg  longitude_deg\n",
       "0      EGLL       LHR     51.470600      -0.461941\n",
       "1      KLAX       LAX     33.942501    -118.407997\n",
       "2      KORD       ORD     41.978600     -87.904800\n",
       "3      KJFK       JFK     40.639447     -73.779317\n",
       "4      KATL       ATL     33.636700     -84.428101\n",
       "...     ...       ...           ...            ...\n",
       "35484  CMB7       NaN     45.253000     -74.807000\n",
       "35485  12CN       NaN     39.268306    -122.116689\n",
       "35486  8CL6       NaN     39.288502    -122.189003\n",
       "35487  0ID2       NaN     44.442902    -113.773003\n",
       "35488   0U8       NaN     44.610199    -113.896004\n",
       "\n",
       "[35489 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter information needed\n",
    "df_global_airports = global_airports[['ident','iata_code','latitude_deg','longitude_deg']]\n",
    "df_global_airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airport_id</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>latitude_deg</th>\n",
       "      <th>longitude_deg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10135</td>\n",
       "      <td>ABE</td>\n",
       "      <td>40.651773</td>\n",
       "      <td>-75.442797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10136</td>\n",
       "      <td>ABI</td>\n",
       "      <td>32.411301</td>\n",
       "      <td>-99.681900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10140</td>\n",
       "      <td>ABQ</td>\n",
       "      <td>35.040199</td>\n",
       "      <td>-106.609001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10141</td>\n",
       "      <td>ABR</td>\n",
       "      <td>45.449100</td>\n",
       "      <td>-98.421799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10146</td>\n",
       "      <td>ABY</td>\n",
       "      <td>31.532946</td>\n",
       "      <td>-84.196215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>15991</td>\n",
       "      <td>YAK</td>\n",
       "      <td>59.508717</td>\n",
       "      <td>-139.660435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>16101</td>\n",
       "      <td>YKM</td>\n",
       "      <td>46.568199</td>\n",
       "      <td>-120.543999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>16133</td>\n",
       "      <td>YNG</td>\n",
       "      <td>41.260700</td>\n",
       "      <td>-80.679100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>16218</td>\n",
       "      <td>YUM</td>\n",
       "      <td>32.656601</td>\n",
       "      <td>-114.606003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>16869</td>\n",
       "      <td>XWA</td>\n",
       "      <td>48.258387</td>\n",
       "      <td>-103.748797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     airport_id iata_code  latitude_deg  longitude_deg\n",
       "0         10135       ABE     40.651773     -75.442797\n",
       "1         10136       ABI     32.411301     -99.681900\n",
       "2         10140       ABQ     35.040199    -106.609001\n",
       "3         10141       ABR     45.449100     -98.421799\n",
       "4         10146       ABY     31.532946     -84.196215\n",
       "..          ...       ...           ...            ...\n",
       "369       15991       YAK     59.508717    -139.660435\n",
       "370       16101       YKM     46.568199    -120.543999\n",
       "371       16133       YNG     41.260700     -80.679100\n",
       "372       16218       YUM     32.656601    -114.606003\n",
       "373       16869       XWA     48.258387    -103.748797\n",
       "\n",
       "[374 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add latitude and longitude to my airports dataset\n",
    "\n",
    "df_airports_merged = df_airports.merge(df_global_airports, on = 'iata_code', how = 'inner' )\n",
    "df_airports_merged = df_airports_merged.drop(columns=['city','ident'])\n",
    "df_airports_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>mkt_unique_carrier</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>origin_city_name</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>dest</th>\n",
       "      <th>dest_city_name</th>\n",
       "      <th>weather_delay_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>DL</td>\n",
       "      <td>N390CA</td>\n",
       "      <td>6248</td>\n",
       "      <td>13296</td>\n",
       "      <td>MHT</td>\n",
       "      <td>Manchester, NH</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>DL</td>\n",
       "      <td>N264SY</td>\n",
       "      <td>4690</td>\n",
       "      <td>11057</td>\n",
       "      <td>CLT</td>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>2692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>HA</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>465</td>\n",
       "      <td>12173</td>\n",
       "      <td>HNL</td>\n",
       "      <td>Honolulu, HI</td>\n",
       "      <td>14222</td>\n",
       "      <td>PPG</td>\n",
       "      <td>Pago Pago, TT</td>\n",
       "      <td>2475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>DL</td>\n",
       "      <td>N921EV</td>\n",
       "      <td>3855</td>\n",
       "      <td>15323</td>\n",
       "      <td>TRI</td>\n",
       "      <td>Bristol/Johnson City/Kingsport, TN</td>\n",
       "      <td>10397</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>2098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>AA</td>\n",
       "      <td>N902EV</td>\n",
       "      <td>3115</td>\n",
       "      <td>10874</td>\n",
       "      <td>CAK</td>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>13930</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>1847</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>AA</td>\n",
       "      <td>N950LR</td>\n",
       "      <td>5849</td>\n",
       "      <td>13851</td>\n",
       "      <td>OKC</td>\n",
       "      <td>Oklahoma City, OK</td>\n",
       "      <td>11298</td>\n",
       "      <td>DFW</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>UA</td>\n",
       "      <td>N88325</td>\n",
       "      <td>6017</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>12992</td>\n",
       "      <td>LIT</td>\n",
       "      <td>Little Rock, AR</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2019-05-11</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24103</td>\n",
       "      <td>4142</td>\n",
       "      <td>14193</td>\n",
       "      <td>PNS</td>\n",
       "      <td>Pensacola, FL</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>DL</td>\n",
       "      <td>N380DA</td>\n",
       "      <td>2164</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>14683</td>\n",
       "      <td>SAT</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>DL</td>\n",
       "      <td>N659BR</td>\n",
       "      <td>4756</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>14696</td>\n",
       "      <td>SBN</td>\n",
       "      <td>South Bend, IN</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        fl_date mkt_unique_carrier tail_num  op_carrier_fl_num  \\\n",
       "0    2019-03-21                 DL   N390CA               6248   \n",
       "1    2018-07-05                 DL   N264SY               4690   \n",
       "2    2018-02-09                 HA   N382HA                465   \n",
       "3    2018-12-09                 DL   N921EV               3855   \n",
       "4    2019-12-24                 AA   N902EV               3115   \n",
       "..          ...                ...      ...                ...   \n",
       "195  2018-12-26                 AA   N950LR               5849   \n",
       "196  2019-03-09                 UA   N88325               6017   \n",
       "197  2019-05-11                 UA   N24103               4142   \n",
       "198  2018-05-15                 DL   N380DA               2164   \n",
       "199  2019-07-19                 DL   N659BR               4756   \n",
       "\n",
       "     origin_airport_id origin                    origin_city_name  \\\n",
       "0                13296    MHT                      Manchester, NH   \n",
       "1                11057    CLT                       Charlotte, NC   \n",
       "2                12173    HNL                        Honolulu, HI   \n",
       "3                15323    TRI  Bristol/Johnson City/Kingsport, TN   \n",
       "4                10874    CAK                           Akron, OH   \n",
       "..                 ...    ...                                 ...   \n",
       "195              13851    OKC                   Oklahoma City, OK   \n",
       "196              12266    IAH                         Houston, TX   \n",
       "197              14193    PNS                       Pensacola, FL   \n",
       "198              12478    JFK                        New York, NY   \n",
       "199              11433    DTW                         Detroit, MI   \n",
       "\n",
       "     dest_airport_id dest         dest_city_name  weather_delay_r  \n",
       "0              11433  DTW            Detroit, MI             2900  \n",
       "1              12478  JFK           New York, NY             2692  \n",
       "2              14222  PPG          Pago Pago, TT             2475  \n",
       "3              10397  ATL            Atlanta, GA             2098  \n",
       "4              13930  ORD            Chicago, IL             1847  \n",
       "..               ...  ...                    ...              ...  \n",
       "195            11298  DFW  Dallas/Fort Worth, TX             1103  \n",
       "196            12992  LIT        Little Rock, AR             1103  \n",
       "197            12266  IAH            Houston, TX             1102  \n",
       "198            14683  SAT        San Antonio, TX             1100  \n",
       "199            14696  SBN         South Bend, IN             1099  \n",
       "\n",
       "[200 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_imp = pd.read_csv(\"../../data/raw/flightsWeatherSample_w.csv\")\n",
    "df_flights_sample = df_flights_imp\n",
    "df_flights_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>mkt_unique_carrier</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>iata_code2</th>\n",
       "      <th>weather_delay_r</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>DL</td>\n",
       "      <td>N390CA</td>\n",
       "      <td>6248</td>\n",
       "      <td>13296</td>\n",
       "      <td>MHT</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>2900</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>DL</td>\n",
       "      <td>N264SY</td>\n",
       "      <td>4690</td>\n",
       "      <td>11057</td>\n",
       "      <td>CLT</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2692</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>HA</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>465</td>\n",
       "      <td>12173</td>\n",
       "      <td>HNL</td>\n",
       "      <td>14222</td>\n",
       "      <td>PPG</td>\n",
       "      <td>2475</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>DL</td>\n",
       "      <td>N921EV</td>\n",
       "      <td>3855</td>\n",
       "      <td>15323</td>\n",
       "      <td>TRI</td>\n",
       "      <td>10397</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2098</td>\n",
       "      <td>Bristol/Johnson City/Kingsport</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>AA</td>\n",
       "      <td>N902EV</td>\n",
       "      <td>3115</td>\n",
       "      <td>10874</td>\n",
       "      <td>CAK</td>\n",
       "      <td>13930</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1847</td>\n",
       "      <td>Akron</td>\n",
       "      <td>OH</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>AA</td>\n",
       "      <td>N950LR</td>\n",
       "      <td>5849</td>\n",
       "      <td>13851</td>\n",
       "      <td>OKC</td>\n",
       "      <td>11298</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1103</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>UA</td>\n",
       "      <td>N88325</td>\n",
       "      <td>6017</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12992</td>\n",
       "      <td>LIT</td>\n",
       "      <td>1103</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2019-05-11</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24103</td>\n",
       "      <td>4142</td>\n",
       "      <td>14193</td>\n",
       "      <td>PNS</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1102</td>\n",
       "      <td>Pensacola</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>DL</td>\n",
       "      <td>N380DA</td>\n",
       "      <td>2164</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>14683</td>\n",
       "      <td>SAT</td>\n",
       "      <td>1100</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>DL</td>\n",
       "      <td>N659BR</td>\n",
       "      <td>4756</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>14696</td>\n",
       "      <td>SBN</td>\n",
       "      <td>1099</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       fl_date mkt_unique_carrier tail_num  op_carrier_fl_num  \\\n",
       "0   2019-03-21                 DL   N390CA               6248   \n",
       "1   2018-07-05                 DL   N264SY               4690   \n",
       "2   2018-02-09                 HA   N382HA                465   \n",
       "3   2018-12-09                 DL   N921EV               3855   \n",
       "4   2019-12-24                 AA   N902EV               3115   \n",
       "..         ...                ...      ...                ...   \n",
       "195 2018-12-26                 AA   N950LR               5849   \n",
       "196 2019-03-09                 UA   N88325               6017   \n",
       "197 2019-05-11                 UA   N24103               4142   \n",
       "198 2018-05-15                 DL   N380DA               2164   \n",
       "199 2019-07-19                 DL   N659BR               4756   \n",
       "\n",
       "     origin_airport_id iata_code  dest_airport_id iata_code2  weather_delay_r  \\\n",
       "0                13296       MHT            11433        DTW             2900   \n",
       "1                11057       CLT            12478        JFK             2692   \n",
       "2                12173       HNL            14222        PPG             2475   \n",
       "3                15323       TRI            10397        ATL             2098   \n",
       "4                10874       CAK            13930        ORD             1847   \n",
       "..                 ...       ...              ...        ...              ...   \n",
       "195              13851       OKC            11298        DFW             1103   \n",
       "196              12266       IAH            12992        LIT             1103   \n",
       "197              14193       PNS            12266        IAH             1102   \n",
       "198              12478       JFK            14683        SAT             1100   \n",
       "199              11433       DTW            14696        SBN             1099   \n",
       "\n",
       "                               city state  \n",
       "0                        Manchester    NH  \n",
       "1                         Charlotte    NC  \n",
       "2                          Honolulu    HI  \n",
       "3    Bristol/Johnson City/Kingsport    TN  \n",
       "4                             Akron    OH  \n",
       "..                              ...   ...  \n",
       "195                   Oklahoma City    OK  \n",
       "196                         Houston    TX  \n",
       "197                       Pensacola    FL  \n",
       "198                        New York    NY  \n",
       "199                         Detroit    MI  \n",
       "\n",
       "[200 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_sample.rename(columns={'origin': 'iata_code','dest': 'iata_code2'}, inplace=True)\n",
    "df_flights_sample['fl_date'] = df_flights_sample['fl_date'].astype('datetime64')\n",
    "df_flights_sample[['city','state']] = df_flights_sample['origin_city_name'].str.split(', ', expand=True)\n",
    "df_flights_sample.drop(columns=['origin_city_name', 'dest_city_name'], inplace=True)\n",
    "df_flights_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_flights_merged = df_flights_sample.merge(df_airports_merged, on='iata_code', how='left')\n",
    "df_flights_merged.rename(columns={'latitude_deg':'lat','longitude_deg':'lon'},inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
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       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>DL</td>\n",
       "      <td>N390CA</td>\n",
       "      <td>6248</td>\n",
       "      <td>13296</td>\n",
       "      <td>MHT</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>2900</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>NH</td>\n",
       "      <td>13296</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>DL</td>\n",
       "      <td>N264SY</td>\n",
       "      <td>4690</td>\n",
       "      <td>11057</td>\n",
       "      <td>CLT</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2692</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>11057</td>\n",
       "      <td>35.214001</td>\n",
       "      <td>-80.943100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>HA</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>465</td>\n",
       "      <td>12173</td>\n",
       "      <td>HNL</td>\n",
       "      <td>14222</td>\n",
       "      <td>PPG</td>\n",
       "      <td>2475</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>HI</td>\n",
       "      <td>12173</td>\n",
       "      <td>21.320620</td>\n",
       "      <td>-157.924228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>DL</td>\n",
       "      <td>N921EV</td>\n",
       "      <td>3855</td>\n",
       "      <td>15323</td>\n",
       "      <td>TRI</td>\n",
       "      <td>10397</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2098</td>\n",
       "      <td>Bristol/Johnson City/Kingsport</td>\n",
       "      <td>TN</td>\n",
       "      <td>15323</td>\n",
       "      <td>36.475201</td>\n",
       "      <td>-82.407401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>AA</td>\n",
       "      <td>N902EV</td>\n",
       "      <td>3115</td>\n",
       "      <td>10874</td>\n",
       "      <td>CAK</td>\n",
       "      <td>13930</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1847</td>\n",
       "      <td>Akron</td>\n",
       "      <td>OH</td>\n",
       "      <td>10874</td>\n",
       "      <td>40.916100</td>\n",
       "      <td>-81.442200</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",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>AA</td>\n",
       "      <td>N950LR</td>\n",
       "      <td>5849</td>\n",
       "      <td>13851</td>\n",
       "      <td>OKC</td>\n",
       "      <td>11298</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1103</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "      <td>13851</td>\n",
       "      <td>35.393101</td>\n",
       "      <td>-97.600700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>UA</td>\n",
       "      <td>N88325</td>\n",
       "      <td>6017</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12992</td>\n",
       "      <td>LIT</td>\n",
       "      <td>1103</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>12266</td>\n",
       "      <td>29.984400</td>\n",
       "      <td>-95.341400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2019-05-11</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24103</td>\n",
       "      <td>4142</td>\n",
       "      <td>14193</td>\n",
       "      <td>PNS</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1102</td>\n",
       "      <td>Pensacola</td>\n",
       "      <td>FL</td>\n",
       "      <td>14193</td>\n",
       "      <td>30.473400</td>\n",
       "      <td>-87.186600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>DL</td>\n",
       "      <td>N380DA</td>\n",
       "      <td>2164</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>14683</td>\n",
       "      <td>SAT</td>\n",
       "      <td>1100</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12478</td>\n",
       "      <td>40.639447</td>\n",
       "      <td>-73.779317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>DL</td>\n",
       "      <td>N659BR</td>\n",
       "      <td>4756</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>14696</td>\n",
       "      <td>SBN</td>\n",
       "      <td>1099</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>MI</td>\n",
       "      <td>11433</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       fl_date mkt_unique_carrier tail_num  op_carrier_fl_num  \\\n",
       "0   2019-03-21                 DL   N390CA               6248   \n",
       "1   2018-07-05                 DL   N264SY               4690   \n",
       "2   2018-02-09                 HA   N382HA                465   \n",
       "3   2018-12-09                 DL   N921EV               3855   \n",
       "4   2019-12-24                 AA   N902EV               3115   \n",
       "..         ...                ...      ...                ...   \n",
       "195 2018-12-26                 AA   N950LR               5849   \n",
       "196 2019-03-09                 UA   N88325               6017   \n",
       "197 2019-05-11                 UA   N24103               4142   \n",
       "198 2018-05-15                 DL   N380DA               2164   \n",
       "199 2019-07-19                 DL   N659BR               4756   \n",
       "\n",
       "     origin_airport_id iata_code  dest_airport_id iata_code2  weather_delay_r  \\\n",
       "0                13296       MHT            11433        DTW             2900   \n",
       "1                11057       CLT            12478        JFK             2692   \n",
       "2                12173       HNL            14222        PPG             2475   \n",
       "3                15323       TRI            10397        ATL             2098   \n",
       "4                10874       CAK            13930        ORD             1847   \n",
       "..                 ...       ...              ...        ...              ...   \n",
       "195              13851       OKC            11298        DFW             1103   \n",
       "196              12266       IAH            12992        LIT             1103   \n",
       "197              14193       PNS            12266        IAH             1102   \n",
       "198              12478       JFK            14683        SAT             1100   \n",
       "199              11433       DTW            14696        SBN             1099   \n",
       "\n",
       "                               city state  airport_id        lat         lon  \n",
       "0                        Manchester    NH       13296  42.932598  -71.435699  \n",
       "1                         Charlotte    NC       11057  35.214001  -80.943100  \n",
       "2                          Honolulu    HI       12173  21.320620 -157.924228  \n",
       "3    Bristol/Johnson City/Kingsport    TN       15323  36.475201  -82.407401  \n",
       "4                             Akron    OH       10874  40.916100  -81.442200  \n",
       "..                              ...   ...         ...        ...         ...  \n",
       "195                   Oklahoma City    OK       13851  35.393101  -97.600700  \n",
       "196                         Houston    TX       12266  29.984400  -95.341400  \n",
       "197                       Pensacola    FL       14193  30.473400  -87.186600  \n",
       "198                        New York    NY       12478  40.639447  -73.779317  \n",
       "199                         Detroit    MI       11433  42.212399  -83.353401  \n",
       "\n",
       "[200 rows x 14 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import meteostast library and datetime (For mor information about this library go to https://meteostat.net/en/)\n",
    "from datetime import datetime\n",
    "from meteostat import Point, Daily\n",
    "\n",
    "start = datetime(2018, 1, 1) # Change start date\n",
    "end = datetime(2019, 12, 31) # Change end date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tmax</th>\n",
       "      <th>prcp</th>\n",
       "      <th>snow</th>\n",
       "      <th>wdir</th>\n",
       "      <th>wspd</th>\n",
       "      <th>wpgt</th>\n",
       "      <th>pres</th>\n",
       "      <th>tsun</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-16.6</td>\n",
       "      <td>-21.1</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>294.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-16.0</td>\n",
       "      <td>-26.1</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-9.1</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-7.3</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>280.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-11.2</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>8.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>997.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143037 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            tavg  tmin  tmax  prcp   snow   wdir  wspd  wpgt    pres  tsun  \\\n",
       "2018-01-01 -16.6 -21.1 -11.7   0.0  300.0  294.0  18.5   NaN  1024.9   NaN   \n",
       "2018-01-02 -16.0 -26.1  -8.3   0.0  300.0    NaN   5.8   NaN  1024.1   NaN   \n",
       "2018-01-03  -9.1 -16.1  -1.1   0.0  280.0    NaN   4.8   NaN  1024.3   NaN   \n",
       "2018-01-04  -7.3 -15.6  -3.3  18.8  280.0  321.0  26.7   NaN   998.5   NaN   \n",
       "2018-01-05 -11.2 -16.1  -6.1   0.0  530.0  279.0  23.2   NaN     NaN   NaN   \n",
       "...          ...   ...   ...   ...    ...    ...   ...   ...     ...   ...   \n",
       "2019-12-27   8.3   3.3  13.9   0.0    0.0  266.0  18.2   NaN  1020.6   NaN   \n",
       "2019-12-28   3.0   1.1   6.1   0.0    0.0    NaN   8.6   NaN  1024.5   NaN   \n",
       "2019-12-29   7.7   3.9  10.0  29.0    0.0   89.0  10.5   NaN  1010.1   NaN   \n",
       "2019-12-30   5.5   1.7   9.4   1.4    0.0  218.0  28.8   NaN   997.8   NaN   \n",
       "2019-12-31  -1.0  -2.0   0.0   0.0   10.0  235.0  32.4   NaN  1002.9   NaN   \n",
       "\n",
       "                  lat        lon  \n",
       "2018-01-01  42.932598 -71.435699  \n",
       "2018-01-02  42.932598 -71.435699  \n",
       "2018-01-03  42.932598 -71.435699  \n",
       "2018-01-04  42.932598 -71.435699  \n",
       "2018-01-05  42.932598 -71.435699  \n",
       "...               ...        ...  \n",
       "2019-12-27  42.212399 -83.353401  \n",
       "2019-12-28  42.212399 -83.353401  \n",
       "2019-12-29  42.212399 -83.353401  \n",
       "2019-12-30  42.212399 -83.353401  \n",
       "2019-12-31  42.212399 -83.353401  \n",
       "\n",
       "[143037 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use weather library meteostat\n",
    "#Be carefull this library is not availble all the time and generate connection errors\n",
    "weather_dfs=[]\n",
    "def get_bulk_weather(df):\n",
    "    for i,r in df.iterrows():\n",
    "        point = Point(r.lat, r.lon)\n",
    "        data = Daily(point, start, end)#,params=['temp','prcp','wspd','tsun','snow'])\n",
    "        data = data.fetch()\n",
    "        data[\"lat\"] = r.lat\n",
    "        data[\"lon\"] = r.lon\n",
    "        weather_dfs.append(data)\n",
    "        #print(data)\n",
    "    weather_final = pd.concat(weather_dfs)\n",
    "    return weather_final \n",
    "\n",
    "df_weather = get_bulk_weather(df_flights_merged)\n",
    "df_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create local file to avoid issue with library\n",
    "from pathlib import Path  \n",
    "filepath = Path('../../data/raw/weatherSampleNew.csv')  \n",
    "filepath.parent.mkdir(parents=True, exist_ok=True)  \n",
    "df_weather.to_csv(filepath)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read weather from local csv to avoid calling the library everytime with the historic data\n",
    "df_weather_f= pd.read_csv(\"../../data/raw/weatherSample.csv\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>mkt_unique_carrier</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>iata_code2</th>\n",
       "      <th>weather_delay_r</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>Key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>DL</td>\n",
       "      <td>N390CA</td>\n",
       "      <td>6248</td>\n",
       "      <td>13296</td>\n",
       "      <td>MHT</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>2900</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>NH</td>\n",
       "      <td>13296</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>DL</td>\n",
       "      <td>N264SY</td>\n",
       "      <td>4690</td>\n",
       "      <td>11057</td>\n",
       "      <td>CLT</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2692</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>11057</td>\n",
       "      <td>35.214001</td>\n",
       "      <td>-80.943100</td>\n",
       "      <td>35.2140007019043-35.2140007019043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>HA</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>465</td>\n",
       "      <td>12173</td>\n",
       "      <td>HNL</td>\n",
       "      <td>14222</td>\n",
       "      <td>PPG</td>\n",
       "      <td>2475</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>HI</td>\n",
       "      <td>12173</td>\n",
       "      <td>21.320620</td>\n",
       "      <td>-157.924228</td>\n",
       "      <td>21.32062-21.32062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>DL</td>\n",
       "      <td>N921EV</td>\n",
       "      <td>3855</td>\n",
       "      <td>15323</td>\n",
       "      <td>TRI</td>\n",
       "      <td>10397</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2098</td>\n",
       "      <td>Bristol/Johnson City/Kingsport</td>\n",
       "      <td>TN</td>\n",
       "      <td>15323</td>\n",
       "      <td>36.475201</td>\n",
       "      <td>-82.407401</td>\n",
       "      <td>36.475201-36.475201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>AA</td>\n",
       "      <td>N902EV</td>\n",
       "      <td>3115</td>\n",
       "      <td>10874</td>\n",
       "      <td>CAK</td>\n",
       "      <td>13930</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1847</td>\n",
       "      <td>Akron</td>\n",
       "      <td>OH</td>\n",
       "      <td>10874</td>\n",
       "      <td>40.916100</td>\n",
       "      <td>-81.442200</td>\n",
       "      <td>40.91609954833984-40.91609954833984</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>AA</td>\n",
       "      <td>N950LR</td>\n",
       "      <td>5849</td>\n",
       "      <td>13851</td>\n",
       "      <td>OKC</td>\n",
       "      <td>11298</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1103</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "      <td>13851</td>\n",
       "      <td>35.393101</td>\n",
       "      <td>-97.600700</td>\n",
       "      <td>35.393101-35.393101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>UA</td>\n",
       "      <td>N88325</td>\n",
       "      <td>6017</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12992</td>\n",
       "      <td>LIT</td>\n",
       "      <td>1103</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>12266</td>\n",
       "      <td>29.984400</td>\n",
       "      <td>-95.341400</td>\n",
       "      <td>29.984399795532227-29.984399795532227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2019-05-11</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24103</td>\n",
       "      <td>4142</td>\n",
       "      <td>14193</td>\n",
       "      <td>PNS</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1102</td>\n",
       "      <td>Pensacola</td>\n",
       "      <td>FL</td>\n",
       "      <td>14193</td>\n",
       "      <td>30.473400</td>\n",
       "      <td>-87.186600</td>\n",
       "      <td>30.4734-30.4734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>DL</td>\n",
       "      <td>N380DA</td>\n",
       "      <td>2164</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>14683</td>\n",
       "      <td>SAT</td>\n",
       "      <td>1100</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12478</td>\n",
       "      <td>40.639447</td>\n",
       "      <td>-73.779317</td>\n",
       "      <td>40.639447-40.639447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>DL</td>\n",
       "      <td>N659BR</td>\n",
       "      <td>4756</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>14696</td>\n",
       "      <td>SBN</td>\n",
       "      <td>1099</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>MI</td>\n",
       "      <td>11433</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       fl_date mkt_unique_carrier tail_num  op_carrier_fl_num  \\\n",
       "0   2019-03-21                 DL   N390CA               6248   \n",
       "1   2018-07-05                 DL   N264SY               4690   \n",
       "2   2018-02-09                 HA   N382HA                465   \n",
       "3   2018-12-09                 DL   N921EV               3855   \n",
       "4   2019-12-24                 AA   N902EV               3115   \n",
       "..         ...                ...      ...                ...   \n",
       "195 2018-12-26                 AA   N950LR               5849   \n",
       "196 2019-03-09                 UA   N88325               6017   \n",
       "197 2019-05-11                 UA   N24103               4142   \n",
       "198 2018-05-15                 DL   N380DA               2164   \n",
       "199 2019-07-19                 DL   N659BR               4756   \n",
       "\n",
       "     origin_airport_id iata_code  dest_airport_id iata_code2  weather_delay_r  \\\n",
       "0                13296       MHT            11433        DTW             2900   \n",
       "1                11057       CLT            12478        JFK             2692   \n",
       "2                12173       HNL            14222        PPG             2475   \n",
       "3                15323       TRI            10397        ATL             2098   \n",
       "4                10874       CAK            13930        ORD             1847   \n",
       "..                 ...       ...              ...        ...              ...   \n",
       "195              13851       OKC            11298        DFW             1103   \n",
       "196              12266       IAH            12992        LIT             1103   \n",
       "197              14193       PNS            12266        IAH             1102   \n",
       "198              12478       JFK            14683        SAT             1100   \n",
       "199              11433       DTW            14696        SBN             1099   \n",
       "\n",
       "                               city state  airport_id        lat         lon  \\\n",
       "0                        Manchester    NH       13296  42.932598  -71.435699   \n",
       "1                         Charlotte    NC       11057  35.214001  -80.943100   \n",
       "2                          Honolulu    HI       12173  21.320620 -157.924228   \n",
       "3    Bristol/Johnson City/Kingsport    TN       15323  36.475201  -82.407401   \n",
       "4                             Akron    OH       10874  40.916100  -81.442200   \n",
       "..                              ...   ...         ...        ...         ...   \n",
       "195                   Oklahoma City    OK       13851  35.393101  -97.600700   \n",
       "196                         Houston    TX       12266  29.984400  -95.341400   \n",
       "197                       Pensacola    FL       14193  30.473400  -87.186600   \n",
       "198                        New York    NY       12478  40.639447  -73.779317   \n",
       "199                         Detroit    MI       11433  42.212399  -83.353401   \n",
       "\n",
       "                                       Key  \n",
       "0                      42.932598-42.932598  \n",
       "1        35.2140007019043-35.2140007019043  \n",
       "2                        21.32062-21.32062  \n",
       "3                      36.475201-36.475201  \n",
       "4      40.91609954833984-40.91609954833984  \n",
       "..                                     ...  \n",
       "195                    35.393101-35.393101  \n",
       "196  29.984399795532227-29.984399795532227  \n",
       "197                        30.4734-30.4734  \n",
       "198                    40.639447-40.639447  \n",
       "199  42.212398529052734-42.212398529052734  \n",
       "\n",
       "[200 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_merged['Key'] = df_flights_merged['lat'].astype(str) +'-'+df_flights_merged['lat'].astype(str) \n",
    "df_flights_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_weather_f['Key'] = df_weather_f['lat'].astype(str) +'-'+df_weather_f['lat'].astype(str)\n",
    "df_weather_f = df_weather_f.drop(columns=['tmin','tmax','wdir','pres','wpgt','lat','lon','tsun']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>-16.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>-7.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>280.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>-11.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>42.932598-42.932598</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143032</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143033</th>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143034</th>\n",
       "      <td>2019-12-29</td>\n",
       "      <td>7.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143035</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143036</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143037 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          fl_date  AvgTemp  Precipitation  Snow_mm  WindSpeed  \\\n",
       "0      2018-01-01    -16.6            0.0    300.0       18.5   \n",
       "1      2018-01-02    -16.0            0.0    300.0        5.8   \n",
       "2      2018-01-03     -9.1            0.0    280.0        4.8   \n",
       "3      2018-01-04     -7.3           18.8    280.0       26.7   \n",
       "4      2018-01-05    -11.2            0.0    530.0       23.2   \n",
       "...           ...      ...            ...      ...        ...   \n",
       "143032 2019-12-27      8.3            0.0      0.0       18.2   \n",
       "143033 2019-12-28      3.0            0.0      0.0        8.6   \n",
       "143034 2019-12-29      7.7           29.0      0.0       10.5   \n",
       "143035 2019-12-30      5.5            1.4      0.0       28.8   \n",
       "143036 2019-12-31     -1.0            0.0     10.0       32.4   \n",
       "\n",
       "                                          Key  \n",
       "0                         42.932598-42.932598  \n",
       "1                         42.932598-42.932598  \n",
       "2                         42.932598-42.932598  \n",
       "3                         42.932598-42.932598  \n",
       "4                         42.932598-42.932598  \n",
       "...                                       ...  \n",
       "143032  42.212398529052734-42.212398529052734  \n",
       "143033  42.212398529052734-42.212398529052734  \n",
       "143034  42.212398529052734-42.212398529052734  \n",
       "143035  42.212398529052734-42.212398529052734  \n",
       "143036  42.212398529052734-42.212398529052734  \n",
       "\n",
       "[143037 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather_f.rename(columns={'Unnamed: 0' :'fl_date','tavg':'AvgTemp', 'prcp':'Precipitation', 'snow':'Snow_mm','wspd':'WindSpeed','tsun':'SunTime'}, inplace=True)\n",
    "df_weather_f['fl_date'] = df_weather_f['fl_date'].astype('datetime64[ns]')\n",
    "df_weather_f.drop_duplicates()\n",
    "df_weather_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>mkt_unique_carrier</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>iata_code2</th>\n",
       "      <th>weather_delay_r</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>Key</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>DL</td>\n",
       "      <td>N390CA</td>\n",
       "      <td>6248</td>\n",
       "      <td>13296</td>\n",
       "      <td>MHT</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>2900</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>NH</td>\n",
       "      <td>13296</td>\n",
       "      <td>42.932598</td>\n",
       "      <td>-71.435699</td>\n",
       "      <td>42.932598-42.932598</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>DL</td>\n",
       "      <td>N264SY</td>\n",
       "      <td>4690</td>\n",
       "      <td>11057</td>\n",
       "      <td>CLT</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2692</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>11057</td>\n",
       "      <td>35.214001</td>\n",
       "      <td>-80.943100</td>\n",
       "      <td>35.2140007019043-35.2140007019043</td>\n",
       "      <td>29.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>HA</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>465</td>\n",
       "      <td>12173</td>\n",
       "      <td>HNL</td>\n",
       "      <td>14222</td>\n",
       "      <td>PPG</td>\n",
       "      <td>2475</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>HI</td>\n",
       "      <td>12173</td>\n",
       "      <td>21.320620</td>\n",
       "      <td>-157.924228</td>\n",
       "      <td>21.32062-21.32062</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>DL</td>\n",
       "      <td>N921EV</td>\n",
       "      <td>3855</td>\n",
       "      <td>15323</td>\n",
       "      <td>TRI</td>\n",
       "      <td>10397</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2098</td>\n",
       "      <td>Bristol/Johnson City/Kingsport</td>\n",
       "      <td>TN</td>\n",
       "      <td>15323</td>\n",
       "      <td>36.475201</td>\n",
       "      <td>-82.407401</td>\n",
       "      <td>36.475201-36.475201</td>\n",
       "      <td>0.6</td>\n",
       "      <td>38.9</td>\n",
       "      <td>130.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>AA</td>\n",
       "      <td>N902EV</td>\n",
       "      <td>3115</td>\n",
       "      <td>10874</td>\n",
       "      <td>CAK</td>\n",
       "      <td>13930</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1847</td>\n",
       "      <td>Akron</td>\n",
       "      <td>OH</td>\n",
       "      <td>10874</td>\n",
       "      <td>40.916100</td>\n",
       "      <td>-81.442200</td>\n",
       "      <td>40.91609954833984-40.91609954833984</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>AA</td>\n",
       "      <td>N950LR</td>\n",
       "      <td>5849</td>\n",
       "      <td>13851</td>\n",
       "      <td>OKC</td>\n",
       "      <td>11298</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1103</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "      <td>13851</td>\n",
       "      <td>35.393101</td>\n",
       "      <td>-97.600700</td>\n",
       "      <td>35.393101-35.393101</td>\n",
       "      <td>12.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>UA</td>\n",
       "      <td>N88325</td>\n",
       "      <td>6017</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12992</td>\n",
       "      <td>LIT</td>\n",
       "      <td>1103</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>12266</td>\n",
       "      <td>29.984400</td>\n",
       "      <td>-95.341400</td>\n",
       "      <td>29.984399795532227-29.984399795532227</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2019-05-11</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24103</td>\n",
       "      <td>4142</td>\n",
       "      <td>14193</td>\n",
       "      <td>PNS</td>\n",
       "      <td>12266</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1102</td>\n",
       "      <td>Pensacola</td>\n",
       "      <td>FL</td>\n",
       "      <td>14193</td>\n",
       "      <td>30.473400</td>\n",
       "      <td>-87.186600</td>\n",
       "      <td>30.4734-30.4734</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>DL</td>\n",
       "      <td>N380DA</td>\n",
       "      <td>2164</td>\n",
       "      <td>12478</td>\n",
       "      <td>JFK</td>\n",
       "      <td>14683</td>\n",
       "      <td>SAT</td>\n",
       "      <td>1100</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12478</td>\n",
       "      <td>40.639447</td>\n",
       "      <td>-73.779317</td>\n",
       "      <td>40.639447-40.639447</td>\n",
       "      <td>17.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>DL</td>\n",
       "      <td>N659BR</td>\n",
       "      <td>4756</td>\n",
       "      <td>11433</td>\n",
       "      <td>DTW</td>\n",
       "      <td>14696</td>\n",
       "      <td>SBN</td>\n",
       "      <td>1099</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>MI</td>\n",
       "      <td>11433</td>\n",
       "      <td>42.212399</td>\n",
       "      <td>-83.353401</td>\n",
       "      <td>42.212398529052734-42.212398529052734</td>\n",
       "      <td>27.8</td>\n",
       "      <td>43.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       fl_date mkt_unique_carrier tail_num  op_carrier_fl_num  \\\n",
       "0   2019-03-21                 DL   N390CA               6248   \n",
       "1   2018-07-05                 DL   N264SY               4690   \n",
       "2   2018-02-09                 HA   N382HA                465   \n",
       "4   2018-12-09                 DL   N921EV               3855   \n",
       "5   2019-12-24                 AA   N902EV               3115   \n",
       "..         ...                ...      ...                ...   \n",
       "583 2018-12-26                 AA   N950LR               5849   \n",
       "585 2019-03-09                 UA   N88325               6017   \n",
       "586 2019-05-11                 UA   N24103               4142   \n",
       "587 2018-05-15                 DL   N380DA               2164   \n",
       "593 2019-07-19                 DL   N659BR               4756   \n",
       "\n",
       "     origin_airport_id iata_code  dest_airport_id iata_code2  weather_delay_r  \\\n",
       "0                13296       MHT            11433        DTW             2900   \n",
       "1                11057       CLT            12478        JFK             2692   \n",
       "2                12173       HNL            14222        PPG             2475   \n",
       "4                15323       TRI            10397        ATL             2098   \n",
       "5                10874       CAK            13930        ORD             1847   \n",
       "..                 ...       ...              ...        ...              ...   \n",
       "583              13851       OKC            11298        DFW             1103   \n",
       "585              12266       IAH            12992        LIT             1103   \n",
       "586              14193       PNS            12266        IAH             1102   \n",
       "587              12478       JFK            14683        SAT             1100   \n",
       "593              11433       DTW            14696        SBN             1099   \n",
       "\n",
       "                               city state  airport_id        lat         lon  \\\n",
       "0                        Manchester    NH       13296  42.932598  -71.435699   \n",
       "1                         Charlotte    NC       11057  35.214001  -80.943100   \n",
       "2                          Honolulu    HI       12173  21.320620 -157.924228   \n",
       "4    Bristol/Johnson City/Kingsport    TN       15323  36.475201  -82.407401   \n",
       "5                             Akron    OH       10874  40.916100  -81.442200   \n",
       "..                              ...   ...         ...        ...         ...   \n",
       "583                   Oklahoma City    OK       13851  35.393101  -97.600700   \n",
       "585                         Houston    TX       12266  29.984400  -95.341400   \n",
       "586                       Pensacola    FL       14193  30.473400  -87.186600   \n",
       "587                        New York    NY       12478  40.639447  -73.779317   \n",
       "593                         Detroit    MI       11433  42.212399  -83.353401   \n",
       "\n",
       "                                       Key  AvgTemp  Precipitation  Snow_mm  \\\n",
       "0                      42.932598-42.932598      4.6            0.0    130.0   \n",
       "1        35.2140007019043-35.2140007019043     29.2            0.0      0.0   \n",
       "2                        21.32062-21.32062     21.6            0.0      0.0   \n",
       "4                      36.475201-36.475201      0.6           38.9    130.0   \n",
       "5      40.91609954833984-40.91609954833984      2.3            0.0      0.0   \n",
       "..                                     ...      ...            ...      ...   \n",
       "583                    35.393101-35.393101     12.8           69.1      0.0   \n",
       "585  29.984399795532227-29.984399795532227     23.8            0.0      0.0   \n",
       "586                        30.4734-30.4734     25.5            0.8      0.0   \n",
       "587                    40.639447-40.639447     17.2           11.2      0.0   \n",
       "593  42.212398529052734-42.212398529052734     27.8           43.6      0.0   \n",
       "\n",
       "     WindSpeed  \n",
       "0         11.2  \n",
       "1          8.6  \n",
       "2         13.7  \n",
       "4          9.0  \n",
       "5          7.2  \n",
       "..         ...  \n",
       "583       29.2  \n",
       "585       15.5  \n",
       "586       15.5  \n",
       "587       17.6  \n",
       "593       11.7  \n",
       "\n",
       "[200 rows x 19 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_weather_merge = df_flights_merged.merge(df_weather_f, on=['Key','fl_date'], how='left')\n",
    "flights_weather_merge.fillna(0, inplace=True)\n",
    "flights_weather_merge.drop_duplicates(inplace=True)\n",
    "flights_weather_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vfDaw2bwzf75afmbz9tz5Wnll89dzYHb5mc9s+y4n7owH+bHvhg0bZouMjLR99913ea7rT3MUO1fy2Wz+85nVZnMtmzc/s/o6W048ec95s5ZltVptDz30kC0yMtL20Ucf2dLS0hzL7LWXunXr2lJSUrI8d8mSJZmOP+6O39kx6nyDq/mMPK54+5jpbjZ/m2MGGP/1ALji3Llz+uSTT/TYY4/p8OHDud6ye/LkyUpPT1f37t116623Oh4PDw933GZm8uTJTm33u+++kyQNHDhQjRs3djweGhqqjz/+WGFhYZo8ebKSkpLcSPU/zuZz5e+Qm7lz50qSnnvuuSy3jenUqZOaN2+e6dtMnsjvbDmJj493fNv5s88+M+RWR670eeHChZKkd999N9OtVSpWrKgnnnhC0pWfPXCGff+9++67mbZZuHBhffDBB7rhhhu0b98+x7cA3eHu/jh69Kg++OAD1apVS6GhoW5t297v2267za3n58WVbFar1fHNuuyudHKVt7NJvs2XnYULF2revHmKiIjQ4MGDPWrLiOPEuHHjtGbNGt1+++16+eWXnXrO77//LunKN0Br1KjheLx48eLq1auXJGn9+vUu9+VaruSzfzO3S5cume5GUqZMGT377LMu9WnGjBmOn8a4+krdsLAwx5UgU6ZMUXp6usuZpP8ds/r375/p26OBgYHq27evatSooTNnzmjNmjWSjB3HJSkhIUHvvPOOnn32WSUlJSk8PNytHNlxJVtKSorjdnbDhg3LdKu4EiVKaPjw4bJYLPrjjz/0999/u9SPc+fO6bXXXlP58uVVqVIlA5Jd4ct833//vU6dOqV27do5XtOSZLFY1KtXL0VFRclisejEiRNezyZ573jpjX3nznsuOTlZ3bt31wMPPOBYPzg4WK+//roqVKig06dP6/Lly273ycixwNV8Rh4vvT0HcyXbP//8o0WLFkmShgwZkmn9kiVLasSIEQoKCtLnn3/u9vFbMm7fLViwQJL0/PPPZ+praGioYzy+ei5s5Fjg7XHOl9kk788vXcln9Fjg7TmYO/vOqOOl2bLlxKxzZ1fzGTkWePs8kSvZvDEWeHPufK3U1FT1799fAQEBmY6HVzP6mGnnrfmzXX5n8/bc+WrOZPO3c2BXy+98Ztt3OXFnPMiPfXf1HYpy429zFDtn80n+9ZlVci2btz6zmiFbTtx5z+VHLWvFihXavXu36tevrzfffFOBgYGOZe3bt1ezZs1UsmRJ7d692/H4wYMH9fLLL6t///6SpNKlS7u9/ewYeb7BlXxGH1e8fcx0JZs/zzEp5vvYtGnTNHnyZJUsWVLjx49X+/btc1x31apVkpTtrTzuuOMOFS9eXDt27FBCQkKu2zx79qxjnbvvvjvL8mLFiik6OlpWq1Vr1651IU1WzuZz5e+Qm5CQEEVGRmY6qF/t5ptvlqQ8/0bOyO9sOfnggw906dIlde/e3elbA+bFlT4vWrRICxYsUO3atbMss99O5OoDaE5SUlJUsWJFVatWLdtbbBUqVMhxsPdk/7mzP9LS0jRgwAClp6fr008/dSrPtc6fP6+jR4+qcuXKHv1udG5cyWb/bZrbbrvN498Uyo9sku/yZScpKclxkvzDDz9U0aJFPWrP0+PEoUOHNGHCBAUHB2vIkCFOv0bt6508eTLLssTEREky5Bb7ruQzsk8rV66UlP24Wa1aNUVGRioxMVHbt293qr1rlShRQtWqVVODBg2yLLNYLLrpppsk/e+YZdQ4bvfZZ59p9uzZuummmzR9+nRDfwfTlWxnzpxRrVq1VK9ePVWsWDHL+qVKldINN9wgm82mU6dOudSPd955RwkJCRo+fLjH77Or+TLfjz/+KOnKFw+z2/bixYu1fPlyt28T6Orr0lvHS2/sO1ez/fTTTwoMDNQzzzyTZf2iRYvq119/1ZIlS9y+VbfRY4Gr+Yw6XubHHMyVbAcOHJDValWxYsWy/U3CihUrqnLlyjp9+rTjdpuuMnLfffDBB/rhhx/UqVOnLMvsH/6vHpeNHAu8Pc75Mlt+zC9dyWf0WODtOZir+87I46XZsmXHzHNnV/MZ1af8OE/kSjZvjAXenDtnt63Y2Fi9++67Oc7pjP5sYOet+bNdfmfz9tz5as5k87dzYFfLz3yS+fZddtwZD/Jr3+3atUuFCxfOVFDPjr/NUeyczSf512dWybVs3vrMaoZs2XHnPZdftSz7Mat79+7Zvs4mTZqkX375JdP7/t1339Xy5ctVt25dzZkzx1F7MoLR5xtcyWfkcSU/jpmuZPPnOWaQV1qF08qXL6/XX39dXbp0UUhISI4vkNOnT+vMmTMKDg7O9qAQGBiom2++Wdu2bdOePXty/W31qydfOV1hHBR05aURHx/vaqRMnM3n7Hp5ef/993Ndvm3bNsf2PJXf2bKzbNkyrV+/XhUqVFDPnj0Na9eVPoeGhmb7rdetW7fqm2++kcViUYcOHfLcZkhIiKZPn57j8osXLzpej55M9N3ZH2PGjNGff/6pDz74wO1B2b6dypUra+zYsfrpp5905MgRx8DRq1cvjwd8V7LZl5UrV07Dhw/XqlWr9Ndff+nGG2/UXXfdpV69euV6HMnvbL7Ml51Ro0YpKSlJ99xzj+644w6327Hz9Djx4Ycfymq16tlnn1X16tWdfl6zZs30xRdfaMWKFfriiy/0+OOPKzQ0VGvXrtWwYcMUGBiY7QdvV7mSr1mzZpo/f75mzJihypUrq23btgoICNCKFSv05ZdfqkiRInrqqaec2u6+ffskSZGRkdkur169uvbu3au4uDjVrVvX5Vxjx47NcVl6erojp/2b1EaN43Y33XSTPvroI3Xo0EFBQUGaPXu2yxly4kq2ihUraubMmTmuf/jwYSUlJSkgIMCl95399w579Oihf/3rX8533gm+yme1WrVnzx6FhoYqKipKCQkJ+uGHH3TgwAEVKVJETZs2VfPmzd0L9f9cySZ553jprX3nSrZjx47p7Nmzql69usLCwnT48GH9+OOPOnr0qG644Qa1bNky28KyK4weC1zdd0YdL/NjDuZKNvtnldxOVtg/q+zfvz/bk815MXLfBQUFqVq1alkeP3HihIYPHy5J6tixoyRjP9NJ3h/nfJktP+aXruQzeqzz9hzMlWxGHy/NlC0nZp47u5rPqLEgP84TuZLNG2OBN+fOV1u7dq2mTJmi+++/Xw899FC2d4b0xmcDybvzZyn/s+XH3NmVbJL/nQOzy+98Ztx32XFnPMiPfXf06FGdP39eUVFRmjVrlhYuXKiDBw+qUKFCql+/vnr27Ok47vnbHMXVfJJ/fWZ1JZu3PrOaIVtO3HnP5Vcta+fOnZKkunXrKikpST/88IPi4uIUFBSkBg0a6J577sny5YPbbrtNTzzxhNq0aWP4xWRGn29wJZ+Rx5X8OGa6ks2f55gU833s0UcfdWo9+zdcSpcurYCA7G+oULZs2Uzr5qRkyZIKCwtTUlKSNm3alOVgcPnyZccb4MyZM071LyfO5nN2PU+sXLlSW7ZsUXBwsFq3bu1xe77OlpGRof/85z+SpBdeeEFFihQxrG13+3zx4kW9+eabOnjwoPbu3auwsDCNGDFCDRs29LhPY8aMUUpKim666aZMtz9xlavZNm3apC+//FItWrTI9goCZ9knnqtXr9bGjRvVsGFDVahQQbGxsVqyZIliYmI0bty4bL8R5ixXstn7s3DhQhUrVkwNGjRw9Oe7777TihUrNGXKFEVFRTndljezSb7Ld62jR49q7ty5slgsTt8OKi+eHCc2bNig1atXKzQ0VM8//7xLz61du7beffddffLJJxozZozGjBnjWFapUiWNHj3akIm/K/nuu+8+xcXFafLkyRo8eHCm20jVqlVLH3/8sVPHgHPnziklJUWScpxYOjtuumPmzJk6fvy4wsLC1KRJEx0+fFiSMeO43QsvvGBMZ110bba8fPrpp5KuXIXj7NWVBw8e1Mcff6xatWoZ9j5zljfzHT9+XFarVeHh4Vq6dKneffddJScnO5Z/8803atq0qUaPHm3Iz+ZcK7tsRh8vfbXvrs32559/Srryvvr66681atQoWa1Wx/pfffWVHnzwQQ0ZMkSFChVyeXveGAtyk92+M+p4mRej5mA5uTab1WpVQECAEhISdPjwYVWpUiXT+mfOnNHBgwcd/+8qb++7YcOGadu2bdq2bZssFoueffZZx5dujfxM54txLr+ySfk3v7xabvny4upYl19zMLvcstnnKEYdL82ULTv+MHe+Wl75jBoL8vM8kTPZqlSpYvhYkB9z57Nnz2rgwIEqX758rhebGH3MlLw/B/NFtvyaOzub7Vr+cA5M8k0+s+87ybPxIDdG7Dv78XbPnj0aPny46tWrp8aNGysuLk4xMTH67bffNHToUD344IN5tmXGOYqr+fzpM6sr2Yyeg5kpW3bcfc/lxxwlNTVVx48fV1BQkPbs2aNXX3010+3jZ8yYoVtvvVUTJkzI9Nnr9ddfd2t7eTH6M6u7+XLiznm+nHh6zHQ1m7/OMSVus+837BOekJCQHNex327l6slRdgICAhy3OH7vvfcyfWPp8uXL+uCDDxy3zUlNTfWk26axZ88eDRo0SNKV2ztld3sQf7N8+XIdPnxYZcuW1cMPP+zr7ki68o3C5cuXa+/evZKu3DYrPj4+00TEHYsWLdLUqVMVEBCgt99+2yu3Tc/O+fPn9dprr+nGG2/Uxx9/7FFb9t98adiwoVauXKmvvvpKX375pVatWqWuXbsqOTlZffv21fnz543outP9ueeee/Tbb79p4sSJmjJlimJiYnTPPffozJkzevnll5WWluZ0W2bJdnWfjMh3ra+//lrp6elq3bq1S1fyeIv9twYff/zxTL9h5Kx//etfatq0qUJCQtSwYUP9+9//1o033qijR49q4sSJLv/GuRFatGihunXrqlixYrrjjjvUpEkTFStWTLt379bkyZMdxYvc2MfCQoUK5XgSyT6m5jVuumrdunUaMWKEJGnAgAEqWrSooeO4L2WXLTcTJkzQzz//rJCQEA0cONCpbVitVvXv3182m02ffvqpgoODPe63s7yd78KFC5Ku3J7wjTfeULNmzbR06VJt3rxZX3/9tapWrao1a9botdde8zzMNXLKZuTx0lf7Lrts9r/1zp07NWLECD3++OP6+eeftXHjRo0ePVqlS5fW4sWLHc9zVX6OBbm9Lo04XubG23Ow7LKFhYWpVatWjseuvtXehQsXNGjQIMfc0p3PKt7ed/PmzdOWLVuUnp6ugIAAJSYm6vTp05KM/Uzni3Euv7JJvplf5pYvN+6MdVL+zsFyy+aN46VZsmXH3+bOzuQzYizwxXmi3LJ5eyzwlrfeektnzpzR8OHDVaJEiRzXM/qYmR9zMF9ky6+5s7PZruUv58B8kc/s+07yfDzIjlH7zj4Pql69un744QdNnz5d48ePV0xMjPr166e0tDS99dZbjmJwTsw6R3E1nz99ZnUlm9FzMDNly46777n8mKNcvHhRkmSz2dSrVy9FRkY65infffedoqOjtWvXLr344otO/466J4z+zGpkPnePK9kx4pjpajZ/nWNKFPP9hv0EjTMvaJvNluc6r7zyiurUqaMjR47ooYceUteuXdWzZ0+1atVKP/74o+PWZvZbSviz7du3q1u3bkpKSlKLFi3y/Qo/b5k6daok6emnn3brijJvqFSpktavX69NmzZp/PjxKlasmCZMmKABAwa43ebs2bM1aNAg2Ww29e/fX3feeaeBPc7dO++8o7/++ksff/yxxxP7YcOG6ccff9SECRNUunRpx+OFChXSW2+9pZo1ayopKUmLFi3ytNtO+eabb7R06VJ9+umnmb4FXbx4cQ0dOlRly5bVoUOH9Pvvv+fZltmyScbmu9q5c+c0f/58SVKPHj0M7bM74uPj9fvvvys4OFjPPvusy89fvXq1HnnkER05ckRLly7Vt99+q8mTJysmJkYPPPCAVq9ere7duztuQZQfFixYoC5dushisejnn3/WlClTNHXqVP38889q1KiRlixZ4tQxxehx01mrVq1Sz549lZqaqs6dOzvuSuCr/hgpp2w5GT16tD777DMFBATo448/dvpb8qNGjVJsbKxef/11Q39vLC/5ke/y5cuSrpy4bNy4sf7zn/+oRo0aKlasmJo2baqvvvpKISEhWrlypdu/cZ2d3LIZebz0xb7LKZv9b33+/Hk99thjeuedd1SlShWVKFFC99xzj8aOHSuLxaJZs2a5/Lts+TkW5LbvjDpe5sTbc7Dcsr333nuqUqWKtm/frnvvvVfdunXT888/r7vvvlu7du3SvffeK0kun6DKj323ePFibdu2TXPmzFG9evW0ZMkSde7cWcnJyYaOBb4YV/Irm+Sb+WVu+XLi7liX33Ow3LIZfbw0U7Zr+ePcOa98Ro4F+X2eKK9s3hoLvGXGjBlauXKlunfvnudvpRp9zPT2HMxX2fJj7uxKtmv5wzkwX+Uz+77zdDzIjpH77pVXXtGKFSs0ffr0TFeNBgQE6IUXXlCLFi2UmpqqWbNm5diGmecorubzp8+srmQzeg5mpmzX8vQ95+05ir1om56eroiICH311Ve67bbbVLRoUdWtW1dTpkxRmTJlFBsbq59//tmtbTjLG59Zjcrn7nElO0YdM93J5m9zTDuK+X7CfgVObt+otg8AOf12yNVCQ0M1ffp0vfLKK4qIiNC2bdu0Y8cO3XnnnVq8eLFuuukmSdINN9xgQO9956efftJTTz2lv//+W3fffbdGjx6d45Ur/uTo0aP6888/FRgYqIceesjX3XGwX01VvHhxtWzZ0jE5/+mnn7R7926X2srIyNDIkSP1zjvvKD09Xf379zfkt7udNXfuXP3000964oknDPkdr0KFCunmm2/O9vZhgYGBuuuuuyRJO3bs8HhbzggJCVGNGjWy/SJI0aJF1bhxY6f7Y7ZskrH5rrZixQqlpKSoRo0aio6ONqSvnli6dKlsNpuaNm3quFWhs9LT0/Xee+8pJSVFI0aMUKVKlRzLihYtqo8//liVK1dWbGysVq1aZXTXs3X+/Hl99NFHCg4O1siRI1WqVCnHslKlSmnUqFEqXry4VqxYobi4uFzbso+bly9fzvFDpn1MNepnSqZPn67evXsrJSVFXbt21XvvvZelP0aN4/ktt2zXSk1N1cCBAzV27FgFBQVp+PDhuv/++53azrp16zRlyhS1aNFCnTt3Nqr7ecqvfFe/1rL7/dqIiAjHmLNu3ToXU2Qvr2xGHS99se9yy3b137pbt25Znnv77berVq1aSktL08aNG13abn6NBbnlM/J4ea38mIPl9bosU6aM5syZo2effVY33nijNm/erH379umBBx7Q4sWLHbfyc/UKrPzYdxUqVFBISIhq166tSZMmKTIyUseOHdPs2bMNHQt8Mc7lVzbJN/PL3PJdy5OxwBdzsNyyGXm8NFu2a/nj3Dm3fEaPBfl9niivfeetscAb9u3bpxEjRqhWrVp65ZVX8lzfyGOmt+dgvszm7bmzq9muZfZzYL7MZ/Z958l4cC1v7LugoCBVrlw5xwuL7FeVZjcP8oc5iqv5/OkzqyvZjJyDmS3btTx9z3l7jnL13WQ6d+6cpYhbvHhxtWvXTpJx52py4o3PrJ7m8+S4ci2jj5nuZPOnOebV/P+y6+uE/bcqTp8+LZvNlu03XO2/NeXsAbFw4cLq1auXevXqlWWZ/XYl/nw7+rFjx+qLL76QzWZTly5d9PbbbyswMNDX3TLETz/9JElq3LhxpitVzKZKlSqqW7eu1q1bp9jYWNWsWdOp5yUnJ6t///5auXKlgoOD9dFHHzlup5NfhgwZIunK++rabx3bP5QOHTpURYoUUadOndSgQQOPtlehQgVJ0qVLlzxqxyhG9sds2ST3+7R8+XJJ0gMPPGB4n9zhSX+OHDmiY8eOqXTp0tm+NwsXLqwmTZroyJEj2rlzp2NS7k3bt2/XxYsXVadOnWzHspIlS6pOnTpavXq1du7cmetvKRUrVkzFihXTxYsXlZiYmO1vPrk6buYkLS1NgwcP1vfffy+LxaJXX301y2+1emMczw/OZLvamTNn1Lt3b23dulXFihXTf/7zH5e+XTtkyBDZbDZZrdYsx94TJ05IunJLrzlz5qhNmzZq06aNe8H+X37nu3rMjoiIyHYd++Oe3rrQ1Ww5cfZ4mZ/7zplsVxc0cvtb79y50+W/tbfHAmfyGXm8vJq352CuvC5vuOEGvf7669n+DqG7n1XyexwvVKiQ7rvvPu3du1e7du1y/C2NGAvyc5zLjjezOcPb88tr813N07HA13Owa7NdfZLQ0+Ol2bJdy9/nztfmq169uuFjga/OE+W077wxFnjDp59+qpSUFIWEhDh+4tHO/lvPs2fP1tq1a9WwYUPdc889kow5Znp7DubLbN6eO7ua7fHHH8+1PbOdA/NlPrPvO6PmhL46f5nTPMjf5yh2rs7zzPiZ1Zm+GvmZ1WzZrmXEe86bc5TixYurUKFCSk1N9fq5mrx44zOrJ/k8Pa5czRvHTHez+csc82oU8/1EWFiYypUrp4SEBB0+fFhVq1bNtDw9PV0HDhyQJKdub7F//34dP35c9evXz3K1g81mc3xLpXbt2sYEyEcZGRl68803tWDBAgUGBuqNN97I9lug/uy3336TJMctP3zl9OnTmjhxoi5evKihQ4dmu479W5PO/jb52bNn1b17d+3atUthYWEaM2aMGjZsaFifnWW/tV9ut5b55ZdfJEl33HFHrsX8y5cva/DgwTp79qw+/vhj3XjjjVnWsU+sypcv70m3nZKQkKDPP/9c//zzjz777LNsv+TibH/Mlk0yNt/VLl++7Dg2+vq9J125Q0d8fLyCg4Pd+gB17tw5Scr1S072Zc6+fz1ldJ8iIyO1ZcsW7d+/P9six/79+yU5N27mJCUlRb1799bq1atVpEgRDRs2LNvXh9HjeH5wNpvdkSNH9PTTT+v48eOKiIjQhAkTVKNGDZe2aT/2rl69Osd11q5dK+nKySRPPoD6Il+5cuUUFhampKQknTp1StWqVcuyjv23Yj35eRdnsxl5vMyvfedstsjISFksFtlsNiUkJKhy5cpZ1rH/ra8+iZIXb48FzubzxjHc23MwV95zsbGxOnXqlJo1a5Yl48WLF7Vt2zYFBASoVq1aTm/fW/tu9OjROnDggN54441s3ydXz4WNHgu8Pc75Klt+zS9dyWdnxFiQH3MwV7IZebw0W7ar+cvc2ZV8RvfJ2+eJXN133hgLvMU+D9q8ebM2b96c7Tpbt27V1q1bFRQUpMcff9ywY6a352C+zObtubOr2Vq1auVX58B8mc9s++7qYr6n44GdN/fdkCFDdOLECb3yyivZzjOymwf5yxxFci2fv31mdSWbkXMws2W7mhHvOW/PUQIDA1WjRg3Fxsbm+JMGRpyryYu3PrO6m8+I44qdt46Z7mTzpznm1fz/fuPXEfvth7IrLq5Zs0YXLlzQLbfc4tQJjQkTJqhHjx5auXJllmUxMTFKSEjQzTffrFtvvdXzjuezt99+WwsWLFBoaKjGjx9f4Ar56enp2rlzpyR5fDW4p0JCQjRr1izNnz8/21voJCUladu2bZLk1G1h/vnnHz3zzDPatWuXqlSpotmzZ/ukkC9Je/bsyfG/4sWLS7ryXtmzZ4/jd3lyUrhwYa1Zs0YrV65UTExMluWpqalatmyZJDluGepNxYsX17Jly7R8+XJt2LAhy/KkpCStWrVKFotFzZo1y7Uts2WTjM13tV27dslqtapMmTJZTkj4gv29VatWLbduy161alUFBgYqISHB8c31q6Wlpem///2vJDl9RYGn7B/wd+zYocTExCzLL1y44MjtzPhkHzft32q9Wnx8vPbu3eu4Yskd6enpjsJUqVKlNG3atFwn20aO497maraEhAQ99dRTOn78uGrXrq3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",
      "text/plain": [
       "<Figure size 2500x2500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4, 1, figsize=(25, 25))#, sharey=True)\n",
    "\n",
    "\n",
    "sns.countplot(x='Snow_mm', data=flights_weather_merge[flights_weather_merge['Snow_mm']>0], ax=ax[0])\n",
    "ax[0].set(xlabel='Snow in mm', ylabel='Flights')\n",
    "\n",
    "\n",
    "sns.countplot(x='Precipitation', data=flights_weather_merge[flights_weather_merge['Precipitation']>10], ax=ax[1])\n",
    "ax[1].set(xlabel='Precipitation in mm', ylabel='Flights')\n",
    "\n",
    "sns.countplot(x='WindSpeed', data=flights_weather_merge[flights_weather_merge['WindSpeed']>20], ax=ax[2])\n",
    "ax[2].set(xlabel='WinSpeed Km/h', ylabel='Flights')\n",
    "#For the wind speed I used the minimun that is considered to affect an airplane. Close to 50Km/h is dangerous and could cause the plane to delay landing or departure.\n",
    "#https://www.skyscanner.com/tips-and-inspiration/what-windspeed-delays-flights\n",
    "\n",
    "\n",
    "sns.countplot(x='AvgTemp', data=flights_weather_merge[flights_weather_merge['AvgTemp']<=0], ax=ax[3])\n",
    "ax[3].set(xlabel='Temperature (0 or less °C)', ylabel='Flights')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 4**: How taxi times changing during the day? Does higher traffic lead to bigger taxi times?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>FlightDate</th>\n",
       "      <th>UniqueMarketingCarrierCode</th>\n",
       "      <th>UniqueScheduledOperatingCarrierCode</th>\n",
       "      <th>TailNumber</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>OriginIATA</th>\n",
       "      <th>DestinationIATA</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>ActualDeparture</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>...</th>\n",
       "      <th>first_dep_time</th>\n",
       "      <th>total_add_gtime</th>\n",
       "      <th>longest_add_gtime</th>\n",
       "      <th>OriginCity</th>\n",
       "      <th>OriginState</th>\n",
       "      <th>DestinationCity</th>\n",
       "      <th>DestinationState</th>\n",
       "      <th>FlightDay</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>FlightYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>N958DL</td>\n",
       "      <td>2718</td>\n",
       "      <td>ATL</td>\n",
       "      <td>BHM</td>\n",
       "      <td>09:58</td>\n",
       "      <td>00:07</td>\n",
       "      <td>209.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>AA</td>\n",
       "      <td>OO</td>\n",
       "      <td>N908EV</td>\n",
       "      <td>3261</td>\n",
       "      <td>ORD</td>\n",
       "      <td>FWA</td>\n",
       "      <td>06:49</td>\n",
       "      <td>06:51</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>IN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11-24</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>N931DN</td>\n",
       "      <td>2293</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ELP</td>\n",
       "      <td>08:38</td>\n",
       "      <td>08:35</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>DL</td>\n",
       "      <td>YX</td>\n",
       "      <td>N216JQ</td>\n",
       "      <td>6155</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DCA</td>\n",
       "      <td>06:00</td>\n",
       "      <td>05:13</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>HA</td>\n",
       "      <td>HA</td>\n",
       "      <td>N490HA</td>\n",
       "      <td>229</td>\n",
       "      <td>KOA</td>\n",
       "      <td>OGG</td>\n",
       "      <td>17:00</td>\n",
       "      <td>16:49</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kona</td>\n",
       "      <td>HI</td>\n",
       "      <td>Kahului</td>\n",
       "      <td>HI</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>DL</td>\n",
       "      <td>OO</td>\n",
       "      <td>N8923A</td>\n",
       "      <td>4698</td>\n",
       "      <td>ILM</td>\n",
       "      <td>ATL</td>\n",
       "      <td>18:25</td>\n",
       "      <td>18:57</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>NC</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>F9</td>\n",
       "      <td>F9</td>\n",
       "      <td>N351FR</td>\n",
       "      <td>778</td>\n",
       "      <td>LAS</td>\n",
       "      <td>DEN</td>\n",
       "      <td>15:22</td>\n",
       "      <td>15:14</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>N923AT</td>\n",
       "      <td>1958</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ORD</td>\n",
       "      <td>01:33</td>\n",
       "      <td>01:28</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>UA</td>\n",
       "      <td>UA</td>\n",
       "      <td>N26215</td>\n",
       "      <td>2144</td>\n",
       "      <td>DEN</td>\n",
       "      <td>FSD</td>\n",
       "      <td>05:05</td>\n",
       "      <td>08:29</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>Sioux Falls</td>\n",
       "      <td>SD</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>N581UW</td>\n",
       "      <td>1939</td>\n",
       "      <td>CLT</td>\n",
       "      <td>MCO</td>\n",
       "      <td>03:19</td>\n",
       "      <td>03:20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>FL</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FlightDate UniqueMarketingCarrierCode  \\\n",
       "0     2018-06-22                         DL   \n",
       "1     2018-04-03                         AA   \n",
       "2     2019-11-24                         DL   \n",
       "3     2018-01-24                         DL   \n",
       "4     2018-09-23                         HA   \n",
       "...          ...                        ...   \n",
       "99995 2018-05-10                         DL   \n",
       "99996 2019-10-28                         F9   \n",
       "99997 2019-10-01                         DL   \n",
       "99998 2018-10-15                         UA   \n",
       "99999 2019-10-14                         AA   \n",
       "\n",
       "      UniqueScheduledOperatingCarrierCode TailNumber  FlightNumber OriginIATA  \\\n",
       "0                                      DL     N958DL          2718        ATL   \n",
       "1                                      OO     N908EV          3261        ORD   \n",
       "2                                      DL     N931DN          2293        ATL   \n",
       "3                                      YX     N216JQ          6155        LGA   \n",
       "4                                      HA     N490HA           229        KOA   \n",
       "...                                   ...        ...           ...        ...   \n",
       "99995                                  OO     N8923A          4698        ILM   \n",
       "99996                                  F9     N351FR           778        LAS   \n",
       "99997                                  DL     N923AT          1958        ATL   \n",
       "99998                                  UA     N26215          2144        DEN   \n",
       "99999                                  AA     N581UW          1939        CLT   \n",
       "\n",
       "      DestinationIATA DepartureTime ActualDeparture  Delay_Departure  ...  \\\n",
       "0                 BHM         09:58           00:07            209.0  ...   \n",
       "1                 FWA         06:49           06:51              2.0  ...   \n",
       "2                 ELP         08:38           08:35             -3.0  ...   \n",
       "3                 DCA         06:00           05:13             -7.0  ...   \n",
       "4                 OGG         17:00           16:49            -11.0  ...   \n",
       "...               ...           ...             ...              ...  ...   \n",
       "99995             ATL         18:25           18:57             32.0  ...   \n",
       "99996             DEN         15:22           15:14             -8.0  ...   \n",
       "99997             ORD         01:33           01:28             -5.0  ...   \n",
       "99998             FSD         05:05           08:29            124.0  ...   \n",
       "99999             MCO         03:19           03:20              1.0  ...   \n",
       "\n",
       "       first_dep_time  total_add_gtime  longest_add_gtime  OriginCity  \\\n",
       "0                 NaN              NaN                NaN     Atlanta   \n",
       "1                 NaN              NaN                NaN     Chicago   \n",
       "2                 NaN              NaN                NaN     Atlanta   \n",
       "3                 NaN              NaN                NaN    New York   \n",
       "4                 NaN              NaN                NaN        Kona   \n",
       "...               ...              ...                ...         ...   \n",
       "99995             NaN              NaN                NaN  Wilmington   \n",
       "99996             NaN              NaN                NaN   Las Vegas   \n",
       "99997             NaN              NaN                NaN     Atlanta   \n",
       "99998             NaN              NaN                NaN      Denver   \n",
       "99999             NaN              NaN                NaN   Charlotte   \n",
       "\n",
       "      OriginState  DestinationCity  DestinationState  FlightDay FlightMonth  \\\n",
       "0              GA       Birmingham                AL         22           6   \n",
       "1              IL       Fort Wayne                IN          3           4   \n",
       "2              GA          El Paso                TX         24          11   \n",
       "3              NY       Washington                DC         24           1   \n",
       "4              HI          Kahului                HI         23           9   \n",
       "...           ...              ...               ...        ...         ...   \n",
       "99995          NC          Atlanta                GA         10           5   \n",
       "99996          NV           Denver                CO         28          10   \n",
       "99997          GA          Chicago                IL          1          10   \n",
       "99998          CO      Sioux Falls                SD         15          10   \n",
       "99999          NC          Orlando                FL         14          10   \n",
       "\n",
       "       FlightYear  \n",
       "0            2018  \n",
       "1            2018  \n",
       "2            2019  \n",
       "3            2018  \n",
       "4            2018  \n",
       "...           ...  \n",
       "99995        2018  \n",
       "99996        2019  \n",
       "99997        2019  \n",
       "99998        2018  \n",
       "99999        2019  \n",
       "\n",
       "[100000 rows x 39 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean data with prepare_data_flight custom function\n",
    "df_flights_sample = pd.read_csv(\"../../data/raw/flights_sample.csv\")\n",
    "df_flights_clean = prepare_data_flights(df_flights_sample)\n",
    "df_flights_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get data related to taxi times\n",
    "df_taxi = df_flights_clean[['FlightDate','DepartureTime','taxi_out','taxi_in','ArrivalTime','OriginCity']]\n",
    "df_taxi[['hourD','minD']] = df_taxi['DepartureTime'].str.split(':', expand=True)\n",
    "df_taxi[['hourA','minA']] = df_taxi['ArrivalTime'].str.split(':', expand=True)\n",
    "\n",
    "df_taxi['hourD'] = pd.to_numeric(df_taxi['hourD'])\n",
    "df_taxi['hourA'] = pd.to_numeric(df_taxi['hourA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(25, 10), sharey=True)\n",
    "\n",
    "sns.barplot(x='hourD', y='taxi_out', data=df_taxi, ax=ax[0])#,ax.set(xlabel='common xlabel', ylabel='common ylabel' )\n",
    "\n",
    "ax[0].set(xlabel='Hours', ylabel='Outgoing Taxi Time(Minutes)')\n",
    "\n",
    "\n",
    "\n",
    "sns.barplot(x='hourA', y='taxi_in', data=df_taxi, ax=ax[1])\n",
    "\n",
    "ax[1].set(xlabel='Hours', ylabel='Incoming Taxi Time(Minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>hourD</th>\n",
       "      <th>taxi_out</th>\n",
       "      <th>FlightIndicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>6094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>4453.0</td>\n",
       "      <td>5558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>4038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4147.0</td>\n",
       "      <td>5018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>2373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3816.0</td>\n",
       "      <td>3696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>3728.0</td>\n",
       "      <td>6158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>8464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>4042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>4321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>3473.0</td>\n",
       "      <td>4047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>6039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>6261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>5981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>3158.0</td>\n",
       "      <td>4618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>3146.0</td>\n",
       "      <td>3801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>2269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>3812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2688.0</td>\n",
       "      <td>2314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2631.0</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hourD  taxi_out  FlightIndicator\n",
       "1       1    4785.0             6094\n",
       "6       6    4453.0             5558\n",
       "4       4    4180.0             4038\n",
       "8       8    4147.0             5018\n",
       "5       5    3965.0             2373\n",
       "3       3    3816.0             3696\n",
       "20     20    3728.0             6158\n",
       "10     10    3704.0             8464\n",
       "23     23    3599.0             4042\n",
       "9       9    3497.0             4321\n",
       "18     18    3473.0             4047\n",
       "15     15    3439.0             6039\n",
       "13     13    3411.0             6261\n",
       "11     11    3183.0             5981\n",
       "12     12    3158.0             4618\n",
       "22     22    3146.0             3801\n",
       "2       2    2977.0             1876\n",
       "0       0    2862.0             2049\n",
       "16     16    2823.0             2269\n",
       "21     21    2790.0             1865\n",
       "17     17    2693.0             3812\n",
       "14     14    2688.0             2314\n",
       "7       7    2631.0             1596\n",
       "19     19    1904.0             2012"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taxi time vs flights\n",
    "df_taxi_calc = df_taxi[['taxi_out','hourD']]\n",
    "df_taxi_calc['FlightIndicator'] = 'F'\n",
    "df_time_taxi_departures = df_taxi_calc.groupby(['hourD','taxi_out']).count().reset_index()\n",
    "\n",
    "df_time_taxi_departures_f = df_time_taxi_departures.groupby(['hourD']).sum().reset_index()\n",
    "df_time_taxi_departures_f.sort_values(by=['taxi_out'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It looks like that a higher traffic leads to higher taxi time, but in some data points it doesn't show a clear relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 5**: What is the average percentage of delays that is already created before departure? (aka are arrival delays caused by departure delays?) Are airlines able to lower the delay during the flights?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>departure_status</th>\n",
       "      <th>arrival_status</th>\n",
       "      <th>no_of_flights</th>\n",
       "      <th>percent_breakdown</th>\n",
       "      <th>avg_dep_delay</th>\n",
       "      <th>avg_arr_delay</th>\n",
       "      <th>avg_time_saved_in_air</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Early Departure</td>\n",
       "      <td>Early Arrival</td>\n",
       "      <td>7981790</td>\n",
       "      <td>50.11</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-14.55</td>\n",
       "      <td>8.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Late Departure</td>\n",
       "      <td>Late Arrival</td>\n",
       "      <td>3900977</td>\n",
       "      <td>24.49</td>\n",
       "      <td>52.94</td>\n",
       "      <td>51.86</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Early Departure</td>\n",
       "      <td>Late Arrival</td>\n",
       "      <td>1374663</td>\n",
       "      <td>8.63</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>10.80</td>\n",
       "      <td>-15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Late Departure</td>\n",
       "      <td>Early Arrival</td>\n",
       "      <td>1308835</td>\n",
       "      <td>8.22</td>\n",
       "      <td>5.94</td>\n",
       "      <td>-8.91</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>On Time Departure</td>\n",
       "      <td>Early Arrival</td>\n",
       "      <td>551015</td>\n",
       "      <td>3.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.01</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>On Time Departure</td>\n",
       "      <td>On Time Arrival</td>\n",
       "      <td>284819</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>On Time Departure</td>\n",
       "      <td>Late Arrival</td>\n",
       "      <td>199969</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.94</td>\n",
       "      <td>-10.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Early Departure</td>\n",
       "      <td>On Time Arrival</td>\n",
       "      <td>192591</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Late Departure</td>\n",
       "      <td>On Time Arrival</td>\n",
       "      <td>132826</td>\n",
       "      <td>0.83</td>\n",
       "      <td>20.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    departure_status   arrival_status  no_of_flights  percent_breakdown  \\\n",
       "0    Early Departure    Early Arrival        7981790              50.11   \n",
       "1     Late Departure     Late Arrival        3900977              24.49   \n",
       "2    Early Departure     Late Arrival        1374663               8.63   \n",
       "3     Late Departure    Early Arrival        1308835               8.22   \n",
       "4  On Time Departure    Early Arrival         551015               3.46   \n",
       "5  On Time Departure  On Time Arrival         284819               1.79   \n",
       "6  On Time Departure     Late Arrival         199969               1.26   \n",
       "7    Early Departure  On Time Arrival         192591               1.21   \n",
       "8     Late Departure  On Time Arrival         132826               0.83   \n",
       "\n",
       "   avg_dep_delay  avg_arr_delay  avg_time_saved_in_air  \n",
       "0          -5.59         -14.55                   8.96  \n",
       "1          52.94          51.86                   1.08  \n",
       "2          -4.54          10.80                 -15.35  \n",
       "3           5.94          -8.91                  14.85  \n",
       "4           0.00         -11.01                  11.00  \n",
       "5           0.00           0.00                   0.00  \n",
       "6           0.00          10.94                 -10.92  \n",
       "7          -4.62           0.00                  -4.54  \n",
       "8          20.20           0.00                   8.22  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/arrival_departure_status.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 6**: How many states cover 50% of US air traffic? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>state</th>\n",
       "      <th>no_of_flights</th>\n",
       "      <th>percent_of_air_traffic</th>\n",
       "      <th>running_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>1738254</td>\n",
       "      <td>10.91</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TX</td>\n",
       "      <td>1572078</td>\n",
       "      <td>9.87</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>1151221</td>\n",
       "      <td>7.23</td>\n",
       "      <td>28.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IL</td>\n",
       "      <td>1008184</td>\n",
       "      <td>6.33</td>\n",
       "      <td>34.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GA</td>\n",
       "      <td>841057</td>\n",
       "      <td>5.28</td>\n",
       "      <td>39.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NY</td>\n",
       "      <td>827854</td>\n",
       "      <td>5.20</td>\n",
       "      <td>44.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NC</td>\n",
       "      <td>736077</td>\n",
       "      <td>4.62</td>\n",
       "      <td>49.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CO</td>\n",
       "      <td>622430</td>\n",
       "      <td>3.91</td>\n",
       "      <td>53.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PA</td>\n",
       "      <td>475258</td>\n",
       "      <td>2.98</td>\n",
       "      <td>56.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DC</td>\n",
       "      <td>472958</td>\n",
       "      <td>2.97</td>\n",
       "      <td>59.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WA</td>\n",
       "      <td>459928</td>\n",
       "      <td>2.89</td>\n",
       "      <td>62.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MI</td>\n",
       "      <td>449393</td>\n",
       "      <td>2.82</td>\n",
       "      <td>65.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AZ</td>\n",
       "      <td>413134</td>\n",
       "      <td>2.59</td>\n",
       "      <td>67.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NV</td>\n",
       "      <td>379296</td>\n",
       "      <td>2.38</td>\n",
       "      <td>69.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OH</td>\n",
       "      <td>362851</td>\n",
       "      <td>2.28</td>\n",
       "      <td>72.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MN</td>\n",
       "      <td>344462</td>\n",
       "      <td>2.16</td>\n",
       "      <td>74.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NJ</td>\n",
       "      <td>332563</td>\n",
       "      <td>2.09</td>\n",
       "      <td>76.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MA</td>\n",
       "      <td>308958</td>\n",
       "      <td>1.94</td>\n",
       "      <td>78.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MO</td>\n",
       "      <td>294386</td>\n",
       "      <td>1.85</td>\n",
       "      <td>80.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TN</td>\n",
       "      <td>279967</td>\n",
       "      <td>1.76</td>\n",
       "      <td>82.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UT</td>\n",
       "      <td>250456</td>\n",
       "      <td>1.57</td>\n",
       "      <td>83.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HI</td>\n",
       "      <td>242222</td>\n",
       "      <td>1.52</td>\n",
       "      <td>85.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OR</td>\n",
       "      <td>226538</td>\n",
       "      <td>1.42</td>\n",
       "      <td>86.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MD</td>\n",
       "      <td>217327</td>\n",
       "      <td>1.36</td>\n",
       "      <td>87.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LA</td>\n",
       "      <td>169508</td>\n",
       "      <td>1.06</td>\n",
       "      <td>88.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>VA</td>\n",
       "      <td>155804</td>\n",
       "      <td>0.98</td>\n",
       "      <td>89.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>IN</td>\n",
       "      <td>144852</td>\n",
       "      <td>0.91</td>\n",
       "      <td>90.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>WI</td>\n",
       "      <td>143063</td>\n",
       "      <td>0.90</td>\n",
       "      <td>91.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SC</td>\n",
       "      <td>138952</td>\n",
       "      <td>0.87</td>\n",
       "      <td>92.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OK</td>\n",
       "      <td>91112</td>\n",
       "      <td>0.57</td>\n",
       "      <td>93.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>AL</td>\n",
       "      <td>87952</td>\n",
       "      <td>0.55</td>\n",
       "      <td>93.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>AK</td>\n",
       "      <td>82625</td>\n",
       "      <td>0.52</td>\n",
       "      <td>94.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>KY</td>\n",
       "      <td>81876</td>\n",
       "      <td>0.51</td>\n",
       "      <td>94.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ID</td>\n",
       "      <td>66659</td>\n",
       "      <td>0.42</td>\n",
       "      <td>95.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NE</td>\n",
       "      <td>67172</td>\n",
       "      <td>0.42</td>\n",
       "      <td>95.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AR</td>\n",
       "      <td>65755</td>\n",
       "      <td>0.41</td>\n",
       "      <td>96.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NM</td>\n",
       "      <td>63794</td>\n",
       "      <td>0.40</td>\n",
       "      <td>96.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CT</td>\n",
       "      <td>62343</td>\n",
       "      <td>0.39</td>\n",
       "      <td>96.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IA</td>\n",
       "      <td>62224</td>\n",
       "      <td>0.39</td>\n",
       "      <td>97.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MT</td>\n",
       "      <td>60062</td>\n",
       "      <td>0.38</td>\n",
       "      <td>97.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PR</td>\n",
       "      <td>57200</td>\n",
       "      <td>0.36</td>\n",
       "      <td>97.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ME</td>\n",
       "      <td>42224</td>\n",
       "      <td>0.27</td>\n",
       "      <td>98.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ND</td>\n",
       "      <td>43155</td>\n",
       "      <td>0.27</td>\n",
       "      <td>98.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>RI</td>\n",
       "      <td>40679</td>\n",
       "      <td>0.26</td>\n",
       "      <td>98.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>KS</td>\n",
       "      <td>33866</td>\n",
       "      <td>0.21</td>\n",
       "      <td>98.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MS</td>\n",
       "      <td>31116</td>\n",
       "      <td>0.20</td>\n",
       "      <td>99.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>SD</td>\n",
       "      <td>31340</td>\n",
       "      <td>0.20</td>\n",
       "      <td>99.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NH</td>\n",
       "      <td>23488</td>\n",
       "      <td>0.15</td>\n",
       "      <td>99.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>VT</td>\n",
       "      <td>23700</td>\n",
       "      <td>0.15</td>\n",
       "      <td>99.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WY</td>\n",
       "      <td>20543</td>\n",
       "      <td>0.13</td>\n",
       "      <td>99.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WV</td>\n",
       "      <td>17172</td>\n",
       "      <td>0.11</td>\n",
       "      <td>99.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>VI</td>\n",
       "      <td>8302</td>\n",
       "      <td>0.05</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>TT</td>\n",
       "      <td>4095</td>\n",
       "      <td>0.03</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  no_of_flights  percent_of_air_traffic  running_total\n",
       "0     CA        1738254                   10.91          10.91\n",
       "1     TX        1572078                    9.87          20.78\n",
       "2     FL        1151221                    7.23          28.01\n",
       "3     IL        1008184                    6.33          34.34\n",
       "4     GA         841057                    5.28          39.62\n",
       "5     NY         827854                    5.20          44.82\n",
       "6     NC         736077                    4.62          49.44\n",
       "7     CO         622430                    3.91          53.35\n",
       "8     PA         475258                    2.98          56.33\n",
       "9     DC         472958                    2.97          59.30\n",
       "10    WA         459928                    2.89          62.19\n",
       "11    MI         449393                    2.82          65.01\n",
       "12    AZ         413134                    2.59          67.60\n",
       "13    NV         379296                    2.38          69.98\n",
       "14    OH         362851                    2.28          72.26\n",
       "15    MN         344462                    2.16          74.42\n",
       "16    NJ         332563                    2.09          76.51\n",
       "17    MA         308958                    1.94          78.45\n",
       "18    MO         294386                    1.85          80.30\n",
       "19    TN         279967                    1.76          82.06\n",
       "20    UT         250456                    1.57          83.63\n",
       "21    HI         242222                    1.52          85.15\n",
       "22    OR         226538                    1.42          86.57\n",
       "23    MD         217327                    1.36          87.93\n",
       "24    LA         169508                    1.06          88.99\n",
       "25    VA         155804                    0.98          89.97\n",
       "26    IN         144852                    0.91          90.88\n",
       "27    WI         143063                    0.90          91.78\n",
       "28    SC         138952                    0.87          92.65\n",
       "29    OK          91112                    0.57          93.22\n",
       "30    AL          87952                    0.55          93.77\n",
       "31    AK          82625                    0.52          94.29\n",
       "32    KY          81876                    0.51          94.80\n",
       "33    ID          66659                    0.42          95.22\n",
       "34    NE          67172                    0.42          95.64\n",
       "35    AR          65755                    0.41          96.05\n",
       "36    NM          63794                    0.40          96.45\n",
       "37    CT          62343                    0.39          96.84\n",
       "38    IA          62224                    0.39          97.23\n",
       "39    MT          60062                    0.38          97.61\n",
       "40    PR          57200                    0.36          97.97\n",
       "41    ME          42224                    0.27          98.24\n",
       "42    ND          43155                    0.27          98.51\n",
       "43    RI          40679                    0.26          98.77\n",
       "44    KS          33866                    0.21          98.98\n",
       "45    MS          31116                    0.20          99.18\n",
       "46    SD          31340                    0.20          99.38\n",
       "47    NH          23488                    0.15          99.53\n",
       "48    VT          23700                    0.15          99.68\n",
       "49    WY          20543                    0.13          99.81\n",
       "50    WV          17172                    0.11          99.92\n",
       "51    VI           8302                    0.05          99.97\n",
       "52    TT           4095                    0.03         100.00"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/percent_of_air_traffic.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 7**: Test the hypothesis whether planes fly faster when there is the departure delay? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DistanceAirports</th>\n",
       "      <th>FlightTime</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>Late</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134</td>\n",
       "      <td>26.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1282</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.122222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>214</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>377</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>628</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>606</td>\n",
       "      <td>99.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>483</td>\n",
       "      <td>71.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.802817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>468</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.685714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97988 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DistanceAirports  FlightTime  Delay_Departure   Late     Speed\n",
       "0                   134        26.0            209.0   True  5.153846\n",
       "1                   157        30.0              2.0   True  5.233333\n",
       "2                  1282       180.0             -3.0  False  7.122222\n",
       "3                   214        44.0             -7.0  False  4.863636\n",
       "4                    84        18.0            -11.0  False  4.666667\n",
       "...                 ...         ...              ...    ...       ...\n",
       "99995               377        65.0             32.0   True  5.800000\n",
       "99996               628        88.0             -8.0  False  7.136364\n",
       "99997               606        99.0             -5.0  False  6.121212\n",
       "99998               483        71.0            124.0   True  6.802817\n",
       "99999               468        70.0              1.0   True  6.685714\n",
       "\n",
       "[97988 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_speed = df_flights_clean[['DistanceAirports','FlightTime','Delay_Departure']]\n",
    "df_flights_speed = df_flights_speed[df_flights_speed.FlightTime != 0] #To avoid division by zero and exclude non applicable data\n",
    "df_flights_speed['Late'] = df_flights_clean['Delay_Departure'].apply(lambda X: 'True' if X > 0 else 'False')\n",
    "#use speed formula distancia divided by time\n",
    "df_flights_speed['Speed'] = df_flights_clean['DistanceAirports'] / df_flights_clean['FlightTime']\n",
    "df_flights_speed = df_flights_speed.dropna()\n",
    "df_flights_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97988 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   DistanceAirports  97988 non-null  int64  \n",
      " 1   FlightTime        97988 non-null  float64\n",
      " 2   Delay_Departure   97988 non-null  float64\n",
      " 3   Late              97988 non-null  object \n",
      " 4   Speed             97988 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(1)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_flights_speed.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Late\n",
       "False    6.601106\n",
       "True     6.756614\n",
       "Name: Speed, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Speed mean for Late and non Late flights\n",
    "sr_mean = df_flights_speed.groupby(\"Late\")[\"Speed\"].mean()\n",
    "sr_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15550776987368753"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cal = abs(sr_mean[0]- sr_mean[1])\n",
    "mean_cal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DistanceAirports</th>\n",
       "      <th>FlightTime</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>Late</th>\n",
       "      <th>Speed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>134</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>5.153846</td>\n",
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       "      <th>1</th>\n",
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       "      <td>30.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>5.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1282</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.122222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>214</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.666667</td>\n",
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       "    <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>99995</th>\n",
       "      <td>377</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>628</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>606</td>\n",
       "      <td>99.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>483</td>\n",
       "      <td>71.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.802817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>468</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.685714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97988 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DistanceAirports  FlightTime  Delay_Departure   Late     Speed\n",
       "0                   134        26.0            209.0   True  5.153846\n",
       "1                   157        30.0              2.0   True  5.233333\n",
       "2                  1282       180.0             -3.0  False  7.122222\n",
       "3                   214        44.0             -7.0  False  4.863636\n",
       "4                    84        18.0            -11.0  False  4.666667\n",
       "...                 ...         ...              ...    ...       ...\n",
       "99995               377        65.0             32.0   True  5.800000\n",
       "99996               628        88.0             -8.0  False  7.136364\n",
       "99997               606        99.0             -5.0  False  6.121212\n",
       "99998               483        71.0            124.0   True  6.802817\n",
       "99999               468        70.0              1.0   True  6.685714\n",
       "\n",
       "[97988 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_late_flights = df_flights_speed[df_flights_speed['Late'] == 'True']\n",
    "df_nolate_flights = df_flights_speed[df_flights_speed['Late'] == 'False']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_speed_late = df_late_flights['Speed']\n",
    "df_speed_nolate = df_nolate_flights['Speed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.153846\n",
       "1        5.233333\n",
       "11       5.444444\n",
       "12       5.235294\n",
       "21       8.403974\n",
       "           ...   \n",
       "99992    7.615385\n",
       "99994    7.539823\n",
       "99995    5.800000\n",
       "99998    6.802817\n",
       "99999    6.685714\n",
       "Name: Speed, Length: 33328, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_speed_late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.663988267035272, 9.719853766413975e-78)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.weightstats import ztest as ztest\n",
    "ztest(df_speed_late,df_speed_nolate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Since p is less than 0.05 we can reject the null hypotesys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 8**: When (which hour) do most 'LONG', 'SHORT', 'MEDIUM' haul flights take off?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def haul_times(df):\n",
    "    #short <=2 hours\n",
    "    #Medium between 2 and 4\n",
    "    #Long > 4\n",
    "    result = df\n",
    "    result['HaulType'] = df['FlightTime'].apply(lambda X: 'Short' if X <=120 else ('Medium' if X < 240 else 'Long'))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>FlightDate</th>\n",
       "      <th>UniqueMarketingCarrierCode</th>\n",
       "      <th>UniqueScheduledOperatingCarrierCode</th>\n",
       "      <th>TailNumber</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>OriginIATA</th>\n",
       "      <th>DestinationIATA</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>ActualDeparture</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>...</th>\n",
       "      <th>first_dep_time</th>\n",
       "      <th>total_add_gtime</th>\n",
       "      <th>longest_add_gtime</th>\n",
       "      <th>OriginCity</th>\n",
       "      <th>OriginState</th>\n",
       "      <th>DestinationCity</th>\n",
       "      <th>DestinationState</th>\n",
       "      <th>FlightDay</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>FlightYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>N958DL</td>\n",
       "      <td>2718</td>\n",
       "      <td>ATL</td>\n",
       "      <td>BHM</td>\n",
       "      <td>09:58</td>\n",
       "      <td>00:07</td>\n",
       "      <td>209.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>AA</td>\n",
       "      <td>OO</td>\n",
       "      <td>N908EV</td>\n",
       "      <td>3261</td>\n",
       "      <td>ORD</td>\n",
       "      <td>FWA</td>\n",
       "      <td>06:49</td>\n",
       "      <td>06:51</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>IN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11-24</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>N931DN</td>\n",
       "      <td>2293</td>\n",
       "      <td>ATL</td>\n",
       "      <td>ELP</td>\n",
       "      <td>08:38</td>\n",
       "      <td>08:35</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>DL</td>\n",
       "      <td>YX</td>\n",
       "      <td>N216JQ</td>\n",
       "      <td>6155</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DCA</td>\n",
       "      <td>06:00</td>\n",
       "      <td>05:13</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>HA</td>\n",
       "      <td>HA</td>\n",
       "      <td>N490HA</td>\n",
       "      <td>229</td>\n",
       "      <td>KOA</td>\n",
       "      <td>OGG</td>\n",
       "      <td>17:00</td>\n",
       "      <td>16:49</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kona</td>\n",
       "      <td>HI</td>\n",
       "      <td>Kahului</td>\n",
       "      <td>HI</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  FlightDate UniqueMarketingCarrierCode UniqueScheduledOperatingCarrierCode  \\\n",
       "0 2018-06-22                         DL                                  DL   \n",
       "1 2018-04-03                         AA                                  OO   \n",
       "2 2019-11-24                         DL                                  DL   \n",
       "3 2018-01-24                         DL                                  YX   \n",
       "4 2018-09-23                         HA                                  HA   \n",
       "\n",
       "  TailNumber  FlightNumber OriginIATA DestinationIATA DepartureTime  \\\n",
       "0     N958DL          2718        ATL             BHM         09:58   \n",
       "1     N908EV          3261        ORD             FWA         06:49   \n",
       "2     N931DN          2293        ATL             ELP         08:38   \n",
       "3     N216JQ          6155        LGA             DCA         06:00   \n",
       "4     N490HA           229        KOA             OGG         17:00   \n",
       "\n",
       "  ActualDeparture  Delay_Departure  ...  first_dep_time  total_add_gtime  \\\n",
       "0           00:07            209.0  ...             NaN              NaN   \n",
       "1           06:51              2.0  ...             NaN              NaN   \n",
       "2           08:35             -3.0  ...             NaN              NaN   \n",
       "3           05:13             -7.0  ...             NaN              NaN   \n",
       "4           16:49            -11.0  ...             NaN              NaN   \n",
       "\n",
       "   longest_add_gtime  OriginCity OriginState  DestinationCity  \\\n",
       "0                NaN     Atlanta          GA       Birmingham   \n",
       "1                NaN     Chicago          IL       Fort Wayne   \n",
       "2                NaN     Atlanta          GA          El Paso   \n",
       "3                NaN    New York          NY       Washington   \n",
       "4                NaN        Kona          HI          Kahului   \n",
       "\n",
       "   DestinationState  FlightDay FlightMonth  FlightYear  \n",
       "0                AL         22           6        2018  \n",
       "1                IN          3           4        2018  \n",
       "2                TX         24          11        2019  \n",
       "3                DC         24           1        2018  \n",
       "4                HI         23           9        2018  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_haul_data = df_flights_clean[['FlightTime','ActualDeparture']]\n",
    "df_haul_data = df_haul_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 98017 entries, 0 to 99999\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   FlightTime       98017 non-null  float64\n",
      " 1   ActualDeparture  98017 non-null  object \n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_haul_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>FlightTime</th>\n",
       "      <th>ActualDeparture</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
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       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>65.0</td>\n",
       "      <td>18:57</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15:14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99.0</td>\n",
       "      <td>01:28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>71.0</td>\n",
       "      <td>08:29</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>70.0</td>\n",
       "      <td>03:20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98017 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FlightTime ActualDeparture  hour min\n",
       "0            26.0           00:07     0  07\n",
       "1            30.0           06:51     6  51\n",
       "2           180.0           08:35     8  35\n",
       "3            44.0           05:13     5  13\n",
       "4            18.0           16:49    16  49\n",
       "...           ...             ...   ...  ..\n",
       "99995        65.0           18:57    18  57\n",
       "99996        88.0           15:14    15  14\n",
       "99997        99.0           01:28     1  28\n",
       "99998        71.0           08:29     8  29\n",
       "99999        70.0           03:20     3  20\n",
       "\n",
       "[98017 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_haul_data[['hour','min']] = df_haul_data['ActualDeparture'].str.split(':', expand=True)\n",
    "df_haul_data['hour'] = pd.to_numeric(df_haul_data['hour'])\n",
    "df_haul_data = df_haul_data.astype({'hour':'int'})\n",
    "df_haul_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightTime</th>\n",
       "      <th>ActualDeparture</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "      <th>HaulType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.0</td>\n",
       "      <td>00:07</td>\n",
       "      <td>0</td>\n",
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       "      <td>Short</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>06:51</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180.0</td>\n",
       "      <td>08:35</td>\n",
       "      <td>8</td>\n",
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       "      <td>Short</td>\n",
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       "      <th>...</th>\n",
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       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "      <td>Short</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15:14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99.0</td>\n",
       "      <td>01:28</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>71.0</td>\n",
       "      <td>08:29</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>70.0</td>\n",
       "      <td>03:20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98017 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FlightTime ActualDeparture  hour min HaulType\n",
       "0            26.0           00:07     0  07    Short\n",
       "1            30.0           06:51     6  51    Short\n",
       "2           180.0           08:35     8  35   Medium\n",
       "3            44.0           05:13     5  13    Short\n",
       "4            18.0           16:49    16  49    Short\n",
       "...           ...             ...   ...  ..      ...\n",
       "99995        65.0           18:57    18  57    Short\n",
       "99996        88.0           15:14    15  14    Short\n",
       "99997        99.0           01:28     1  28    Short\n",
       "99998        71.0           08:29     8  29    Short\n",
       "99999        70.0           03:20     3  20    Short\n",
       "\n",
       "[98017 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_haul_processed = haul_times(df_haul_data)\n",
    "df_haul_processed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ShortHaulCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ShortHaulCount\n",
       "hour                \n",
       "10              4831"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hour with most Short haul takeoff\n",
    "df_haul_short = df_haul_processed[df_haul_processed['HaulType'] == 'Short']\n",
    "df_haul_short = df_haul_short[['hour','HaulType']].groupby(['hour'], group_keys=False).count()\n",
    "df_haul_short.rename(columns={'HaulType': 'ShortHaulCount'}, inplace=True)\n",
    "df_haul_short.nlargest(1,'ShortHaulCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>MediumHaulCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MediumHaulCount\n",
       "hour                 \n",
       "10               2157"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hour with most Medium haul takeoff\n",
    "df_haul_medium = df_haul_processed[df_haul_processed['HaulType'] == 'Medium']\n",
    "df_haul_medium = df_haul_medium[['hour','HaulType']].groupby(['hour'],group_keys=False).count()\n",
    "df_haul_medium.rename(columns={'HaulType': 'MediumHaulCount'}, inplace=True)\n",
    "df_haul_medium.nlargest(1,'MediumHaulCount')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>LongHaulCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LongHaulCount\n",
       "hour               \n",
       "13              601"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hour with most Long haul takeoff\n",
    "df_haul_long = df_haul_processed[df_haul_processed['HaulType'] == 'Long']\n",
    "df_haul_long = df_haul_long[['hour','HaulType']].groupby(['hour'], group_keys=False).count()\n",
    "df_haul_long.rename(columns={'HaulType': 'LongHaulCount'}, inplace=True)\n",
    "df_haul_long.nlargest(1,'LongHaulCount')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphs with all the Haul Types by hours\n",
    "fig, ax = plt.subplots(3, 1, figsize=(25, 25), sharey=True)\n",
    "fig.suptitle('Flights by Haul Type and Departure time')\n",
    "\n",
    "sns.countplot(x='hour', data= df_haul_processed[df_haul_processed['HaulType'] == 'Short'] , ax=ax[0])\n",
    "ax[0].set(xlabel='Departure Time', ylabel='Flights Count')\n",
    "ax[0].set_title('Short Haul (< 2 hours)')\n",
    "sns.countplot(x='hour', data= df_haul_processed[df_haul_processed['HaulType'] == 'Medium'] , ax=ax[1])\n",
    "ax[1].set(xlabel='Departure Time', ylabel='Flights Count')\n",
    "ax[1].set_title('Medium Haul (Between 2 and 4 hours)')\n",
    "sns.countplot(x='hour', data= df_haul_processed[df_haul_processed['HaulType'] == 'Long'] , ax=ax[2])\n",
    "ax[2].set(xlabel='Departure Time', ylabel='Flights Count')\n",
    "ax[2].set_title('Long Haul (> 4 hours)')\n",
    "\n",
    "    #short <=2 hours\n",
    "    #Medium between 2 and 4\n",
    "    #Long > 4\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 9**: Find the top 10 the bussiest airports. Does the biggest number of flights mean that the biggest number of passengers went through the particular airport? How much traffic do these 10 airports cover?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>origin_city_name</th>\n",
       "      <th>total_flights_18_19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>962032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>785850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>599657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>584688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>552134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>501557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>500840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>472958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>472442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>379367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        origin_city_name  total_flights_18_19\n",
       "0            Chicago, IL               962032\n",
       "1            Atlanta, GA               785850\n",
       "2           New York, NY               599657\n",
       "3  Dallas/Fort Worth, TX               584688\n",
       "4             Denver, CO               552134\n",
       "5          Charlotte, NC               501557\n",
       "6        Los Angeles, CA               500840\n",
       "7         Washington, DC               472958\n",
       "8            Houston, TX               472442\n",
       "9            Seattle, WA               379367"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/top10_airports_flights.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>origin_city_name</th>\n",
       "      <th>total_flights_15_19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>2626134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>2182679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>1983029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>1678322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>1632958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1431533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>1404708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>1295165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>1286340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>1089147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        origin_city_name  total_flights_15_19\n",
       "0            Chicago, IL              2626134\n",
       "1            Atlanta, GA              2182679\n",
       "2           New York, NY              1983029\n",
       "3  Dallas/Fort Worth, TX              1678322\n",
       "4        Los Angeles, CA              1632958\n",
       "5            Houston, TX              1431533\n",
       "6             Denver, CO              1404708\n",
       "7         Washington, DC              1295165\n",
       "8          Charlotte, NC              1286340\n",
       "9            Seattle, WA              1089147"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/top10_airports_passengers.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Task 10**: Do bigger delays lead to bigger fuel comsumption per passenger? \n",
    "We need to do four things to answer this as accurate as possible:\n",
    "- Find out average monthly delay per air carrier (monthly delay is sum of all delays in 1 month)\n",
    "- Find out distance covered monthly by different air carriers\n",
    "- Find out number of passengers that were carried by different air carriers\n",
    "- Find out total fuel comsumption per air carrier.\n",
    "\n",
    "Use this information to get the average fuel comsumption per passenger per km. Is this higher for the airlines with bigger average delays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 39 columns):\n",
      " #   Column                               Non-Null Count   Dtype         \n",
      "---  ------                               --------------   -----         \n",
      " 0   FlightDate                           100000 non-null  datetime64[ns]\n",
      " 1   UniqueMarketingCarrierCode           100000 non-null  object        \n",
      " 2   UniqueScheduledOperatingCarrierCode  100000 non-null  object        \n",
      " 3   TailNumber                           99664 non-null   object        \n",
      " 4   FlightNumber                         100000 non-null  int64         \n",
      " 5   OriginIATA                           100000 non-null  object        \n",
      " 6   DestinationIATA                      100000 non-null  object        \n",
      " 7   DepartureTime                        100000 non-null  object        \n",
      " 8   ActualDeparture                      98387 non-null   object        \n",
      " 9   Delay_Departure                      98356 non-null   float64       \n",
      " 10  taxi_out                             98302 non-null   float64       \n",
      " 11  wheels_off                           98302 non-null   float64       \n",
      " 12  wheels_on                            98248 non-null   float64       \n",
      " 13  taxi_in                              98248 non-null   float64       \n",
      " 14  ArrivalTime                          100000 non-null  object        \n",
      " 15  arr_time                             98276 non-null   float64       \n",
      " 16  arr_delay                            98023 non-null   float64       \n",
      " 17  cancelled                            100000 non-null  int64         \n",
      " 18  cancellation_code                    1687 non-null    object        \n",
      " 19  diverted                             100000 non-null  int64         \n",
      " 20  ElapsedTimeFlight,                   99999 non-null   float64       \n",
      " 21  actual_elapsed_time                  98045 non-null   float64       \n",
      " 22  FlightTime                           98017 non-null   float64       \n",
      " 23  DistanceAirports                     100000 non-null  int64         \n",
      " 24  Delay_Carrier                        18778 non-null   float64       \n",
      " 25  Delay_Weather                        18778 non-null   float64       \n",
      " 26  Delay_NationalAirSystem              18778 non-null   float64       \n",
      " 27  Delay_Security                       18778 non-null   float64       \n",
      " 28  Delay_LateAircraft                   18778 non-null   float64       \n",
      " 29  first_dep_time                       699 non-null     float64       \n",
      " 30  total_add_gtime                      699 non-null     float64       \n",
      " 31  longest_add_gtime                    699 non-null     float64       \n",
      " 32  OriginCity                           100000 non-null  object        \n",
      " 33  OriginState                          100000 non-null  object        \n",
      " 34  DestinationCity                      100000 non-null  object        \n",
      " 35  DestinationState                     100000 non-null  object        \n",
      " 36  FlightDay                            100000 non-null  int64         \n",
      " 37  FlightMonth                          100000 non-null  int64         \n",
      " 38  FlightYear                           100000 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(18), int64(7), object(13)\n",
      "memory usage: 29.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_flights_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>UniqueMarketingCarrierCode</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>DistanceAirports</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>FlightYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DL</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>209.0</td>\n",
       "      <td>134</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>2.0</td>\n",
       "      <td>157</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DL</td>\n",
       "      <td>2019-11-24</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1282</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DL</td>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HA</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>DL</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>377</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>F9</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>628</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>DL</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>606</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>UA</td>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>124.0</td>\n",
       "      <td>483</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>AA</td>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>468</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98356 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      UniqueMarketingCarrierCode FlightDate  Delay_Departure  \\\n",
       "0                             DL 2018-06-22            209.0   \n",
       "1                             AA 2018-04-03              2.0   \n",
       "2                             DL 2019-11-24             -3.0   \n",
       "3                             DL 2018-01-24             -7.0   \n",
       "4                             HA 2018-09-23            -11.0   \n",
       "...                          ...        ...              ...   \n",
       "99995                         DL 2018-05-10             32.0   \n",
       "99996                         F9 2019-10-28             -8.0   \n",
       "99997                         DL 2019-10-01             -5.0   \n",
       "99998                         UA 2018-10-15            124.0   \n",
       "99999                         AA 2019-10-14              1.0   \n",
       "\n",
       "       DistanceAirports  FlightMonth  FlightYear  \n",
       "0                   134            6        2018  \n",
       "1                   157            4        2018  \n",
       "2                  1282           11        2019  \n",
       "3                   214            1        2018  \n",
       "4                    84            9        2018  \n",
       "...                 ...          ...         ...  \n",
       "99995               377            5        2018  \n",
       "99996               628           10        2019  \n",
       "99997               606           10        2019  \n",
       "99998               483           10        2018  \n",
       "99999               468           10        2019  \n",
       "\n",
       "[98356 rows x 6 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delays_flights = df_flights_clean[['UniqueMarketingCarrierCode','FlightDate','Delay_Departure','DistanceAirports' ,'FlightMonth','FlightYear']]\n",
    "\n",
    "df_delays_flights = df_delays_flights.dropna()\n",
    "\n",
    "df_delays_flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueMarketingCarrierCode</th>\n",
       "      <th>FlightYear</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>Delay_Departure</th>\n",
       "      <th>DistanceAirports</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
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       "      <td>10219.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>6129.0</td>\n",
       "      <td>663704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>12456.0</td>\n",
       "      <td>750173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>7742.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>4368.0</td>\n",
       "      <td>533726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>508397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>5706.0</td>\n",
       "      <td>534127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>564685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    UniqueMarketingCarrierCode  FlightYear  FlightMonth  Delay_Departure  \\\n",
       "0                           AA        2018            1          10335.0   \n",
       "1                           AA        2018            2          10219.0   \n",
       "2                           AA        2018            3           4184.0   \n",
       "3                           AA        2018            4           6129.0   \n",
       "4                           AA        2018            5          12456.0   \n",
       "..                         ...         ...          ...              ...   \n",
       "238                         WN        2019            8           7742.0   \n",
       "239                         WN        2019            9           4368.0   \n",
       "240                         WN        2019           10           6348.0   \n",
       "241                         WN        2019           11           5706.0   \n",
       "242                         WN        2019           12           9674.0   \n",
       "\n",
       "     DistanceAirports  \n",
       "0              699310  \n",
       "1              650011  \n",
       "2              731911  \n",
       "3              663704  \n",
       "4              750173  \n",
       "..                ...  \n",
       "238            486001  \n",
       "239            533726  \n",
       "240            508397  \n",
       "241            534127  \n",
       "242            564685  \n",
       "\n",
       "[243 rows x 5 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_monthly_delay = df_delays_flights.groupby(['UniqueMarketingCarrierCode','FlightYear','FlightMonth'], group_keys=False).sum()\n",
    "df_sum_monthly_delay\n",
    "\n",
    "df_sum_monthly = df_sum_monthly_delay.index.to_frame().join(df_sum_monthly_delay)\n",
    "df_sum_monthly = df_sum_monthly.reset_index(level=0, drop=True)\n",
    "df_sum_monthly = df_sum_monthly.reset_index(level=0, drop=True)\n",
    "df_sum_monthly = df_sum_monthly.reset_index(level=0, drop=True)\n",
    "df_sum_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>486001</td>\n",
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       "      <th>239</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
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       "      <th>240</th>\n",
       "      <td>WN</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>5706.0</td>\n",
       "      <td>534127</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>9674.0</td>\n",
       "      <td>564685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AirlineCode  FlightYear  FlightMonth  Delay_Departure  DistanceAirports\n",
       "0            AA        2018            1          10335.0            699310\n",
       "1            AA        2018            2          10219.0            650011\n",
       "2            AA        2018            3           4184.0            731911\n",
       "3            AA        2018            4           6129.0            663704\n",
       "4            AA        2018            5          12456.0            750173\n",
       "..          ...         ...          ...              ...               ...\n",
       "238          WN        2019            8           7742.0            486001\n",
       "239          WN        2019            9           4368.0            533726\n",
       "240          WN        2019           10           6348.0            508397\n",
       "241          WN        2019           11           5706.0            534127\n",
       "242          WN        2019           12           9674.0            564685\n",
       "\n",
       "[243 rows x 5 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_monthly.rename(columns={'UniqueMarketingCarrierCode': 'AirlineCode'}, inplace=True) \n",
    "df_sum_monthly\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirlineCode</th>\n",
       "      <th>FlightYear</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>Delay_Departure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>10335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>10219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>4184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>6129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>12456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>7742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>4368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>6348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>5706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>9674.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AirlineCode  FlightYear  FlightMonth  Delay_Departure\n",
       "0            AA        2018            1          10335.0\n",
       "1            AA        2018            2          10219.0\n",
       "2            AA        2018            3           4184.0\n",
       "3            AA        2018            4           6129.0\n",
       "4            AA        2018            5          12456.0\n",
       "..          ...         ...          ...              ...\n",
       "238          WN        2019            8           7742.0\n",
       "239          WN        2019            9           4368.0\n",
       "240          WN        2019           10           6348.0\n",
       "241          WN        2019           11           5706.0\n",
       "242          WN        2019           12           9674.0\n",
       "\n",
       "[243 rows x 4 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Monthly Delay by Carrier\n",
    "df_sum_montly_delays=df_sum_monthly[['AirlineCode','FlightYear','FlightMonth','Delay_Departure']]\n",
    "df_sum_montly_delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirlineCode</th>\n",
       "      <th>FlightYear</th>\n",
       "      <th>FlightMonth</th>\n",
       "      <th>DistanceAirports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2018</td>\n",
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       "      <td>650011</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>731911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>663704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>750173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>486001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>533726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>508397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>534127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>WN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>564685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AirlineCode  FlightYear  FlightMonth  DistanceAirports\n",
       "0            AA        2018            1            699310\n",
       "1            AA        2018            2            650011\n",
       "2            AA        2018            3            731911\n",
       "3            AA        2018            4            663704\n",
       "4            AA        2018            5            750173\n",
       "..          ...         ...          ...               ...\n",
       "238          WN        2019            8            486001\n",
       "239          WN        2019            9            533726\n",
       "240          WN        2019           10            508397\n",
       "241          WN        2019           11            534127\n",
       "242          WN        2019           12            564685\n",
       "\n",
       "[243 rows x 4 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Montly Distance by carrier\n",
    "df_sum_montly_distance =df_sum_monthly[['AirlineCode','FlightYear','FlightMonth','DistanceAirports']]\n",
    "df_sum_montly_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seats</th>\n",
       "      <th>passengers</th>\n",
       "      <th>distance</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>GV</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>K2</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>160</td>\n",
       "      <td>85</td>\n",
       "      <td>1430</td>\n",
       "      <td>AA</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>5V</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4004</td>\n",
       "      <td>3484</td>\n",
       "      <td>999</td>\n",
       "      <td>WN</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>F</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>3900</td>\n",
       "      <td>3635</td>\n",
       "      <td>599</td>\n",
       "      <td>AA</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>669</td>\n",
       "      <td>625</td>\n",
       "      <td>4101</td>\n",
       "      <td>DL</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>2095</td>\n",
       "      <td>GL</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>9858</td>\n",
       "      <td>8825</td>\n",
       "      <td>2381</td>\n",
       "      <td>B6</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2976</td>\n",
       "      <td>2557</td>\n",
       "      <td>1452</td>\n",
       "      <td>F9</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seats  passengers  distance unique_carrier  year  month class\n",
       "0          9           0       176             GV  2018      9     F\n",
       "1          6           3        24             K2  2019     10     L\n",
       "2        160          85      1430             AA  2018     11     F\n",
       "3          9           2       128             5V  2019     11     L\n",
       "4       4004        3484       999             WN  2018      8     F\n",
       "...      ...         ...       ...            ...   ...    ...   ...\n",
       "99995   3900        3635       599             AA  2019      8     F\n",
       "99996    669         625      4101             DL  2018      9     F\n",
       "99997    173           0      2095             GL  2019      9     L\n",
       "99998   9858        8825      2381             B6  2019      5     F\n",
       "99999   2976        2557      1452             F9  2018      7     F\n",
       "\n",
       "[100000 rows x 7 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Passenger carried by Airline\n",
    "df_passengers = pd.read_csv(\"../../data/raw/passengersSample.csv\")\n",
    "df_passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirlineCode</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>K2</td>\n",
       "      <td>3</td>\n",
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       "      <td>85</td>\n",
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       "      <td>WN</td>\n",
       "      <td>3484</td>\n",
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       "      <th>6</th>\n",
       "      <td>UA</td>\n",
       "      <td>3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>99992</th>\n",
       "      <td>0CQ</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>AA</td>\n",
       "      <td>3635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>DL</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>B6</td>\n",
       "      <td>8825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>F9</td>\n",
       "      <td>2557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82149 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AirlineCode  passengers\n",
       "1              K2           3\n",
       "2              AA          85\n",
       "3              5V           2\n",
       "4              WN        3484\n",
       "6              UA        3499\n",
       "...           ...         ...\n",
       "99992         0CQ           1\n",
       "99995          AA        3635\n",
       "99996          DL         625\n",
       "99998          B6        8825\n",
       "99999          F9        2557\n",
       "\n",
       "[82149 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_passengers_total = df_passengers[['unique_carrier','passengers']]\n",
    "\n",
    "df_passengers_total= df_passengers_total[df_passengers_total['passengers'] != 0]\n",
    "df_passengers_total.rename(columns={'unique_carrier': 'AirlineCode'}, inplace=True) \n",
    "df_passengers_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>WN</td>\n",
       "      <td>35217616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>AA</td>\n",
       "      <td>32784968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>DL</td>\n",
       "      <td>32553519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>UA</td>\n",
       "      <td>23883750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>B6</td>\n",
       "      <td>8799146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0UQ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>WRD</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>RVQ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10Q</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>FP</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>277 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AirlineCode  passengers\n",
       "259          WN    35217616\n",
       "79           AA    32784968\n",
       "118          DL    32553519\n",
       "243          UA    23883750\n",
       "97           B6     8799146\n",
       "..          ...         ...\n",
       "9           0UQ           4\n",
       "260         WRD           4\n",
       "223         RVQ           4\n",
       "11          10Q           2\n",
       "133          FP           2\n",
       "\n",
       "[277 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_passengers_airline = df_passengers_total.groupby(['AirlineCode']).sum().reset_index()\n",
    "\n",
    "df_sum_passengers_airline.sort_values(by=['passengers'], ascending=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>unique_carrier</th>\n",
       "      <th>carrier_name</th>\n",
       "      <th>total_gallons</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09Q</td>\n",
       "      <td>Swift Air, LLC d/b/a Eastern Air Lines d/b/a E...</td>\n",
       "      <td>1235210.0</td>\n",
       "      <td>3449957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0WQ</td>\n",
       "      <td>Avjet Corporation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1BQ</td>\n",
       "      <td>Eastern Airlines f/k/a Dynamic Airways, LLC</td>\n",
       "      <td>60821.0</td>\n",
       "      <td>176988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27Q</td>\n",
       "      <td>Jet Aviation Flight Services, Inc.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2HQ</td>\n",
       "      <td>Elite Airways LLC</td>\n",
       "      <td>410186.0</td>\n",
       "      <td>1518919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>X9</td>\n",
       "      <td>Omni Air International LLC</td>\n",
       "      <td>2531738.0</td>\n",
       "      <td>6172701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>XP</td>\n",
       "      <td>XTRA Airways</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>YV</td>\n",
       "      <td>Mesa Airlines Inc.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>YX</td>\n",
       "      <td>Republic Airline</td>\n",
       "      <td>21048.0</td>\n",
       "      <td>50043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines Co.</td>\n",
       "      <td>175889193.0</td>\n",
       "      <td>357550908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1058 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_carrier                                       carrier_name  \\\n",
       "0               09Q  Swift Air, LLC d/b/a Eastern Air Lines d/b/a E...   \n",
       "1               0WQ                                  Avjet Corporation   \n",
       "2               1BQ        Eastern Airlines f/k/a Dynamic Airways, LLC   \n",
       "3               27Q                 Jet Aviation Flight Services, Inc.   \n",
       "4               2HQ                                  Elite Airways LLC   \n",
       "...             ...                                                ...   \n",
       "1053             X9                         Omni Air International LLC   \n",
       "1054             XP                                       XTRA Airways   \n",
       "1055             YV                                 Mesa Airlines Inc.   \n",
       "1056             YX                                   Republic Airline   \n",
       "1057             WN                             Southwest Airlines Co.   \n",
       "\n",
       "      total_gallons  total_cost  \n",
       "0         1235210.0     3449957  \n",
       "1               0.0           0  \n",
       "2           60821.0      176988  \n",
       "3               0.0           0  \n",
       "4          410186.0     1518919  \n",
       "...             ...         ...  \n",
       "1053      2531738.0     6172701  \n",
       "1054            0.0           0  \n",
       "1055            0.0           0  \n",
       "1056        21048.0       50043  \n",
       "1057    175889193.0   357550908  \n",
       "\n",
       "[1058 rows x 4 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total fuel comsumption per air carrier.\n",
    "df_fuel  = pd.read_csv(\"../../data/raw/fuel_comsumption.csv\")\n",
    "df_fuel_comsumption = df_fuel[['unique_carrier','carrier_name','total_gallons','total_cost']]\n",
    "df_fuel_comsumption.dropna()\n",
    "df_fuel_comsumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>AirlineCode</th>\n",
       "      <th>AirlineName</th>\n",
       "      <th>total_gallons</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09Q</td>\n",
       "      <td>Swift Air, LLC d/b/a Eastern Air Lines d/b/a E...</td>\n",
       "      <td>1235210.0</td>\n",
       "      <td>3449957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1BQ</td>\n",
       "      <td>Eastern Airlines f/k/a Dynamic Airways, LLC</td>\n",
       "      <td>60821.0</td>\n",
       "      <td>176988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2HQ</td>\n",
       "      <td>Elite Airways LLC</td>\n",
       "      <td>410186.0</td>\n",
       "      <td>1518919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5V</td>\n",
       "      <td>Tatonduk Outfitters Limited d/b/a Everts Air A...</td>\n",
       "      <td>304851.0</td>\n",
       "      <td>792440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5X</td>\n",
       "      <td>United Parcel Service</td>\n",
       "      <td>73210000.0</td>\n",
       "      <td>133562000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>WI</td>\n",
       "      <td>Sky Lease Cargo</td>\n",
       "      <td>788547.0</td>\n",
       "      <td>1793535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>WL</td>\n",
       "      <td>Caribbean Sun Airlines, Inc. d/b/a World Atlan...</td>\n",
       "      <td>64970.0</td>\n",
       "      <td>168103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>X9</td>\n",
       "      <td>Omni Air International LLC</td>\n",
       "      <td>2531738.0</td>\n",
       "      <td>6172701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>YX</td>\n",
       "      <td>Republic Airline</td>\n",
       "      <td>21048.0</td>\n",
       "      <td>50043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines Co.</td>\n",
       "      <td>175889193.0</td>\n",
       "      <td>357550908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>828 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AirlineCode                                        AirlineName  \\\n",
       "0            09Q  Swift Air, LLC d/b/a Eastern Air Lines d/b/a E...   \n",
       "2            1BQ        Eastern Airlines f/k/a Dynamic Airways, LLC   \n",
       "4            2HQ                                  Elite Airways LLC   \n",
       "6             5V  Tatonduk Outfitters Limited d/b/a Everts Air A...   \n",
       "7             5X                              United Parcel Service   \n",
       "...          ...                                                ...   \n",
       "1051          WI                                    Sky Lease Cargo   \n",
       "1052          WL  Caribbean Sun Airlines, Inc. d/b/a World Atlan...   \n",
       "1053          X9                         Omni Air International LLC   \n",
       "1056          YX                                   Republic Airline   \n",
       "1057          WN                             Southwest Airlines Co.   \n",
       "\n",
       "      total_gallons  total_cost  \n",
       "0         1235210.0     3449957  \n",
       "2           60821.0      176988  \n",
       "4          410186.0     1518919  \n",
       "6          304851.0      792440  \n",
       "7        73210000.0   133562000  \n",
       "...             ...         ...  \n",
       "1051       788547.0     1793535  \n",
       "1052        64970.0      168103  \n",
       "1053      2531738.0     6172701  \n",
       "1056        21048.0       50043  \n",
       "1057    175889193.0   357550908  \n",
       "\n",
       "[828 rows x 4 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fuel_comsumption = df_fuel_comsumption[df_fuel_comsumption['total_gallons'] != 0]\n",
    "df_fuel_comsumption.rename(columns={'unique_carrier': 'AirlineCode'}, inplace=True)\n",
    "df_fuel_comsumption.rename(columns={'carrier_name': 'AirlineName'}, inplace=True)\n",
    "df_fuel_comsumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>AirlineCode</th>\n",
       "      <th>total_gallons</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AA</td>\n",
       "      <td>6.416096e+09</td>\n",
       "      <td>13069982821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DL</td>\n",
       "      <td>6.310645e+09</td>\n",
       "      <td>13415228189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>UA</td>\n",
       "      <td>6.123717e+09</td>\n",
       "      <td>12627729183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>WN</td>\n",
       "      <td>3.648209e+09</td>\n",
       "      <td>7489788379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FX</td>\n",
       "      <td>2.075059e+09</td>\n",
       "      <td>4248786486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5X</td>\n",
       "      <td>1.523490e+09</td>\n",
       "      <td>3115165000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B6</td>\n",
       "      <td>1.515474e+09</td>\n",
       "      <td>3291672014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AS</td>\n",
       "      <td>1.275982e+09</td>\n",
       "      <td>2878694746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5Y</td>\n",
       "      <td>9.677969e+08</td>\n",
       "      <td>2125488689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NK</td>\n",
       "      <td>7.666939e+08</td>\n",
       "      <td>1687812540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HA</td>\n",
       "      <td>4.753299e+08</td>\n",
       "      <td>967317832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F9</td>\n",
       "      <td>4.728716e+08</td>\n",
       "      <td>931830411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>G4</td>\n",
       "      <td>3.404517e+08</td>\n",
       "      <td>769787007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PO</td>\n",
       "      <td>3.201268e+08</td>\n",
       "      <td>651374333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OO</td>\n",
       "      <td>1.750844e+08</td>\n",
       "      <td>424845558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9S</td>\n",
       "      <td>1.512969e+08</td>\n",
       "      <td>338466203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KAQ</td>\n",
       "      <td>1.336774e+08</td>\n",
       "      <td>309403902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SY</td>\n",
       "      <td>9.167172e+07</td>\n",
       "      <td>273419896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>X9</td>\n",
       "      <td>7.764875e+07</td>\n",
       "      <td>186815355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KD</td>\n",
       "      <td>5.140127e+07</td>\n",
       "      <td>107214104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M6</td>\n",
       "      <td>4.192460e+07</td>\n",
       "      <td>93763529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>WI</td>\n",
       "      <td>3.067892e+07</td>\n",
       "      <td>69832020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09Q</td>\n",
       "      <td>3.030427e+07</td>\n",
       "      <td>76146698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>N8</td>\n",
       "      <td>1.924860e+07</td>\n",
       "      <td>63705015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8C</td>\n",
       "      <td>1.554831e+07</td>\n",
       "      <td>38367573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GL</td>\n",
       "      <td>1.156546e+07</td>\n",
       "      <td>32522889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>U7</td>\n",
       "      <td>1.094250e+07</td>\n",
       "      <td>23739832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>8.446353e+06</td>\n",
       "      <td>19617730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KH</td>\n",
       "      <td>8.404593e+06</td>\n",
       "      <td>17063469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5V</td>\n",
       "      <td>7.895278e+06</td>\n",
       "      <td>21989573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PFQ</td>\n",
       "      <td>7.434311e+06</td>\n",
       "      <td>17180208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GFQ</td>\n",
       "      <td>6.741599e+06</td>\n",
       "      <td>18027305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2HQ</td>\n",
       "      <td>6.080250e+06</td>\n",
       "      <td>23754366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>L2</td>\n",
       "      <td>5.305314e+06</td>\n",
       "      <td>13662876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KLQ</td>\n",
       "      <td>4.134408e+06</td>\n",
       "      <td>11189462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ABX</td>\n",
       "      <td>3.271677e+06</td>\n",
       "      <td>4973218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>WL</td>\n",
       "      <td>2.425627e+06</td>\n",
       "      <td>5543326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XP</td>\n",
       "      <td>1.900924e+06</td>\n",
       "      <td>4575063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1BQ</td>\n",
       "      <td>1.862466e+06</td>\n",
       "      <td>5964173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>YX</td>\n",
       "      <td>5.255570e+05</td>\n",
       "      <td>1241169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0WQ</td>\n",
       "      <td>9.156700e+04</td>\n",
       "      <td>327406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AirlineCode  total_gallons   total_cost\n",
       "9           AA   6.416096e+09  13069982821\n",
       "13          DL   6.310645e+09  13415228189\n",
       "34          UA   6.123717e+09  12627729183\n",
       "37          WN   3.648209e+09   7489788379\n",
       "15          FX   2.075059e+09   4248786486\n",
       "5           5X   1.523490e+09   3115165000\n",
       "12          B6   1.515474e+09   3291672014\n",
       "11          AS   1.275982e+09   2878694746\n",
       "6           5Y   9.677969e+08   2125488689\n",
       "28          NK   7.666939e+08   1687812540\n",
       "19          HA   4.753299e+08    967317832\n",
       "14          F9   4.728716e+08    931830411\n",
       "16          G4   3.404517e+08    769787007\n",
       "31          PO   3.201268e+08    651374333\n",
       "29          OO   1.750844e+08    424845558\n",
       "8           9S   1.512969e+08    338466203\n",
       "20         KAQ   1.336774e+08    309403902\n",
       "32          SY   9.167172e+07    273419896\n",
       "38          X9   7.764875e+07    186815355\n",
       "21          KD   5.140127e+07    107214104\n",
       "25          M6   4.192460e+07     93763529\n",
       "35          WI   3.067892e+07     69832020\n",
       "0          09Q   3.030427e+07     76146698\n",
       "26          N8   1.924860e+07     63705015\n",
       "7           8C   1.554831e+07     38367573\n",
       "18          GL   1.156546e+07     32522889\n",
       "33          U7   1.094250e+07     23739832\n",
       "27          NC   8.446353e+06     19617730\n",
       "22          KH   8.404593e+06     17063469\n",
       "4           5V   7.895278e+06     21989573\n",
       "30         PFQ   7.434311e+06     17180208\n",
       "17         GFQ   6.741599e+06     18027305\n",
       "3          2HQ   6.080250e+06     23754366\n",
       "24          L2   5.305314e+06     13662876\n",
       "23         KLQ   4.134408e+06     11189462\n",
       "10         ABX   3.271677e+06      4973218\n",
       "36          WL   2.425627e+06      5543326\n",
       "39          XP   1.900924e+06      4575063\n",
       "2          1BQ   1.862466e+06      5964173\n",
       "40          YX   5.255570e+05      1241169\n",
       "1          0WQ   9.156700e+04       327406"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fuel_comsumption_carrier = df_fuel_comsumption.groupby(['AirlineCode']).sum().reset_index()\n",
    "\n",
    "\n",
    "df_fuel_comsumption_carrier.sort_values(by=['total_gallons'], ascending=False) "
   ]
  }
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