EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"flights2018.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7213446, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7213446 entries, 0 to 7213445\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Dtype  \n",
      "---  ------               -----  \n",
      " 0   FL_DATE              object \n",
      " 1   OP_CARRIER           object \n",
      " 2   OP_CARRIER_FL_NUM    int64  \n",
      " 3   ORIGIN               object \n",
      " 4   DEST                 object \n",
      " 5   CRS_DEP_TIME         int64  \n",
      " 6   DEP_TIME             float64\n",
      " 7   DEP_DELAY            float64\n",
      " 8   TAXI_OUT             float64\n",
      " 9   WHEELS_OFF           float64\n",
      " 10  WHEELS_ON            float64\n",
      " 11  TAXI_IN              float64\n",
      " 12  CRS_ARR_TIME         int64  \n",
      " 13  ARR_TIME             float64\n",
      " 14  ARR_DELAY            float64\n",
      " 15  CANCELLED            float64\n",
      " 16  CANCELLATION_CODE    object \n",
      " 17  DIVERTED             float64\n",
      " 18  CRS_ELAPSED_TIME     float64\n",
      " 19  ACTUAL_ELAPSED_TIME  float64\n",
      " 20  AIR_TIME             float64\n",
      " 21  DISTANCE             float64\n",
      " 22  CARRIER_DELAY        float64\n",
      " 23  WEATHER_DELAY        float64\n",
      " 24  NAS_DELAY            float64\n",
      " 25  SECURITY_DELAY       float64\n",
      " 26  LATE_AIRCRAFT_DELAY  float64\n",
      " 27  Unnamed: 27          float64\n",
      "dtypes: float64(20), int64(3), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>FL_DATE</th>\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>DEST</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>WHEELS_OFF</th>\n",
       "      <th>...</th>\n",
       "      <th>CRS_ELAPSED_TIME</th>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <th>AIR_TIME</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>CARRIER_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "      <th>NAS_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>Unnamed: 27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>UA</td>\n",
       "      <td>2429</td>\n",
       "      <td>EWR</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1517</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>...</td>\n",
       "      <td>268.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>UA</td>\n",
       "      <td>2427</td>\n",
       "      <td>LAS</td>\n",
       "      <td>SFO</td>\n",
       "      <td>1115</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>UA</td>\n",
       "      <td>2426</td>\n",
       "      <td>SNA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1335</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>846.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>UA</td>\n",
       "      <td>2425</td>\n",
       "      <td>RSW</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1546</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>...</td>\n",
       "      <td>190.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>UA</td>\n",
       "      <td>2424</td>\n",
       "      <td>ORD</td>\n",
       "      <td>ALB</td>\n",
       "      <td>630</td>\n",
       "      <td>650.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FL_DATE OP_CARRIER  OP_CARRIER_FL_NUM ORIGIN DEST  CRS_DEP_TIME  \\\n",
       "0  2018-01-01         UA               2429    EWR  DEN          1517   \n",
       "1  2018-01-01         UA               2427    LAS  SFO          1115   \n",
       "2  2018-01-01         UA               2426    SNA  DEN          1335   \n",
       "3  2018-01-01         UA               2425    RSW  ORD          1546   \n",
       "4  2018-01-01         UA               2424    ORD  ALB           630   \n",
       "\n",
       "   DEP_TIME  DEP_DELAY  TAXI_OUT  WHEELS_OFF  ...  CRS_ELAPSED_TIME  \\\n",
       "0    1512.0       -5.0      15.0      1527.0  ...             268.0   \n",
       "1    1107.0       -8.0      11.0      1118.0  ...              99.0   \n",
       "2    1330.0       -5.0      15.0      1345.0  ...             134.0   \n",
       "3    1552.0        6.0      19.0      1611.0  ...             190.0   \n",
       "4     650.0       20.0      13.0       703.0  ...             112.0   \n",
       "\n",
       "   ACTUAL_ELAPSED_TIME  AIR_TIME  DISTANCE  CARRIER_DELAY  WEATHER_DELAY  \\\n",
       "0                250.0     225.0    1605.0            NaN            NaN   \n",
       "1                 83.0      65.0     414.0            NaN            NaN   \n",
       "2                126.0     106.0     846.0            NaN            NaN   \n",
       "3                182.0     157.0    1120.0            NaN            NaN   \n",
       "4                106.0      83.0     723.0            NaN            NaN   \n",
       "\n",
       "  NAS_DELAY  SECURITY_DELAY  LATE_AIRCRAFT_DELAY  Unnamed: 27  \n",
       "0       NaN             NaN                  NaN          NaN  \n",
       "1       NaN             NaN                  NaN          NaN  \n",
       "2       NaN             NaN                  NaN          NaN  \n",
       "3       NaN             NaN                  NaN          NaN  \n",
       "4       NaN             NaN                  NaN          NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7213446 entries, 0 to 7213445\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Dtype  \n",
      "---  ------               -----  \n",
      " 0   FL_DATE              object \n",
      " 1   OP_CARRIER           object \n",
      " 2   OP_CARRIER_FL_NUM    int64  \n",
      " 3   ORIGIN               object \n",
      " 4   DEST                 object \n",
      " 5   CRS_DEP_TIME         int64  \n",
      " 6   DEP_TIME             float64\n",
      " 7   DEP_DELAY            float64\n",
      " 8   TAXI_OUT             float64\n",
      " 9   WHEELS_OFF           float64\n",
      " 10  WHEELS_ON            float64\n",
      " 11  TAXI_IN              float64\n",
      " 12  CRS_ARR_TIME         int64  \n",
      " 13  ARR_TIME             float64\n",
      " 14  ARR_DELAY            float64\n",
      " 15  CANCELLED            float64\n",
      " 16  CANCELLATION_CODE    object \n",
      " 17  DIVERTED             float64\n",
      " 18  CRS_ELAPSED_TIME     float64\n",
      " 19  ACTUAL_ELAPSED_TIME  float64\n",
      " 20  AIR_TIME             float64\n",
      " 21  DISTANCE             float64\n",
      " 22  CARRIER_DELAY        float64\n",
      " 23  WEATHER_DELAY        float64\n",
      " 24  NAS_DELAY            float64\n",
      " 25  SECURITY_DELAY       float64\n",
      " 26  LATE_AIRCRAFT_DELAY  float64\n",
      " 27  Unnamed: 27          float64\n",
      "dtypes: float64(20), int64(3), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FL_DATE                      0\n",
       "OP_CARRIER                   0\n",
       "OP_CARRIER_FL_NUM            0\n",
       "ORIGIN                       0\n",
       "DEST                         0\n",
       "CRS_DEP_TIME                 0\n",
       "DEP_TIME                112317\n",
       "DEP_DELAY               117234\n",
       "TAXI_OUT                115830\n",
       "WHEELS_OFF              115829\n",
       "WHEELS_ON               119246\n",
       "TAXI_IN                 119246\n",
       "CRS_ARR_TIME                 0\n",
       "ARR_TIME                119245\n",
       "ARR_DELAY               137040\n",
       "CANCELLED                    0\n",
       "CANCELLATION_CODE      7096862\n",
       "DIVERTED                     0\n",
       "CRS_ELAPSED_TIME            10\n",
       "ACTUAL_ELAPSED_TIME     134442\n",
       "AIR_TIME                134442\n",
       "DISTANCE                     0\n",
       "CARRIER_DELAY          5860736\n",
       "WEATHER_DELAY          5860736\n",
       "NAS_DELAY              5860736\n",
       "SECURITY_DELAY         5860736\n",
       "LATE_AIRCRAFT_DELAY    5860736\n",
       "Unnamed: 27            7213446\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of Delated Flights')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATG0lEQVR4nO3dfbRldV3H8fdHHnxiEGluioCMKD6QpdKoGWVkpjxo1KqUVhIqRVaYFlYYplhZWOkqE1NSBEEwC0zE5QOmhJYKA4HyEIk44ogwFxVh1Az02x97X+dwOPfec5l77vzOzPu11ln33H32+e3v7+45n/vbv7333FQVkqR23WtrFyBJWphBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOIN6yiR5c5I/Waa2HppkU5Id+u8vTPLry9F2394Hkhy1XO0tYbt/nuSWJDdNqP3Tkvz5JNoesa3nJ/nElr6v38/7jvneSvKIpW5Tk2NQNyTJ+iTfTnJ7kluT/GeSFyX5/n6qqhdV1Z+N2dbTF1qnqm6oql2q6rvLUPuJSc4cav+Qqjp9S9teYh17A8cB+1fVg0e8flCS7/XBtSnJhiTvTvLECdWzrL/8htpe04fqpoHHFaPW7ffz9cuwzXv0i0NbxqBuz7OrahWwD3AS8EfA25Z7I0l2XO42G7EP8NWq2rjAOjdW1S7AKuDHgP8GPp7kZ1aiwAnYrQ/iXarqcVu7GC0/g7pRVfWNqjoPeC5wVJLHwl0Pu5OsTnJ+P/r+WpKPJ7lXkjOAhwLv60dZfzgw+jo6yQ3ARweWDYb2w5NcnOQbSd6bZPd+Wwcl2TBY49yoPcnBwB8Dzx0c1Q2OJvu6XpHki0k2JnlHkgf0r83VcVSSG/ppixPm+9kkeUD//tm+vVf07T8duAB4SF/HaYv8jKuqNlTVK4G3Aq8d2Majk1zQ/1yvTfKceWp5YL8PZpN8vX++V//aa4CfBN7Y1/PGxdpO8gNJzktyW5KLgYcv1IdxDU5n9Nt4X7+NS/qpouFR8tOTfK7v08npPAZ4M/CUvj+39u0dmuTq/kjwy0lethw1azODunFVdTGwge4DP+y4/rUZ4EF0YVlVdSRwA93ofJeq+quB9/wU8BjgmfNs8teAFwIPAe4E3jBGjR8E/gL4pwVGdc/vHz8N7AvsArxxaJ2fAB4F/Azwyj4YRvl74AF9Oz/V1/yCqvoIcAj9iLmqnr9Y7QPOBQ5Icv8k96cL/LOAHwR+BXhTkh8a8b57AW+nG8k/FPj2XL+q6gTg48CxfT3HjtH2ycD/AnvQ7YcXLqEP4zoZ+CbwYOCo/jHsWcATgccBzwGeWVXXAC8CPtn3Z7d+3bcBv9kfCT4W+OgEat6uTSyok5zaj5yuHHP95/S/la9Kctak6ppSNwK7j1h+B90Hep+quqOqPl6L/+ctJ1bVN6vq2/O8fkZVXVlV3wT+BHhO+pONW+hXgddX1fVVtQl4OXDE0Gj+1VX17aq6AriCLiTuoq/lucDLq+r2qloPvA44cgvruxEIsBtdSK2vqrdX1Z1VdRlwDvBLw2+qqq9W1TlV9a2quh14Dd0vj/nM23bft18EXtnvoyuBceb4b+mPqm5dbDQ7sI1X9TVfPc82TqqqW6vqBuBjwOMXaPYOYP8ku1bV1/s+aRlNckR9GnDwOCsm2Y/ug3tgVf0Q8NIJ1jWN9gS+NmL5XwPXAR9Ocn2S48do60tLeP2LwE7A6rGqXNhD+vYG296R7khgzuBVGt+iG3UPWw3sPKKtPbewvj2BAm6lGx0/eSD8bqX7RTPq5OT9kryln4K5DbgI2G2BX24LtT1D9zMZ3geLWV1Vu/WPv1lk3VHbGPVvYpx9MecXgUOBLyb59yRPGaNmLcHEgrqqLmIoXJI8PMkHk1yabj710f1LvwGcXFVf79+70Img7Uq6qxH2BO52pr0fUR5XVfsCzwZ+P5tPiM03sl5sxL33wPOH0o2WbqE7VL7fQF070H3ox233RrqQGmz7TuDmRd437Ja+puG2vrzEdob9AnBZfyTxJeDfB8Jv7mTdb41433F00zVPrqpdgaf2y9N/Hf65LNT2LN3PZHgfLKe5bew1sGzvedYd5W77uaouqarD6aZy/hV49xZVqLtZ6TnqU4AXV9WPAi8D3tQvfyTwyCT/keRT/cmp7VqSXZM8C3gXcGZVfXbEOs9K8ogkAW4Dvts/oAvAsa6bHfK8JPsnuR/wp8C/9Jfv/Q9wnySHJdkJeAVw74H33QysycClhEPOBn4vycOS7MLmOe07l1JcX8u7gdckWZVkH+D3gTMXfufd9SfI9kzyKuDX6eb4Ac6n+/d4ZJKd+scT55kzX0U3L31ruhOvrxp6fXg/zNt237dzgRP7kfr+jJ4/vsdGbOPRdHP847oZ2CvJzgBJdk7yq0keUFV3sPnfoZbRigV1/+H8ceCfk1wOvIVufhW6Q7H9gIPoTq68Ncluo9rZDrwvye10I68TgNcDL5hn3f2AjwCbgE8Cb6qqC/vX/hJ4xTjzlkPOoJu2ugm4D/C70F2FAvw23dURX6YbYQ9eBfLP/devJhk1R3lq3/ZFwBfoTpi9eAl1DXpxv/3r6Y40zurbH9dDkmyi+7ldAvwwcFBVfRi6IxXgGcARdEcCN9FdEXLvEW39LXBfupH+p4APDr3+d3Tzz19P8oYx2j6WbprhJrr98PYl9Gtcx9KdjL2Jbp+cDXxnzPd+FLgKuCnJLf2yI4H1/dTPi4DnLW+5yiT/cECSNcD5VfXYJLsC11bVHiPWezPwqao6rf/+34Djq+qSiRUnCYAkrwUeXFUrfhepxrNiI+qqug34QpJfhu8fds6d1f9Xusu2SLKabipki++iknR36a7j/pH+M/gk4GjgPVu7Ls1vkpfnnU13OP6odLfpHk13dvvodDdEXAUc3q/+IbpD5qvpLgX6g6r66qRqk7Zzq+jmqb9JN9//OuC9W7UiLWiiUx+SpC3nnYmS1LiJ/Mc8q1evrjVr1kyiaUnaJl166aW3VNXMqNcmEtRr1qxh3bp1k2hakrZJSea9C9WpD0lqnEEtSY0zqCWpcQa1JDXOoJakxhnUktQ4g1qSGjfWddRJ1gO30/0/s3dW1dpJFiVJ2mwpN7z8dFXdsvhqkqTlNJE7EydlzfHvH7l8/UmHrXAlkrRyxp2jLro/oHppkmNGrZDkmCTrkqybnZ1dvgolaTs3blAfWFUHAIcAv5PkqcMrVNUpVbW2qtbOzIz8f0UkSffAWEFdVTf2XzfS/SWIJ02yKEnSZosGdZL7J1k195zuD3NeOenCJEmdcU4mPgh4T5K59c+qquG/tCxJmpBFg7qqrgcet9h6kqTJ8M5ESWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1DiDWpIaZ1BLUuMMaklqnEEtSY0zqCWpcQa1JDXOoJakxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1DiDWpIaZ1BLUuPGDuokOyT5ryTnT7IgSdJdLWVE/RLgmkkVIkkabaygTrIXcBjw1smWI0kaNu6I+m+BPwS+N98KSY5Jsi7JutnZ2WUpTpI0RlAneRawsaouXWi9qjqlqtZW1dqZmZllK1CStnfjjKgPBH4uyXrgXcDTkpw50aokSd+3aFBX1curaq+qWgMcAXy0qp438cokSYDXUUtS83ZcyspVdSFw4UQqkSSN5IhakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1DiDWpIaZ1BLUuMMaklqnEEtSY0zqCWpcQa1JDXOoJakxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1LhFgzrJfZJcnOSKJFclefVKFCZJ6uw4xjrfAZ5WVZuS7AR8IskHqupTE65NksQYQV1VBWzqv92pf9Qki5IkbTbWHHWSHZJcDmwELqiqT0+2LEnSnLGCuqq+W1WPB/YCnpTkscPrJDkmybok62ZnZ5e7Tknabi3pqo+quhW4EDh4xGunVNXaqlo7MzOzTOVJksa56mMmyW798/sCTwf+e9KFSZI641z1sQdwepId6IL93VV1/mTLkiTNGeeqj88AT1iBWiRJI3hnoiQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1DiDWpIaZ1BLUuMMaklqnEEtSY0zqCWpcQa1JDXOoJakxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxiwZ1kr2TfCzJNUmuSvKSlShMktTZcYx17gSOq6rLkqwCLk1yQVVdPeHaJEmMMaKuqq9U1WX989uBa4A9J12YJKmzpDnqJGuAJwCfnkQxkqS7Gzuok+wCnAO8tKpuG/H6MUnWJVk3Ozu7nDVK0nZtrKBOshNdSL+zqs4dtU5VnVJVa6tq7czMzHLWKEnbtXGu+gjwNuCaqnr95EuSJA0aZ0R9IHAk8LQkl/ePQydclySpt+jleVX1CSArUIskaQTvTJSkxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMaN84cDmrfm+PfP+9r6kw5bwUokafk5opakxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTGGdSS1DiDWpIaZ1BLUuMMaklqnEEtSY0zqCWpcYsGdZJTk2xMcuVKFCRJuqtxRtSnAQdPuA5J0jwWDeqqugj42grUIkkaYdnmqJMck2RdknWzs7PL1awkbfeWLair6pSqWltVa2dmZparWUna7nnVhyQ1zqCWpMaNc3ne2cAngUcl2ZDk6MmXJUmas+NiK1TVr6xEIZKk0Zz6kKTGGdSS1DiDWpIaZ1BLUuMMaklqnEEtSY0zqCWpcQa1JDXOoJakxhnUktQ4g1qSGmdQS1LjDGpJapxBLUmNM6glqXEGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWqcQS1JjTOoJalxBrUkNc6glqTG7bi1C5i0Nce/f97X1p902ApWIkn3jCNqSWqcQS1JjTOoJalxBrUkNc6glqTGbfNXfSzEK0IkTYOxRtRJDk5ybZLrkhw/6aIkSZstOqJOsgNwMvCzwAbgkiTnVdXVky5ua5pvtO1IW9JKG2fq40nAdVV1PUCSdwGHA9t0UM/H6RJJK22coN4T+NLA9xuAJw+vlOQY4Jj+201Jrt3y8sa2GrhlBbc3Ul677E020a9lti32CezXtGmxX/vM98I4QZ0Ry+puC6pOAU5ZQlHLJsm6qlq7NbY9Sdtiv7bFPoH9mjbT1q9xTiZuAPYe+H4v4MbJlCNJGjZOUF8C7JfkYUl2Bo4AzptsWZKkOYtOfVTVnUmOBT4E7ACcWlVXTbyypdkqUy4rYFvs17bYJ7Bf02aq+pWqu003S5Ia4i3kktQ4g1qSGjfVQT3tt7YnWZ/ks0kuT7KuX7Z7kguSfK7/+sCB9V/e9/XaJM/cepXfVZJTk2xMcuXAsiX3I8mP9j+P65K8IcmoS0NXxDx9OjHJl/v9dXmSQwdea75PfT17J/lYkmuSXJXkJf3yad9f8/Vr6vcZAFU1lQ+6E5ufB/YFdgauAPbf2nUtsQ/rgdVDy/4KOL5/fjzw2v75/n0f7w08rO/7Dlu7D31tTwUOAK7ckn4AFwNPobt2/wPAIY316UTgZSPWnYo+9fXsARzQP18F/E9f/7Tvr/n6NfX7rKqmekT9/Vvbq+r/gLlb26fd4cDp/fPTgZ8fWP6uqvpOVX0BuI7uZ7DVVdVFwNeGFi+pH0n2AHatqk9W92l5x8B7Vtw8fZrPVPQJoKq+UlWX9c9vB66hu/t42vfXfP2az1T0a840B/WoW9sX2jEtKuDDSS7tb8EHeFBVfQW6f3zAD/bLp62/S+3Hnv3z4eWtOTbJZ/qpkbnpgansU5I1wBOAT7MN7a+hfsE2sM+mOajHurW9cQdW1QHAIcDvJHnqAutuC/2F+fsxDf37B+DhwOOBrwCv65dPXZ+S7AKcA7y0qm5baNURy5rt24h+bRP7bJqDeupvba+qG/uvG4H30E1l3NwfftF/3divPm39XWo/NvTPh5c3o6purqrvVtX3gH9k89TTVPUpyU50YfbOqjq3Xzz1+2tUv7aVfTbNQT3Vt7YnuX+SVXPPgWcAV9L14ah+taOA9/bPzwOOSHLvJA8D9qM76dGqJfWjP9y+PcmP9WfZf23gPU2YC7LeL9DtL5iiPvV1vA24pqpeP/DSVO+v+fq1LewzYHqv+ujm+TmU7uzu54ETtnY9S6x9X7qzzlcAV83VD/wA8G/A5/qvuw+854S+r9fSwJnogbrOpjusvINuRHL0PekHsJbug/R54I30d8421KczgM8Cn6H7oO8xTX3q6/kJukP5zwCX949Dt4H9NV+/pn6fVZW3kEtS66Z56kOStgsGtSQ1zqCWpMYZ1JLUOINakhpnUEtS4wxqSWrc/wNYVaFcMcLkDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of delayed flights \n",
    "plt.hist(df.DEP_DELAY, bins=45)\n",
    "plt.gca().set(title='Distribution of Delated Flights')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.096212e+06\n",
       "mean     9.969858e+00\n",
       "std      4.482964e+01\n",
       "min     -1.220000e+02\n",
       "25%     -5.000000e+00\n",
       "50%     -2.000000e+00\n",
       "75%      7.000000e+00\n",
       "max      2.710000e+03\n",
       "Name: DEP_DELAY, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.DEP_DELAY.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preliminary analysis: above chart presents a highly skewed distribution. One possible problem stemming from the nature of the dataset is that when we attempt to fit a classification model to predict delayed time, the predicted result tends to converge to the mode of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.OP_CARRIER.value_counts(normalize=True).plot.pie()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['DISTANCE','NAS_DELAY','CARRIER_DELAY','WEATHER_DELAY','SECURITY_DELAY','LATE_AIRCRAFT_DELAY']].corr()\n",
    "sns.heatmap(df[['DISTANCE','NAS_DELAY','CARRIER_DELAY','WEATHER_DELAY','SECURITY_DELAY','LATE_AIRCRAFT_DELAY']].corr(), annot=True, cmap = 'Blues')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['CRS_DEP_TIME','NAS_DELAY','CARRIER_DELAY','WEATHER_DELAY','SECURITY_DELAY','LATE_AIRCRAFT_DELAY']].corr()\n",
    "sns.heatmap(df[['CRS_DEP_TIME','NAS_DELAY','CARRIER_DELAY','WEATHER_DELAY','SECURITY_DELAY','LATE_AIRCRAFT_DELAY']].corr(), annot=True, cmap = 'Greens')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORIGIN  OP_CARRIER\n",
       "ABE     9E             352\n",
       "        DL             363\n",
       "        EV             470\n",
       "        G4             912\n",
       "        OH             997\n",
       "                      ... \n",
       "XNA     YV             225\n",
       "        YX            1431\n",
       "YAK     AS             722\n",
       "YNG     G4               2\n",
       "YUM     OO            1376\n",
       "Length: 1967, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Explore on the subset which delayed time is positive -- the actual delayed flights\n",
    "df[(df.DEP_DELAY >= 0)]\n",
    "df.groupby(['ORIGIN','OP_CARRIER']).size()\n",
    "\n",
    "# this shows the number of flights in each origin operated by each carrier  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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