test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to pandas data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Senin     0\n",
       "Selasa    0\n",
       "Rabu      1\n",
       "Kamis     0\n",
       "Jumat     1\n",
       "Sabtu     1\n",
       "Minggu    1\n",
       "Name: List kehadiran, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1 = pd.Series([random.randint(0, 1) for _ in range(7)], index=['Senin', 'Selasa', 'Rabu', 'Kamis', 'Jumat', 'Sabtu', \"Minggu\"], name=\"List kehadiran\")\n",
    "\n",
    "series1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kelas A    10\n",
       "Kelas B     5\n",
       "Kelas C    17\n",
       "Kelas D    29\n",
       "Name: Jumlah Siswa, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series2 = pd.Series({'Kelas A': 10, 'Kelas B': 5, 'Kelas C': 17, 'Kelas D': 29}, name=\"Jumlah Siswa\")\n",
    "\n",
    "series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kelas A    2\n",
       "Kelas B    1\n",
       "Kelas C    0\n",
       "Kelas D    0\n",
       "Name: Jumlah siswa absen, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3 = pd.Series({'Kelas A': 2, 'Kelas B': 1, 'Kelas C': 0, 'Kelas D': 0}, name=\"Jumlah siswa absen\")\n",
    "series3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>jumlah siswa</th>\n",
       "      <th>Jumlah siswa absen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kelas A</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelas B</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelas C</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelas D</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         jumlah siswa  Jumlah siswa absen\n",
       "Kelas A            10                   2\n",
       "Kelas B             5                   1\n",
       "Kelas C            17                   0\n",
       "Kelas D            29                   0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe1 = pd.DataFrame({'jumlah siswa': series2, 'Jumlah siswa absen': series3})\n",
    "dataframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0  1.0  5.0\n",
       "1  2.0  6.0\n",
       "2  3.0  7.0\n",
       "3  4.0  8.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe2 = pd.DataFrame({'one': [1., 2., 3., 4.], 'two': [5., 6., 7., 8.]})\n",
    "dataframe2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index \"Kelas A\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3['Kelas A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label \"jumlah siswa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kelas A    10\n",
       "Kelas B     5\n",
       "Kelas C    17\n",
       "Kelas D    29\n",
       "Name: jumlah siswa, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe1['jumlah siswa']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mendapatkan data 'jumlah siswa' di 'Kelas A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan data 'jumlah siswa' di 'Kelas A'\n",
    "dataframe1.loc['Kelas A', 'jumlah siswa']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mendapatkan data index ke 0 dan 1, di 'Kelas B'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.0\n",
       "1    6.0\n",
       "Name: two, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe2.iloc[:2, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data dapat diunduh di\n",
    "[Kaggle Flight Delay](https://www.kaggle.com/divyansh22/flight-delay-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Jan_2020_ontime.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menampilkan 5 baris teratas dari dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>DAY_OF_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>OP_UNIQUE_CARRIER</th>\n",
       "      <th>OP_CARRIER_AIRLINE_ID</th>\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>ORIGIN_AIRPORT_SEQ_ID</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>...</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEP_DEL15</th>\n",
       "      <th>DEP_TIME_BLK</th>\n",
       "      <th>ARR_TIME</th>\n",
       "      <th>ARR_DEL15</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>N48901</td>\n",
       "      <td>4397</td>\n",
       "      <td>13930</td>\n",
       "      <td>1393007</td>\n",
       "      <td>ORD</td>\n",
       "      <td>...</td>\n",
       "      <td>GRB</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>N16976</td>\n",
       "      <td>4401</td>\n",
       "      <td>15370</td>\n",
       "      <td>1537002</td>\n",
       "      <td>TUL</td>\n",
       "      <td>...</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>N12167</td>\n",
       "      <td>4404</td>\n",
       "      <td>11618</td>\n",
       "      <td>1161802</td>\n",
       "      <td>EWR</td>\n",
       "      <td>...</td>\n",
       "      <td>TYS</td>\n",
       "      <td>1848.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800-1859</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>N14902</td>\n",
       "      <td>4405</td>\n",
       "      <td>10781</td>\n",
       "      <td>1078105</td>\n",
       "      <td>BTR</td>\n",
       "      <td>...</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800-1859</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>N606UX</td>\n",
       "      <td>4407</td>\n",
       "      <td>14524</td>\n",
       "      <td>1452401</td>\n",
       "      <td>RIC</td>\n",
       "      <td>...</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY_OF_MONTH  DAY_OF_WEEK OP_UNIQUE_CARRIER  OP_CARRIER_AIRLINE_ID  \\\n",
       "0             1            3                EV                  20366   \n",
       "1             1            3                EV                  20366   \n",
       "2             1            3                EV                  20366   \n",
       "3             1            3                EV                  20366   \n",
       "4             1            3                EV                  20366   \n",
       "\n",
       "  OP_CARRIER TAIL_NUM  OP_CARRIER_FL_NUM  ORIGIN_AIRPORT_ID  \\\n",
       "0         EV   N48901               4397              13930   \n",
       "1         EV   N16976               4401              15370   \n",
       "2         EV   N12167               4404              11618   \n",
       "3         EV   N14902               4405              10781   \n",
       "4         EV   N606UX               4407              14524   \n",
       "\n",
       "   ORIGIN_AIRPORT_SEQ_ID ORIGIN  ...  DEST  DEP_TIME DEP_DEL15  DEP_TIME_BLK  \\\n",
       "0                1393007    ORD  ...   GRB    1003.0       0.0     1000-1059   \n",
       "1                1537002    TUL  ...   ORD    1027.0       0.0     1000-1059   \n",
       "2                1161802    EWR  ...   TYS    1848.0       0.0     1800-1859   \n",
       "3                1078105    BTR  ...   IAH    1846.0       0.0     1800-1859   \n",
       "4                1452401    RIC  ...   IAH    1038.0       0.0     1000-1059   \n",
       "\n",
       "   ARR_TIME ARR_DEL15  CANCELLED  DIVERTED  DISTANCE  Unnamed: 21  \n",
       "0    1117.0       0.0        0.0       0.0     174.0          NaN  \n",
       "1    1216.0       0.0        0.0       0.0     585.0          NaN  \n",
       "2    2120.0       0.0        0.0       0.0     631.0          NaN  \n",
       "3    2004.0       0.0        0.0       0.0     253.0          NaN  \n",
       "4    1330.0       0.0        0.0       0.0    1157.0          NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Menampilkan 5 teratas\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalisasi dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>DAY_OF_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>OP_CARRIER_AIRLINE_ID</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>ORIGIN_AIRPORT_SEQ_ID</th>\n",
       "      <th>DEST_AIRPORT_ID</th>\n",
       "      <th>DEST_AIRPORT_SEQ_ID</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEP_DEL15</th>\n",
       "      <th>ARR_TIME</th>\n",
       "      <th>ARR_DEL15</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>6.073460e+05</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>6.073460e+05</td>\n",
       "      <td>600682.000000</td>\n",
       "      <td>600647.000000</td>\n",
       "      <td>600271.000000</td>\n",
       "      <td>599268.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>607346.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.014354</td>\n",
       "      <td>3.955735</td>\n",
       "      <td>19994.137492</td>\n",
       "      <td>2622.365261</td>\n",
       "      <td>12657.389167</td>\n",
       "      <td>1.265743e+06</td>\n",
       "      <td>12657.196320</td>\n",
       "      <td>1.265724e+06</td>\n",
       "      <td>1331.512559</td>\n",
       "      <td>0.136326</td>\n",
       "      <td>1477.968924</td>\n",
       "      <td>0.137309</td>\n",
       "      <td>0.011407</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>798.022341</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.990719</td>\n",
       "      <td>1.910205</td>\n",
       "      <td>375.754232</td>\n",
       "      <td>1822.545302</td>\n",
       "      <td>1524.407203</td>\n",
       "      <td>1.524405e+05</td>\n",
       "      <td>1524.279269</td>\n",
       "      <td>1.524277e+05</td>\n",
       "      <td>497.095168</td>\n",
       "      <td>0.343135</td>\n",
       "      <td>525.543294</td>\n",
       "      <td>0.344174</td>\n",
       "      <td>0.106193</td>\n",
       "      <td>0.043473</td>\n",
       "      <td>587.282639</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19393.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10135.000000</td>\n",
       "      <td>1.013506e+06</td>\n",
       "      <td>10135.000000</td>\n",
       "      <td>1.013506e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19790.000000</td>\n",
       "      <td>1070.000000</td>\n",
       "      <td>11292.000000</td>\n",
       "      <td>1.129202e+06</td>\n",
       "      <td>11292.000000</td>\n",
       "      <td>1.129202e+06</td>\n",
       "      <td>919.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1058.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19977.000000</td>\n",
       "      <td>2177.000000</td>\n",
       "      <td>12889.000000</td>\n",
       "      <td>1.288903e+06</td>\n",
       "      <td>12889.000000</td>\n",
       "      <td>1.288903e+06</td>\n",
       "      <td>1326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1513.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20378.000000</td>\n",
       "      <td>4108.000000</td>\n",
       "      <td>14027.000000</td>\n",
       "      <td>1.402702e+06</td>\n",
       "      <td>14027.000000</td>\n",
       "      <td>1.402702e+06</td>\n",
       "      <td>1739.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20452.000000</td>\n",
       "      <td>6860.000000</td>\n",
       "      <td>16869.000000</td>\n",
       "      <td>1.686901e+06</td>\n",
       "      <td>16869.000000</td>\n",
       "      <td>1.686901e+06</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5095.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DAY_OF_MONTH    DAY_OF_WEEK  OP_CARRIER_AIRLINE_ID  OP_CARRIER_FL_NUM  \\\n",
       "count  607346.000000  607346.000000          607346.000000      607346.000000   \n",
       "mean       16.014354       3.955735           19994.137492        2622.365261   \n",
       "std         8.990719       1.910205             375.754232        1822.545302   \n",
       "min         1.000000       1.000000           19393.000000           1.000000   \n",
       "25%         8.000000       2.000000           19790.000000        1070.000000   \n",
       "50%        16.000000       4.000000           19977.000000        2177.000000   \n",
       "75%        24.000000       5.000000           20378.000000        4108.000000   \n",
       "max        31.000000       7.000000           20452.000000        6860.000000   \n",
       "\n",
       "       ORIGIN_AIRPORT_ID  ORIGIN_AIRPORT_SEQ_ID  DEST_AIRPORT_ID  \\\n",
       "count      607346.000000           6.073460e+05    607346.000000   \n",
       "mean        12657.389167           1.265743e+06     12657.196320   \n",
       "std          1524.407203           1.524405e+05      1524.279269   \n",
       "min         10135.000000           1.013506e+06     10135.000000   \n",
       "25%         11292.000000           1.129202e+06     11292.000000   \n",
       "50%         12889.000000           1.288903e+06     12889.000000   \n",
       "75%         14027.000000           1.402702e+06     14027.000000   \n",
       "max         16869.000000           1.686901e+06     16869.000000   \n",
       "\n",
       "       DEST_AIRPORT_SEQ_ID       DEP_TIME      DEP_DEL15       ARR_TIME  \\\n",
       "count         6.073460e+05  600682.000000  600647.000000  600271.000000   \n",
       "mean          1.265724e+06    1331.512559       0.136326    1477.968924   \n",
       "std           1.524277e+05     497.095168       0.343135     525.543294   \n",
       "min           1.013506e+06       1.000000       0.000000       1.000000   \n",
       "25%           1.129202e+06     919.000000       0.000000    1058.000000   \n",
       "50%           1.288903e+06    1326.000000       0.000000    1513.000000   \n",
       "75%           1.402702e+06    1739.000000       0.000000    1918.000000   \n",
       "max           1.686901e+06    2400.000000       1.000000    2400.000000   \n",
       "\n",
       "           ARR_DEL15      CANCELLED       DIVERTED       DISTANCE  Unnamed: 21  \n",
       "count  599268.000000  607346.000000  607346.000000  607346.000000          0.0  \n",
       "mean        0.137309       0.011407       0.001893     798.022341          NaN  \n",
       "std         0.344174       0.106193       0.043473     587.282639          NaN  \n",
       "min         0.000000       0.000000       0.000000      31.000000          NaN  \n",
       "25%         0.000000       0.000000       0.000000     369.000000          NaN  \n",
       "50%         0.000000       0.000000       0.000000     641.000000          NaN  \n",
       "75%         0.000000       0.000000       0.000000    1037.000000          NaN  \n",
       "max         1.000000       1.000000       1.000000    5095.000000          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label \"day of month\" dan \"Flight Number\""
   ]
  },
  {
   "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>DAY_OF_MONTH</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607341</th>\n",
       "      <td>31</td>\n",
       "      <td>4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607342</th>\n",
       "      <td>31</td>\n",
       "      <td>4813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607343</th>\n",
       "      <td>31</td>\n",
       "      <td>4814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607344</th>\n",
       "      <td>31</td>\n",
       "      <td>4815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607345</th>\n",
       "      <td>31</td>\n",
       "      <td>4815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607346 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DAY_OF_MONTH  OP_CARRIER_FL_NUM\n",
       "0                  1               4397\n",
       "1                  1               4401\n",
       "2                  1               4404\n",
       "3                  1               4405\n",
       "4                  1               4407\n",
       "...              ...                ...\n",
       "607341            31               4812\n",
       "607342            31               4813\n",
       "607343            31               4814\n",
       "607344            31               4815\n",
       "607345            31               4815\n",
       "\n",
       "[607346 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:, ['DAY_OF_MONTH', 'OP_CARRIER_FL_NUM']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1\n",
       "1          1\n",
       "2          1\n",
       "3          1\n",
       "4          1\n",
       "          ..\n",
       "607341    31\n",
       "607342    31\n",
       "607343    31\n",
       "607344    31\n",
       "607345    31\n",
       "Name: DAY_OF_MONTH, Length: 607346, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DAY_OF_MONTH']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nama-nama kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAY_OF_MONTH', 'DAY_OF_WEEK', 'OP_UNIQUE_CARRIER',\n",
       "       'OP_CARRIER_AIRLINE_ID', 'OP_CARRIER', 'TAIL_NUM', 'OP_CARRIER_FL_NUM',\n",
       "       'ORIGIN_AIRPORT_ID', 'ORIGIN_AIRPORT_SEQ_ID', 'ORIGIN',\n",
       "       'DEST_AIRPORT_ID', 'DEST_AIRPORT_SEQ_ID', 'DEST', 'DEP_TIME',\n",
       "       'DEP_DEL15', 'DEP_TIME_BLK', 'ARR_TIME', 'ARR_DEL15', 'CANCELLED',\n",
       "       'DIVERTED', 'DISTANCE', 'Unnamed: 21'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List nama label\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>DAY_OF_MONTH</th>\n",
       "      <th>OP_UNIQUE_CARRIER</th>\n",
       "      <th>OP_CARRIER_AIRLINE_ID</th>\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>ORIGIN_AIRPORT_SEQ_ID</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>DEST_AIRPORT_ID</th>\n",
       "      <th>DEST_AIRPORT_SEQ_ID</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DEP_TIME_BLK</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "      <td>82389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "      <td>76644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "      <td>96799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "      <td>103676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "      <td>103812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "      <td>65520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "      <td>78506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DAY_OF_MONTH  OP_UNIQUE_CARRIER  OP_CARRIER_AIRLINE_ID  \\\n",
       "DAY_OF_WEEK                                                           \n",
       "1                   82389              82389                  82389   \n",
       "2                   76644              76644                  76644   \n",
       "3                   96799              96799                  96799   \n",
       "4                  103676             103676                 103676   \n",
       "5                  103812             103812                 103812   \n",
       "6                   65520              65520                  65520   \n",
       "7                   78506              78506                  78506   \n",
       "\n",
       "             OP_CARRIER  OP_CARRIER_FL_NUM  ORIGIN_AIRPORT_ID  \\\n",
       "DAY_OF_WEEK                                                     \n",
       "1                 82389              82389              82389   \n",
       "2                 76644              76644              76644   \n",
       "3                 96799              96799              96799   \n",
       "4                103676             103676             103676   \n",
       "5                103812             103812             103812   \n",
       "6                 65520              65520              65520   \n",
       "7                 78506              78506              78506   \n",
       "\n",
       "             ORIGIN_AIRPORT_SEQ_ID  ORIGIN  DEST_AIRPORT_ID  \\\n",
       "DAY_OF_WEEK                                                   \n",
       "1                            82389   82389            82389   \n",
       "2                            76644   76644            76644   \n",
       "3                            96799   96799            96799   \n",
       "4                           103676  103676           103676   \n",
       "5                           103812  103812           103812   \n",
       "6                            65520   65520            65520   \n",
       "7                            78506   78506            78506   \n",
       "\n",
       "             DEST_AIRPORT_SEQ_ID    DEST  DEP_TIME_BLK  CANCELLED  DIVERTED  \\\n",
       "DAY_OF_WEEK                                                                   \n",
       "1                          82389   82389         82389      82389     82389   \n",
       "2                          76644   76644         76644      76644     76644   \n",
       "3                          96799   96799         96799      96799     96799   \n",
       "4                         103676  103676        103676     103676    103676   \n",
       "5                         103812  103812        103812     103812    103812   \n",
       "6                          65520   65520         65520      65520     65520   \n",
       "7                          78506   78506         78506      78506     78506   \n",
       "\n",
       "             DISTANCE  \n",
       "DAY_OF_WEEK            \n",
       "1               82389  \n",
       "2               76644  \n",
       "3               96799  \n",
       "4              103676  \n",
       "5              103812  \n",
       "6               65520  \n",
       "7               78506  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['DAY_OF_WEEK']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "busyDays = data['DAY_OF_WEEK'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggunakan matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.bar(busyDays.index, busyDays)\n",
    "# plt.title(\"Busy days frequency\")\n",
    "# plt.xlabel(\"No. of days\")\n",
    "# plt.ylabel(\"Frequency\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggunakan seaborn (lebih berwarna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(busyDays.index, busyDays,palette=\"rocket\")\n",
    "plt.title(\"Busy days frequency\")\n",
    "plt.xlabel(\"No. of days\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping CANCELED (YES, NO) dan Carrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>DAY_OF_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>OP_UNIQUE_CARRIER</th>\n",
       "      <th>OP_CARRIER_AIRLINE_ID</th>\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>OP_CARRIER_FL_NUM</th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>ORIGIN_AIRPORT_SEQ_ID</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>DEST_AIRPORT_ID</th>\n",
       "      <th>DEST_AIRPORT_SEQ_ID</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DEP_TIME_BLK</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>4397</td>\n",
       "      <td>13930</td>\n",
       "      <td>1393007</td>\n",
       "      <td>ORD</td>\n",
       "      <td>11977</td>\n",
       "      <td>1197705</td>\n",
       "      <td>GRB</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>4401</td>\n",
       "      <td>15370</td>\n",
       "      <td>1537002</td>\n",
       "      <td>TUL</td>\n",
       "      <td>13930</td>\n",
       "      <td>1393007</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>4404</td>\n",
       "      <td>11618</td>\n",
       "      <td>1161802</td>\n",
       "      <td>EWR</td>\n",
       "      <td>15412</td>\n",
       "      <td>1541205</td>\n",
       "      <td>TYS</td>\n",
       "      <td>1800-1859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>4405</td>\n",
       "      <td>10781</td>\n",
       "      <td>1078105</td>\n",
       "      <td>BTR</td>\n",
       "      <td>12266</td>\n",
       "      <td>1226603</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1800-1859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>EV</td>\n",
       "      <td>4407</td>\n",
       "      <td>14524</td>\n",
       "      <td>1452401</td>\n",
       "      <td>RIC</td>\n",
       "      <td>12266</td>\n",
       "      <td>1226603</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1157.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213587</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>DL</td>\n",
       "      <td>812</td>\n",
       "      <td>10397</td>\n",
       "      <td>1039707</td>\n",
       "      <td>ATL</td>\n",
       "      <td>13930</td>\n",
       "      <td>1393007</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1000-1059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213730</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>DL</td>\n",
       "      <td>1012</td>\n",
       "      <td>11503</td>\n",
       "      <td>1150305</td>\n",
       "      <td>EGE</td>\n",
       "      <td>10397</td>\n",
       "      <td>1039707</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1300-1359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214192</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>DL</td>\n",
       "      <td>1519</td>\n",
       "      <td>13930</td>\n",
       "      <td>1393007</td>\n",
       "      <td>ORD</td>\n",
       "      <td>13487</td>\n",
       "      <td>1348702</td>\n",
       "      <td>MSP</td>\n",
       "      <td>1200-1259</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228934</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>HA</td>\n",
       "      <td>19690</td>\n",
       "      <td>HA</td>\n",
       "      <td>53</td>\n",
       "      <td>14771</td>\n",
       "      <td>1477104</td>\n",
       "      <td>SFO</td>\n",
       "      <td>12173</td>\n",
       "      <td>1217305</td>\n",
       "      <td>HNL</td>\n",
       "      <td>0700-0759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265121</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>HA</td>\n",
       "      <td>19690</td>\n",
       "      <td>HA</td>\n",
       "      <td>374</td>\n",
       "      <td>12982</td>\n",
       "      <td>1298202</td>\n",
       "      <td>LIH</td>\n",
       "      <td>12173</td>\n",
       "      <td>1217305</td>\n",
       "      <td>HNL</td>\n",
       "      <td>1700-1759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DAY_OF_MONTH  DAY_OF_WEEK OP_UNIQUE_CARRIER  OP_CARRIER_AIRLINE_ID  \\\n",
       "0                  1            3                EV                  20366   \n",
       "1                  1            3                EV                  20366   \n",
       "2                  1            3                EV                  20366   \n",
       "3                  1            3                EV                  20366   \n",
       "4                  1            3                EV                  20366   \n",
       "...              ...          ...               ...                    ...   \n",
       "213587            11            6                DL                  19790   \n",
       "213730            11            6                DL                  19790   \n",
       "214192            11            6                DL                  19790   \n",
       "228934            12            7                HA                  19690   \n",
       "265121            14            2                HA                  19690   \n",
       "\n",
       "       OP_CARRIER  OP_CARRIER_FL_NUM  ORIGIN_AIRPORT_ID  \\\n",
       "0              EV               4397              13930   \n",
       "1              EV               4401              15370   \n",
       "2              EV               4404              11618   \n",
       "3              EV               4405              10781   \n",
       "4              EV               4407              14524   \n",
       "...           ...                ...                ...   \n",
       "213587         DL                812              10397   \n",
       "213730         DL               1012              11503   \n",
       "214192         DL               1519              13930   \n",
       "228934         HA                 53              14771   \n",
       "265121         HA                374              12982   \n",
       "\n",
       "        ORIGIN_AIRPORT_SEQ_ID ORIGIN  DEST_AIRPORT_ID  DEST_AIRPORT_SEQ_ID  \\\n",
       "0                     1393007    ORD            11977              1197705   \n",
       "1                     1537002    TUL            13930              1393007   \n",
       "2                     1161802    EWR            15412              1541205   \n",
       "3                     1078105    BTR            12266              1226603   \n",
       "4                     1452401    RIC            12266              1226603   \n",
       "...                       ...    ...              ...                  ...   \n",
       "213587                1039707    ATL            13930              1393007   \n",
       "213730                1150305    EGE            10397              1039707   \n",
       "214192                1393007    ORD            13487              1348702   \n",
       "228934                1477104    SFO            12173              1217305   \n",
       "265121                1298202    LIH            12173              1217305   \n",
       "\n",
       "       DEST DEP_TIME_BLK  CANCELLED  DIVERTED  DISTANCE  \n",
       "0       GRB    1000-1059        0.0       0.0     174.0  \n",
       "1       ORD    1000-1059        0.0       0.0     585.0  \n",
       "2       TYS    1800-1859        0.0       0.0     631.0  \n",
       "3       IAH    1800-1859        0.0       0.0     253.0  \n",
       "4       IAH    1000-1059        0.0       0.0    1157.0  \n",
       "...     ...          ...        ...       ...       ...  \n",
       "213587  ORD    1000-1059        1.0       0.0     606.0  \n",
       "213730  ATL    1300-1359        1.0       0.0    1312.0  \n",
       "214192  MSP    1200-1259        1.0       0.0     334.0  \n",
       "228934  HNL    0700-0759        1.0       0.0    2398.0  \n",
       "265121  HNL    1700-1759        1.0       0.0     102.0  \n",
       "\n",
       "[170 rows x 16 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['CANCELLED','OP_UNIQUE_CARRIER']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WN    18.073717\n",
       "DL    13.183095\n",
       "AA    12.558904\n",
       "OO    11.716550\n",
       "UA     7.969263\n",
       "YX     4.795125\n",
       "MQ     4.313851\n",
       "B6     4.068356\n",
       "OH     4.002496\n",
       "9E     3.798164\n",
       "AS     3.475778\n",
       "YV     2.936712\n",
       "NK     2.877602\n",
       "F9     2.052701\n",
       "EV     1.739371\n",
       "G4     1.279995\n",
       "HA     1.158318\n",
       "Name: OP_UNIQUE_CARRIER, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalFlightsPerCarrier = data['OP_UNIQUE_CARRIER'].value_counts()\n",
    "totalFlightsPerCarrier.apply(lambda x: (x/len(data))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax.pie(totalFlightsPerCarrier, \n",
    "       autopct=lambda x: \"{:.2f}%\".format(x), textprops=dict(color=\"w\"),\n",
    "      pctdistance=0.9)\n",
    "ax.legend(totalFlightsPerCarrier.index, title=\"Airlines\", loc=\"lower left\",bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "ax.set_title(\"Flight Carriers\", color=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengubah python array menjadi numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1 = [1, 2, 3, 4, 5, 6]\n",
    "narray1 = np.array(array1)\n",
    "narray1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan dimensi/shape/axes dari numpy array\n",
    "narray1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "narray2 = np.arange(15).reshape(3, 5)\n",
    "narray2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape/axes: (3, 5)\n",
      "Dimension of array: 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape/axes: {}\\nDimension of array: {}\".format(narray2.shape, narray2.ndim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.random((4, 2))\n",
    "B = np.random.random((4,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5000262 , 0.35306749],\n",
       "       [0.95267548, 0.23249119],\n",
       "       [0.50669833, 0.73748558],\n",
       "       [0.82063668, 0.76445778]])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39704342, 0.35966615],\n",
       "       [0.67126213, 0.60642839],\n",
       "       [0.42873526, 0.88479837],\n",
       "       [0.91391103, 0.78685374]])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89706963, 0.71273365],\n",
       "       [1.62393761, 0.83891958],\n",
       "       [0.93543359, 1.62228396],\n",
       "       [1.73454771, 1.55131152]])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89706963, 0.71273365],\n",
       "       [1.62393761, 0.83891958],\n",
       "       [0.93543359, 1.62228396],\n",
       "       [1.73454771, 1.55131152]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "narray2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "narray2[:1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n",
      "[5 6 7 8 9]\n",
      "[10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "for row in narray2:\n",
    "    print(row)"
   ]
  },
  {
   "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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}