barcharts_heatmaps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "parallel-alpha",
   "metadata": {},
   "source": [
    "# Barcharts and Heatmaps\n",
    "---\n",
    "\n",
    "In this notebook, you'll learn about bar charts and heatmaps. Barcharts are great for showing comparisons visually. Heatmaps are useful when it comes to visualising the distribution of a data involving categorical variables.\n",
    "\n",
    "First of all, we need to import the necessary libraries and read the data prepared for visualisation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "yellow-steal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprising-plumbing",
   "metadata": {},
   "source": [
    "[Dataset](https://www.kaggle.com/yuanyuwendymu/airline-delay-and-cancellation-data-2009-2018) used comprises flight info of U.S. domestic flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "local-syria",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>2015-01-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>195</td>\n",
       "      <td>MCO</td>\n",
       "      <td>FLL</td>\n",
       "      <td>2147</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>177.0</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>2015-01-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>197</td>\n",
       "      <td>LGA</td>\n",
       "      <td>FLL</td>\n",
       "      <td>1050</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>194.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1076.0</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>2015-01-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>198</td>\n",
       "      <td>FLL</td>\n",
       "      <td>MCO</td>\n",
       "      <td>700</td>\n",
       "      <td>712.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>199</td>\n",
       "      <td>IAH</td>\n",
       "      <td>LAS</td>\n",
       "      <td>2240</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>196.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1222.0</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>2015-01-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>200</td>\n",
       "      <td>IAH</td>\n",
       "      <td>ORD</td>\n",
       "      <td>623</td>\n",
       "      <td>620.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>...</td>\n",
       "      <td>152.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>925.0</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  2015-01-01         NK                195    MCO  FLL          2147   \n",
       "1  2015-01-01         NK                197    LGA  FLL          1050   \n",
       "2  2015-01-01         NK                198    FLL  MCO           700   \n",
       "3  2015-01-01         NK                199    IAH  LAS          2240   \n",
       "4  2015-01-01         NK                200    IAH  ORD           623   \n",
       "\n",
       "   DEP_TIME  DEP_DELAY  TAXI_OUT  WHEELS_OFF  ...  CRS_ELAPSED_TIME  \\\n",
       "0    2143.0       -4.0      15.0      2158.0  ...              63.0   \n",
       "1    1104.0       14.0      20.0      1124.0  ...             194.0   \n",
       "2     712.0       12.0      19.0       731.0  ...              57.0   \n",
       "3    2251.0       11.0       8.0      2259.0  ...             196.0   \n",
       "4     620.0       -3.0      15.0       635.0  ...             152.0   \n",
       "\n",
       "   ACTUAL_ELAPSED_TIME  AIR_TIME  DISTANCE  CARRIER_DELAY  WEATHER_DELAY  \\\n",
       "0                 62.0      40.0     177.0            NaN            NaN   \n",
       "1                179.0     150.0    1076.0            NaN            NaN   \n",
       "2                 61.0      32.0     177.0            0.0            0.0   \n",
       "3                176.0     164.0    1222.0            NaN            NaN   \n",
       "4                140.0     115.0     925.0            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      16.0             0.0                  0.0          NaN  \n",
       "3       NaN             NaN                  NaN          NaN  \n",
       "4       NaN             NaN                  NaN          NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_data = pd.read_csv('../../datasets/flight-delays/2015.csv')\n",
    "org_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naked-hydrogen",
   "metadata": {},
   "source": [
    "We first do data transformations so that we can perform visualisations on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "automatic-duplicate",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>AA</th>\n",
       "      <th>AS</th>\n",
       "      <th>B6</th>\n",
       "      <th>DL</th>\n",
       "      <th>EV</th>\n",
       "      <th>F9</th>\n",
       "      <th>HA</th>\n",
       "      <th>MQ</th>\n",
       "      <th>NK</th>\n",
       "      <th>OO</th>\n",
       "      <th>UA</th>\n",
       "      <th>US</th>\n",
       "      <th>VX</th>\n",
       "      <th>WN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.955843</td>\n",
       "      <td>-0.320888</td>\n",
       "      <td>7.347281</td>\n",
       "      <td>-2.043847</td>\n",
       "      <td>8.537497</td>\n",
       "      <td>18.357238</td>\n",
       "      <td>3.512640</td>\n",
       "      <td>18.164974</td>\n",
       "      <td>11.398054</td>\n",
       "      <td>10.889894</td>\n",
       "      <td>6.352729</td>\n",
       "      <td>3.107457</td>\n",
       "      <td>1.420702</td>\n",
       "      <td>3.389466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.530204</td>\n",
       "      <td>-0.782923</td>\n",
       "      <td>18.657673</td>\n",
       "      <td>5.614745</td>\n",
       "      <td>10.417236</td>\n",
       "      <td>27.424179</td>\n",
       "      <td>6.029967</td>\n",
       "      <td>21.301627</td>\n",
       "      <td>16.474466</td>\n",
       "      <td>9.588895</td>\n",
       "      <td>7.260662</td>\n",
       "      <td>7.114455</td>\n",
       "      <td>7.784410</td>\n",
       "      <td>3.501363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.693587</td>\n",
       "      <td>-0.544731</td>\n",
       "      <td>10.741317</td>\n",
       "      <td>2.077965</td>\n",
       "      <td>6.730101</td>\n",
       "      <td>20.074855</td>\n",
       "      <td>3.468383</td>\n",
       "      <td>11.018418</td>\n",
       "      <td>10.039118</td>\n",
       "      <td>3.181693</td>\n",
       "      <td>4.892212</td>\n",
       "      <td>3.330787</td>\n",
       "      <td>5.348207</td>\n",
       "      <td>3.263341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.931778</td>\n",
       "      <td>-3.009003</td>\n",
       "      <td>2.780105</td>\n",
       "      <td>0.083343</td>\n",
       "      <td>4.821253</td>\n",
       "      <td>12.640440</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>5.131228</td>\n",
       "      <td>8.766224</td>\n",
       "      <td>3.223796</td>\n",
       "      <td>4.376092</td>\n",
       "      <td>2.660290</td>\n",
       "      <td>0.995507</td>\n",
       "      <td>2.996399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.173878</td>\n",
       "      <td>-1.716398</td>\n",
       "      <td>-0.709019</td>\n",
       "      <td>0.149333</td>\n",
       "      <td>7.724290</td>\n",
       "      <td>13.007554</td>\n",
       "      <td>0.826426</td>\n",
       "      <td>5.466790</td>\n",
       "      <td>22.397347</td>\n",
       "      <td>4.141162</td>\n",
       "      <td>6.827695</td>\n",
       "      <td>0.681605</td>\n",
       "      <td>7.102021</td>\n",
       "      <td>5.680777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OP_CARRIER        AA        AS         B6        DL         EV         F9  \\\n",
       "month                                                                       \n",
       "1           6.955843 -0.320888   7.347281 -2.043847   8.537497  18.357238   \n",
       "2           7.530204 -0.782923  18.657673  5.614745  10.417236  27.424179   \n",
       "3           6.693587 -0.544731  10.741317  2.077965   6.730101  20.074855   \n",
       "4           4.931778 -3.009003   2.780105  0.083343   4.821253  12.640440   \n",
       "5           5.173878 -1.716398  -0.709019  0.149333   7.724290  13.007554   \n",
       "\n",
       "OP_CARRIER        HA         MQ         NK         OO        UA        US  \\\n",
       "month                                                                       \n",
       "1           3.512640  18.164974  11.398054  10.889894  6.352729  3.107457   \n",
       "2           6.029967  21.301627  16.474466   9.588895  7.260662  7.114455   \n",
       "3           3.468383  11.018418  10.039118   3.181693  4.892212  3.330787   \n",
       "4           0.011022   5.131228   8.766224   3.223796  4.376092  2.660290   \n",
       "5           0.826426   5.466790  22.397347   4.141162  6.827695  0.681605   \n",
       "\n",
       "OP_CARRIER        VX        WN  \n",
       "month                           \n",
       "1           1.420702  3.389466  \n",
       "2           7.784410  3.501363  \n",
       "3           5.348207  3.263341  \n",
       "4           0.995507  2.996399  \n",
       "5           7.102021  5.680777  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we retain only the columns we need\n",
    "flight_data = org_data.loc[:,['FL_DATE', 'OP_CARRIER', 'ARR_DELAY']]\n",
    "\n",
    "# drop rows with missing ARR_DELAY values\n",
    "flight_data.dropna(inplace=True)\n",
    "\n",
    "# parse FL_DATE column to string, then to datetime format and retain only the month\n",
    "flight_data['month'] = pd.to_datetime(flight_data['FL_DATE'], format='%Y-%m-%d').dt.month\n",
    "\n",
    "# we take the mean of arrival delay time, grouped by month and carrier; reset index to drop multiindex\n",
    "flight_data = flight_data.groupby(['month', 'OP_CARRIER']).mean('ARR_DELAY').reset_index()\n",
    "\n",
    "# converting from long to wide format\n",
    "flight_data = flight_data.pivot(index='month', columns='OP_CARRIER', values='ARR_DELAY')\n",
    "flight_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "foster-soldier",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>OP_CARRIER</th>\n",
       "      <th>AA</th>\n",
       "      <th>AS</th>\n",
       "      <th>B6</th>\n",
       "      <th>DL</th>\n",
       "      <th>EV</th>\n",
       "      <th>F9</th>\n",
       "      <th>HA</th>\n",
       "      <th>MQ</th>\n",
       "      <th>NK</th>\n",
       "      <th>OO</th>\n",
       "      <th>UA</th>\n",
       "      <th>US</th>\n",
       "      <th>VX</th>\n",
       "      <th>WN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.193907</td>\n",
       "      <td>2.503899</td>\n",
       "      <td>9.280950</td>\n",
       "      <td>0.653114</td>\n",
       "      <td>5.154422</td>\n",
       "      <td>9.175737</td>\n",
       "      <td>7.448029</td>\n",
       "      <td>1.896565</td>\n",
       "      <td>20.519018</td>\n",
       "      <td>5.606689</td>\n",
       "      <td>5.014041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.106221</td>\n",
       "      <td>5.532108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.432732</td>\n",
       "      <td>-1.813800</td>\n",
       "      <td>3.539154</td>\n",
       "      <td>-3.703377</td>\n",
       "      <td>0.851062</td>\n",
       "      <td>0.978460</td>\n",
       "      <td>3.696915</td>\n",
       "      <td>-2.167268</td>\n",
       "      <td>8.000101</td>\n",
       "      <td>1.530896</td>\n",
       "      <td>-1.794265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070998</td>\n",
       "      <td>-1.336260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.580930</td>\n",
       "      <td>-2.993617</td>\n",
       "      <td>3.676787</td>\n",
       "      <td>-5.011516</td>\n",
       "      <td>2.303760</td>\n",
       "      <td>0.082127</td>\n",
       "      <td>0.467074</td>\n",
       "      <td>-3.735054</td>\n",
       "      <td>6.810736</td>\n",
       "      <td>1.750897</td>\n",
       "      <td>-2.456542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.254278</td>\n",
       "      <td>-0.688851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.772630</td>\n",
       "      <td>-1.916516</td>\n",
       "      <td>1.418299</td>\n",
       "      <td>-3.175414</td>\n",
       "      <td>4.415930</td>\n",
       "      <td>11.164527</td>\n",
       "      <td>-2.719894</td>\n",
       "      <td>0.220061</td>\n",
       "      <td>7.543881</td>\n",
       "      <td>4.925548</td>\n",
       "      <td>0.281064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116370</td>\n",
       "      <td>0.995684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.149684</td>\n",
       "      <td>-1.846681</td>\n",
       "      <td>13.839290</td>\n",
       "      <td>2.504595</td>\n",
       "      <td>6.685176</td>\n",
       "      <td>9.346221</td>\n",
       "      <td>-1.706475</td>\n",
       "      <td>0.662486</td>\n",
       "      <td>12.733123</td>\n",
       "      <td>10.947612</td>\n",
       "      <td>7.012079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.498720</td>\n",
       "      <td>6.720893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OP_CARRIER        AA        AS         B6        DL        EV         F9  \\\n",
       "month                                                                      \n",
       "8           3.193907  2.503899   9.280950  0.653114  5.154422   9.175737   \n",
       "9          -1.432732 -1.813800   3.539154 -3.703377  0.851062   0.978460   \n",
       "10         -0.580930 -2.993617   3.676787 -5.011516  2.303760   0.082127   \n",
       "11          0.772630 -1.916516   1.418299 -3.175414  4.415930  11.164527   \n",
       "12          4.149684 -1.846681  13.839290  2.504595  6.685176   9.346221   \n",
       "\n",
       "OP_CARRIER        HA        MQ         NK         OO        UA  US         VX  \\\n",
       "month                                                                           \n",
       "8           7.448029  1.896565  20.519018   5.606689  5.014041 NaN   5.106221   \n",
       "9           3.696915 -2.167268   8.000101   1.530896 -1.794265 NaN   0.070998   \n",
       "10          0.467074 -3.735054   6.810736   1.750897 -2.456542 NaN   2.254278   \n",
       "11         -2.719894  0.220061   7.543881   4.925548  0.281064 NaN   0.116370   \n",
       "12         -1.706475  0.662486  12.733123  10.947612  7.012079 NaN  13.498720   \n",
       "\n",
       "OP_CARRIER        WN  \n",
       "month                 \n",
       "8           5.532108  \n",
       "9          -1.336260  \n",
       "10         -0.688851  \n",
       "11          0.995684  \n",
       "12          6.720893  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last five rows of the data\n",
    "flight_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cardiovascular-inclusion",
   "metadata": {},
   "source": [
    "## Bar Charts\n",
    "\n",
    "Now that the dataset is loaded, we can create a bar chart showing the average arrival delay for, say, Spirit Airlines (airline code: NK) flights, by month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "balanced-detail",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrival delay (in minutes)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Average Arrival Delay for Spirit Airlines Flights, by Month\")\n",
    "\n",
    "# Bar chart showing average arrival delay for Spirit Airlines flights by month\n",
    "sns.barplot(x=flight_data.index, y=flight_data['NK'])\n",
    "\n",
    "# Add label for vertical axis\n",
    "plt.ylabel(\"Arrival delay (in minutes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollywood-seating",
   "metadata": {},
   "source": [
    "The commands for customizing the text (title and vertical axis label) and size of the figure are familiar from the previous chapter. The code that creates the bar chart is new:\n",
    "\n",
    "```python\n",
    "# Bar chart showing average arrival delay for Spirit Airlines flights by month\n",
    "sns.barplot(x=flight_data.index, y=flight_data['NK'])\n",
    "```\n",
    "It has three main components:\n",
    "\n",
    "+ `sns.barplot` - This tells the notebook that we want to create a bar chart.       \n",
    "+ `x=flight_data.index` - This determines what to use on the horizontal axis. In this case, we have selected the index column containing the months\n",
    "+ `y=flight_data['NK']` - This sets the column in the data that will be used to determine the height of each bar. In this case, we select the 'NK' column.\n",
    "\n",
    "**Important Note:** It is not possible to use `flight_data['Month']` for indexing column in this case (which will return an error). This is because when we loaded the dataset, the \"Month\" column was used to index the rows, thus `flight_data['Month']` will return an error stating 'Month' is not found among the dataframe's attributes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "split-guinea",
   "metadata": {},
   "source": [
    "We can also flip to show horizontal bar plot, passing in `orient='h'` into `sns.barplot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "golden-custody",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Arrival delay (in minutes)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.title(\"Average Arrival Delay for Spirit Airlines Flights, by Month\")\n",
    "\n",
    "# Horizontal bar chart; note that we flipped the x and y compared to previous example\n",
    "sns.barplot(x=flight_data['NK'], y=flight_data.index, orient='h')\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"Arrival delay (in minutes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loose-measurement",
   "metadata": {},
   "source": [
    "## Heatmap\n",
    "\n",
    "We have one more plot type to learn about: heatmaps!\n",
    "\n",
    "In the code cell below, we create a heatmap to quickly visualize patterns in flight_data. Each cell is color-coded according to its corresponding value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "second-visibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 42.0, 'Airline')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(14,7))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Average Arrival Delay for Each Airline, by Month\")\n",
    "\n",
    "# Heatmap showing average arrival delay for each airline by month\n",
    "sns.heatmap(data=flight_data, annot=True)\n",
    "\n",
    "# Add label for horizontal axis\n",
    "plt.xlabel(\"Airline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-senior",
   "metadata": {},
   "source": [
    "The relevant code to create the heatmap is as follows:\n",
    "\n",
    "```python\n",
    "# Heatmap showing average arrival delay for each airline by month\n",
    "sns.heatmap(data=flight_data, annot=True)`\n",
    "```\n",
    "This code has three main components:\n",
    "\n",
    "+ `sns.heatmap` - This tells the notebook that we want to create a heatmap.\n",
    "+ `data=flight_data` - This tells the notebook to use all of the entries in flight_data to create the heatmap.\n",
    "+ `annot=True` - This ensures that the values for each cell appear on the chart. (Leaving this out removes the numbers from each of the cells!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-wisconsin",
   "metadata": {},
   "source": [
    "Next up, we'll learn about [scatterplots](https://github.com/colintwh/visualisation/blob/master/scatterplots.ipynb)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}