json_in_python_and_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Write JSON to a File\n",
    "In the first example we are going to use the Python module json to write JSON to a file. It's very simple, we just need to import json. All we need to do is to store our data in a dict object. This is the code for the tutorial [How to Read & Write JSON files using Python and Pandas](https://www.marsja.se/how-to-read-and-write-json-files-using-python-and-pandas/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Sub_ID': ['1', '2', '3', '4', '5', '6', '7', '8'], 'Name': ['Erik', 'Daniel', 'Michael', 'Sven', 'Gary', 'Carol', 'Lisa', 'Elisabeth'], 'Salary': ['723.3', '515.2', '621', '731', '844.15', '558', '642.8', '732.5'], 'StartDate': ['1/1/2011', '7/23/2013', '12/15/2011', '6/11/2013', '3/27/2011', '5/21/2012', '7/30/2013', '6/17/2014'], 'Department': ['IT', 'Manegement', 'IT', 'HR', 'Finance', 'IT', 'Manegement', 'IT'], 'Sex': ['M', 'M', 'M', 'M', 'M', 'F', 'F', 'F']}\n"
     ]
    }
   ],
   "source": [
    "data = {\"Sub_ID\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\" ],\n",
    "        \"Name\":[\"Erik\", \"Daniel\", \"Michael\", \"Sven\",\n",
    "                \"Gary\", \"Carol\",\"Lisa\", \"Elisabeth\" ],\n",
    "        \"Salary\":[\"723.3\", \"515.2\", \"621\", \"731\", \n",
    "                  \"844.15\",\"558\", \"642.8\", \"732.5\" ],\n",
    "        \"StartDate\":[ \"1/1/2011\", \"7/23/2013\", \"12/15/2011\",\n",
    "                     \"6/11/2013\", \"3/27/2011\",\"5/21/2012\", \n",
    "                     \"7/30/2013\", \"6/17/2014\"],\n",
    "        \"Department\":[ \"IT\", \"Manegement\", \"IT\", \"HR\", \n",
    "                      \"Finance\", \"IT\", \"Manegement\", \"IT\"],\n",
    "        \"Sex\":[ \"M\", \"M\", \"M\", \n",
    "              \"M\", \"M\", \"F\", \"F\", \"F\"]}\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving to a JSON file\n",
    "import json\n",
    "\n",
    "with open('data.json', 'w') as outfile:\n",
    "    json.dump(data, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Sub_ID': ['1', '2', '3', '4', '5', '6', '7', '8'], 'Name': ['Erik', 'Daniel', 'Michael', 'Sven', 'Gary', 'Carol', 'Lisa', 'Elisabeth'], 'Salary': ['723.3', '515.2', '621', '731', '844.15', '558', '642.8', '732.5'], 'StartDate': ['1/1/2011', '7/23/2013', '12/15/2011', '6/11/2013', '3/27/2011', '5/21/2012', '7/30/2013', '6/17/2014'], 'Department': ['IT', 'Manegement', 'IT', 'HR', 'Finance', 'IT', 'Manegement', 'IT'], 'Sex': ['M', 'M', 'M', 'M', 'M', 'F', 'F', 'F']}\n"
     ]
    }
   ],
   "source": [
    "# Open the file using json\n",
    "with open('data.json') as json_file:\n",
    "    data = json.load(json_file)\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Load Data from JSON using Pandas\n",
    "In the next example we are going to use Pandas read_json method to read the JSON file we wrote earlier (i.e., data.txt). It's fairly simple we start by importing pandas as pd:"
   ]
  },
  {
   "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>Sub_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>Department</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Erik</td>\n",
       "      <td>723.30</td>\n",
       "      <td>1/1/2011</td>\n",
       "      <td>IT</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>515.20</td>\n",
       "      <td>7/23/2013</td>\n",
       "      <td>Manegement</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Michael</td>\n",
       "      <td>621.00</td>\n",
       "      <td>12/15/2011</td>\n",
       "      <td>IT</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sven</td>\n",
       "      <td>731.00</td>\n",
       "      <td>6/11/2013</td>\n",
       "      <td>HR</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Gary</td>\n",
       "      <td>844.15</td>\n",
       "      <td>3/27/2011</td>\n",
       "      <td>Finance</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Carol</td>\n",
       "      <td>558.00</td>\n",
       "      <td>5/21/2012</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>642.80</td>\n",
       "      <td>7/30/2013</td>\n",
       "      <td>Manegement</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>732.50</td>\n",
       "      <td>6/17/2014</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sub_ID       Name  Salary   StartDate  Department Sex\n",
       "0       1       Erik  723.30    1/1/2011          IT   M\n",
       "1       2     Daniel  515.20   7/23/2013  Manegement   M\n",
       "2       3    Michael  621.00  12/15/2011          IT   M\n",
       "3       4       Sven  731.00   6/11/2013          HR   M\n",
       "4       5       Gary  844.15   3/27/2011     Finance   M\n",
       "5       6      Carol  558.00   5/21/2012          IT   F\n",
       "6       7       Lisa  642.80   7/30/2013  Manegement   F\n",
       "7       8  Elisabeth  732.50   6/17/2014          IT   F"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json('data.json')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation using Pandas\n",
    "Here we are going to use Pandas inplace method to modify our dataframe. We start by setting the Sub_ID column as index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>Department</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub_ID</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>Erik</td>\n",
       "      <td>723.30</td>\n",
       "      <td>1/1/2011</td>\n",
       "      <td>IT</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>515.20</td>\n",
       "      <td>7/23/2013</td>\n",
       "      <td>Manegement</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Michael</td>\n",
       "      <td>621.00</td>\n",
       "      <td>12/15/2011</td>\n",
       "      <td>IT</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sven</td>\n",
       "      <td>731.00</td>\n",
       "      <td>6/11/2013</td>\n",
       "      <td>HR</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gary</td>\n",
       "      <td>844.15</td>\n",
       "      <td>3/27/2011</td>\n",
       "      <td>Finance</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Carol</td>\n",
       "      <td>558.00</td>\n",
       "      <td>5/21/2012</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>642.80</td>\n",
       "      <td>7/30/2013</td>\n",
       "      <td>Manegement</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>732.50</td>\n",
       "      <td>6/17/2014</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Salary   StartDate  Department Sex\n",
       "Sub_ID                                               \n",
       "1            Erik  723.30    1/1/2011          IT   M\n",
       "2          Daniel  515.20   7/23/2013  Manegement   M\n",
       "3         Michael  621.00  12/15/2011          IT   M\n",
       "4            Sven  731.00   6/11/2013          HR   M\n",
       "5            Gary  844.15   3/27/2011     Finance   M\n",
       "6           Carol  558.00   5/21/2012          IT   F\n",
       "7            Lisa  642.80   7/30/2013  Manegement   F\n",
       "8       Elisabeth  732.50   6/17/2014          IT   F"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Sub_ID', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas JSON to CSV\n",
    "If we want to save the JSON file to a csv file this is also possible. We just use the to_csv method from Pandas to save the dataframe to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Load JSON from an URL\n",
    "In the next example we are going to learn how to load a JSON file from the web. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>base</th>\n",
       "      <th>date</th>\n",
       "      <th>time_last_updated</th>\n",
       "      <th>rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AED</th>\n",
       "      <td>USD</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>1564531802</td>\n",
       "      <td>3.672091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARS</th>\n",
       "      <td>USD</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>1564531802</td>\n",
       "      <td>43.822753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUD</th>\n",
       "      <td>USD</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>1564531802</td>\n",
       "      <td>1.451794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGN</th>\n",
       "      <td>USD</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>1564531802</td>\n",
       "      <td>1.753879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRL</th>\n",
       "      <td>USD</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>1564531802</td>\n",
       "      <td>3.785435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    base       date  time_last_updated      rates\n",
       "AED  USD 2019-07-31         1564531802   3.672091\n",
       "ARS  USD 2019-07-31         1564531802  43.822753\n",
       "AUD  USD 2019-07-31         1564531802   1.451794\n",
       "BGN  USD 2019-07-31         1564531802   1.753879\n",
       "BRL  USD 2019-07-31         1564531802   3.785435"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://api.exchangerate-api.com/v4/latest/USD\"\n",
    "df = pd.read_json(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load JSON from an URL Second Example\n",
    "When loading some data from JSON, using Pandas read_json seems to create a dataframe with dictionaries within each cell. One way to deal with these dictionaries, nested within dictionaries, is to work with the Python module request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airport</th>\n",
       "      <th>carrier</th>\n",
       "      <th>statistics</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'code': 'ATL', 'name': 'Atlanta, GA: Hartsfie...</td>\n",
       "      <td>{'code': 'AA', 'name': 'American Airlines Inc.'}</td>\n",
       "      <td>{'flights': {'cancelled': 5, 'on time': 561, '...</td>\n",
       "      <td>{'label': '2003/6', 'year': 2003, 'month': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'code': 'BOS', 'name': 'Boston, MA: Logan Int...</td>\n",
       "      <td>{'code': 'AA', 'name': 'American Airlines Inc.'}</td>\n",
       "      <td>{'flights': {'cancelled': 7, 'on time': 1034, ...</td>\n",
       "      <td>{'label': '2003/6', 'year': 2003, 'month': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'code': 'BWI', 'name': 'Baltimore, MD: Baltim...</td>\n",
       "      <td>{'code': 'AA', 'name': 'American Airlines Inc.'}</td>\n",
       "      <td>{'flights': {'cancelled': 1, 'on time': 490, '...</td>\n",
       "      <td>{'label': '2003/6', 'year': 2003, 'month': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'code': 'CLT', 'name': 'Charlotte, NC: Charlo...</td>\n",
       "      <td>{'code': 'AA', 'name': 'American Airlines Inc.'}</td>\n",
       "      <td>{'flights': {'cancelled': 4, 'on time': 151, '...</td>\n",
       "      <td>{'label': '2003/6', 'year': 2003, 'month': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'code': 'DCA', 'name': 'Washington, DC: Ronal...</td>\n",
       "      <td>{'code': 'AA', 'name': 'American Airlines Inc.'}</td>\n",
       "      <td>{'flights': {'cancelled': 5, 'on time': 793, '...</td>\n",
       "      <td>{'label': '2003/6', 'year': 2003, 'month': 6}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             airport  \\\n",
       "0  {'code': 'ATL', 'name': 'Atlanta, GA: Hartsfie...   \n",
       "1  {'code': 'BOS', 'name': 'Boston, MA: Logan Int...   \n",
       "2  {'code': 'BWI', 'name': 'Baltimore, MD: Baltim...   \n",
       "3  {'code': 'CLT', 'name': 'Charlotte, NC: Charlo...   \n",
       "4  {'code': 'DCA', 'name': 'Washington, DC: Ronal...   \n",
       "\n",
       "                                            carrier  \\\n",
       "0  {'code': 'AA', 'name': 'American Airlines Inc.'}   \n",
       "1  {'code': 'AA', 'name': 'American Airlines Inc.'}   \n",
       "2  {'code': 'AA', 'name': 'American Airlines Inc.'}   \n",
       "3  {'code': 'AA', 'name': 'American Airlines Inc.'}   \n",
       "4  {'code': 'AA', 'name': 'American Airlines Inc.'}   \n",
       "\n",
       "                                          statistics  \\\n",
       "0  {'flights': {'cancelled': 5, 'on time': 561, '...   \n",
       "1  {'flights': {'cancelled': 7, 'on time': 1034, ...   \n",
       "2  {'flights': {'cancelled': 1, 'on time': 490, '...   \n",
       "3  {'flights': {'cancelled': 4, 'on time': 151, '...   \n",
       "4  {'flights': {'cancelled': 5, 'on time': 793, '...   \n",
       "\n",
       "                                            time  \n",
       "0  {'label': '2003/6', 'year': 2003, 'month': 6}  \n",
       "1  {'label': '2003/6', 'year': 2003, 'month': 6}  \n",
       "2  {'label': '2003/6', 'year': 2003, 'month': 6}  \n",
       "3  {'label': '2003/6', 'year': 2003, 'month': 6}  \n",
       "4  {'label': '2003/6', 'year': 2003, 'month': 6}  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://think.cs.vt.edu/corgis/json/airlines/airlines.json\"\n",
    "df = pd.read_json(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airport.code</th>\n",
       "      <th>airport.name</th>\n",
       "      <th>carrier.code</th>\n",
       "      <th>carrier.name</th>\n",
       "      <th>statistics.# of delays.carrier</th>\n",
       "      <th>statistics.# of delays.late aircraft</th>\n",
       "      <th>statistics.# of delays.national aviation system</th>\n",
       "      <th>statistics.# of delays.security</th>\n",
       "      <th>statistics.# of delays.weather</th>\n",
       "      <th>statistics.flights.cancelled</th>\n",
       "      <th>...</th>\n",
       "      <th>statistics.flights.total</th>\n",
       "      <th>statistics.minutes delayed.carrier</th>\n",
       "      <th>statistics.minutes delayed.late aircraft</th>\n",
       "      <th>statistics.minutes delayed.national aviation system</th>\n",
       "      <th>statistics.minutes delayed.security</th>\n",
       "      <th>statistics.minutes delayed.total</th>\n",
       "      <th>statistics.minutes delayed.weather</th>\n",
       "      <th>time.label</th>\n",
       "      <th>time.month</th>\n",
       "      <th>time.year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta, GA: Hartsfield-Jackson Atlanta Intern...</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>752</td>\n",
       "      <td>1367</td>\n",
       "      <td>1269</td>\n",
       "      <td>3817</td>\n",
       "      <td>139</td>\n",
       "      <td>8314</td>\n",
       "      <td>1722</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOS</td>\n",
       "      <td>Boston, MA: Logan International</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>69</td>\n",
       "      <td>46</td>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1266</td>\n",
       "      <td>4201</td>\n",
       "      <td>3043</td>\n",
       "      <td>3067</td>\n",
       "      <td>45</td>\n",
       "      <td>12139</td>\n",
       "      <td>1783</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD: Baltimore/Washington Internatio...</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>593</td>\n",
       "      <td>1058</td>\n",
       "      <td>1600</td>\n",
       "      <td>1708</td>\n",
       "      <td>0</td>\n",
       "      <td>5698</td>\n",
       "      <td>1332</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CLT</td>\n",
       "      <td>Charlotte, NC: Charlotte Douglas International</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>968</td>\n",
       "      <td>653</td>\n",
       "      <td>558</td>\n",
       "      <td>127</td>\n",
       "      <td>3048</td>\n",
       "      <td>742</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DCA</td>\n",
       "      <td>Washington, DC: Ronald Reagan Washington National</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>947</td>\n",
       "      <td>2048</td>\n",
       "      <td>1566</td>\n",
       "      <td>2767</td>\n",
       "      <td>3</td>\n",
       "      <td>7868</td>\n",
       "      <td>1484</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  airport.code                                       airport.name  \\\n",
       "0          ATL  Atlanta, GA: Hartsfield-Jackson Atlanta Intern...   \n",
       "1          BOS                    Boston, MA: Logan International   \n",
       "2          BWI  Baltimore, MD: Baltimore/Washington Internatio...   \n",
       "3          CLT     Charlotte, NC: Charlotte Douglas International   \n",
       "4          DCA  Washington, DC: Ronald Reagan Washington National   \n",
       "\n",
       "  carrier.code            carrier.name  statistics.# of delays.carrier  \\\n",
       "0           AA  American Airlines Inc.                              34   \n",
       "1           AA  American Airlines Inc.                              69   \n",
       "2           AA  American Airlines Inc.                              18   \n",
       "3           AA  American Airlines Inc.                              17   \n",
       "4           AA  American Airlines Inc.                              43   \n",
       "\n",
       "   statistics.# of delays.late aircraft  \\\n",
       "0                                    18   \n",
       "1                                    46   \n",
       "2                                    25   \n",
       "3                                    10   \n",
       "4                                    26   \n",
       "\n",
       "   statistics.# of delays.national aviation system  \\\n",
       "0                                              105   \n",
       "1                                               84   \n",
       "2                                               38   \n",
       "3                                               10   \n",
       "4                                               53   \n",
       "\n",
       "   statistics.# of delays.security  statistics.# of delays.weather  \\\n",
       "0                                2                              28   \n",
       "1                                2                              24   \n",
       "2                                0                              20   \n",
       "3                                2                              10   \n",
       "4                                0                              25   \n",
       "\n",
       "   statistics.flights.cancelled    ...      statistics.flights.total  \\\n",
       "0                             5    ...                           752   \n",
       "1                             7    ...                          1266   \n",
       "2                             1    ...                           593   \n",
       "3                             4    ...                           203   \n",
       "4                             5    ...                           947   \n",
       "\n",
       "   statistics.minutes delayed.carrier  \\\n",
       "0                                1367   \n",
       "1                                4201   \n",
       "2                                1058   \n",
       "3                                 968   \n",
       "4                                2048   \n",
       "\n",
       "   statistics.minutes delayed.late aircraft  \\\n",
       "0                                      1269   \n",
       "1                                      3043   \n",
       "2                                      1600   \n",
       "3                                       653   \n",
       "4                                      1566   \n",
       "\n",
       "   statistics.minutes delayed.national aviation system  \\\n",
       "0                                               3817     \n",
       "1                                               3067     \n",
       "2                                               1708     \n",
       "3                                                558     \n",
       "4                                               2767     \n",
       "\n",
       "   statistics.minutes delayed.security  statistics.minutes delayed.total  \\\n",
       "0                                  139                              8314   \n",
       "1                                   45                             12139   \n",
       "2                                    0                              5698   \n",
       "3                                  127                              3048   \n",
       "4                                    3                              7868   \n",
       "\n",
       "   statistics.minutes delayed.weather  time.label  time.month  time.year  \n",
       "0                                1722      2003/6           6       2003  \n",
       "1                                1783      2003/6           6       2003  \n",
       "2                                1332      2003/6           6       2003  \n",
       "3                                 742      2003/6           6       2003  \n",
       "4                                1484      2003/6           6       2003  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "url = \"https://think.cs.vt.edu/corgis/json/airlines/airlines.json\"\n",
    "resp = requests.get(url=url)\n",
    "\n",
    "df = json_normalize(resp.json())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning up the column names\n",
    "As can be seen in the image above, the column names are quite long. This is quite impractical when we are going to create a time series plot, later, using Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airport_code</th>\n",
       "      <th>airport_name</th>\n",
       "      <th>carrier_code</th>\n",
       "      <th>carrier_name</th>\n",
       "      <th>delays_carrier</th>\n",
       "      <th>delays_late aircraft</th>\n",
       "      <th>delays_national aviation system</th>\n",
       "      <th>delays_security</th>\n",
       "      <th>delays_weather</th>\n",
       "      <th>flights_cancelled</th>\n",
       "      <th>...</th>\n",
       "      <th>flights_total</th>\n",
       "      <th>minutes delayed_carrier</th>\n",
       "      <th>minutes delayed_late aircraft</th>\n",
       "      <th>minutes delayed_national aviation system</th>\n",
       "      <th>minutes delayed_security</th>\n",
       "      <th>minutes delayed_total</th>\n",
       "      <th>minutes delayed_weather</th>\n",
       "      <th>time_label</th>\n",
       "      <th>time_month</th>\n",
       "      <th>time_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta, GA: Hartsfield-Jackson Atlanta Intern...</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>752</td>\n",
       "      <td>1367</td>\n",
       "      <td>1269</td>\n",
       "      <td>3817</td>\n",
       "      <td>139</td>\n",
       "      <td>8314</td>\n",
       "      <td>1722</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOS</td>\n",
       "      <td>Boston, MA: Logan International</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>69</td>\n",
       "      <td>46</td>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1266</td>\n",
       "      <td>4201</td>\n",
       "      <td>3043</td>\n",
       "      <td>3067</td>\n",
       "      <td>45</td>\n",
       "      <td>12139</td>\n",
       "      <td>1783</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD: Baltimore/Washington Internatio...</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>593</td>\n",
       "      <td>1058</td>\n",
       "      <td>1600</td>\n",
       "      <td>1708</td>\n",
       "      <td>0</td>\n",
       "      <td>5698</td>\n",
       "      <td>1332</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CLT</td>\n",
       "      <td>Charlotte, NC: Charlotte Douglas International</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>968</td>\n",
       "      <td>653</td>\n",
       "      <td>558</td>\n",
       "      <td>127</td>\n",
       "      <td>3048</td>\n",
       "      <td>742</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DCA</td>\n",
       "      <td>Washington, DC: Ronald Reagan Washington National</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>947</td>\n",
       "      <td>2048</td>\n",
       "      <td>1566</td>\n",
       "      <td>2767</td>\n",
       "      <td>3</td>\n",
       "      <td>7868</td>\n",
       "      <td>1484</td>\n",
       "      <td>2003/6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  airport_code                                       airport_name  \\\n",
       "0          ATL  Atlanta, GA: Hartsfield-Jackson Atlanta Intern...   \n",
       "1          BOS                    Boston, MA: Logan International   \n",
       "2          BWI  Baltimore, MD: Baltimore/Washington Internatio...   \n",
       "3          CLT     Charlotte, NC: Charlotte Douglas International   \n",
       "4          DCA  Washington, DC: Ronald Reagan Washington National   \n",
       "\n",
       "  carrier_code            carrier_name   delays_carrier  \\\n",
       "0           AA  American Airlines Inc.               34   \n",
       "1           AA  American Airlines Inc.               69   \n",
       "2           AA  American Airlines Inc.               18   \n",
       "3           AA  American Airlines Inc.               17   \n",
       "4           AA  American Airlines Inc.               43   \n",
       "\n",
       "    delays_late aircraft   delays_national aviation system   delays_security  \\\n",
       "0                     18                               105                 2   \n",
       "1                     46                                84                 2   \n",
       "2                     25                                38                 0   \n",
       "3                     10                                10                 2   \n",
       "4                     26                                53                 0   \n",
       "\n",
       "    delays_weather  flights_cancelled    ...      flights_total  \\\n",
       "0               28                  5    ...                752   \n",
       "1               24                  7    ...               1266   \n",
       "2               20                  1    ...                593   \n",
       "3               10                  4    ...                203   \n",
       "4               25                  5    ...                947   \n",
       "\n",
       "   minutes delayed_carrier  minutes delayed_late aircraft  \\\n",
       "0                     1367                           1269   \n",
       "1                     4201                           3043   \n",
       "2                     1058                           1600   \n",
       "3                      968                            653   \n",
       "4                     2048                           1566   \n",
       "\n",
       "   minutes delayed_national aviation system  minutes delayed_security  \\\n",
       "0                                      3817                       139   \n",
       "1                                      3067                        45   \n",
       "2                                      1708                         0   \n",
       "3                                       558                       127   \n",
       "4                                      2767                         3   \n",
       "\n",
       "   minutes delayed_total  minutes delayed_weather  time_label  time_month  \\\n",
       "0                   8314                     1722      2003/6           6   \n",
       "1                  12139                     1783      2003/6           6   \n",
       "2                   5698                     1332      2003/6           6   \n",
       "3                   3048                      742      2003/6           6   \n",
       "4                   7868                     1484      2003/6           6   \n",
       "\n",
       "   time_year  \n",
       "0       2003  \n",
       "1       2003  \n",
       "2       2003  \n",
       "3       2003  \n",
       "4       2003  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "df.rename(columns=lambda x: re.sub(\"statistics.# of\",\"\",x), \n",
    "          inplace=True)\n",
    "df.rename(columns=lambda x: re.sub(\"statistics.\",\"\",x), \n",
    "          inplace=True)\n",
    "\n",
    "df.columns = df.columns.str.replace(\"[.]\", \"_\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series Plot\n",
    "Plotting the data obtained from JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f273fde9b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "g = sns.lineplot(x=\"time_year\", y=\"flights_cancelled\", ci=False,\n",
    "             hue=\"carrier_code\", data=df)\n",
    "\n",
    "g.set_ylabel(\"Flights Cancelled\",fontsize=20)\n",
    "g.set_xlabel(\"Year\",fontsize=20)\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}