practice_json.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andew</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bhuvan</td>\n",
       "      <td>18</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Drake</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eisha</td>\n",
       "      <td>13</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  age grade\n",
       "0    Andew   12     A\n",
       "1   Bhuvan   18     B\n",
       "2  Clinton   11     A\n",
       "3    Drake   12     C\n",
       "4    Eisha   13     B"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read a simple json file into a dataframe\n",
    "data = pd.read_json('datasets/simple.json')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andew</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bhuvan</td>\n",
       "      <td>18</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Drake</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eisha</td>\n",
       "      <td>13</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  age grade\n",
       "0    Andew   12     A\n",
       "1   Bhuvan   18     B\n",
       "2  Clinton   11     A\n",
       "3    Drake   12     C\n",
       "4    Eisha   13     B"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if we have json data in multiple rows then we cant read it directly\n",
    "data = pd.read_json('datasets/simple_records.json', lines = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'student_roll_no': 101, 'details': {'name': 'Andew', 'age': 12, 'grade': 'A'}}, {'student_roll_no': 102, 'details': {'name': 'Bhuvan', 'age': 18, 'grade': 'B'}}, {'student_roll_no': 103, 'details': {'name': 'Clinton', 'age': 11, 'grade': 'A'}}, {'student_roll_no': 104, 'details': {'name': 'Drake', 'age': 12, 'grade': 'C'}}, {'student_roll_no': 105, 'details': {'name': 'Eisha', 'age': 13, 'grade': 'B'}}, {'student_roll_no': 106, 'details': {'name': 'Farhan', 'age': 22, 'grade': 'C'}}, {'student_roll_no': 107, 'details': {'name': 'Garima', 'age': 11, 'grade': 'A'}}, {'student_roll_no': 108, 'details': {'name': 'Himanshu', 'age': 19, 'grade': 'A'}}, {'student_roll_no': 109, 'details': {'name': 'Ishaan', 'age': 10, 'grade': 'D'}}, {'student_roll_no': 110, 'details': {'name': 'Jason', 'age': 9, 'grade': 'B'}}]\n"
     ]
    }
   ],
   "source": [
    "#if the data that we have is in the format of the nested json then we cant directly read it and then we use json module\n",
    "import json\n",
    "with open('datasets/nested.json') as f:\n",
    "    my_data = json.load(f)\n",
    "print(my_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'details': {'age': 12, 'grade': 'A', 'name': 'Andew'},\n",
      "  'student_roll_no': 101},\n",
      " {'details': {'age': 18, 'grade': 'B', 'name': 'Bhuvan'},\n",
      "  'student_roll_no': 102},\n",
      " {'details': {'age': 11, 'grade': 'A', 'name': 'Clinton'},\n",
      "  'student_roll_no': 103},\n",
      " {'details': {'age': 12, 'grade': 'C', 'name': 'Drake'},\n",
      "  'student_roll_no': 104},\n",
      " {'details': {'age': 13, 'grade': 'B', 'name': 'Eisha'},\n",
      "  'student_roll_no': 105},\n",
      " {'details': {'age': 22, 'grade': 'C', 'name': 'Farhan'},\n",
      "  'student_roll_no': 106},\n",
      " {'details': {'age': 11, 'grade': 'A', 'name': 'Garima'},\n",
      "  'student_roll_no': 107},\n",
      " {'details': {'age': 19, 'grade': 'A', 'name': 'Himanshu'},\n",
      "  'student_roll_no': 108},\n",
      " {'details': {'age': 10, 'grade': 'D', 'name': 'Ishaan'},\n",
      "  'student_roll_no': 109},\n",
      " {'details': {'age': 9, 'grade': 'B', 'name': 'Jason'}, 'student_roll_no': 110}]\n"
     ]
    }
   ],
   "source": [
    "#since this data is in really unstructured format so we can use pprint to view it in a structured mannner\n",
    "from pprint import pprint\n",
    "pprint(my_data)\n",
    "#spo we can see all these nested structure is a part of the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'student_roll_no': 101, 'details': {'name': 'Andew', 'age': 12, 'grade': 'A'}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data[0]\n",
    "#if i want to find out age of each we must first access details dictionary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data[0]['details']['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "18\n",
      "11\n",
      "12\n",
      "13\n",
      "22\n",
      "11\n",
      "19\n",
      "10\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "#in this way we can iterate through all of the ages\n",
    "for data in my_data:\n",
    "    print(data['details']['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'Himanshu', 'age': 19}, {'name': 'Himanshu', 'age': 19}, {'name': 'Himanshu', 'age': 19}]\n"
     ]
    }
   ],
   "source": [
    "#now lets say i want to find out the names of all those student with age greater than 15\n",
    "rel_data = list()\n",
    "filtered_var = dict()\n",
    "for data in my_data:\n",
    "    if data['details']['age'] > 15:\n",
    "        filtered_var['name'] = data['details']['name']\n",
    "        filtered_var['age']  = data['details']['age']\n",
    "        rel_data.append(filtered_var)\n",
    "\n",
    "print(rel_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('datasets/created_json.json', 'w')as f:\n",
    "    json.dump(rel_data, f, indent = 4)"
   ]
  }
 ],
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