id_card_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "substantial-maker",
   "metadata": {},
   "source": [
    "# ID Card Identification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finished-choir",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "useful-senate",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "wired-muslim",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Module for data manipulation\n",
    "import pandas as pd\n",
    "# Module for linear algebra calculation\n",
    "import numpy as np\n",
    "# Module for timing\n",
    "from datetime import datetime\n",
    "# Module for binary search\n",
    "from bisect import bisect_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "regulation-final",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_search(a, x):\n",
    "    elem = bisect_left(a, x)\n",
    "    # check the data\n",
    "    status = False\n",
    "    if elem != len(a) and a[elem] == x:\n",
    "        status = True\n",
    "    return status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "phantom-difference",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1234567891234567"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID = int('1234567891234567')\n",
    "ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "monetary-bridal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check length of ID\n",
    "def checkLength(ID):\n",
    "    length = len(str(ID))\n",
    "    # check length\n",
    "    status = False\n",
    "    if length == 16:\n",
    "        status = True\n",
    "    return (status, length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "satisfied-disclaimer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 16)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkLength(ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "crazy-board",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check DOB\n",
    "def checkDOB(ID):\n",
    "    dob = str(ID)[6:12]\n",
    "    dob_person = int(dob)\n",
    "    # Check the woman's dob\n",
    "    if dob_person > 400000:\n",
    "        dob_person = dob_person - 400000\n",
    "    # Convert into datetime\n",
    "    try:\n",
    "        dob_date = datetime.strptime(str(dob_person), '%d%m%y')\n",
    "        if dob_date > datetime.now():\n",
    "            dob_date = dob_date.replace(year = dob_date.year - 100)\n",
    "    except:\n",
    "        dob_date = None\n",
    "    # Check age\n",
    "    status = False\n",
    "    age_int = None\n",
    "    if dob_date != None:\n",
    "        age = (datetime.now() - dob_date).days / 365.2425\n",
    "        dob_date = dob_date.strftime('%d-%m-%Y')\n",
    "        if age >= 17:\n",
    "            age_int = int(age)\n",
    "            status = True\n",
    "    return (status, dob_date, age_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "orange-dover",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, None, None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkDOB(ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bearing-physics",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check gender\n",
    "def checkGender(ID):\n",
    "    gender = str(ID)[6:7]\n",
    "    # Check status\n",
    "    status = False\n",
    "    sex = None\n",
    "    if int(gender) in range(8):\n",
    "        if int(gender) in range(4):\n",
    "            sex = 'Man'\n",
    "        else:\n",
    "            sex = 'Woman'\n",
    "        status = True\n",
    "    return (status, sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "driving-louisville",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 'Woman')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkGender(ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "criminal-providence",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check computerized last number\n",
    "def checkComputerizedLastNumber(ID):\n",
    "    last_num = str(ID)[12:]\n",
    "    # Check last number\n",
    "    status = False\n",
    "    if status != '0000':\n",
    "        status = True\n",
    "    return (status, last_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "engaging-joshua",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, '4567')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkComputerizedLastNumber(ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "genetic-identification",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('../data/csv/Admin Area Code - ID.csv', sep = ',', dtype = {'code': object})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "nervous-month",
   "metadata": {
    "scrolled": true
   },
   "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>code</th>\n",
       "      <th>province</th>\n",
       "      <th>district</th>\n",
       "      <th>subdistrict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110101</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Bakongan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110102</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Kluet Utara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110103</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Kluet Selatan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110104</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Labuhan Haji</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110105</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Meukek</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code province           district    subdistrict\n",
       "0  110101     Aceh  Kab. Aceh Selatan       Bakongan\n",
       "1  110102     Aceh  Kab. Aceh Selatan    Kluet Utara\n",
       "2  110103     Aceh  Kab. Aceh Selatan  Kluet Selatan\n",
       "3  110104     Aceh  Kab. Aceh Selatan   Labuhan Haji\n",
       "4  110105     Aceh  Kab. Aceh Selatan         Meukek"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "assisted-award",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'123456'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(ID)[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "willing-impossible",
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkAdminArea(ID, data):\n",
    "    admin_code = str(ID)[:6]\n",
    "    # Check the admin area\n",
    "    status = False\n",
    "    prov, district, subdistrict = None, None, None\n",
    "    bin_status = binary_search(a = data['code'], x = admin_code)\n",
    "    if bin_status:\n",
    "        status = True\n",
    "        # Get the values\n",
    "        prov, district, subdistrict = df[df['code'] == admin_code].values.tolist()[0][1:]\n",
    "    return (status, prov, district, subdistrict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "southwest-moment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, None, None, None)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkLength(ID, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sporting-aaron",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "useful-schedule",
   "metadata": {},
   "outputs": [],
   "source": [
    "class identificationID:\n",
    "    def __init__(self, ID):\n",
    "        self.ID = ID\n",
    "    # Get value\n",
    "    def get_value(self):\n",
    "        return str(self.ID)\n",
    "    # Check length of ID\n",
    "    def checkLength(self):\n",
    "        length = len(str(self.ID))\n",
    "        # check length\n",
    "        status = False\n",
    "        if length == 16:\n",
    "            status = True\n",
    "        return (status, length)\n",
    "    # Check administrative area\n",
    "    def checkAdminArea(self, data):\n",
    "        admin_code = str(self.ID)[:6]\n",
    "        # Check the admin area\n",
    "        status = False\n",
    "        prov, district, subdistrict = None, None, None\n",
    "        bin_status = binary_search(a = data['code'], x = admin_code)\n",
    "        if bin_status:\n",
    "            status = True\n",
    "            # Get the values\n",
    "            prov, district, subdistrict = df[df['code'] == admin_code].values.tolist()[0][1:]\n",
    "        return (status, prov, district, subdistrict)\n",
    "    # Check DOB\n",
    "    def checkDOB(self):\n",
    "        dob = str(self.ID)[6:12]\n",
    "        dob_person = int(dob)\n",
    "        # Check the woman's dob\n",
    "        if dob_person > 400000:\n",
    "            dob_person = dob_person - 400000\n",
    "        # Convert into datetime\n",
    "        try:\n",
    "            dob_date = datetime.strptime(str(dob_person), '%d%m%y')\n",
    "            if dob_date > datetime.now():\n",
    "                dob_date = dob_date.replace(year = dob_date.year - 100)\n",
    "        except:\n",
    "            dob_date = None\n",
    "        # Check age\n",
    "        status = False\n",
    "        age_int = None\n",
    "        if dob_date != None:\n",
    "            age = (datetime.now() - dob_date).days / 365.2425\n",
    "            dob_date = dob_date.strftime('%d-%m-%Y')\n",
    "            if age >= 17:\n",
    "                age_int = int(age)\n",
    "                status = True\n",
    "        return (status, dob_date, age_int)\n",
    "    # Check Gender\n",
    "    def checkGender(self):\n",
    "        gender = str(self.ID)[6:7]\n",
    "        # Check status\n",
    "        status = False\n",
    "        sex = None\n",
    "        if int(gender) in range(8):\n",
    "            if int(gender) in range(4):\n",
    "                sex = 'Man'\n",
    "            else:\n",
    "                sex = 'Woman'\n",
    "            status = True\n",
    "        return (status, sex)\n",
    "    # Check computerized number\n",
    "    def checkComputerizedNumber(self):\n",
    "        last_num = str(self.ID)[12:]\n",
    "        # Check last number\n",
    "        status = False\n",
    "        if status != '0000':\n",
    "            status = True\n",
    "        return (status, last_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "photographic-acrylic",
   "metadata": {},
   "outputs": [],
   "source": [
    "NIK = identificationID('1234567891234567')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "exterior-boost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 16)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIK.checkLength()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "protecting-elevation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, None, None, None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIK.checkAdminArea(data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "amber-average",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, None, None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIK.checkDOB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "numerical-burke",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 'Woman')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIK.checkGender()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "smaller-trash",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, '4567')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIK.checkComputerizedNumber()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "planned-ethics",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "informational-flight",
   "metadata": {},
   "outputs": [],
   "source": [
    "NIKs = ['1234567891234567', '1234567891234567', '1234567891234567', '1234567891234567']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "improving-german",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('../data/csv/Admin Area Code - ID.csv', sep = ',', dtype = {'code': object})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "respected-hepatitis",
   "metadata": {
    "scrolled": true
   },
   "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>code</th>\n",
       "      <th>province</th>\n",
       "      <th>district</th>\n",
       "      <th>subdistrict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110101</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Bakongan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110102</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Kluet Utara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110103</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Kluet Selatan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110104</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Labuhan Haji</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110105</td>\n",
       "      <td>Aceh</td>\n",
       "      <td>Kab. Aceh Selatan</td>\n",
       "      <td>Meukek</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code province           district    subdistrict\n",
       "0  110101     Aceh  Kab. Aceh Selatan       Bakongan\n",
       "1  110102     Aceh  Kab. Aceh Selatan    Kluet Utara\n",
       "2  110103     Aceh  Kab. Aceh Selatan  Kluet Selatan\n",
       "3  110104     Aceh  Kab. Aceh Selatan   Labuhan Haji\n",
       "4  110105     Aceh  Kab. Aceh Selatan         Meukek"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "invalid-score",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables for summaries\n",
    "sum_number = 0\n",
    "sum_valid_number = 0\n",
    "sum_length = 0\n",
    "sum_area = 0\n",
    "sum_dob = 0\n",
    "sum_gender = 0\n",
    "sum_computerized = 0\n",
    "\n",
    "# json objects\n",
    "data_full = {}\n",
    "data_limited = {}\n",
    "\n",
    "# Looping\n",
    "for i in range(len(NIKs)):\n",
    "    # Assign NIK into object\n",
    "    NIK = identificationID(NIKs[i])\n",
    "    NIK_value = NIK.get_value()\n",
    "    \n",
    "    # NIK's length\n",
    "    stat_length, nik_length = NIK.checkLength()\n",
    "    # NIK's admin area\n",
    "    stat_area, nik_prov, nik_district, nik_subdistrict = NIK.checkAdminArea(data = df)\n",
    "    # NIK's DOB\n",
    "    stat_dob, nik_dob, nik_age = NIK.checkDOB()\n",
    "    # NIK's gender\n",
    "    stat_gender, nik_gender = NIK.checkGender()\n",
    "    # NIK's last computerized number\n",
    "    stat_comp, nik_comp = NIK.checkComputerizedNumber()\n",
    "    \n",
    "    # Summary\n",
    "    valid_all = stat_length & stat_area & stat_dob & stat_gender & stat_comp\n",
    "    sum_number += 1\n",
    "    sum_valid_number += valid_all\n",
    "    sum_length += stat_length\n",
    "    sum_area += stat_area\n",
    "    sum_dob += stat_dob\n",
    "    sum_gender += stat_gender\n",
    "    sum_computerized += stat_comp\n",
    "    \n",
    "    # Convert into json - data full\n",
    "    dic_data_full = {\n",
    "        NIK_value: {\n",
    "            'data': {\n",
    "                'length': {\n",
    "                    'value': NIK_value,\n",
    "                    'valid': stat_length\n",
    "                },\n",
    "                'area': {\n",
    "                    'value': {\n",
    "                        'province': nik_prov,\n",
    "                        'district': nik_district,\n",
    "                        'subdistrict': nik_subdistrict\n",
    "                    },\n",
    "                    'valid': stat_area\n",
    "                    },\n",
    "                'dob': {\n",
    "                    'value': {\n",
    "                        'dob': nik_dob,\n",
    "                        'age': nik_age\n",
    "                    },\n",
    "                    'valid': stat_dob\n",
    "                },\n",
    "                'gender': {\n",
    "                    'value': nik_gender,\n",
    "                    'valid': stat_gender\n",
    "                },\n",
    "                'computerized': {\n",
    "                    'value': nik_comp,\n",
    "                    'valid': stat_comp\n",
    "                }\n",
    "            },\n",
    "            'valid': valid_all\n",
    "        }\n",
    "    }\n",
    "    # Convert into json - data limited\n",
    "    dic_data_limited = {\n",
    "        NIK_value: valid_all\n",
    "    }\n",
    "    \n",
    "    # Append into json\n",
    "    data_full = {**data_full, **dic_data_full}\n",
    "    data_limited = {**data_limited, **dic_data_limited}\n",
    "\n",
    "# Convert into json - summary\n",
    "summary = {\n",
    "    'number': sum_number,\n",
    "    'valid_number': sum_valid_number,\n",
    "    'valid': {\n",
    "        'length': sum_length,\n",
    "        'area': sum_area,\n",
    "        'dob': sum_dob,\n",
    "        'gender': sum_gender,\n",
    "        'computerized': sum_computerized\n",
    "    }\n",
    "}\n",
    "# Convert into json - master\n",
    "json_data_full = {'message': 'success','data': data_full, 'summary': summary}\n",
    "json_data_limited = {'message': 'success','data': data_limited, 'summary': summary}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "interracial-chicago",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'number': 4,\n",
       " 'valid_number': 0,\n",
       " 'valid': {'length': 4, 'area': 0, 'dob': 0, 'gender': 4, 'computerized': 4}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "material-mattress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1234567891234567': {'data': {'length': {'value': '1234567891234567',\n",
       "    'valid': True},\n",
       "   'area': {'value': {'province': None, 'district': None, 'subdistrict': None},\n",
       "    'valid': False},\n",
       "   'dob': {'value': {'dob': None, 'age': None}, 'valid': False},\n",
       "   'gender': {'value': 'Woman', 'valid': True},\n",
       "   'computerized': {'value': '4567', 'valid': True}},\n",
       "  'valid': False}}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "advanced-bryan",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "macro-czech",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'message': 'success',\n",
       " 'data': {'1234567891234567': {'data': {'length': {'value': '1234567891234567',\n",
       "     'valid': True},\n",
       "    'area': {'value': {'province': None,\n",
       "      'district': None,\n",
       "      'subdistrict': None},\n",
       "     'valid': False},\n",
       "    'dob': {'value': {'dob': None, 'age': None}, 'valid': False},\n",
       "    'gender': {'value': 'Woman', 'valid': True},\n",
       "    'computerized': {'value': '4567', 'valid': True}},\n",
       "   'valid': False}},\n",
       " 'summary': {'number': 4,\n",
       "  'valid_number': 0,\n",
       "  'valid': {'length': 4, 'area': 0, 'dob': 0, 'gender': 4, 'computerized': 4}}}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "received-cornwall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1234567891234567': False}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_limited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "sufficient-spoke",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_limited)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "suitable-feeding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'message': 'success',\n",
       " 'data': {'1234567891234567': False},\n",
       " 'summary': {'number': 4,\n",
       "  'valid_number': 0,\n",
       "  'valid': {'length': 4, 'area': 0, 'dob': 0, 'gender': 4, 'computerized': 4}}}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data_limited"
   ]
  }
 ],
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