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‎Assignment-13.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## CSV Vs EXCEL :\n",
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" - CSV file can’t perform operations on data while Excel can perform operations on the data.\n",
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" - CSV files are faster and also consumes less memory whereas Excel consumes more memory while importing data.\n",
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" - CSV files can be opened with any text editor in windows while Excel files can't be opened with text editors."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## FileObject from open function"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The mode for reader Object is read-binary ('rb') and for writer Object is write-binary ('wb')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"with open('movies.csv', 'w', newline='') as file:\n",
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" writer = csv.writer(file)\n",
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"## writerow function takes a list argument and writes it to a CSV file\n",
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" writer.writerow([\"SrNum\", \"Movie\", \"Genre\"])\n",
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" writer.writerow([1, \"Venom\", \"Sci-Fi\"])\n",
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" writer.writerow([2, \"A Quiet Place\", \"Horror\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['SrNum', 'Movie', 'Genre']\n",
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"['1', 'Venom', 'Sci-Fi']\n",
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"['2', 'A Quiet Place', 'Horror']\n"
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]
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}
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],
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"source": [
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"## to check contents of csv\n",
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"with open('movies.csv', 'r') as file:\n",
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" reader = csv.reader(file)\n",
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" for row in reader:\n",
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" print(row)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The delimiter argument used to separate data bydefault comma(,) in it. A lineterminator is a string used to terminate lines produced by writer objects. The default value is \\r\\n. You can change its value by passing any string as a lineterminator parameter."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\"name\": \"John\", \"age\": 31, \"Salary\": 25000}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"str"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import json \n",
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" \n",
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"# {key:value mapping} ## Its type is dictonary \n",
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"a ={\"name\":\"John\", \n",
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" \"age\":31, \n",
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" \"Salary\":25000} \n",
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"# conversion to JSON done by dumps() function \n",
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"b = json.dumps(a) \n",
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"print(b) \n",
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"type(b)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"a ={\"name\":\"John\", \n",
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" \"age\":31, \n",
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" \"Salary\":25000} "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type(a)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"json_var =\"\"\" \n",
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"{ \n",
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" \"Employee\": { \n",
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" \"name\": \"Akash Borgalli\", \n",
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" \"Languages_spoken\": [ \n",
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" { \n",
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" \"names\": [\"Hindi\", \"English\", \"Kanada\", \"Marathi\"] \n",
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" } \n",
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" ] \n",
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" } \n",
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"} \n",
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"\"\"\"\n",
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"## json loads() is a function that takes a string of JSON data and returns a dictionary data structure\n",
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"var = json.loads(json_var) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'str'>\n",
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"{'Employee': {'name': 'Akash Borgalli', 'Languages_spoken': [{'names': ['Hindi', 'English', 'Kanada', 'Marathi']}]}}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"dict"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(type(json_var))\n",
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"print(var)\n",
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"type(var)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"str"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type(json_var)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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