adding a json making notebook
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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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"# Making (mini) datasets using JSON files"
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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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"Ways of thickening, layering, ..., text."
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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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"----------------------------------"
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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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"## JSON?\n",
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"\n",
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"JSON is an open standard file format that saves information in **key, value paired** objects. \n",
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"\n",
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"It looks like this:\n",
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"\n",
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"```\n",
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"{\n",
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" \"name\": \"XPUB1\",\n",
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" \"date\": \"26-10-2020\",\n",
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" \"number_of_students\": 10\n",
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"}\n",
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"```\n",
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"\n",
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"In a JSON file, you can store both **strings** and **numbers**, but also **lists** or another **dictionary** object.\n",
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"\n",
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"Look for example how the number `10` is written without `\"`s around it."
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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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"## dictionaries & JSON files\n",
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"\n",
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"Python uses a **dictionary** to store data in the same **key, value paired** way. \n",
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"\n",
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"`dict = {}`\n",
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"\n",
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"We will use a dictionary to store the data, and then save it as a JSON file.\n",
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"\n",
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"In this way, we can *add* one (of multiple) layer(s) of *value* to a word ..., such as:\n",
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"\n",
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"```\n",
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"{\n",
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" \"common\": \"English\",\n",
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" \"language\": \"communication system\",\n",
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" \"formal\": 6,\n",
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" \"semantic\": ['language', 'formal', 'informat'],\n",
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" \"semantic\": { \n",
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" \"type\": \"word\",\n",
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" \"number of letters\": 7\n",
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" }\n",
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"}\n",
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"```\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Adding a new key to the dictionary, assigning a string as value:\n",
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"dataset['new'] = 'NEW WORD'\n",
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"# or assigning a number as value:\n",
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"dataset['new'] = 10 "
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Printing the tag of a word in the dictionary\n",
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"print(dataset['are'])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Printing all the keys in the dataset\n",
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"print(dataset.keys())"
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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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"## Making a sample dataset"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is sample data, a list of words and POS tags:\n",
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"data = [('Common', 'JJ'), ('languages', 'NNS'), ('like', 'IN'), ('English', 'NNP'), ('are', 'VBP'), ('both', 'DT'), ('formal', 'JJ'), ('and', 'CC'), ('semantic', 'JJ'), (';', ':'), ('although', 'IN'), ('their', 'PRP$'), ('scope', 'NN'), ('extends', 'VBZ'), ('beyond', 'IN'), ('the', 'DT'), ('formal', 'JJ'), (',', ','), ('anything', 'NN'), ('that', 'WDT'), ('can', 'MD'), ('be', 'VB'), ('expressed', 'VBN'), ('in', 'IN'), ('a', 'DT'), ('computer', 'NN'), ('control', 'NN'), ('language', 'NN'), ('can', 'MD'), ('also', 'RB'), ('be', 'VB'), ('expressed', 'VBN'), ('in', 'IN'), ('common', 'JJ'), ('language', 'NN'), ('.', '.')]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Making a dataset with only verbs\n",
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"dataset = {}\n",
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"\n",
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"for word, tag in data:\n",
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" if 'VB' in tag:\n",
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" dataset[word] = tag\n",
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"\n",
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"print(dataset)"
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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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"## Saving as JSON file"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"out = json.dumps(dataset, indent=4)\n",
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"print(out)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"f = open('json-dataset.json', 'w')\n",
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"f.write(out)\n",
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"f.close()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.7.3"
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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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