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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "def79524",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ " ![\"Skills](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\")
\n",
+ " \n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3297b8f-d940-495e-b617-311976f3374a",
+ "metadata": {},
+ "source": [
+ "Extracting and Visualizing Stock Data
\n",
+ "Description
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a63757ca-0a1f-4a61-bd6c-3c25f234e7fb",
+ "metadata": {},
+ "source": [
+ "Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11a783a7-56d2-4e99-97cf-ea53bfc8aa81",
+ "metadata": {},
+ "source": [
+ "Table of Contents
\n",
+ "\n",
+ "
\n",
+ " - Define a Function that Makes a Graph
\n",
+ " - Question 1: Use yfinance to Extract Stock Data
\n",
+ " - Question 2: Use Webscraping to Extract Tesla Revenue Data
\n",
+ " - Question 3: Use yfinance to Extract Stock Data
\n",
+ " - Question 4: Use Webscraping to Extract GME Revenue Data
\n",
+ " - Question 5: Plot Tesla Stock Graph
\n",
+ " - Question 6: Plot GameStop Stock Graph
\n",
+ "
\n",
+ "
\n",
+ " Estimated Time Needed: 30 min
\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "be6e0c97-5ff6-4c62-8123-c41697152060",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: yfinance==0.1.67 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.1.67)\n",
+ "Requirement already satisfied: pandas>=0.24 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.3.5)\n",
+ "Requirement already satisfied: numpy>=1.15 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.21.6)\n",
+ "Requirement already satisfied: requests>=2.20 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (2.29.0)\n",
+ "Requirement already satisfied: multitasking>=0.0.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (0.0.11)\n",
+ "Requirement already satisfied: lxml>=4.5.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (4.9.2)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.24->yfinance==0.1.67) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.24->yfinance==0.1.67) (2023.3)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (3.1.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (3.4)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (1.26.15)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (2023.5.7)\n",
+ "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance==0.1.67) (1.16.0)\n",
+ "\n",
+ " __ __ __ __\n",
+ " / \\ / \\ / \\ / \\\n",
+ " / \\/ \\/ \\/ \\\n",
+ "███████████████/ /██/ /██/ /██/ /████████████████████████\n",
+ " / / \\ / \\ / \\ / \\ \\____\n",
+ " / / \\_/ \\_/ \\_/ \\ o \\__,\n",
+ " / _/ \\_____/ `\n",
+ " |/\n",
+ " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n",
+ " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
+ " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
+ " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
+ " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n",
+ " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n",
+ "\n",
+ " mamba (1.4.2) supported by @QuantStack\n",
+ "\n",
+ " GitHub: https://github.com/mamba-org/mamba\n",
+ " Twitter: https://twitter.com/QuantStack\n",
+ "\n",
+ "█████████████████████████████████████████████████████████████\n",
+ "\n",
+ "\n",
+ "Looking for: ['bs4==4.10.0']\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/r/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/noarch No change\n",
+ "pkgs/r/linux-64 No change\n",
+ "pkgs/main/linux-64 No change\n",
+ "pkgs/r/noarch No change\n",
+ "\u001b[?25h\n",
+ "Pinned packages:\n",
+ " - python 3.7.*\n",
+ "\n",
+ "\n",
+ "Transaction\n",
+ "\n",
+ " Prefix: /home/jupyterlab/conda/envs/python\n",
+ "\n",
+ " All requested packages already installed\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G\u001b[?25hRequirement already satisfied: nbformat==4.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (4.2.0)\n",
+ "Requirement already satisfied: ipython-genutils in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (0.2.0)\n",
+ "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (4.17.3)\n",
+ "Requirement already satisfied: jupyter-core in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (4.12.0)\n",
+ "Requirement already satisfied: traitlets>=4.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (5.9.0)\n",
+ "Requirement already satisfied: attrs>=17.4.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (23.1.0)\n",
+ "Requirement already satisfied: importlib-metadata in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (4.11.4)\n",
+ "Requirement already satisfied: importlib-resources>=1.4.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (5.12.0)\n",
+ "Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (1.3.10)\n",
+ "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (0.19.3)\n",
+ "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (4.5.0)\n",
+ "Requirement already satisfied: zipp>=3.1.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (3.15.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install yfinance==0.1.67\n",
+ "!mamba install bs4==4.10.0 -y\n",
+ "!pip install nbformat==4.2.0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "9860fee0-de38-4cd2-936b-7479745b0f4e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import yfinance as yf\n",
+ "import pandas as pd\n",
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "import plotly.graph_objects as go\n",
+ "from plotly.subplots import make_subplots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2d6a13c-efae-4024-85e5-4edda1895c92",
+ "metadata": {},
+ "source": [
+ "## Define Graphing Function\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c0e1b5f-6137-4d71-b130-5aabd2cfb7f5",
+ "metadata": {},
+ "source": [
+ "In this section, we define the function `make_graph`. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "id": "0dcaaa5e-e5ed-4337-beb4-134b5da043d6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def make_graph(stock_data, revenue_data, stock):\n",
+ " fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=(\"Historical Share Price\", \"Historical Revenue\"), vertical_spacing = .3)\n",
+ " stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']\n",
+ " revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']\n",
+ " fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype(\"float\"), name=\"Share Price\"), row=1, col=1)\n",
+ " fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype(\"float\"), name=\"Revenue\"), row=2, col=1)\n",
+ " fig.update_xaxes(title_text=\"Date\", row=1, col=1)\n",
+ " fig.update_xaxes(title_text=\"Date\", row=2, col=1)\n",
+ " fig.update_yaxes(title_text=\"Price ($US)\", row=1, col=1)\n",
+ " fig.update_yaxes(title_text=\"Revenue ($US Millions)\", row=2, col=1)\n",
+ " fig.update_layout(showlegend=False,\n",
+ " height=900,\n",
+ " title=stock,\n",
+ " xaxis_rangeslider_visible=True)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fbaa3d01-1420-4087-9d79-3a56522c1610",
+ "metadata": {},
+ "source": [
+ "## Question 1: Use yfinance to Extract Stock Data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d574fd10-1aab-4e46-a924-53d028709d24",
+ "metadata": {},
+ "source": [
+ "Using the `Ticker` function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is `TSLA`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "408e0d71-3e76-4731-8cb0-60e7efd7c9ae",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "tesla = yf.Ticker(\"TSLA\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13c78686-b22b-490c-acac-b66d49024e46",
+ "metadata": {},
+ "source": [
+ "Using the ticker object and the function `history` extract stock information and save it in a dataframe named `tesla_data`. Set the `period` parameter to `max` so we get information for the maximum amount of time.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "7f9763e4-9122-41ec-9443-a53343c59b60",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
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+ " \n",
+ "
\n",
+ "
Click here if you need help locating the table
\n",
+ "\n",
+ "```\n",
+ " \n",
+ "Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab\n",
+ " \n",
+ "soup.find_all(\"tbody\")[1]\n",
+ " \n",
+ "If you want to use the read_html function the table is located at index 1\n",
+ "\n",
+ "\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Revenue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 2021 | \n",
+ " $53,823 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 2020 | \n",
+ " $31,536 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2019 | \n",
+ " $24,578 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2018 | \n",
+ " $21,461 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2017 | \n",
+ " $11,759 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 2016 | \n",
+ " $7,000 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 2015 | \n",
+ " $4,046 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 2014 | \n",
+ " $3,198 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 2013 | \n",
+ " $2,013 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2012 | \n",
+ " $413 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 2011 | \n",
+ " $204 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 2010 | \n",
+ " $117 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 2009 | \n",
+ " $112 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Revenue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 8 | \n",
+ " 2013 | \n",
+ " 2013 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2012 | \n",
+ " 413 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 2011 | \n",
+ " 204 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 2010 | \n",
+ " 117 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 2009 | \n",
+ " 112 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Open | \n",
+ " High | \n",
+ " Low | \n",
+ " Close | \n",
+ " Volume | \n",
+ " Dividends | \n",
+ " Stock Splits | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 2002-02-13 | \n",
+ " 1.620128 | \n",
+ " 1.693350 | \n",
+ " 1.603296 | \n",
+ " 1.691667 | \n",
+ " 76216000 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 2002-02-14 | \n",
+ " 1.712708 | \n",
+ " 1.716074 | \n",
+ " 1.670626 | \n",
+ " 1.683251 | \n",
+ " 11021600 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2002-02-15 | \n",
+ " 1.683250 | \n",
+ " 1.687458 | \n",
+ " 1.658001 | \n",
+ " 1.674834 | \n",
+ " 8389600 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2002-02-19 | \n",
+ " 1.666418 | \n",
+ " 1.666418 | \n",
+ " 1.578047 | \n",
+ " 1.607504 | \n",
+ " 7410400 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2002-02-20 | \n",
+ " 1.615920 | \n",
+ " 1.662210 | \n",
+ " 1.603296 | \n",
+ " 1.662210 | \n",
+ " 6892800 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Click here if you need help locating the table
\n",
+ "\n",
+ "```\n",
+ " \n",
+ "Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab\n",
+ " \n",
+ "soup.find_all(\"tbody\")[1]\n",
+ " \n",
+ "If you want to use the read_html function the table is located at index 1\n",
+ "\n",
+ "\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Revenue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 2021 | \n",
+ " 53823 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 2020 | \n",
+ " 31536 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2019 | \n",
+ " 24578 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2018 | \n",
+ " 21461 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2017 | \n",
+ " 11759 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Revenue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 8 | \n",
+ " 2013 | \n",
+ " 2013 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2012 | \n",
+ " 413 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 2011 | \n",
+ " 204 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 2010 | \n",
+ " 117 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 2009 | \n",
+ " 112 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
About the Authors:
\n",
+ "\n",
+ "Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
+ "\n",
+ "Azim Hirjani\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0241bee-799c-4d33-8a95-0d5572066f14",
+ "metadata": {},
+ "source": [
+ "## Change Log\n",
+ "\n",
+ "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
+ "| ----------------- | ------- | ------------- | ------------------------- |\n",
+ "| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |\n",
+ "| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |\n",
+ "| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |\n",
+ "\n",
+ "
\n",
+ "\n",
+ "## © IBM Corporation 2020. All rights reserved. \n",
+ "\n",
+ "
\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python",
+ "language": "python",
+ "name": "conda-env-python-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}