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Webscraping_Engineer_Peer_Review_Assignment.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/dsimanoliveira/603e5cba4dcfb721e24cf68454fa499e/webscraping_engineer_peer_review_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "733501fd-f799-4dbc-8825-0d1942ddeecb" | |
| }, | |
| "source": [ | |
| "<p style=\"text-align:center\">\n", | |
| " <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0221ENSkillsNetwork899-2023-01-01\">\n", | |
| " <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\" />\n", | |
| " </a>\n", | |
| "</p>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "6475f542-e53f-4d38-b94d-667b0f0b9813" | |
| }, | |
| "source": [ | |
| "# Peer Review Assignment - Data Engineer - Webscraping\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5634bd5b-2073-4abf-ac5e-60addf1e8059" | |
| }, | |
| "source": [ | |
| "Estimated time needed: **20** minutes\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4cbadd10-bc26-404c-9f0f-8e16390af86c" | |
| }, | |
| "source": [ | |
| "## Objectives\n", | |
| "\n", | |
| "In this part you will:\n", | |
| "\n", | |
| "- Use webscraping to get bank information\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d1f34239-7d19-4df7-aab3-ac6d88827eda" | |
| }, | |
| "source": [ | |
| "## Imports\n", | |
| "\n", | |
| "Import any additional libraries you may need here.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "22f312ab-d5ed-4da3-9016-278ebf7518ad" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from bs4 import BeautifulSoup\n", | |
| "import html5lib\n", | |
| "import requests\n", | |
| "import pandas as pd" | |
| ], | |
| "execution_count": null | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "764e78fa-dda1-488b-830d-4c42fa046624" | |
| }, | |
| "source": [ | |
| "## Extract Data Using Web Scraping\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "68802876-ac84-43e3-b24a-8df5261d80f7" | |
| }, | |
| "source": [ | |
| "The wikipedia webpage https://web.archive.org/web/20200318083015/https://en.wikipedia.org/wiki/List_of_largest_banks provides information about largest banks in the world by various parameters. Scrape the data from the table 'By market capitalization' and store it in a JSON file.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bd044e14-2b76-487b-a9fd-6bc9520635ad" | |
| }, | |
| "source": [ | |
| "### Webpage Contents\n", | |
| "\n", | |
| "Gather the contents of the webpage in text format using the `requests` library and assign it to the variable <code>html_data</code>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = requests.get('https://web.archive.org/web/20200318083015/https://en.wikipedia.org/wiki/List_of_largest_banks')\n", | |
| "html_data = response.content\n" | |
| ], | |
| "metadata": { | |
| "id": "fzL2q_6M7kId" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3bdf785c-6c25-413f-87f2-d55335c16a46" | |
| }, | |
| "source": [ | |
| "<b>Question 1</b> Print out the output of the following line, and remember it as it will be a quiz question:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "html_data[760:783]" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "p_Z3eL3B8OaD", | |
| "outputId": "f56820ea-8112-491b-ac4a-84fbfa72b908" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "b'\" href=\"/_static/css/ba'" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 112 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "183111fc-9af4-4f7f-96ca-9eab98e4bcba" | |
| }, | |
| "source": [ | |
| "### Scraping the Data\n", | |
| "\n", | |
| "<b> Question 2</b> Using the contents and `beautiful soup` load the data from the `By market capitalization` table into a `pandas` dataframe. The dataframe should have the bank `Name` and `Market Cap (US$ Billion)` as column names. Display the first five rows using head.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ce4d7391-008e-4458-84c1-d1fc7b9d0375" | |
| }, | |
| "source": [ | |
| "Using BeautifulSoup parse the contents of the webpage.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "soup = BeautifulSoup(html_data, 'html.parser')" | |
| ], | |
| "metadata": { | |
| "id": "gsmVGxWs8ZjT" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "eefd343b-f629-4865-add4-14ce9e9aed51" | |
| }, | |
| "source": [ | |
| "Load the data from the `By market capitalization` table into a pandas dataframe. The dataframe should have the bank `Name` and `Market Cap (US$ Billion)` as column names. Using the empty dataframe `data` and the given loop extract the necessary data from each row and append it to the empty dataframe.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data = pd.DataFrame(columns=[\"Name\", \"Market Cap (US$ Billion)\"])\n", | |
| "\n", | |
| "for row in soup.find_all('tbody')[2].find_all('tr'):\n", | |
| " col = row.find_all('td')\n", | |
| " #Write your code here\n", | |
| " if col:\n", | |
| " new_df_row = {\n", | |
| " 'Name': col[1].find_all('a')[-1].text,\n", | |
| " 'Market Cap (US$ Billion)': float(col[-1].text)\n", | |
| " }\n", | |
| " data = pd.concat([data, pd.DataFrame(new_df_row, index=[0])], ignore_index=True)\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "rUwQYeUE_tuK" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "fc5391c0-33f5-4229-b4fe-765495e4ac68" | |
| }, | |
| "source": [ | |
| "**Question 3** Display the first five rows using the `head` function.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "w6HMq1RiMBip", | |
| "outputId": "544f1804-350f-48f3-9daa-b9438dcec8da" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Name Market Cap (US$ Billion)\n", | |
| "0 JPMorgan Chase 390.934\n", | |
| "1 Industrial and Commercial Bank of China 345.214\n", | |
| "2 Bank of America 325.331\n", | |
| "3 Wells Fargo 308.013\n", | |
| "4 China Construction Bank 257.399" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-9689971c-bf5d-4d42-85b8-d5c3c212b32a\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
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| " .dataframe tbody tr th {\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Name</th>\n", | |
| " <th>Market Cap (US$ Billion)</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>JPMorgan Chase</td>\n", | |
| " <td>390.934</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Industrial and Commercial Bank of China</td>\n", | |
| " <td>345.214</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Bank of America</td>\n", | |
| " <td>325.331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Wells Fargo</td>\n", | |
| " <td>308.013</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>China Construction Bank</td>\n", | |
| " <td>257.399</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9689971c-bf5d-4d42-85b8-d5c3c212b32a')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-9689971c-bf5d-4d42-85b8-d5c3c212b32a button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-9689971c-bf5d-4d42-85b8-d5c3c212b32a');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-b3d98d79-bcfe-4c9c-aa1d-6d8d6181e0f9\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3d98d79-bcfe-4c9c-aa1d-6d8d6181e0f9')\"\n", | |
| " title=\"Suggest charts.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-b3d98d79-bcfe-4c9c-aa1d-6d8d6181e0f9 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 103 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7f03eda7-0d24-48a6-b851-e2e81ec8a1ed" | |
| }, | |
| "source": [ | |
| "\n", | |
| "### Loading the Data\n", | |
| "\n", | |
| "Load the `pandas` dataframe created above into a JSON named `bank_market_cap.json` using the `to_json()` function.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data.to_json('bank_market_cap.json')" | |
| ], | |
| "metadata": { | |
| "id": "OarwAbrDNIoL" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "c46b715f-9cdf-4db1-8fc6-78cb57f14ff0" | |
| }, | |
| "source": [ | |
| "## Authors\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e4f97a2e-adf1-41d6-89fe-e4e63c47f721" | |
| }, | |
| "source": [ | |
| "Ramesh Sannareddy, Joseph Santarcangelo and Azim Hirjani\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1589609f-21f1-4438-8edb-f51b0781d4a9" | |
| }, | |
| "source": [ | |
| "### Other Contributors\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "de7e35ec-5271-4a04-a721-6a2102cde9a7" | |
| }, | |
| "source": [ | |
| "Rav Ahuja\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3d790194-f2df-49ee-bc2a-110a812e60a2" | |
| }, | |
| "source": [ | |
| "## Change Log\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "40f0770b-5380-49e7-b6c3-e7c8d9522fd0" | |
| }, | |
| "source": [ | |
| "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", | |
| "| ----------------- | ------- | ----------------- | ---------------------------------- |\n", | |
| "| 2022-07-12 | 0.2 | Appalabhaktula Hema | Corrected the code and markdown |\n", | |
| "| 2020-11-25 | 0.1 | Ramesh Sannareddy | Created initial version of the lab |\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "6b5e1afb-c515-43d1-b8d3-60cd03658f45" | |
| }, | |
| "source": [ | |
| "Copyright © 2020 IBM Corporation.\n" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| }, | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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