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Final 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/mhoangvslev/02b463eba19b3ad1c0fbd998c228507e/final-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": "c728d388-e4bc-4591-b9cc-26e4e92cd191" | |
| }, | |
| "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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork900-2022-01-01\" target=\"_blank\">\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": "d8821c88-c218-4a9d-93b5-c2f37d322062" | |
| }, | |
| "source": [ | |
| "<h1>Extracting and Visualizing Stock Data</h1>\n", | |
| "<h2>Description</h2>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2538bdd2-4ceb-44b6-a4ca-b0b5b0e30c6f" | |
| }, | |
| "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", | |
| "metadata": { | |
| "id": "79e8b53b-e939-4002-b56b-10b0299d1714" | |
| }, | |
| "source": [ | |
| "<h2>Table of Contents</h2>\n", | |
| "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n", | |
| " <ul>\n", | |
| " <li>Define a Function that Makes a Graph</li>\n", | |
| " <li>Question 1: Use yfinance to Extract Stock Data</li>\n", | |
| " <li>Question 2: Use Webscraping to Extract Tesla Revenue Data</li>\n", | |
| " <li>Question 3: Use yfinance to Extract Stock Data</li>\n", | |
| " <li>Question 4: Use Webscraping to Extract GME Revenue Data</li>\n", | |
| " <li>Question 5: Plot Tesla Stock Graph</li>\n", | |
| " <li>Question 6: Plot GameStop Stock Graph</li>\n", | |
| " </ul>\n", | |
| "<p>\n", | |
| " Estimated Time Needed: <strong>30 min</strong></p>\n", | |
| "</div>\n", | |
| "\n", | |
| "<hr>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d17f6a23-965c-4058-9d72-194d85e5c11e" | |
| }, | |
| "source": [ | |
| "***Note***:- If you are working Locally using anaconda, please uncomment the following code and execute it.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "caa30075-4302-4e8e-ba7c-1c811d92b993" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#!pip install yfinance==0.2.38\n", | |
| "#!pip install pandas==2.2.2\n", | |
| "#!pip install nbformat" | |
| ], | |
| "execution_count": null | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3abe7b75-617d-4173-9dfd-a2a83c4e76e5", | |
| "outputId": "b28d233c-8be9-486e-fe22-f9a060e65113" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.40)\n", | |
| "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.0.3)\n", | |
| "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.25.2)\n", | |
| "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.31.0)\n", | |
| "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance) (0.0.11)\n", | |
| "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.9.4)\n", | |
| "Requirement already satisfied: platformdirs>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.2.2)\n", | |
| "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2023.4)\n", | |
| "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.4.4)\n", | |
| "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.10/dist-packages (from yfinance) (3.17.6)\n", | |
| "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.12.3)\n", | |
| "Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.1)\n", | |
| "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n", | |
| "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (1.16.0)\n", | |
| "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (0.5.1)\n", | |
| "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n", | |
| "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2024.1)\n", | |
| "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.3.2)\n", | |
| "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.7)\n", | |
| "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2.0.7)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2024.7.4)\n", | |
| "Collecting bs4\n", | |
| " Downloading bs4-0.0.2-py2.py3-none-any.whl (1.2 kB)\n", | |
| "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from bs4) (4.12.3)\n", | |
| "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->bs4) (2.5)\n", | |
| "Installing collected packages: bs4\n", | |
| "Successfully installed bs4-0.0.2\n", | |
| "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (5.10.4)\n", | |
| "Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.10/dist-packages (from nbformat) (2.20.0)\n", | |
| "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat) (4.19.2)\n", | |
| "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.7.2)\n", | |
| "Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.7.1)\n", | |
| "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (23.2.0)\n", | |
| "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (2023.12.1)\n", | |
| "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.35.1)\n", | |
| "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.19.0)\n", | |
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| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install yfinance\n", | |
| "!pip install bs4\n", | |
| "!pip install nbformat" | |
| ], | |
| "execution_count": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "c737dcae-8ac5-4a32-b288-962816d7c900" | |
| }, | |
| "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" | |
| ], | |
| "execution_count": 2 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "94be5979-1678-40aa-9acc-4235777942f6" | |
| }, | |
| "source": [ | |
| "In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "59c53c5a-54b3-4f08-9a40-27e5d1223dda" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "# Ignore all warnings\n", | |
| "warnings.filterwarnings(\"ignore\", category=FutureWarning)" | |
| ], | |
| "execution_count": 3 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "a2ed8143-11ca-47eb-ae5e-0921727ea169" | |
| }, | |
| "source": [ | |
| "## Define Graphing Function\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "48e845f6-f9a2-49e2-aa45-18052c213677" | |
| }, | |
| "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", | |
| "metadata": { | |
| "id": "542e2635-6f1e-4ff8-9e93-2a080e2418d3" | |
| }, | |
| "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), 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), 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()" | |
| ], | |
| "execution_count": 4 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "f51c2575-6124-4ac6-b7dc-770e05418748" | |
| }, | |
| "source": [ | |
| "Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.\n", | |
| "> **Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.**\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "74e55a13-875e-4aa6-b421-6dd805955af0" | |
| }, | |
| "source": [ | |
| "## Question 1: Use yfinance to Extract Stock Data\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "069fde57-803a-4c35-9565-6b1960bed7d3" | |
| }, | |
| "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", | |
| "metadata": { | |
| "id": "c8d7b40c-f2c7-42d9-abb9-6ab590d00c89" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "tesla = yf.Ticker(\"TSLA\")" | |
| ], | |
| "execution_count": 5 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1c991913-8dcf-4df1-8111-94b063e26b18" | |
| }, | |
| "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", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 455 | |
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| "id": "528c4075-61e6-45ca-a791-c8793d5cd9b7", | |
| "outputId": "343519b9-f189-4086-b281-c60efad1c816" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Open High Low Close \\\n", | |
| "Date \n", | |
| "2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 \n", | |
| "2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 \n", | |
| "2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 \n", | |
| "2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 \n", | |
| "2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 \n", | |
| "... ... ... ... ... \n", | |
| "2024-07-08 00:00:00-04:00 247.710007 259.440002 244.570007 252.940002 \n", | |
| "2024-07-09 00:00:00-04:00 251.000000 265.609985 250.300003 262.329987 \n", | |
| "2024-07-10 00:00:00-04:00 262.799988 267.589996 257.859985 263.260010 \n", | |
| "2024-07-11 00:00:00-04:00 263.299988 271.000000 239.649994 241.029999 \n", | |
| "2024-07-12 00:00:00-04:00 235.800003 251.839996 233.089996 248.229996 \n", | |
| "\n", | |
| " Volume Dividends Stock Splits \n", | |
| "Date \n", | |
| "2010-06-29 00:00:00-04:00 281494500 0.0 0.0 \n", | |
| "2010-06-30 00:00:00-04:00 257806500 0.0 0.0 \n", | |
| "2010-07-01 00:00:00-04:00 123282000 0.0 0.0 \n", | |
| "2010-07-02 00:00:00-04:00 77097000 0.0 0.0 \n", | |
| "2010-07-06 00:00:00-04:00 103003500 0.0 0.0 \n", | |
| "... ... ... ... \n", | |
| "2024-07-08 00:00:00-04:00 157219600 0.0 0.0 \n", | |
| "2024-07-09 00:00:00-04:00 160210900 0.0 0.0 \n", | |
| "2024-07-10 00:00:00-04:00 128519400 0.0 0.0 \n", | |
| "2024-07-11 00:00:00-04:00 221707300 0.0 0.0 \n", | |
| "2024-07-12 00:00:00-04:00 155694400 0.0 0.0 \n", | |
| "\n", | |
| "[3533 rows x 7 columns]" | |
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| " <th></th>\n", | |
| " <th>Open</th>\n", | |
| " <th>High</th>\n", | |
| " <th>Low</th>\n", | |
| " <th>Close</th>\n", | |
| " <th>Volume</th>\n", | |
| " <th>Dividends</th>\n", | |
| " <th>Stock Splits</th>\n", | |
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| " <th>2010-06-29 00:00:00-04:00</th>\n", | |
| " <td>1.266667</td>\n", | |
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| " <th>2010-06-30 00:00:00-04:00</th>\n", | |
| " <td>1.719333</td>\n", | |
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| " <th>2010-07-01 00:00:00-04:00</th>\n", | |
| " <td>1.666667</td>\n", | |
| " <td>1.728000</td>\n", | |
| " <td>1.351333</td>\n", | |
| " <td>1.464000</td>\n", | |
| " <td>123282000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
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| " <th>2010-07-02 00:00:00-04:00</th>\n", | |
| " <td>1.533333</td>\n", | |
| " <td>1.540000</td>\n", | |
| " <td>1.247333</td>\n", | |
| " <td>1.280000</td>\n", | |
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| " <th>2010-07-06 00:00:00-04:00</th>\n", | |
| " <td>1.333333</td>\n", | |
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| " <td>103003500</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2024-07-08 00:00:00-04:00</th>\n", | |
| " <td>247.710007</td>\n", | |
| " <td>259.440002</td>\n", | |
| " <td>244.570007</td>\n", | |
| " <td>252.940002</td>\n", | |
| " <td>157219600</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2024-07-09 00:00:00-04:00</th>\n", | |
| " <td>251.000000</td>\n", | |
| " <td>265.609985</td>\n", | |
| " <td>250.300003</td>\n", | |
| " <td>262.329987</td>\n", | |
| " <td>160210900</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2024-07-10 00:00:00-04:00</th>\n", | |
| " <td>262.799988</td>\n", | |
| " <td>267.589996</td>\n", | |
| " <td>257.859985</td>\n", | |
| " <td>263.260010</td>\n", | |
| " <td>128519400</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2024-07-11 00:00:00-04:00</th>\n", | |
| " <td>263.299988</td>\n", | |
| " <td>271.000000</td>\n", | |
| " <td>239.649994</td>\n", | |
| " <td>241.029999</td>\n", | |
| " <td>221707300</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2024-07-12 00:00:00-04:00</th>\n", | |
| " <td>235.800003</td>\n", | |
| " <td>251.839996</td>\n", | |
| " <td>233.089996</td>\n", | |
| " <td>248.229996</td>\n", | |
| " <td>155694400</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>3533 rows × 7 columns</p>\n", | |
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| " }\n", | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "tesla_data", | |
| "summary": "{\n \"name\": \"tesla_data\",\n \"rows\": 3533,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2010-06-29 00:00:00-04:00\",\n \"max\": \"2024-07-12 00:00:00-04:00\",\n \"num_unique_values\": 3533,\n \"samples\": [\n \"2011-10-11 00:00:00-04:00\",\n \"2015-12-29 00:00:00-05:00\",\n \"2019-01-10 00:00:00-05:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102.25051660341896,\n \"min\": 1.0759999752044678,\n \"max\": 411.4700012207031,\n \"num_unique_values\": 3227,\n \"samples\": [\n 15.27066707611084,\n 2.2073330879211426,\n 12.533332824707031\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 104.52280432687249,\n \"min\": 1.108667016029358,\n \"max\": 414.4966735839844,\n \"num_unique_values\": 3221,\n \"samples\": [\n 2.246000051498413,\n 2.50266695022583,\n 9.463333129882812\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 99.78050835654766,\n \"min\": 0.9986670017242432,\n \"max\": 405.6666564941406,\n \"num_unique_values\": 3237,\n \"samples\": [\n 15.633333206176758,\n 179.8300018310547,\n 15.305999755859375\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102.18809698026372,\n \"min\": 1.053333044052124,\n \"max\": 409.9700012207031,\n \"num_unique_values\": 3333,\n \"samples\": [\n 2.2233328819274902,\n 337.7966613769531,\n 14.524666786193848\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 78783175,\n \"min\": 1777500,\n \"max\": 914082000,\n \"num_unique_values\": 3502,\n \"samples\": [\n 181006400,\n 10117500,\n 110263500\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Dividends\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Stock Splits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.09808743684656393,\n \"min\": 0.0,\n \"max\": 5.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ], | |
| "source": [ | |
| "tesla_data = tesla.history(period=\"max\")\n", | |
| "tesla_data" | |
| ], | |
| "execution_count": 7 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "87f9ef1f-68fc-4ef6-a280-47d6fe775430" | |
| }, | |
| "source": [ | |
| "**Reset the index** using the `reset_index(inplace=True)` function on the tesla_data DataFrame and display the first five rows of the `tesla_data` dataframe using the `head` function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tesla_data.reset_index(inplace=True)\n", | |
| "tesla_data.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "RFRmsAGAEY4A", | |
| "outputId": "93abbe49-7615-4642-f40d-2b0d4218a986" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " index Date Open High Low Close \\\n", | |
| "0 0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 \n", | |
| "1 1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 \n", | |
| "2 2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 \n", | |
| "3 3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 \n", | |
| "4 4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 \n", | |
| "\n", | |
| " Volume Dividends Stock Splits \n", | |
| "0 281494500 0.0 0.0 \n", | |
| "1 257806500 0.0 0.0 \n", | |
| "2 123282000 0.0 0.0 \n", | |
| "3 77097000 0.0 0.0 \n", | |
| "4 103003500 0.0 0.0 " | |
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| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>index</th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Open</th>\n", | |
| " <th>High</th>\n", | |
| " <th>Low</th>\n", | |
| " <th>Close</th>\n", | |
| " <th>Volume</th>\n", | |
| " <th>Dividends</th>\n", | |
| " <th>Stock Splits</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>2010-06-29 00:00:00-04:00</td>\n", | |
| " <td>1.266667</td>\n", | |
| " <td>1.666667</td>\n", | |
| " <td>1.169333</td>\n", | |
| " <td>1.592667</td>\n", | |
| " <td>281494500</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2010-06-30 00:00:00-04:00</td>\n", | |
| " <td>1.719333</td>\n", | |
| " <td>2.028000</td>\n", | |
| " <td>1.553333</td>\n", | |
| " <td>1.588667</td>\n", | |
| " <td>257806500</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2</td>\n", | |
| " <td>2010-07-01 00:00:00-04:00</td>\n", | |
| " <td>1.666667</td>\n", | |
| " <td>1.728000</td>\n", | |
| " <td>1.351333</td>\n", | |
| " <td>1.464000</td>\n", | |
| " <td>123282000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>3</td>\n", | |
| " <td>2010-07-02 00:00:00-04:00</td>\n", | |
| " <td>1.533333</td>\n", | |
| " <td>1.540000</td>\n", | |
| " <td>1.247333</td>\n", | |
| " <td>1.280000</td>\n", | |
| " <td>77097000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>4</td>\n", | |
| " <td>2010-07-06 00:00:00-04:00</td>\n", | |
| " <td>1.333333</td>\n", | |
| " <td>1.333333</td>\n", | |
| " <td>1.055333</td>\n", | |
| " <td>1.074000</td>\n", | |
| " <td>103003500</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</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-bd262d49-5112-41f8-85a0-4d91e917a76b')\"\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", | |
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| " </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", | |
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| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-bd262d49-5112-41f8-85a0-4d91e917a76b 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-bd262d49-5112-41f8-85a0-4d91e917a76b');\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", | |
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| "\n", | |
| "\n", | |
| "<div id=\"df-a03578c9-e401-4798-a8ed-3de810dc483b\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a03578c9-e401-4798-a8ed-3de810dc483b')\"\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", | |
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| " </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-a03578c9-e401-4798-a8ed-3de810dc483b button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "tesla_data", | |
| "summary": "{\n \"name\": \"tesla_data\",\n \"rows\": 3533,\n \"fields\": [\n {\n \"column\": \"index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1020,\n \"min\": 0,\n \"max\": 3532,\n \"num_unique_values\": 3533,\n \"samples\": [\n 325,\n 1385,\n 2148\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2010-06-29 00:00:00-04:00\",\n \"max\": \"2024-07-12 00:00:00-04:00\",\n \"num_unique_values\": 3533,\n \"samples\": [\n \"2011-10-11 00:00:00-04:00\",\n \"2015-12-29 00:00:00-05:00\",\n \"2019-01-10 00:00:00-05:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102.25051660341896,\n \"min\": 1.0759999752044678,\n \"max\": 411.4700012207031,\n \"num_unique_values\": 3227,\n \"samples\": [\n 15.27066707611084,\n 2.2073330879211426,\n 12.533332824707031\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 104.52280432687249,\n \"min\": 1.108667016029358,\n \"max\": 414.4966735839844,\n \"num_unique_values\": 3221,\n \"samples\": [\n 2.246000051498413,\n 2.50266695022583,\n 9.463333129882812\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 99.78050835654766,\n \"min\": 0.9986670017242432,\n \"max\": 405.6666564941406,\n \"num_unique_values\": 3237,\n \"samples\": [\n 15.633333206176758,\n 179.8300018310547,\n 15.305999755859375\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102.18809698026372,\n \"min\": 1.053333044052124,\n \"max\": 409.9700012207031,\n \"num_unique_values\": 3333,\n \"samples\": [\n 2.2233328819274902,\n 337.7966613769531,\n 14.524666786193848\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 78783175,\n \"min\": 1777500,\n \"max\": 914082000,\n \"num_unique_values\": 3502,\n \"samples\": [\n 181006400,\n 10117500,\n 110263500\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Dividends\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Stock Splits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.09808743684656393,\n \"min\": 0.0,\n \"max\": 5.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e3bd7036-80dc-4589-b1c7-847c8b930692" | |
| }, | |
| "source": [ | |
| "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "727cbaf8-8841-45f7-b287-651eedfa3bae" | |
| }, | |
| "source": [ | |
| "Use the `requests` library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named `html_data`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3d01a50b-131b-4087-831f-6fef439d9dcd" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "html_data = requests.get(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm\").text" | |
| ], | |
| "execution_count": 13 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9f2b9bdb-5fc5-4920-a311-ec8e98eb0af9" | |
| }, | |
| "source": [ | |
| "Parse the html data using `beautiful_soup` using parser i.e `html5lib` or `html.parser`. Make sure to use the `html_data` with the content parameter as follow `html_data.content` .\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ab847066-8188-48d3-82e1-751eb1db5a24" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "soup = BeautifulSoup(html_data, \"html5lib\")" | |
| ], | |
| "execution_count": null | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "13b57dc9-8ae2-45a3-ba0e-54cffb288108" | |
| }, | |
| "source": [ | |
| "Using `BeautifulSoup` or the `read_html` function extract the table with `Tesla Revenue` and store it into a dataframe named `tesla_revenue`. The dataframe should have columns `Date` and `Revenue`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bbc0cde4-fb57-435f-97bd-5d0f05cf9b69" | |
| }, | |
| "source": [ | |
| "<details><summary>Step-by-step instructions</summary>\n", | |
| "\n", | |
| "```\n", | |
| "\n", | |
| "Here are the step-by-step instructions:\n", | |
| "\n", | |
| "1. Find All Tables: Start by searching for all HTML tables on a webpage using `soup.find_all('table')`.\n", | |
| "2. Identify the Relevant Table: then loops through each table. If a table contains the text “Tesla Quarterly Revenue,”, select that table.\n", | |
| "3. Initialize a DataFrame: Create an empty Pandas DataFrame called `tesla_revenue` with columns “Date” and “Revenue.”\n", | |
| "4. Loop Through Rows: For each row in the relevant table, extract the data from the first and second columns (date and revenue).\n", | |
| "5. Clean Revenue Data: Remove dollar signs and commas from the revenue value.\n", | |
| "6. Add Rows to DataFrame: Create a new row in the DataFrame with the extracted date and cleaned revenue values.\n", | |
| "7. Repeat for All Rows: Continue this process for all rows in the table.\n", | |
| "\n", | |
| "```\n", | |
| "</details>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8ac3887d-e8fc-4622-8262-ea2120ef12c2" | |
| }, | |
| "source": [ | |
| "<details><summary>Click here if you need help locating the table</summary>\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", | |
| "We are focusing on quarterly revenue in the lab.\n", | |
| "```\n", | |
| "\n", | |
| "</details>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tesla_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n", | |
| "\n", | |
| "for table in soup.find_all(\"table\"):\n", | |
| " if \"Tesla Quarterly Revenue\" in table.find(\"th\").text:\n", | |
| " for row in soup.find(\"tbody\").find_all('tr'):\n", | |
| " cols = row.find_all(\"td\")\n", | |
| " data = cols[0].text\n", | |
| " revenue = cols[1].text\n", | |
| " tesla_revenue = pd.concat([tesla_revenue, pd.DataFrame([[data, revenue]], columns=[\"Date\", \"Revenue\"])], ignore_index=True)\n", | |
| "tesla_revenue" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 457 | |
| }, | |
| "id": "C5FXVIjiHMTZ", | |
| "outputId": "37ff8984-09b5-4128-d027-3abb3960142d" | |
| }, | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
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| "data": { | |
| "text/plain": [ | |
| " Date Revenue\n", | |
| "0 2021 $53,823\n", | |
| "1 2020 $31,536\n", | |
| "2 2019 $24,578\n", | |
| "3 2018 $21,461\n", | |
| "4 2017 $11,759\n", | |
| "5 2016 $7,000\n", | |
| "6 2015 $4,046\n", | |
| "7 2014 $3,198\n", | |
| "8 2013 $2,013\n", | |
| "9 2012 $413\n", | |
| "10 2011 $204\n", | |
| "11 2010 $117\n", | |
| "12 2009 $112" | |
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| " <th>5</th>\n", | |
| " <td>2016</td>\n", | |
| " <td>$7,000</td>\n", | |
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| " <th>6</th>\n", | |
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| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "tesla_revenue", | |
| "summary": "{\n \"name\": \"tesla_revenue\",\n \"rows\": 13,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"2010\",\n \"2012\",\n \"2021\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Revenue\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"$117\",\n \"$413\",\n \"$53,823\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e6df862a-ee9b-47a8-8a85-5134e5c3f49c" | |
| }, | |
| "source": [ | |
| "Execute the following line to remove the comma and dollar sign from the `Revenue` column.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "e1950a9e-31a0-417c-bc2e-80eaffb7bb5a" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "tesla_revenue[\"Revenue\"] = tesla_revenue['Revenue'].str.replace(',|\\$',\"\", regex=True)" | |
| ], | |
| "execution_count": 31 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1aa4dd2a-45af-4cf0-a036-2fbb54880f92" | |
| }, | |
| "source": [ | |
| "Execute the following lines to remove an null or empty strings in the Revenue column.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9ffc183b-db10-4de9-b8e4-c633f544fde8" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "tesla_revenue.dropna(inplace=True)\n", | |
| "\n", | |
| "tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]" | |
| ], | |
| "execution_count": 32 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "f8bc39a5-a7dc-4c2d-83be-2e7a323ff3a8" | |
| }, | |
| "source": [ | |
| "Display the last 5 row of the `tesla_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "b2403842-f7ad-4c34-b62c-53dc8fa00389", | |
| "outputId": "54e637f6-a986-480a-f316-24ad58fb1183" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Date Revenue\n", | |
| "8 2013 2013\n", | |
| "9 2012 413\n", | |
| "10 2011 204\n", | |
| "11 2010 117\n", | |
| "12 2009 112" | |
| ], | |
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| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>2012</td>\n", | |
| " <td>413</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>2011</td>\n", | |
| " <td>204</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>2010</td>\n", | |
| " <td>117</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>2009</td>\n", | |
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| } | |
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| "source": [ | |
| "tesla_revenue.tail(5)" | |
| ], | |
| "execution_count": 33 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9de1ca67-7ccc-4426-ac8b-83149ba32438" | |
| }, | |
| "source": [ | |
| "## Question 3: Use yfinance to Extract Stock Data\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "b7d30f5c-0e1f-45e3-ab6c-5371c6d7a32f" | |
| }, | |
| "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 GameStop and its ticker symbol is `GME`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "d31d752d-33bb-440f-89c4-c5928b4bfdfc" | |
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| "outputs": [], | |
| "source": [ | |
| "gme = yf.Ticker(\"GME\")" | |
| ], | |
| "execution_count": 34 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bbcd64ca-5178-484a-b3a2-b34f8011c801" | |
| }, | |
| "source": [ | |
| "Using the ticker object and the function `history` extract stock information and save it in a dataframe named `gme_data`. Set the `period` parameter to ` \"max\" ` so we get information for the maximum amount of time.\n" | |
| ] | |
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| " Open High Low Close \\\n", | |
| "Date \n", | |
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| "2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 \n", | |
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| "... ... ... ... ... \n", | |
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| "2024-07-12 00:00:00-04:00 25.760000 26.889999 25.420000 26.049999 \n", | |
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| "2024-07-11 00:00:00-04:00 13032700 0.0 0.0 \n", | |
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| " <th>2024-07-08 00:00:00-04:00</th>\n", | |
| " <td>24.120001</td>\n", | |
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| " <th>2024-07-11 00:00:00-04:00</th>\n", | |
| " <td>25.440001</td>\n", | |
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| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
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| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 35 | |
| } | |
| ], | |
| "source": [ | |
| "gme_data = gme.history(period=\"max\")\n", | |
| "gme_data" | |
| ], | |
| "execution_count": 35 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "177976c9-c793-44cf-b4ad-2f28c2dbd616" | |
| }, | |
| "source": [ | |
| "**Reset the index** using the `reset_index(inplace=True)` function on the gme_data DataFrame and display the first five rows of the `gme_data` dataframe using the `head` function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "b641e124-3a42-49b3-b93e-9743f72fc7f9", | |
| "outputId": "396a3043-439c-4060-c72b-5758ec04faf3" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Date Open High Low Close Volume \\\n", | |
| "0 2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000 \n", | |
| "1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 \n", | |
| "2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600 \n", | |
| "3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 \n", | |
| "4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 \n", | |
| "\n", | |
| " Dividends Stock Splits \n", | |
| "0 0.0 0.0 \n", | |
| "1 0.0 0.0 \n", | |
| "2 0.0 0.0 \n", | |
| "3 0.0 0.0 \n", | |
| "4 0.0 0.0 " | |
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| " <th></th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Open</th>\n", | |
| " <th>High</th>\n", | |
| " <th>Low</th>\n", | |
| " <th>Close</th>\n", | |
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| " <th>0</th>\n", | |
| " <td>2002-02-13 00:00:00-05:00</td>\n", | |
| " <td>1.620129</td>\n", | |
| " <td>1.693350</td>\n", | |
| " <td>1.603296</td>\n", | |
| " <td>1.691667</td>\n", | |
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| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2002-02-14 00:00:00-05:00</td>\n", | |
| " <td>1.712707</td>\n", | |
| " <td>1.716074</td>\n", | |
| " <td>1.670626</td>\n", | |
| " <td>1.683250</td>\n", | |
| " <td>11021600</td>\n", | |
| " <td>0.0</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2002-02-15 00:00:00-05:00</td>\n", | |
| " <td>1.683250</td>\n", | |
| " <td>1.687458</td>\n", | |
| " <td>1.658001</td>\n", | |
| " <td>1.674834</td>\n", | |
| " <td>8389600</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2002-02-19 00:00:00-05:00</td>\n", | |
| " <td>1.666418</td>\n", | |
| " <td>1.666418</td>\n", | |
| " <td>1.578047</td>\n", | |
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| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
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| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2002-02-20 00:00:00-05:00</td>\n", | |
| " <td>1.615920</td>\n", | |
| " <td>1.662210</td>\n", | |
| " <td>1.603296</td>\n", | |
| " <td>1.662210</td>\n", | |
| " <td>6892800</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</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-33966e1e-1136-48e7-97af-164c03095ab0')\"\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-33966e1e-1136-48e7-97af-164c03095ab0 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-33966e1e-1136-48e7-97af-164c03095ab0');\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-93c33e7d-18b9-459d-8f30-c7676c101393\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93c33e7d-18b9-459d-8f30-c7676c101393')\"\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", | |
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| " </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-93c33e7d-18b9-459d-8f30-c7676c101393 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "gme_data", | |
| "summary": "{\n \"name\": \"gme_data\",\n \"rows\": 5641,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2002-02-13 00:00:00-05:00\",\n \"max\": \"2024-07-12 00:00:00-04:00\",\n \"num_unique_values\": 5641,\n \"samples\": [\n \"2010-07-22 00:00:00-04:00\",\n \"2008-07-07 00:00:00-04:00\",\n \"2015-12-29 00:00:00-05:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10.623018732696528,\n \"min\": 0.6438432736492175,\n \"max\": 94.92749786376953,\n \"num_unique_values\": 5389,\n \"samples\": [\n 6.533273319267075,\n 4.495960979811188,\n 20.59000015258789\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.285177945758598,\n \"min\": 0.672458315127611,\n \"max\": 120.75,\n \"num_unique_values\": 5413,\n \"samples\": [\n 1.0604474530931778,\n 4.401698911435514,\n 3.474476940999407\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.970282813620916,\n \"min\": 0.6312187118812991,\n \"max\": 72.87750244140625,\n \"num_unique_values\": 5406,\n \"samples\": [\n 3.9236582112407232,\n 7.386912984150538,\n 1.5075000524520874\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10.530068780958818,\n \"min\": 0.6387933492660522,\n \"max\": 86.87750244140625,\n \"num_unique_values\": 4621,\n \"samples\": [\n 45.98500061035156,\n 1.7758289575576782,\n 2.695725202560425\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 29830497,\n \"min\": 260000,\n \"max\": 788631600,\n \"num_unique_values\": 5354,\n \"samples\": [\n 2746300,\n 13963600,\n 1287200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Dividends\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.00600817477759431,\n \"min\": 0.0,\n \"max\": 0.095,\n \"num_unique_values\": 8,\n \"samples\": [\n 0.0375,\n 0.09,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Stock Splits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05953963182276481,\n \"min\": 0.0,\n \"max\": 4.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.0,\n 2.0,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 36 | |
| } | |
| ], | |
| "source": [ | |
| "gme_data.reset_index(inplace=True)\n", | |
| "gme_data.head()" | |
| ], | |
| "execution_count": 36 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ebc33ea7-ff12-4c71-b0ee-003bca92fe25" | |
| }, | |
| "source": [ | |
| "## Question 4: Use Webscraping to Extract GME Revenue Data\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "b0e65b52-3c85-47f8-a362-097b55bf6b8b" | |
| }, | |
| "source": [ | |
| "Use the `requests` library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named `html_data_2`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "c0ab5ab4-e87c-4786-8b10-4d5419b6e70b" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "html_data_2 = requests.get(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html\").text" | |
| ], | |
| "execution_count": 38 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "56d90bfb-4a44-4a83-afda-77cd08f053c3" | |
| }, | |
| "source": [ | |
| "Parse the html data using `beautiful_soup` using parser i.e `html5lib` or `html.parser`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "fcb28dd0-98a6-4afe-9ae3-8f071e409a67" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "soup = BeautifulSoup(html_data_2, \"html5lib\")\n", | |
| "soup" | |
| ], | |
| "execution_count": null | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d1fac6e4-906d-47b2-836a-d8f10a63a221" | |
| }, | |
| "source": [ | |
| "Using `BeautifulSoup` or the `read_html` function extract the table with `GameStop Revenue` and store it into a dataframe named `gme_revenue`. The dataframe should have columns `Date` and `Revenue`. Make sure the comma and dollar sign is removed from the `Revenue` column.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7d25ed7f-690d-48ae-95a6-70ccda568bf3" | |
| }, | |
| "source": [ | |
| "> **Note: Use the method similar to what you did in question 2.** \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "354212fe-7de1-4dd1-8671-ac37f9ced1a2" | |
| }, | |
| "source": [ | |
| "<details><summary>Click here if you need help locating the table</summary>\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", | |
| "</details>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 551 | |
| }, | |
| "id": "b4c9b89e-54f0-455e-bdd8-18297e0cd822", | |
| "outputId": "cd6fa4df-c2d4-41fb-c26e-ba9d5d5a9638" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Date Revenue\n", | |
| "0 2020 6466\n", | |
| "1 2019 8285\n", | |
| "2 2018 8547\n", | |
| "3 2017 7965\n", | |
| "4 2016 9364\n", | |
| "5 2015 9296\n", | |
| "6 2014 9040\n", | |
| "7 2013 8887\n", | |
| "8 2012 9551\n", | |
| "9 2011 9474\n", | |
| "10 2010 9078\n", | |
| "11 2009 8806\n", | |
| "12 2008 7094\n", | |
| "13 2007 5319\n", | |
| "14 2006 3092\n", | |
| "15 2005 1843" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-bc94c345-42e6-432a-8d29-cb185bf76066\" class=\"colab-df-container\">\n", | |
| " <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>Date</th>\n", | |
| " <th>Revenue</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2020</td>\n", | |
| " <td>6466</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2019</td>\n", | |
| " <td>8285</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018</td>\n", | |
| " <td>8547</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2017</td>\n", | |
| " <td>7965</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2016</td>\n", | |
| " <td>9364</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>2015</td>\n", | |
| " <td>9296</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>2014</td>\n", | |
| " <td>9040</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>2013</td>\n", | |
| " <td>8887</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>2012</td>\n", | |
| " <td>9551</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>2011</td>\n", | |
| " <td>9474</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>2010</td>\n", | |
| " <td>9078</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>2009</td>\n", | |
| " <td>8806</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>2008</td>\n", | |
| " <td>7094</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>2007</td>\n", | |
| " <td>5319</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>2006</td>\n", | |
| " <td>3092</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>2005</td>\n", | |
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| " 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", | |
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| " background-color: #3B4455;\n", | |
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| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-bc94c345-42e6-432a-8d29-cb185bf76066');\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", | |
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| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
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| "\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", | |
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| " const charts = await google.colab.kernel.invokeFunction(\n", | |
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| " .colab-df-generate {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
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| " cursor: pointer;\n", | |
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| " 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", | |
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| " [theme=dark] .colab-df-generate:hover {\n", | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "gme_revenue", | |
| "summary": "{\n \"name\": \"gme_revenue\",\n \"rows\": 16,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"2020\",\n \"2019\",\n \"2015\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Revenue\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"6466\",\n \"8285\",\n \"9296\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 43 | |
| } | |
| ], | |
| "source": [ | |
| "gme_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n", | |
| "for table in soup.find_all(\"table\"):\n", | |
| " if \"GameStop Quarterly Revenue\" in table.find(\"th\").text:\n", | |
| " for row in soup.find(\"tbody\").find_all('tr'):\n", | |
| " cols = row.find_all(\"td\")\n", | |
| " data = cols[0].text\n", | |
| " revenue = cols[1].text\n", | |
| " gme_revenue = pd.concat([gme_revenue, pd.DataFrame([[data, revenue]], columns=[\"Date\", \"Revenue\"])], ignore_index=True)\n", | |
| "\n", | |
| "gme_revenue[\"Revenue\"] = gme_revenue['Revenue'].str.replace(',|\\$',\"\", regex=True)\n", | |
| "gme_revenue.dropna(inplace=True)\n", | |
| "gme_revenue = gme_revenue[gme_revenue['Revenue'] != \"\"]\n", | |
| "gme_revenue" | |
| ], | |
| "execution_count": 43 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e63773d5-6778-460c-9a71-77ae61382760" | |
| }, | |
| "source": [ | |
| "Display the last five rows of the `gme_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "9ae2e74b-806a-453a-9bf8-e875e1c68885", | |
| "outputId": "16e7f62e-60d8-49f6-e068-ab0d173e78a4" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Date Revenue\n", | |
| "11 2009 8806\n", | |
| "12 2008 7094\n", | |
| "13 2007 5319\n", | |
| "14 2006 3092\n", | |
| "15 2005 1843" | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Revenue</th>\n", | |
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| " <tbody>\n", | |
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| " <td>7094</td>\n", | |
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| " <tr>\n", | |
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| " <td>2007</td>\n", | |
| " <td>5319</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>2006</td>\n", | |
| " <td>3092</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>2005</td>\n", | |
| " <td>1843</td>\n", | |
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| " background-color: #3B4455;\n", | |
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| " [theme=dark] .colab-df-convert:hover {\n", | |
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| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
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| " const buttonEl =\n", | |
| " document.querySelector('#df-b976aa8f-dc80-4bcd-9aa2-5dd4b9382c76 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
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| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-b976aa8f-dc80-4bcd-9aa2-5dd4b9382c76');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
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| " 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", | |
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| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d624e7f8-12a9-43d1-a407-3cdd4ddb72e7')\"\n", | |
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| " .colab-df-quickchart {\n", | |
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| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
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| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
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| " spin 1s steps(1) infinite;\n", | |
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| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
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| " 30% {\n", | |
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| " 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", | |
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| " 60% {\n", | |
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| " async function quickchart(key) {\n", | |
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| " 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", | |
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| " 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-d624e7f8-12a9-43d1-a407-3cdd4ddb72e7 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
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| "metadata": {}, | |
| "execution_count": 44 | |
| } | |
| ], | |
| "source": [ | |
| "gme_revenue.tail(5)" | |
| ], | |
| "execution_count": 44 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1582f654-dbc2-440e-8a0c-20c8917952c3" | |
| }, | |
| "source": [ | |
| "## Question 5: Plot Tesla Stock Graph\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1b73f5e1-ebdd-49b6-95e4-771841d98434" | |
| }, | |
| "source": [ | |
| "Use the `make_graph` function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "39b8b70f-821d-4bc3-bf91-47d1bfff1941" | |
| }, | |
| "source": [ | |
| "<details><summary>Hint</summary>\n", | |
| "\n", | |
| "```\n", | |
| "\n", | |
| "You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.\n", | |
| "\n", | |
| "```\n", | |
| " \n", | |
| "</details>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 917 | |
| }, | |
| "id": "f0a7c6d9-619e-44ce-8f2f-b68d9715d7f0", | |
| "outputId": "c043a356-7c77-4b73-a45a-df924bf2814b" | |
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| { | |
| "output_type": "display_data", | |
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| "make_graph(tesla_data, tesla_revenue, 'Tesla')" | |
| ], | |
| "execution_count": 45 | |
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| "## Question 6: Plot GameStop Stock Graph\n" | |
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| "Use the `make_graph` function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`. Note the graph will only show data upto June 2021.\n" | |
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| "<details><summary>Hint</summary>\n", | |
| "\n", | |
| "```\n", | |
| "\n", | |
| "You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`\n", | |
| "\n", | |
| "```\n", | |
| " \n", | |
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| " \n", | |
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| "var x = new MutationObserver(function (mutations, observer) {{\n", | |
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| " console.log([gd, 'removed!']);\n", | |
| " Plotly.purge(gd);\n", | |
| " observer.disconnect();\n", | |
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| "}});\n", | |
| "\n", | |
| "// Listen for the removal of the full notebook cells\n", | |
| "var notebookContainer = gd.closest('#notebook-container');\n", | |
| "if (notebookContainer) {{\n", | |
| " x.observe(notebookContainer, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| "// Listen for the clearing of the current output cell\n", | |
| "var outputEl = gd.closest('.output');\n", | |
| "if (outputEl) {{\n", | |
| " x.observe(outputEl, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| " }) }; </script> </div>\n", | |
| "</body>\n", | |
| "</html>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "make_graph(gme_data, gme_revenue, 'GameStop')" | |
| ], | |
| "execution_count": 46 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "838c62e8-2acb-457b-b789-f0f28388fb15" | |
| }, | |
| "source": [ | |
| "<h2>About the Authors:</h2>\n", | |
| "\n", | |
| "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> 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", | |
| "metadata": { | |
| "id": "6f2f1b09-b0dd-460c-9be9-08134a4c3e90" | |
| }, | |
| "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", | |
| "<hr>\n", | |
| "\n", | |
| "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n", | |
| "\n", | |
| "<p>\n" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
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| }, | |
| "file_extension": ".py", | |
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| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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