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Extraction and visualization of stock data and revenue of Tesla and GameStop
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Final Assignment Tesla.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "<center>\n <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n</center>\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<h1>Extracting and Visualizing Stock Data</h1>\n<h2>Description</h2>\n"
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "!pip install yfinance\n#!pip install pandas\n#!pip install requests\n!pip install bs4\n#!pip install plotly",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "Collecting yfinance\n Downloading yfinance-0.1.55.tar.gz (23 kB)\nRequirement already satisfied: pandas>=0.24 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (1.0.5)\nRequirement already satisfied: numpy>=1.15 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (1.18.5)\nRequirement already satisfied: requests>=2.20 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (2.24.0)\nCollecting multitasking>=0.0.7\n Downloading multitasking-0.0.9.tar.gz (8.1 kB)\nRequirement already satisfied: lxml>=4.5.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (4.5.1)\nRequirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2.8.1)\nRequirement already satisfied: pytz>=2017.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2020.1)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (3.0.4)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2020.12.5)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (1.25.9)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2.9)\nRequirement already satisfied: six>=1.5 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)\nBuilding wheels for collected packages: yfinance, multitasking\n Building wheel for yfinance (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22616 sha256=486d672f5f3079ae28b0e94aed69f46b148776e83008c1120291a13df4fee7a3\n Stored in directory: /tmp/wsuser/.cache/pip/wheels/aa/8a/36/59ed4f6fbcb6100967618eeb0696046bf9777a41ac2ff1f9b9\n Building wheel for multitasking (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for multitasking: filename=multitasking-0.0.9-py3-none-any.whl size=8366 sha256=a226fe6e010fa3324358e3fc624358bcc705ca9692717d74fc8e027ae2a90abf\n Stored in directory: /tmp/wsuser/.cache/pip/wheels/ae/25/47/4d68431a7ec1b6c4b5233365934b74c1d4e665bf5f968d363a\nSuccessfully built yfinance multitasking\nInstalling collected packages: multitasking, yfinance\nSuccessfully installed multitasking-0.0.9 yfinance-0.1.55\nCollecting bs4\n Downloading bs4-0.0.1.tar.gz (1.1 kB)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from bs4) (4.9.1)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from beautifulsoup4->bs4) (2.0.1)\nBuilding wheels for collected packages: bs4\n Building wheel for bs4 (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for bs4: filename=bs4-0.0.1-py3-none-any.whl size=1272 sha256=4ef439e02ec66242ef6f6be5c3252cd736eced78a1ac247ad1051cdf59781bdf\n Stored in directory: /tmp/wsuser/.cache/pip/wheels/0a/9e/ba/20e5bbc1afef3a491f0b3bb74d508f99403aabe76eda2167ca\nSuccessfully built bs4\nInstalling collected packages: bs4\nSuccessfully installed bs4-0.0.1\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "import yfinance as yf\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Define Graphing Function\n"
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"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 fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data.Date, infer_datetime_format=True), y=stock_data.Close.astype(\"float\"), name=\"Share Price\"), row=1, col=1)\n fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data.Date, infer_datetime_format=True), y=revenue_data.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": 5,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Question 1: Use yfinance to Extract Stock Data\n"
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "tesla=yf.Ticker('TSLA')",
"execution_count": 54,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "tesla_data=tesla.history(period=\"max\")",
"execution_count": 55,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "tesla_data.reset_index(inplace=True)\ntesla_data.head()",
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 56,
"data": {
"text/plain": " Date Open High Low Close Volume Dividends Stock Splits\n0 2010-06-29 3.800 5.000 3.508 4.778 93831500 0 0.0\n1 2010-06-30 5.158 6.084 4.660 4.766 85935500 0 0.0\n2 2010-07-01 5.000 5.184 4.054 4.392 41094000 0 0.0\n3 2010-07-02 4.600 4.620 3.742 3.840 25699000 0 0.0\n4 2010-07-06 4.000 4.000 3.166 3.222 34334500 0 0.0",
"text/html": "<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>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>2010-06-29</td>\n <td>3.800</td>\n <td>5.000</td>\n <td>3.508</td>\n <td>4.778</td>\n <td>93831500</td>\n <td>0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2010-06-30</td>\n <td>5.158</td>\n <td>6.084</td>\n <td>4.660</td>\n <td>4.766</td>\n <td>85935500</td>\n <td>0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2010-07-01</td>\n <td>5.000</td>\n <td>5.184</td>\n <td>4.054</td>\n <td>4.392</td>\n <td>41094000</td>\n <td>0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2010-07-02</td>\n <td>4.600</td>\n <td>4.620</td>\n <td>3.742</td>\n <td>3.840</td>\n <td>25699000</td>\n <td>0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2010-07-06</td>\n <td>4.000</td>\n <td>4.000</td>\n <td>3.166</td>\n <td>3.222</td>\n <td>34334500</td>\n <td>0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Use the `requests` library to download the webpage [https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue](https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). Save the text of the response as a variable named `html_data`.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "url=\" https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue.\"\nhtml_data=requests.get(url).text",
"execution_count": 93,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Parse the html data using `beautiful_soup`.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "beautiful_soup=BeautifulSoup(html_data,\"html5lib\")",
"execution_count": 94,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Using beautiful soup extract the table with `Tesla Quarterly Revenue` and store it into a dataframe named `tesla_revenue`. The dataframe should have columns `Date` and `Revenue`. Make sure the comma and dollar sign is removed from the `Revenue` column. \n"
},
{
"metadata": {},
"cell_type": "code",
"source": "tables=beautiful_soup.find_all(\"table\")\nfor index,table in enumerate(tables):\n if(\"Tesla Quarterly Revenue\" in str(table)):\n table_index=index\ntesla_revenue=pd.DataFrame(columns=[\"Date\",\"Revenue\"])\n\nfor row in tables[table_index].tbody.find_all('tr'):\n col=row.find_all(\"td\")\n if(col!=[]):\n date=col[0].text\n revenue=col[1].text.strip().replace(\"$\",\"\").replace(\",\",\"\")\n tesla_revenue=tesla_revenue.append({\"Date\":date,\"Revenue\":revenue},ignore_index=True)\ntesla_revenue.head()",
"execution_count": 98,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 98,
"data": {
"text/plain": " Date Revenue\n0 2020-12-31 10744\n1 2020-09-30 8771\n2 2020-06-30 6036\n3 2020-03-31 5985\n4 2019-12-31 7384",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Date</th>\n <th>Revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2020-12-31</td>\n <td>10744</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2020-09-30</td>\n <td>8771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2020-06-30</td>\n <td>6036</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2020-03-31</td>\n <td>5985</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-12-31</td>\n <td>7384</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<details><summary>Click here if you need help removing the dollar sign and comma</summary>\n\n```\n \nIf you parsed the HTML table by row and column you can use the replace function on the string\n \n revenue = col[1].text.replace(\"$\", \"\").replace(\",\", \"\")\n \nIf you use the read_html function you can use the replace function on the string representation of the column\n\n tesla_revenue[\"Revenue\"] = tesla_revenue[\"Revenue\"].str.replace(\"$\", \"\").str.replace(\",\", \"\")\n\n```\n\n</details>\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Remove the rows in the dataframe that are empty strings or are NaN in the Revenue column. Print the entire `tesla_revenue` DataFrame to see if you have any.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "print(tesla_revenue)\n\ntesla_revenue.dropna(inplace=True)\nnot_empty=tesla_revenue[\"Revenue\"]!=\"\"\ntesla_revenue=tesla_revenue[not_empty]",
"execution_count": 99,
"outputs": [
{
"output_type": "stream",
"text": " Date Revenue\n0 2020-12-31 10744\n1 2020-09-30 8771\n2 2020-06-30 6036\n3 2020-03-31 5985\n4 2019-12-31 7384\n5 2019-09-30 6303\n6 2019-06-30 6350\n7 2019-03-31 4541\n8 2018-12-31 7226\n9 2018-09-30 6824\n10 2018-06-30 4002\n11 2018-03-31 3409\n12 2017-12-31 3288\n13 2017-09-30 2985\n14 2017-06-30 2790\n15 2017-03-31 2696\n16 2016-12-31 2285\n17 2016-09-30 2298\n18 2016-06-30 1270\n19 2016-03-31 1147\n20 2015-12-31 1214\n21 2015-09-30 937\n22 2015-06-30 955\n23 2015-03-31 940\n24 2014-12-31 957\n25 2014-09-30 852\n26 2014-06-30 769\n27 2014-03-31 621\n28 2013-12-31 615\n29 2013-09-30 431\n30 2013-06-30 405\n31 2013-03-31 562\n32 2012-12-31 306\n33 2012-09-30 50\n34 2012-06-30 27\n35 2012-03-31 30\n36 2011-12-31 39\n37 2011-09-30 58\n38 2011-06-30 58\n39 2011-03-31 49\n40 2010-12-31 36\n41 2010-09-30 31\n42 2010-06-30 28\n43 2010-03-31 21\n44 2009-12-31 \n45 2009-09-30 46\n46 2009-06-30 27\n47 2008-12-31 \n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<details><summary>Click here if you need help removing the Nan or empty strings</summary>\n\n```\n \nIf you have NaN in the Revenue column\n \n tesla_revenue.dropna(inplace=True)\n \nIf you have emtpty string in the Revenue column\n\n tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]\n\n```\n\n</details>\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Display the last 5 row of the `tesla_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "tesla_revenue.tail()",
"execution_count": 100,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 100,
"data": {
"text/plain": " Date Revenue\n41 2010-09-30 31\n42 2010-06-30 28\n43 2010-03-31 21\n45 2009-09-30 46\n46 2009-06-30 27",
"text/html": "<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>41</th>\n <td>2010-09-30</td>\n <td>31</td>\n </tr>\n <tr>\n <th>42</th>\n <td>2010-06-30</td>\n <td>28</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2010-03-31</td>\n <td>21</td>\n </tr>\n <tr>\n <th>45</th>\n <td>2009-09-30</td>\n <td>46</td>\n </tr>\n <tr>\n <th>46</th>\n <td>2009-06-30</td>\n <td>27</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Question 3: Use yfinance to Extract Stock Data\n"
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "gmestop=yf.Ticker(\"GME\")",
"execution_count": 62,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "gme_data=gmestop.history(period=\"max\")",
"execution_count": 63,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"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"
},
{
"metadata": {},
"cell_type": "code",
"source": "gme_data.reset_index(inplace=True)\ngme_data.head()",
"execution_count": 68,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 68,
"data": {
"text/plain": " index Date Open High Low Close Volume \\\n0 0 2002-02-13 6.480513 6.773399 6.413183 6.766666 19054000 \n1 1 2002-02-14 6.850831 6.864296 6.682506 6.733003 2755400 \n2 2 2002-02-15 6.733001 6.749833 6.632006 6.699336 2097400 \n3 3 2002-02-19 6.665671 6.665671 6.312189 6.430017 1852600 \n4 4 2002-02-20 6.463681 6.648838 6.413183 6.648838 1723200 \n\n Dividends Stock Splits \n0 0.0 0.0 \n1 0.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 0.0 ",
"text/html": "<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>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>2002-02-13</td>\n <td>6.480513</td>\n <td>6.773399</td>\n <td>6.413183</td>\n <td>6.766666</td>\n <td>19054000</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>2002-02-14</td>\n <td>6.850831</td>\n <td>6.864296</td>\n <td>6.682506</td>\n <td>6.733003</td>\n <td>2755400</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>2002-02-15</td>\n <td>6.733001</td>\n <td>6.749833</td>\n <td>6.632006</td>\n <td>6.699336</td>\n <td>2097400</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>2002-02-19</td>\n <td>6.665671</td>\n <td>6.665671</td>\n <td>6.312189</td>\n <td>6.430017</td>\n <td>1852600</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>2002-02-20</td>\n <td>6.463681</td>\n <td>6.648838</td>\n <td>6.413183</td>\n <td>6.648838</td>\n <td>1723200</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Question 4: Use Webscraping to Extract GME Revenue Data\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Use the `requests` library to download the webpage [https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue](https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). Save the text of the response as a variable named `html_data`.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "url=\"https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue.\"\nhtml_data=requests.get(url).text",
"execution_count": 69,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Parse the html data using `beautiful_soup`.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "beautiful_soup=BeautifulSoup(html_data,\"html.parser\")",
"execution_count": 73,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Using beautiful soup extract the table with `GameStop Quarterly 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 using a method similar to what you did in Question 2.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "tables=beautiful_soup.find_all(\"table\")\nfor index,table in enumerate(tables):\n if(str(table)==\"GameStop Quarterly Revenue\"):\n table_index=index\n\ngme_revenue=pd.DataFrame(columns=[\"Date\",\"Revenue\"])\n\nfor row in tables[table_index].tbody.find_all(\"tr\"):\n col=row.find_all(\"td\")\n if(col!=[]):\n date=col[0].text\n revenue=col[1].text.replace(\"$\",\"\").replace(\",\",\"\")\n gme_revenue=gme_revenue.append({\"Date\":date,\"Revenue\":revenue},ignore_index=True)\ngme_revenue.head()",
"execution_count": 81,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 81,
"data": {
"text/plain": " Date Revenue\n0 2020-10-31 1005\n1 2020-07-31 942\n2 2020-04-30 1021\n3 2020-01-31 2194\n4 2019-10-31 1439",
"text/html": "<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>2020-10-31</td>\n <td>1005</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2020-07-31</td>\n <td>942</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2020-04-30</td>\n <td>1021</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2020-01-31</td>\n <td>2194</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-10-31</td>\n <td>1439</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Display the last five rows of the `gme_revenue` dataframe using the `tail` function. Take a screenshot of the results.\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "gme_revenue.tail()",
"execution_count": 82,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 82,
"data": {
"text/plain": " Date Revenue\n59 2006-01-31 1667\n60 2005-10-31 534\n61 2005-07-31 416\n62 2005-04-30 475\n63 2005-01-31 709",
"text/html": "<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>59</th>\n <td>2006-01-31</td>\n <td>1667</td>\n </tr>\n <tr>\n <th>60</th>\n <td>2005-10-31</td>\n <td>534</td>\n </tr>\n <tr>\n <th>61</th>\n <td>2005-07-31</td>\n <td>416</td>\n </tr>\n <tr>\n <th>62</th>\n <td>2005-04-30</td>\n <td>475</td>\n </tr>\n <tr>\n <th>63</th>\n <td>2005-01-31</td>\n <td>709</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Question 5: Plot Tesla Stock Graph\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Use the `make_graph` function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`\n"
},
{
"metadata": {},
"cell_type": "code",
"source": "make_graph(tesla_data,tesla_revenue,'Tesla')",
"execution_count": 87,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
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"source": "## Question 6: Plot GameStop Stock Graph\n"
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"source": "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')`.\n"
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"source": "make_graph(gme_data,gme_revenue,'GameStop')",
"execution_count": 88,
"outputs": [
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