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Final Assignment.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h1>Extracting and Visualizing Stock Data</h1>\n<h2>Description</h2>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.\n"
},
{
"cell_type": "markdown",
"metadata": {},
"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": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n from cryptography.utils import int_from_bytes\n/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n from cryptography.utils import int_from_bytes\nRequirement already satisfied: yfinance in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (0.1.59)\nRequirement already satisfied: multitasking>=0.0.7 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (0.0.9)\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: 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)\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: 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: 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: 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: 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: 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)\n/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n from cryptography.utils import int_from_bytes\n/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n from cryptography.utils import int_from_bytes\nRequirement already satisfied: bs4 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (0.0.1)\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)\n"
}
],
"source": "!pip install yfinance\n#!pip install pandas\n#!pip install requests\n!pip install bs4\n#!pip install plotly"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"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"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Define Graphing Function\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In this section, we define the function `make_graph`. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.\n"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"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 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()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 1: Use yfinance to Extract Stock Data\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Using the `Ticker` function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is `TSLA`.\n"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "yfinance.Ticker object <TSLA>"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "tesla = yf.Ticker(\"TSLA\")\ntesla"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Using the ticker object and the function `history` extract stock information and save it in a dataframe named `tesla_data`. Set the `period` parameter to `max` so we get information for the maximum amount of time.\n"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": "tesla_data = tesla.history('max')"
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"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>",
"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"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "tesla_data.reset_index(inplace=True)\ntesla_data.head(5)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 2: Use Webscraping to Extract Tesla Revenue Data\n"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": "url =\"https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue\"\nhtml_data = requests.get(url).text"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Parse the html data using `beautiful_soup`.\n"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": "beautiful_soup = BeautifulSoup(html_data, \"html.parser\")"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"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>2021-03-31</td>\n <td>10389</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2020-12-31</td>\n <td>10744</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2020-09-30</td>\n <td>8771</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2020-06-30</td>\n <td>6036</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2020-03-31</td>\n <td>5985</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Date Revenue\n0 2021-03-31 10389\n1 2020-12-31 10744\n2 2020-09-30 8771\n3 2020-06-30 6036\n4 2020-03-31 5985"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "tesla_revenue=pd.DataFrame(columns= [\"Date\",\"Revenue\"])\n\nfor table in beautiful_soup.find_all('table', attrs={'class':'historical_data_table table'}):\n if table.find('th').getText().startswith(\"Tesla Quarterly Revenue\"):\n for row in table.find('tbody').find_all('tr'):\n tds = row.find_all('td')\n if len(tds) != 2: continue\n date = tds[0].getText()\n revenue = tds[1].getText()\n revenue = revenue.replace('$', '').replace(',', '')\n \n tesla_revenue = tesla_revenue.append({\"Date\":date, \"Revenue\":revenue}, ignore_index=True)\n \ntesla_revenue.head(5)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<details><summary>Click here if you need help removing the dollar sign and comma</summary>\n\n```\n \nIf you parsed the HTML table by row and column you can use the replace function on the string\n \n revenue = col[1].text.replace(\"$\", \"\").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"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": "tesla_revenue.dropna(inplace=True)\ntesla_revenue=tesla_revenue[tesla_revenue['Revenue']!=\"\"]"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"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>42</th>\n <td>2010-09-30</td>\n <td>31</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2010-06-30</td>\n <td>28</td>\n </tr>\n <tr>\n <th>44</th>\n <td>2010-03-31</td>\n <td>21</td>\n </tr>\n <tr>\n <th>46</th>\n <td>2009-09-30</td>\n <td>46</td>\n </tr>\n <tr>\n <th>47</th>\n <td>2009-06-30</td>\n <td>27</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Date Revenue\n42 2010-09-30 31\n43 2010-06-30 28\n44 2010-03-31 21\n46 2009-09-30 46\n47 2009-06-30 27"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "tesla_revenue.tail(5)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 3: Use yfinance to Extract Stock Data\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Using the `Ticker` function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is `GME`.\n"
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": "gamestop = yf.Ticker(\"GME\")"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": "gme_data = gamestop.history(period=\"max\")"
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"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>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>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>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>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>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>",
"text/plain": " Date Open High Low Close Volume Dividends \\\n0 2002-02-13 6.480513 6.773399 6.413183 6.766666 19054000 0.0 \n1 2002-02-14 6.850831 6.864296 6.682506 6.733003 2755400 0.0 \n2 2002-02-15 6.733001 6.749833 6.632006 6.699336 2097400 0.0 \n3 2002-02-19 6.665671 6.665671 6.312189 6.430017 1852600 0.0 \n4 2002-02-20 6.463681 6.648838 6.413183 6.648838 1723200 0.0 \n\n Stock Splits \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 "
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "gme_data.reset_index(inplace=True)\ngme_data.head(5)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 4: Use Webscraping to Extract GME Revenue Data\n"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": "html_data = requests.get(\"https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue\").text"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Parse the html data using `beautiful_soup`.\n"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": "soup=BeautifulSoup(html_data, \"html.parser\")"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": "gme_revenue = pd.DataFrame(columns=[\"Date\",\"Revenue\"])\n\nfor table in soup.find_all('table', attrs={'Class':'historical_data_table table'}):\n if table.find('th').getText().startwith(\"GameStop Quarterly Revenue\"):\n for row in table.find('tbody').find_all('tr'):\n col = row.find_all('td')\n if len(col) !=2:continue\n date = col[0].getText()\n revenue=col[1].getText().replace('$','').replace(',','')\n \ngme_revenue= gme_revenue.append({\"Date\": date, \"Revenue\":revenue}, ignore_index=True)"
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"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>2008-12-31</td>\n <td></td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Date Revenue\n0 2008-12-31 "
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "gme_revenue.tail(5)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 5: Plot Tesla Stock Graph\n"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'subplot_titles' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-414eb0627c2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msubplot_titles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmake_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtesla_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtesla_revenue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Tesla'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'subplot_titles' is not defined"
]
}
],
"source": "subplot_titles()\nmake_graph(tesla_data, tesla_revenue, 'Tesla')"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Question 6: Plot GameStop Stock Graph\n"
},
{
"cell_type": "markdown",
"metadata": {},
"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"
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
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"output_type": "display_data"
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],
"source": "gme_revenue.dropna(inplace=True)\ngme_revenue=gme_revenue[gme_revenue['Revenue']!=\"\"]\nmake_graph(gme_data, gme_revenue, 'GameStop')"
},
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"cell_type": "markdown",
"metadata": {},
"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\nAzim Hirjani\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n| ----------------- | ------- | ------------- | ------------------------- |\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\"> \u00a9 IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n"
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