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Final Assignment.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"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": {},
"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": "markdown",
"metadata": {},
"source": [
"***Note***:- If you are working Locally using anaconda, please uncomment the following code and execute it.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!pip install yfinance==0.2.38\n",
"#!pip install pandas==2.2.2\n",
"#!pip install nbformat"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: yfinance in ./.env/lib/python3.9/site-packages (0.2.41)\n",
"Requirement already satisfied: numpy>=1.16.5 in ./.env/lib/python3.9/site-packages (from yfinance) (2.0.1)\n",
"Requirement already satisfied: pytz>=2022.5 in ./.env/lib/python3.9/site-packages (from yfinance) (2024.1)\n",
"Requirement already satisfied: lxml>=4.9.1 in ./.env/lib/python3.9/site-packages (from yfinance) (5.2.2)\n",
"Requirement already satisfied: requests>=2.31 in ./.env/lib/python3.9/site-packages (from yfinance) (2.32.3)\n",
"Requirement already satisfied: frozendict>=2.3.4 in ./.env/lib/python3.9/site-packages (from yfinance) (2.4.4)\n",
"Requirement already satisfied: beautifulsoup4>=4.11.1 in ./.env/lib/python3.9/site-packages (from yfinance) (4.12.3)\n",
"Requirement already satisfied: pandas>=1.3.0 in ./.env/lib/python3.9/site-packages (from yfinance) (2.2.2)\n",
"Requirement already satisfied: multitasking>=0.0.7 in ./.env/lib/python3.9/site-packages (from yfinance) (0.0.11)\n",
"Requirement already satisfied: html5lib>=1.1 in ./.env/lib/python3.9/site-packages (from yfinance) (1.1)\n",
"Requirement already satisfied: platformdirs>=2.0.0 in ./.env/lib/python3.9/site-packages (from yfinance) (4.2.2)\n",
"Requirement already satisfied: peewee>=3.16.2 in ./.env/lib/python3.9/site-packages (from yfinance) (3.17.6)\n",
"Requirement already satisfied: soupsieve>1.2 in ./.env/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n",
"Requirement already satisfied: six>=1.9 in ./.env/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
"Requirement already satisfied: webencodings in ./.env/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in ./.env/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\n",
"Requirement already satisfied: tzdata>=2022.7 in ./.env/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2024.1)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in ./.env/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.3.2)\n",
"Requirement already satisfied: certifi>=2017.4.17 in ./.env/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2024.7.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in ./.env/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2.2.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in ./.env/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.7)\n",
"\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n",
"You should consider upgrading via the '/Users/lex/code/uol/coursera/python-project-for-data-science/.env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: bs4 in ./.env/lib/python3.9/site-packages (0.0.2)\n",
"Requirement already satisfied: beautifulsoup4 in ./.env/lib/python3.9/site-packages (from bs4) (4.12.3)\n",
"Requirement already satisfied: soupsieve>1.2 in ./.env/lib/python3.9/site-packages (from beautifulsoup4->bs4) (2.5)\n",
"\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n",
"You should consider upgrading via the '/Users/lex/code/uol/coursera/python-project-for-data-science/.env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: nbformat in ./.env/lib/python3.9/site-packages (5.10.4)\n",
"Requirement already satisfied: traitlets>=5.1 in ./.env/lib/python3.9/site-packages (from nbformat) (5.14.3)\n",
"Requirement already satisfied: jsonschema>=2.6 in ./.env/lib/python3.9/site-packages (from nbformat) (4.23.0)\n",
"Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in ./.env/lib/python3.9/site-packages (from nbformat) (5.7.2)\n",
"Requirement already satisfied: fastjsonschema>=2.15 in ./.env/lib/python3.9/site-packages (from nbformat) (2.20.0)\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in ./.env/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat) (2023.12.1)\n",
"Requirement already satisfied: attrs>=22.2.0 in ./.env/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat) (23.2.0)\n",
"Requirement already satisfied: referencing>=0.28.4 in ./.env/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat) (0.35.1)\n",
"Requirement already satisfied: rpds-py>=0.7.1 in ./.env/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat) (0.19.0)\n",
"Requirement already satisfied: platformdirs>=2.5 in ./.env/lib/python3.9/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.2.2)\n",
"\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n",
"You should consider upgrading via the '/Users/lex/code/uol/coursera/python-project-for-data-science/.env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install yfinance\n",
"!pip install bs4\n",
"!pip install nbformat"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import yfinance as yf\n",
"import pandas as pd\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"# Ignore all warnings\n",
"warnings.filterwarnings(\"ignore\", category=FutureWarning)"
]
},
{
"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": 5,
"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",
" 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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": {},
"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": 9,
"metadata": {},
"outputs": [],
"source": [
"tsla = yf.Ticker(\"TSLA\")"
]
},
{
"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": 10,
"metadata": {},
"outputs": [],
"source": [
"tesla_data = tsla.history(period=\"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": 11,
"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 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>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>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>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>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>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 \n",
"1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 \n",
"2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 \n",
"3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 \n",
"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 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tesla_data.reset_index(inplace=True)\n",
"tesla_data.head()"
]
},
{
"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://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",
"execution_count": 12,
"metadata": {},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"soup = BeautifulSoup(html_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": {},
"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": {},
"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",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"tesla_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n",
"for tr in soup.find_all(\"tbody\")[1].find_all(\"tr\"):\n",
" col =tr.find_all(\"td\")\n",
" date = col[0].text\n",
" revenue = col[1].text\n",
" \n",
" # Finally we append the data of each row to the table\n",
" tesla_revenue = pd.concat([tesla_revenue,pd.DataFrame({\"Date\":[date], \"Revenue\":[revenue]})], ignore_index=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execute the following line to remove the comma and dollar sign from the `Revenue` column. \n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"tesla_revenue[\"Revenue\"] = tesla_revenue['Revenue'].str.replace(',|\\$',\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execute the following lines to remove an null or empty strings in the Revenue column.\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"tesla_revenue.dropna(inplace=True)\n",
"\n",
"tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != \"\"]"
]
},
{
"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": 39,
"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>48</th>\n",
" <td>2010-09-30</td>\n",
" <td>$31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2010-06-30</td>\n",
" <td>$28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2010-03-31</td>\n",
" <td>$21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>2009-09-30</td>\n",
" <td>$46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>2009-06-30</td>\n",
" <td>$27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Revenue\n",
"48 2010-09-30 $31\n",
"49 2010-06-30 $28\n",
"50 2010-03-31 $21\n",
"52 2009-09-30 $46\n",
"53 2009-06-30 $27"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tesla_revenue.tail()"
]
},
{
"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": 41,
"metadata": {},
"outputs": [],
"source": [
"gme = 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": 42,
"metadata": {},
"outputs": [],
"source": [
"gme_data = gme.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": 43,
"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 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",
" <td>76216000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2002-02-14 00:00:00-05:00</td>\n",
" <td>1.712708</td>\n",
" <td>1.716074</td>\n",
" <td>1.670626</td>\n",
" <td>1.683251</td>\n",
" <td>11021600</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2002-02-15 00:00:00-05:00</td>\n",
" <td>1.683251</td>\n",
" <td>1.687459</td>\n",
" <td>1.658002</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>1.666417</td>\n",
" <td>1.666417</td>\n",
" <td>1.578047</td>\n",
" <td>1.607504</td>\n",
" <td>7410400</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2002-02-20 00:00:00-05:00</td>\n",
" <td>1.615920</td>\n",
" <td>1.662209</td>\n",
" <td>1.603296</td>\n",
" <td>1.662209</td>\n",
" <td>6892800</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 \\\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 1.712708 1.716074 1.670626 1.683251 11021600 \n",
"2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 \n",
"3 2002-02-19 00:00:00-05:00 1.666417 1.666417 1.578047 1.607504 7410400 \n",
"4 2002-02-20 00:00:00-05:00 1.615920 1.662209 1.603296 1.662209 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 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gme_data.reset_index(inplace=True)\n",
"gme_data.head()"
]
},
{
"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://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",
"execution_count": 44,
"metadata": {},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parse the html data using `beautiful_soup` using parser i.e `html5lib` or `html.parser`.\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"soup2 = BeautifulSoup(html_data_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": {},
"source": [
"> **Note: Use the method similar to what you did in question 2.** \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"gme_revenue = pd.DataFrame(columns=[\"Date\", \"Revenue\"])\n",
"\n",
"for tr in soup2.find_all(\"tbody\")[1].find_all(\"tr\"):\n",
" col =tr.find_all(\"td\")\n",
" date = col[0].text\n",
" revenue = col[1].text\n",
" \n",
" # Finally we append the data of each row to the table\n",
" gme_revenue = pd.concat([gme_revenue,pd.DataFrame({\"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": 48,
"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>57</th>\n",
" <td>2006-01-31</td>\n",
" <td>$1,667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>2005-10-31</td>\n",
" <td>$534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>2005-07-31</td>\n",
" <td>$416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>2005-04-30</td>\n",
" <td>$475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>2005-01-31</td>\n",
" <td>$709</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Revenue\n",
"57 2006-01-31 $1,667\n",
"58 2005-10-31 $534\n",
"59 2005-07-31 $416\n",
"60 2005-04-30 $475\n",
"61 2005-01-31 $709"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gme_revenue.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 5: Plot Tesla Stock Graph\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `make_graph` function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Hint</summary>\n",
"\n",
"```\n",
"\n",
"You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.\n",
"\n",
"```\n",
" \n",
"</details>\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"tesla_revenue.Revenue = tesla_revenue.Revenue.str.replace(\"$\", \"\").str.replace(\",\", \"\").astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" define('plotly', function(require, exports, module) {\n",
" /**\n",
"* plotly.js v2.34.0\n",
"* Copyright 2012-2024, Plotly, Inc.\n",
"* All rights reserved.\n",
"* Licensed under the MIT license\n",
"*/\n",
"/*! For license information please see plotly.min.js.LICENSE.txt */\n",
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