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.
Estimated Time Needed: 30 min
!pip install yfinance==0.1.67
#!pip install pandas==1.3.3
#!pip install requests==2.26.0
!mamba install bs4==4.10.0 -y
#!pip install plotly==5.3.1
Requirement already satisfied: yfinance==0.1.67 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (0.1.67) Requirement already satisfied: lxml>=4.5.1 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (4.7.1) Requirement already satisfied: pandas>=0.24 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (1.3.4) Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (0.0.10) Requirement already satisfied: requests>=2.20 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (2.26.0) Requirement already satisfied: numpy>=1.15 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (1.20.3) Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pandas>=0.24->yfinance==0.1.67) (2.8.2) Requirement already satisfied: pytz>=2017.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pandas>=0.24->yfinance==0.1.67) (2021.3) Requirement already satisfied: six>=1.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance==0.1.67) (1.15.0) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (1.26.7) Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (2021.10.8) Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (3.3) Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (2.0.4) /usr/bin/sh: mamba: command not found
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
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.
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
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
.
Tesla=yf.Ticker("TSLA")
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.
tesla_data=Tesla.history(period='max')
tesla_data
Open | High | Low | Close | Volume | Dividends | Stock Splits | |
---|---|---|---|---|---|---|---|
Date | |||||||
2010-06-29 | 3.800000 | 5.000000 | 3.508000 | 4.778000 | 93831500 | 0 | 0.0 |
2010-06-30 | 5.158000 | 6.084000 | 4.660000 | 4.766000 | 85935500 | 0 | 0.0 |
2010-07-01 | 5.000000 | 5.184000 | 4.054000 | 4.392000 | 41094000 | 0 | 0.0 |
2010-07-02 | 4.600000 | 4.620000 | 3.742000 | 3.840000 | 25699000 | 0 | 0.0 |
2010-07-06 | 4.000000 | 4.000000 | 3.166000 | 3.222000 | 34334500 | 0 | 0.0 |
... | ... | ... | ... | ... | ... | ... | ... |
2022-03-07 | 856.299988 | 866.140015 | 804.570007 | 804.580017 | 24164700 | 0 | 0.0 |
2022-03-08 | 795.530029 | 849.989990 | 782.169983 | 824.400024 | 26799700 | 0 | 0.0 |
2022-03-09 | 839.479980 | 860.559998 | 832.010010 | 858.969971 | 19728000 | 0 | 0.0 |
2022-03-10 | 851.450012 | 854.450012 | 810.359985 | 838.299988 | 19549500 | 0 | 0.0 |
2022-03-11 | 840.200012 | 843.799988 | 793.770020 | 795.349976 | 22272800 | 0 | 0.0 |
2947 rows × 7 columns
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.
tesla_data.reset_index(inplace=True)
tesla_data.head()
Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
---|---|---|---|---|---|---|---|---|
0 | 2010-06-29 | 3.800 | 5.000 | 3.508 | 4.778 | 93831500 | 0 | 0.0 |
1 | 2010-06-30 | 5.158 | 6.084 | 4.660 | 4.766 | 85935500 | 0 | 0.0 |
2 | 2010-07-01 | 5.000 | 5.184 | 4.054 | 4.392 | 41094000 | 0 | 0.0 |
3 | 2010-07-02 | 4.600 | 4.620 | 3.742 | 3.840 | 25699000 | 0 | 0.0 |
4 | 2010-07-06 | 4.000 | 4.000 | 3.166 | 3.222 | 34334500 | 0 | 0.0 |
Use the requests
library to download the webpage https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue. Save the text of the response as a variable named html_data
.
html_data=requests.get("https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork23455606-2021-01-01").text
Parse the html data using beautiful_soup
.
soup=BeautifulSoup(html_data,'html.parser')
Using BeautifulSoup
or the read_html
function extract the table with Tesla Quarterly Revenue
and store it into a dataframe named tesla_revenue
. The dataframe should have columns Date
and Revenue
.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
tesla_tables = soup.find_all('table')
for index,table in enumerate(tesla_tables):
if ("Tesla Quarterly Revenue" in str(table)):
tesla_table_index = index
tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])
for row in tesla_tables[tesla_table_index].tbody.find_all("tr"):
col = row.find_all("td")
if (col !=[]):
date = col[0].text
revenue = col[1].text.replace("$", "").replace(",", "")
tesla_revenue = tesla_revenue.append({"Date" : date, "Revenue" : revenue}, ignore_index=True)
Execute the following line to remove the comma and dollar sign from the Revenue
column.
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"",regex=True)
Execute the following lines to remove an null or empty strings in the Revenue column.
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
Display the last 5 row of the tesla_revenue
dataframe using the tail
function. Take a screenshot of the results.
tesla_revenue.tail()
Date | Revenue | |
---|---|---|
45 | 2010-09-30 | 31 |
46 | 2010-06-30 | 28 |
47 | 2010-03-31 | 21 |
49 | 2009-09-30 | 46 |
50 | 2009-06-30 | 27 |
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
.
GameStop=yf.Ticker('GME')
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.
gme_data=GameStop.history('max')
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.
gme_data.reset_index(inplace=True)
gme_data.head()
Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
---|---|---|---|---|---|---|---|---|
0 | 2002-02-13 | 6.480514 | 6.773400 | 6.413184 | 6.766667 | 19054000 | 0.0 | 0.0 |
1 | 2002-02-14 | 6.850828 | 6.864294 | 6.682503 | 6.733001 | 2755400 | 0.0 | 0.0 |
2 | 2002-02-15 | 6.733001 | 6.749833 | 6.632006 | 6.699336 | 2097400 | 0.0 | 0.0 |
3 | 2002-02-19 | 6.665672 | 6.665672 | 6.312190 | 6.430017 | 1852600 | 0.0 | 0.0 |
4 | 2002-02-20 | 6.463682 | 6.648839 | 6.413184 | 6.648839 | 1723200 | 0.0 | 0.0 |
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%3E;. Save the text of the response as a variable named html_data
.
html_data=requests.get("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html").text
Parse the html data using beautiful_soup
.
soup=BeautifulSoup(html_data,"html.parser")
Using BeautifulSoup
or the read_html
function 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.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
gme_tables = soup.find_all('table')
for index,table in enumerate(gme_tables):
if ("GameStop Quarterly Revenue" in str(table)):
gme_table_index = index
gme_revenue = pd.DataFrame(columns=["Date", "Revenue"])
for row in gme_tables[gme_table_index].tbody.find_all("tr"):
col = row.find_all("td")
if (col !=[]):
date = col[0].text
revenue = col[1].text.replace("$", "").replace(",", "")
gme_revenue = gme_revenue.append({"Date" : date, "Revenue" : revenue}, ignore_index=True)
Display the last five rows of the gme_revenue
dataframe using the tail
function. Take a screenshot of the results.
gme_revenue.tail()
Date | Revenue | |
---|---|---|
57 | 2006-01-31 | 1667 |
58 | 2005-10-31 | 534 |
59 | 2005-07-31 | 416 |
60 | 2005-04-30 | 475 |
61 | 2005-01-31 | 709 |
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')
. Note the graph will only show data upto June 2021.
make_graph(tesla_data, tesla_revenue, 'Tesla')
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.
Joseph Santarcangelo 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.
Azim Hirjani
Date (YYYY-MM-DD) | Version | Changed By | Change Description |
---|---|---|---|
2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |