Stock Market Data And Analysis In Python – Part VI


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See Part I for instructions on how to get pandas_datareader or yfinance module to retrieve the data, and Part II to learn how to get stock market data for different geographies. In Part III and Part IV, review the tutorial on how to analyse the stock market data for all the stocks which make up the S&P 500. Part V explains how to use Quandl to get stock market data.

Fundamental Data

We have used yfinance to get the fundamental data.

The first step is to set the ticker and then call the appropriate properties to get the right stock market data.

If yfinance is not installed on your computer, then run the below line of code from your Jupyter Notebook to install yfinance.

!pip install yfinance
install hosted with ❤ by GitHub
# Import yfinance
import yfinance as yf

# Set the ticker as MSFT
msft = yf.Ticker("MSFT")
Import yfinance and set the hosted with ❤ by GitHub

Key Ratios

You can fetch the latest price to book ratio and price to earnings ratio as shown below.

# get price to book
pb =['priceToBook']
pe =['regularMarketPrice']/['epsTrailingTwelveMonths']
print('Price to Book Ratio is: %.2f' % pb)
print('Price to Earnings Ratio is: %.2f' % pe)
Get price to hosted with ❤ by GitHub


# show revenues
revenue = msft.financials.loc['Total Revenue'], revenue.values)
plt.ylabel("Total Revenues") hosted with ❤ by GitHub

Earnings Before Interest and Taxes (EBIT)

EBIT = msft.financials.loc['Earnings Before Interest and Taxes'], EBIT.values)
plt.ylabel("EBIT") hosted with ❤ by GitHub

Balance sheet, cash flows and other information

# show income statement
# show balance heet
# show cashflow
# show other info
Show hosted with ❤ by GitHub

Stay tuned for next installment in which Ishan Shah will share a tutorial on Stock Market Data Visualization and Analysis.

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