I am trying to use yfinance to download financial data from S&P's 500 firms. However, I would also like to include the GICS sector code for each firm, in order to then divide the data into smaller dataset according to the GICS code. This is my attempt:
import pandas as pd
import yfinance as yf
import datetime
payload=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
first_table = payload[0]
df = first_table
df.head()
tickers_symbols=df['Symbol'].values.tolist()
GICS_sectors = df['GICS Sector'].values.tolist()
GICS=pd.DataFrame(GICS_sectors)
data = yf.download(tickers_symbols, period='1mo')
data['GICS']=GICS
print(data.head)
data.to_csv('stock_prices.csv')
However, I noticed that, besides giving me an empty list when I save the output in a CSV file, it yields a column vector, which is incompatible with the stock dataframe which as days as rows and firms as columns. Any idea on how to solve this issue? Solutions including other packages are also welcome.
Although it is not as fast as getting all the stocks, getting the values for each stock in an empty data frame and adding the stock name and industry category is an easy format (vertical format) to complete for future analysis.
import pandas as pd
import yfinance as yf
import datetime
payload = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
df = pd.DataFrame(payload[0])
tickers_symbols = df['Symbol'].values.tolist()
GICS_sectors = df['GICS Sector'].values.tolist()
data = pd.DataFrame()
for t,s in zip(tickers_symbols, GICS_sectors):
tmp = yf.download(t, period='1mo', progress=False)
tmp.reset_index(inplace=True)
tmp['Ticker'] = t
tmp['GICS'] = s
data = data.append(tmp, ignore_index=True)
data.to_csv('stock_prices.csv', sep=',')
Date Open High Low Close Adj Close Volume Ticker GICS
0 2021-08-09 197.649994 198.149994 196.779999 197.429993 195.934311 1193300.0 MMM Industrials
1 2021-08-10 198.240005 199.490005 197.699997 199.250000 197.740524 1598400.0 MMM Industrials
2 2021-08-11 200.000000 201.770004 199.309998 201.570007 200.042969 2217400.0 MMM Industrials
3 2021-08-12 201.479996 202.369995 200.360001 201.429993 199.904007 1231800.0 MMM Industrials
4 2021-08-13 201.229996 201.710007 200.289993 200.580002 199.060455 1910700.0 MMM Industrials