Applying function to Pandas Data frame and getting result as a new data frame

Question

I have this code:

import yfinance as yF
import datetime
import pandas as pd

df = pd.DataFrame({'ID':['1', '2'], 'Ticker': ['AIN', 'TILE'], 'Company':['Albany International', 'Interface']})


def get_returns(tic,com):
    df_Stock = yF.download(tickers = tic, period = "max", interval = "1mo", prepost = False, repair = False)
    df_Stock[com + ' % Growth'] = df_Stock['High']-df_Stock['Open']
    df_Stock[com + ' % Growth'] = (df_Stock[com + ' % Growth'] * 100 )/df_Stock['Open']
    
    return df_Stock[com + ' % Growth']

get_returns('AIN','AIN')

Everything work as expected until this point.

df1 = df.apply(lambda x: get_returns(x.Ticker, x.Company), axis=1)

Here I am trying to use this function

get_returns()

with Pandas apply and lambda on the dataframe df defined above. The desired output is another dataframe df1 which has 3 columns namely

1. Date
2. Company 1 - Albany International % Returns
3. Company 2 - Interface % Returns

If there are more rows in dataframe df, each company will be a new column in df1 and its monthly return would be the row value for the given time period under the Date column.

Answer 1

This is the output I got when running your code for the following data frame

	ID	Ticker	Company
0	1	AIN	Albany International
1	2	TILE	Interface

Output

Date	1985-01-01	1985-02-01	1985-03-01	1985-04-01	1985-05-01	1985-06-01	1985-07-01	1985-08-01	1985-09-01	1985-10-01	...	2023-01-01	2023-02-01	2023-03-01	2023-04-01	2023-05-01	2023-06-01	2023-07-01	2023-08-01	2023-09-01	2023-10-01
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	13.081953	3.767988	1.609221	3.527832	3.188085	11.953453	5.196635	0.891469	1.352506	0.290596
1	inf	inf	inf	inf	inf	inf	inf	inf	inf	inf	...	15.577892	8.091470	5.604387	1.231532	2.809710	27.745666	12.328766	8.771934	3.567984	1.530608

I believe that the code that you're running creates an entry for each index in the original data frame. So row 0 corresponds to 'AIN' and row 1 corresponds to 'TILE'.

To get it into the desired format you need to transpose df1 and rename the columns based on df.

df1 = df1.T
df1.rename(columns=df['Company'] + ' % Returns', inplace=True)
df1.rename(columns={v: f"Company {k+1} - {v}" for k, v in enumerate(df1.columns)}, inplace=True)

Output:

Date	Company 1 - Albany International % Returns	Company 2 - Interface % Returns
1985-01-01	NaN	inf
1985-02-01	NaN	inf
1985-03-01	NaN	inf
1985-04-01	NaN	inf
1985-05-01	NaN	inf
...	...	...
2023-06-01	11.953453	27.745666
2023-07-01	5.196635	12.328766
2023-08-01	0.891469	8.771934
2023-09-01	1.352506	3.567984
2023-10-01	0.290596	1.530608