Creating new df columns for each iteration of "for" loop

Question

I am trying to calculate the diff_chg of S&P sectors for 4 different dates (given in start_return) :

start_return = [-30,-91,-182,-365]
for date in start_return:
    diff_chg = closingprices[-1].divide(closingprices[date]) 
    for i in sectors:                                  #Sectors is XLK, XLY , etc                                                          
        diff_chg[i] = diff_chg[sectordict[i]].mean()    #finds the % chg of all sectors
        diff_df = diff_chg.to_frame

My expected output is to have 4 columns in the df, each one with the returns of each sector for the given period (-30,-91, -182,-365.) .

As of now when I run this code, it returns the sum of the returns of all 4 periods in the diff_df. I would like it to create a new column in the df for each period.

my code returns:

XLK     1.859907
XLI     1.477272
XLF     1.603589
XLE     1.415377
XLB     1.526237

but I want it to return:

        1mo (-30)        3mo (-61)         6mo (-182)         1yr (-365
XLK     1.086547         values here       etc               etc
XLI     1.0334
XLF     1.07342
XLE     .97829
XLB     1.0281

Answer 1

Try something like this:

start_return = [-30,-91,-182,-365]
diff_chg = pd.DataFrame()
for date in start_return:
    diff_chg[date] = closingprices[-1].divide(closingprices[date])

What this does is to add columns for each date in start_return to a single DataFrame created at the beginning.