Hello, once we fetch data from DSWS we generally get this ouput:
# Set Start and End Dates with the following format "yyyy-mm-dd" StartDate = "2023-03-01" #Set start date EndDate = "2023-05-31" #Set end date # Set The Frequency Of The Data Equal to D,W or M Freq = 'M' # Initializations sp500_data = pd.DataFrame() ism_data = pd.DataFrame() # Fetch daily data and add 1 period return sp500_data = ds.get_data(tickers='S&PCOMP', fields=['PI'],start=StartDate, end=EndDate, freq=Freq)
The sp500_data dataframe will looks like this:
Instrument S&PCOMP Field PI Currency U$ Dates 2023-03-01 3951.39 2023-04-01 4124.51 2023-05-01 4167.87
Now, how to convert that output into a better dataframe similar to the following:
Dates S&PCOMP 3/1/2023 3951.39 4/1/2023 4124.51 5/1/2023 4167.87
I am not able to find a proper solution, looks like it originally fetches data using MultiIndex that always create a mess when you have to manage data into dataframes. BE aware that the ticker is near the "Dates" not ABOVE so a reset_index will not solve here (Dates is my "index").