python finance 9 compare same stock current and past by shift days

(Stock(past x days) - Stock(current)) / Stock(current)
Date
2011-01-03    0.004695
2011-01-04   -0.002670
2011-01-05    0.006426
2011-01-06    0.005454
2011-01-07   -0.006085
                ...
2019-10-28   -0.002914
2019-10-29   -0.010520
2019-10-30   -0.002215
2019-10-31    0.030043
2019-11-01         NaN
Name: XOM_1d, Length: 2224, dtype: float64
Date
2011-01-03    0.002012
2011-01-04    0.003738
2011-01-05    0.011914
2011-01-06   -0.000665
2011-01-07    0.001323
                ...
2019-10-28   -0.013403
2019-10-29   -0.012712
2019-10-30    0.027761
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_2d, Length: 2224, dtype: float64
Date
2011-01-03    0.008451
2011-01-04    0.009212
2011-01-05    0.005757
2011-01-06    0.006784
2011-01-07    0.013097
                ...
2019-10-28   -0.015589
2019-10-29    0.016949
2019-10-30         NaN
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_3d, Length: 2224, dtype: float64
Date
2011-01-03    0.013950
2011-01-04    0.003071
2011-01-05    0.013253
2011-01-06    0.018622
2011-01-07    0.014817
                ...
2019-10-28    0.013986
2019-10-29         NaN
2019-10-30         NaN
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_4d, Length: 2224, dtype: float64
Date
2011-01-03    0.007780
2011-01-04    0.010547
2011-01-05    0.025167
2011-01-06    0.020351
2011-01-07    0.029766
                ...
2019-10-28         NaN
2019-10-29         NaN
2019-10-30         NaN
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_5d, Length: 2224, dtype: float64
Date
2011-01-03    0.015292
2011-01-04    0.022430
2011-01-05    0.026908
2011-01-06    0.035382
2011-01-07    0.041275
                ...
2019-10-28         NaN
2019-10-29         NaN
2019-10-30         NaN
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_6d, Length: 2224, dtype: float64
Date
2011-01-03    0.027230
2011-01-04    0.024166
2011-01-05    0.042035
2011-01-06    0.046954
2011-01-07    0.035058
                ...
2019-10-28         NaN
2019-10-29         NaN
2019-10-30         NaN
2019-10-31         NaN
2019-11-01         NaN
Name: XOM_7d, Length: 2224, dtype: float64

---------------------
def process_data_for_labels(ticker):
    hm_days = 7
    df = pd.read_csv('sp500_joined_closes.csv', index_col=0)
    tickers = df.columns.values.tolist()
    df.fillna(0, inplace=True)

    for i in range(1, hm_days+1):
        df['{}_{}d'.format(ticker, i)] = (df[ticker].shift(-i) - df[ticker]) / df[ticker]
        print(df['{}_{}d'.format(ticker, i)])

    df.fillna(0, inplace=True)
    return tickers, df

process_data_for_labels('XOM')

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reference:
https://www.youtube.com/watch?v=Z-5wNWgRJpk&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ&index=9