To automate this process, we can write a function that accepts stock data in the and outputs the histogram of daily returns along with the percentages that fall within 1, 2, and 3 standard deviations from the mean. The following function does this and replaces the methods with their symbol counterparts:

>>> def test_return_normality(stock_data):
        close = stock_data['Close']
        daily_return = close.pct_change().dropna()
        daily_return.hist(bins=20)
        mean = daily_return.mean() 
        std = daily_return.std()

        abs_z_score = abs(daily_return - mean) / std 
        pcts = [abs_z_score.lt(i).mean() for i in range(1,4)]
 
        print('{:.3f} fall within 1 standard deviation. '
              '{:.3f} within 2 and {:.3f} within 3'.format(*pcts))

>>> slb = pd.read_csv('data/slb_stock.csv', index_col='Date',
                      parse_dates=['Date'])
>>> test_return_normality(slb)
0.742 fall within 1 standard deviation. 0.946 within 2 and 0.986 within 3