Как сделать вычисления производимые в коде видимыми

from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
from pandas.tools.plotting import autocorrelation_plot
 
def parser(x):
    return datetime.strptime('190'+x, '%Y-%m')
 
series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
autocorrelation_plot(series)
 
pyplot.show()

def autocorrelation_plot(series, ax=None, **kwds):
    """Autocorrelation plot for time series.
 
    Parameters:
    -----------
    series: Time series
    ax: Matplotlib axis object, optional
    kwds : keywords
        Options to pass to matplotlib plotting method
 
    Returns:
    -----------
    ax: Matplotlib axis object
    """
    import matplotlib.pyplot as plt
    n = len(series)
    data = np.asarray(series)
    if ax is None:
        ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0))
    mean = np.mean(data)
    c0 = np.sum((data - mean) ** 2) / float(n)
 
    def r(h):
        return ((data[:n - h] - mean) *
                (data[h:] - mean)).sum() / float(n) / c0
    x = np.arange(n) + 1
    y = lmap(r, x)
    z95 = 1.959963984540054
    z99 = 2.5758293035489004
    ax.axhline(y=z99 / np.sqrt(n), linestyle='--', color='grey')
    ax.axhline(y=z95 / np.sqrt(n), color='grey')
    ax.axhline(y=0.0, color='black')
    ax.axhline(y=-z95 / np.sqrt(n), color='grey')
    ax.axhline(y=-z99 / np.sqrt(n), linestyle='--', color='grey')
    ax.set_xlabel("Lag")
    ax.set_ylabel("Autocorrelation")
    ax.plot(x, y, **kwds)
    if 'label' in kwds:
        ax.legend()
    ax.grid()
    return ax