cross correlation plot in statsmodels-CodePudding

Consider the simple example below, borrowed from

However, this seems to be a lot of code for something that that conceptually is very simple (just appending two lists). Can this code be streamlined?

Thanks!

CodePudding user response：

Well, you can try "just appending to lists":

# also
# cc = list(backards)   list(forwards[1:])
cc = np.concatenate([backwards, forwards[1:]])
full = pd.DataFrame({'lag':np.arange(len(cc))-len(backwards), 
                     'value':cc})
full.plot(x='lag')

Also:

full = (pd.DataFrame({'value':np.concatenate([backwards, forwards[1:]])})
          .assign(lag=lambda x: x.index - len(backwards) )
       )

Output:

Note if all you want is to plot the two arrays, then this would do

plt.plot(-np.arange(len(backwards)), backwards, c='C0')
plt.plot(forwards, c='C0')