Logarithmic returns in pandas dataframe

Question

Python pandas has a pct_change function which I use to calculate the returns for stock prices in a dataframe:

ndf[\'Return\']= ndf[\'TypicalPrice\'].pct_chan         


        

Answer 1

The results might seem similar, but that is just because of the Taylor expansion for the logarithm. Since log(1 + x) ~ x, the results can be similar.

However,

I am using the following code to get logarithmic returns, but it gives the exact same values as the pct.change() function.

is not quite correct.

import pandas as pd

df = pd.DataFrame({'p': range(10)})

df['pct_change'] = df.pct_change()
df['log_stuff'] = \
    np.log(df['p'].astype('float64')/df['p'].astype('float64').shift(1))
df[['pct_change', 'log_stuff']].plot();