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_change() 

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

ndf['retlog']=np.log(ndf['TypicalPrice'].astype('float64')/ndf['TypicalPrice'].astype('float64').shift(1)) #np is for numpy 

Answer 1

Here is one way to calculate log return using .shift(). And the result is similar to but not the same as the gross return calculated by pct_change(). Can you upload a copy of your sample data (dropbox share link) to reproduce the inconsistency you saw?

import pandas as pd import numpy as np  np.random.seed(0) df = pd.DataFrame(100 + np.random.randn(100).cumsum(), columns=['price']) df['pct_change'] = df.price.pct_change() df['log_ret'] = np.log(df.price) - np.log(df.price.shift(1))  Out[56]:         price  pct_change  log_ret 0   101.7641         NaN      NaN 1   102.1642      0.0039   0.0039 2   103.1429      0.0096   0.0095 3   105.3838      0.0217   0.0215 4   107.2514      0.0177   0.0176 5   106.2741     -0.0091  -0.0092 6   107.2242      0.0089   0.0089 7   107.0729     -0.0014  -0.0014 ..       ...         ...      ... 92  101.6160      0.0021   0.0021 93  102.5926      0.0096   0.0096 94  102.9490      0.0035   0.0035 95  103.6555      0.0069   0.0068 96  103.6660      0.0001   0.0001 97  105.4519      0.0172   0.0171 98  105.5788      0.0012   0.0012 99  105.9808      0.0038   0.0038  [100 rows x 3 columns]