We already showed how to download the factor-related data directly from prof. French's website. As an alternative, we can use the functionalities of pandas_datareader and avoid some manual preprocessing steps:

1. Import the libraries:

from pandas_datareader.famafrench import get_available_datasets
import pandas_datareader.data as web

2. Print the available datasets (only the first five):

get_available_datasets()[:5]

The preceding code results in the following output:

['F-F_Research_Data_Factors',
 'F-F_Research_Data_Factors_weekly',
 'F-F_Research_Data_Factors_daily',
 'F-F_Research_Data_5_Factors_2x3',
 'F-F_Research_Data_5_Factors_2x3_daily']

3. Download the selected dataset:

ff_dict = web.DataReader('F-F_Research_Data_Factors', 'famafrench', 
                         start='2014-01-01')

The default behavior of web.DataReader downloads the last 5 years' worth of data. The resulting object is a dictionary and we can inspect its contents by running the following command:

ff_dict.keys()

This results in the following output:

dict_keys([0, 1, 'DESCR'])

4. Inspect the description of the dataset:

print(ff_dict['DESCR'])

The description is as follows:

5. View the monthly dataset:

ff_dict[0].head()

The dataset looks like the one we manually downloaded from prof. French's website:

The yearly values are stored under the key of 1 and can be accessed by using ff_dict[1].