Pandas (http://pandas.pydata.org/pandas-docs/stable/) is distributed as an open-source, BSD-licensed Python library package that provides fast, flexible, and expressive data structures designed to begin working with relational or labeled data. Pandas has been successfully used in the fields of finance, statistics, economics, analytics, and big data. Instructions for installing pandas can be found at http://pandas.pydata.org/pandas-docs/stable/install.html. 

Once pandas is installed on your system, it is easy to import into your codebase. It can be referenced as follows:

#Method 1
import pandas 

#Method 2
import pandas as pd

#Method 3
from pandas import DataFrame, Series

It would not be possible for us to cover every aspect of pandas in this book. We are not here to explore the possibilities of pandas, but to explore how these libraries can be useful in the facilitation of big-data modeling. To learn more about this library, check out Learning pandas by Michael Heydt.