All pandas programs and examples in this book will always start by importing pandas (and NumPy) into the Python environment. There is a common convention used in many publications (web and print) of importing pandas and NumPy, which will also be used throughout this book. All workbooks and examples for chapters will start with code similar to the following to initialize the pandas library within Python.

In [1]:
   # import numpy and pandas, and DataFrame / Series
   import numpy as np
   import pandas as pd
   from pandas import DataFrame, Series

   # Set some pandas options
   pd.set_option('display.notebook_repr_html', False)
   pd.set_option('display.max_columns', 10)
   pd.set_option('display.max_rows', 10)

   # And some items for matplotlib
   %matplotlib inline 
   import matplotlib.pyplot as plt
   pd.options.display.mpl_style = 'default'

NumPy and pandas go hand-in-hand, as much of pandas is built on NumPy. It is, therefore, very convenient to import NumPy and put it in a np. namespace. Likewise, pandas is imported and referenced with a pd. prefix. Since DataFrame and Series objects of pandas are used so frequently, the third line then imports the Series and DataFrame objects into the global namespace so that we can use them without a pd. prefix.

The three pd.set_options() method calls set up some defaults for IPython Notebooks and console output from pandas. These specify how wide and high any output will be, and how many columns it will contain. They can be used to modify the output of IPython and pandas to fit your personal needs to display results. The options set here are convenient for formatting the output of the examples to the constraints of the text.