Summary

In this chapter, we covered the basics of handling pandas DataFrames to format them as inputs for different visualization functions in libraries such as pandas , seaborn and more, and we covered some essential concepts in generating and modifying plots to create pleasing figures.

The pandas library contains functions such as read_csv(), read_excel(), and read_json() to read structured text data files. Functions such as describe() and info() are useful to get information on the summary statistics and memory usage of the features in a DataFrame. Other important operations on pandas DataFrames include subletting based on user-specified conditions/constraints, adding new columns to a DataFrame, transforming existing columns with built-in Python functions as well as user-defined functions, deleting specific columns in a DataFrame, and writing a modified DataFrame to a file on the local system.

Once equipped with knowledge of these common operations on pandas DataFrames, we...