The Pandas library builds on NumPy by introducing several useful data structures and functionalities to read and process data. Pandas is a great tool for general data munging. It easily handles common tasks such as dealing with missing data, manipulating shapes and sizes, converting between data formats and structures, and importing data from different sources.
The main data structures introduced by Pandas are:
Series
The DataFrame
Panel
The DataFrame is probably the most widely used. It is a two-dimensional structure that is effectively a table created from either a NumPy array, lists, dicts, or series. You can also create a DataFrame by reading from a file.
Probably the best way to get a feel for Pandas is to go through a typical use case. Let's say that we are given the task of discovering how the daily maximum temperature has changed over time. For this example, we will be working with historical weather observations from the Hobart weather station in Tasmania. Download the following...
