If you wish to designate a specific column in your dataframe to become the index you can do so using the Pandas _index() function. Use set_index() to add a single-level index Note that when the dataframe is created, Pandas will add its own numeric index without the need for you to explicitly set a column to use as the index.ĭf = pd. Then either import data into Pandas from a CSV file or create a dataframe of dummy data to work with. To get started, open a Jupyter notebook and import the Pandas library using the import pandas as pd naming convention. In this quick tutorial I’ll show you how to use them.

Similarly, you can remove, drop, or reset indexes using the _index() method. However, you can also add explicitly named indexes with a single level or a multi-level or hierarchical index using the _index() method. While many Pandas operations don’t require or benefit from an explicitly named index on the dataframe, named indexes (or indices) can be beneficial for some tasks because a wide range of functions are designed to let you manipulate the data based on index values.īy default, Pandas will add a basic numeric index and this is fine for most tasks.

Pandas set index