When it comes to working with data in a tabular form, most people reach for a spreadsheet. That’s not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for massaging tables of data. But what if you want more control, precision, and power than Excel alone delivers?
In that case, the open source Pandas library for Python might be what you are looking for. Pandas augments Python with new data types for loading data fast from tabular sources, and for manipulating, aligning, merging, and doing other processing at scale.
Your first Pandas data set
Pandas is not part of the Python standard library. It’s a third-party project, so you’ll need to install it in your Python runtime with pip install pandas. Once installed, you can import it into Python with import pandas.
Pandas gives you two new data types: Series and DataFrame. The DataFrame represents your entire spreadsheet or rectangular data, whereas the Series is a single column of the DataFrame. In Python terms, you can think of the Pandas DataFrame as a dictionary or collection of Series objects. You’ll also find later that you can use dictionary- and list-like methods for finding elements in a DataFrame.
