Data Structures in pandas
Explore pandas data structures such as Series and DataFrame to gain skills in data manipulation, cleaning, and analysis. Learn to index and select data, handle missing values, merge datasets, and perform aggregation using GroupBy, preparing you to manage and analyze data more effectively with Python.
We'll cover the following...
In Python, pandas is an open-source library providing high-performance, easy-to-use data structures and data analysis tools. Today, pandas is actively supported by a community of like-minded individuals around the world who contribute their valuable time and energy to help make pandas open source.
In this section of the course, we’ll learn to use pandas for data analysis. To compare pandas with a familiar program, we can think of pandas as being an extremely powerful version of Excel with a lot more features. We’ll cover the following key concepts in this lesson:
- Data structures—Series and DataFrame.
- Indexing and selection.
- Hierarchical indexing.
- Data cleaning and preparation.
- Handling missing data.
- Data wrangling—merging/joining, combining/concatenation.
- Data aggregation and GroupBy.
Data structure in pandas
Series and DataFrame are two powerful data structures in pandas. Let’s talk about the Series first:
Series
A Series is a one-dimensional, array-like object containing values and an array of labels associated with the values. Series can be indexed using ...