Data Structures in pandas
Explore the core pandas data structures, Series and DataFrame, and how to use indexing, selection, and data cleaning methods. Understand key concepts like handling missing data, merging datasets, and performing group operations to efficiently analyze data 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. pandas is maintained by an active global community of contributors.
This section introduces pandas for data analysis. pandas can be compared to Excel, with more advanced data manipulation and analysis capabilities. This lesson covers the following concepts:
- 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 labels.
Note: Series is similar to the NumPy array. It’s built ...