Function Chaining with pipe()

Explore how to use the pandas pipe() method to chain multiple data transformation functions in a readable and reusable way. This lesson helps you apply complex sequences of data manipulations efficiently, improving clarity and workflow when working with pandas DataFrames.

We'll cover the following...

Introduction
Example of pipe() for data transformation

Introduction

The pipe() method in pandas is a highly useful method for working with data transformations. At its core, pipe() is used for function chaining, which is connecting two or more functions in a sequence so that the output of one function serves as the input of the next.

It provides a way to streamline data transformations and manipulations by enabling users to chain function calls together in a readable, reusable, and efficient manner. It also helps to eliminate the cognitive burden of naming variables at each intermediate step of the transformation process.

We’ll work with a mock retail transactions dataset in this lesson, as ...

1.Before We Begin

2.Reading Data into pandas

3.Combining Data

4.Reshaping and Manipulating Data

5.Encoding Data Types

6.Handling Numerical Data

7.Handling Categorical Data

8.Handling Text Data

9.Handling Time Series Data

10.Handling Sparse Data Structures

11.Handling Missing Data

Project

12.Leveraging Further Features of pandas

13.Utilizing Extended Libraries

14.Wrap Up

15.Appendix

Project

Function Chaining with pipe()

Introduction