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The Pandas Workshop

You're reading from  The Pandas Workshop

Product type Book
Published in Jun 2022
Publisher Packt
ISBN-13 9781800208933
Pages 744 pages
Edition 1st Edition
Languages
Authors (4):
Blaine Bateman Blaine Bateman
Profile icon Blaine Bateman
Saikat Basak Saikat Basak
Profile icon Saikat Basak
Thomas V. Joseph Thomas V. Joseph
Profile icon Thomas V. Joseph
William So William So
Profile icon William So
View More author details

Table of Contents (21) Chapters

Preface Part 1 – Introduction to pandas
Chapter 1: Introduction to pandas Chapter 2: Working with Data Structures Chapter 3: Data I/O Chapter 4: Pandas Data Types Part 2 – Working with Data
Chapter 5: Data Selection – DataFrames Chapter 6: Data Selection – Series Chapter 7: Data Exploration and Transformation Chapter 8: Understanding Data Visualization Part 3 – Data Modeling
Chapter 9: Data Modeling – Preprocessing Chapter 10: Data Modeling – Modeling Basics Chapter 11: Data Modeling – Regression Modeling Part 4 – Additional Use Cases for pandas
Chapter 12: Using Time in pandas Chapter 13: Exploring Time Series Chapter 14: Applying pandas Data Processing for Case Studies Chapter 15: Appendix Other Books You May Enjoy

Exploring dependent and independent variables

In this chapter, you will learn about dependent and independent variables. You will learn about the need for scaling and normalization of data, in addition to performing those operations. You will also use some basic modeling methods to analyze your data.

At a high level, we can say a dependent variable is related to one or more independent variables in a linear or non-linear way. Linear models are easy to understand. A linear model relating one Y to one X is just a line. With multiple X variables, each one has a coefficient that gives its effect on Y, and since all those effects are independent, we just add all the effects together in a multivariate linear model. In a non-linear model, Y depends on X in a more complex way, such as Y being a function of X2. We can create non-linear models nearly as easily as linear models in pandas using some simple additional modules. We'll explore how to do that in the following chapter.

Much...

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