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The Statistics and Calculus with Python Workshop

You're reading from  The Statistics and Calculus with Python Workshop

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209763
Pages 740 pages
Edition 1st Edition
Languages
Concepts
Authors (6):
Peter Farrell Peter Farrell
Profile icon Peter Farrell
Alvaro Fuentes Alvaro Fuentes
Profile icon Alvaro Fuentes
Ajinkya Sudhir Kolhe Ajinkya Sudhir Kolhe
Profile icon Ajinkya Sudhir Kolhe
Quan Nguyen Quan Nguyen
Profile icon Quan Nguyen
Alexander Joseph Sarver Alexander Joseph Sarver
Profile icon Alexander Joseph Sarver
Marios Tsatsos Marios Tsatsos
Profile icon Marios Tsatsos
View More author details

Table of Contents (14) Chapters

Preface
1. Fundamentals of Python 2. Python's Main Tools for Statistics 3. Python's Statistical Toolbox 4. Functions and Algebra with Python 5. More Mathematics with Python 6. Matrices and Markov Chains with Python 7. Doing Basic Statistics with Python 8. Foundational Probability Concepts and Their Applications 9. Intermediate Statistics with Python 10. Foundational Calculus with Python 11. More Calculus with Python 12. Intermediate Calculus with Python Appendix

Types of Data in Statistics

In statistics, there are two main types of data: categorical data and numerical data. Depending on which type an attribute or a variable in your dataset belongs to, its data processing, modeling, analysis, and visualization techniques might differ. In this section, we will explain the details of these two main data types and discuss relevant points for each of them, which are summarized in the following table:

Figure 3.1: Data type comparison

For the rest of this section, we will go into more detail about each of the preceding comparisons, starting with categorical data in the next subsection.

Categorical Data

When an attribute or a variable is categorical, the possible values it can take belong to a predetermined and fixed set of values. For example, in a weather-related dataset, you might have an attribute to describe the overall weather for each day, in which case that attribute might be among a list of discrete values such...

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