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Modern Python Cookbook

You're reading from   Modern Python Cookbook 133 recipes to develop flawless and expressive programs in Python 3.8

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800207455
Length 822 pages
Edition 2nd Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Toc

Table of Contents (18) Chapters Close

Preface 1. Numbers, Strings, and Tuples 2. Statements and Syntax FREE CHAPTER 3. Function Definitions 4. Built-In Data Structures Part 1: Lists and Sets 5. Built-In Data Structures Part 2: Dictionaries 6. User Inputs and Outputs 7. Basics of Classes and Objects 8. More Advanced Class Design 9. Functional Programming Features 10. Input/Output, Physical Format, and Logical Layout 11. Testing 12. Web Services 13. Application Integration: Configuration 14. Application Integration: Combination 15. Statistical Programming and Linear Regression 16. Other Books You May Enjoy
17. Index

Using the built-in statistics library

A great deal of exploratory data analysis (EDA) involves getting a summary of the data. There are several kinds of summary that might be interesting:

  • Central Tendency: Values such as the mean, mode, and median can characterize the center of a set of data.
  • Extrema: The minimum and maximum are as important as the central measures of a set of data.
  • Variance: The variance and standard deviation are used to describe the dispersal of the data. A large variance means the data is widely distributed; a small variance means the data clusters tightly around the central value.

This recipe will show how to create basic descriptive statistics in Python.

Getting ready

We'll look at some simple data that can be used for statistical analysis. We've been given a file of raw data, called anscombe.json. It's a JSON document that has four series of (x,y) pairs.

We can read this data with the...

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