You're reading from Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781803231105

Length 382 pages

Edition 1st Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Ayodele Oluleye

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Table of Contents (13) Chapters

Preface

1. Chapter 1: Generating Summary Statistics

2. Chapter 2: Preparing Data for EDA FREE CHAPTER

3. Chapter 3: Visualizing Data in Python

4. Chapter 4: Performing Univariate Analysis in Python

5. Chapter 5: Performing Bivariate Analysis in Python

6. Chapter 6: Performing Multivariate Analysis in Python

7. Chapter 7: Analyzing Time Series Data in Python

8. Chapter 8: Analysing Text Data in Python

9. Chapter 9: Dealing with Outliers and Missing Values

10. Chapter 10: Performing Automated Exploratory Data Analysis in Python

11. Index

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12. Other Books You May Enjoy

Analyzing the interquartile range (IQR) of a dataset

The IQR also measures the spread or variability of a dataset. It is simply the distance between the first and third quartiles. The IQR is a very useful statistic, especially when we need to identify where the middle 50% of values in a dataset lie. Unlike the range, which can be skewed by very high or low numbers (outliers), the IQR isn’t affected by outliers since it focuses on the middle 50. It is also useful when we need to compute for outliers in a dataset.

To analyze the IQR of a dataset, we will use the IQR method from the stats module within the scipy library in Python.

Getting ready

We will work with the COVID-19 cases again for this recipe.

How to do it…

We will explore how to compute the IQR using the scipy library:

Import pandas and import the stats module from the scipy library:
```
import pandas as pd
from scipy import stats
```
Load the .csv into a dataframe using read_csv. Then subset...