Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Exploratory Data Analysis with Python Cookbook

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

Arrow left icon
Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

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 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:

  1. Import pandas and import the stats module from the scipy library:
    import pandas as pd
    from scipy import stats
  2. Load the .csv into a dataframe using read_csv. Then subset...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime