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

View More author details

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

Checking the quartiles of a dataset

The quartile is like the percentile because it can be used to measure the spread and identify the center of a dataset. Percentiles and quartiles are called quantiles. While the percentile divides the dataset into 100 equal portions, the quartile divides the dataset into 4 equal portions. Typically, three quartiles will split your dataset into four equal portions.

To analyze the quartiles of a dataset, we will use the quantiles method from the numpy library in Python. Unlike percentile, the quartile doesn’t have a specific method dedicated to it.

Getting ready

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

How to do it…

We will compute the quartiles using the numpy library:

Import the numpy and pandas libraries:
```
import numpy as np
import pandas as pd
```
Load the .csv into a dataframe using read_csv. Then subset the dataframe to include only relevant columns:
```
covid_data = pd.read_csv("covid-data...
```