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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

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
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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

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:

  1. Import the numpy and pandas libraries:
    import numpy as np
    import pandas as pd
  2. 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...
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