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Python Data Cleaning Cookbook - Second Edition

You're reading from   Python Data Cleaning Cookbook - Second Edition Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

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Product type Book
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Pages 486 pages
Edition 2nd Edition
Languages
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

Using groupby to organize data by groups

At a certain point in most data analysis projects, we have to generate summary statistics by groups. While this can be done using the approaches in the previous recipe, in most cases the pandas DataFrame groupby method is a better choice. If groupby can handle an aggregation task—and it usually can—it is likely the most efficient way to accomplish that task. We make good use of groupby in the next few recipes. We go over the basics in this recipe.

Getting ready

We will work with the COVID-19 daily data in this recipe.

How to do it…

We will create a pandas groupby DataFrame and use it to generate summary statistics by group:

  1. Import pandas and numpy, and load the COVID-19 daily data:
    import pandas as pd
    coviddaily = pd.read_csv("data/coviddaily.csv", parse_dates=["casedate"])
    
  2. Create a pandas groupby DataFrame:
    countrytots = coviddaily.groupby([&apos...
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