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Python Data Cleaning Cookbook

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

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 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

Examining both distribution shape and outliers with violin plots

Violin plots combine histograms and boxplots in one plot. They show the IQR, median, and whiskers, as well as the frequency of observations at all ranges of values. It is hard to visualize how that is possible without seeing an actual violin plot. We generate a few violin plots on the same data we used for boxplots in the previous recipe, to make it easier to grasp how they work.

Getting ready

We will work with the NLS data. You need Matplotlib and Seaborn installed on your computer to run the code in this recipe.

How to do it…

We do violin plots to view both the spread and shape of the distribution on the same graphic. We then do violin plots by groups:

  1. Load pandas, matplotlib, and seaborn, and the NLS data:
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    nls97 = pd.read_csv("data/nls97f.csv", low_memory=False)
    nls97.set_index...
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