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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565661
Length 436 pages
Edition 1st Edition
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Authors (2):
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Michael B Walker Michael B Walker
Author Profile Icon Michael B Walker
Michael B Walker
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas 2. Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas FREE CHAPTER 3. Chapter 3: Taking the Measure of Your Data 4. Chapter 4: Identifying Missing Values and Outliers in Subsets of Data 5. Chapter 5: Using Visualizations for the Identification of Unexpected Values 6. Chapter 6: Cleaning and Exploring Data with Series Operations 7. Chapter 7: Fixing Messy Data when Aggregating 8. Chapter 8: Addressing Data Issues When Combining DataFrames 9. Chapter 9: Tidying and Reshaping Data 10. Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning 11. Other Books You May Enjoy

Examining both the 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 and the Covid case 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 Covid case and NLS data:
    >>> import pandas as pd
    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> import seaborn as sns
    &gt...
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