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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

Using unstack and pivot to reshape data from long to wide

Sometimes, we actually have to move data from a tidy to an untidy structure. This is often because we need to prepare the data for analysis with software packages that do not handle relational data well, or because we are submitting data to some external authority that has requested it in an untidy format. unstack and pivot can be helpful when we need to reshape data from long to wide format. unstack does the opposite of what we did with stack, and pivot does the opposite of melt.

Getting ready...

We continue to work with the NLS data on weeks worked and college enrollment in this recipe.

How to do it…

We use unstack and pivot to return the melted NLS DataFrame to its original state:

  1. Import pandas and load the stacked and melted NLS data:
    >>> import pandas as pd
    >>> nls97 = pd.read_csv("data/nls97f.csv")
    >>> nls97.set_index(['originalid'], inplace=True...
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