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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
Languages
Tools
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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

Generating frequencies for categorical variables

Many years ago, a very seasoned researcher said to me, "90% of what we're going to find, we'll see in the frequency distributions." That message has stayed with me. The more one-way and two-way frequency distributions (crosstabs) I do on a DataFrame, the better I understand it. We will do one-way distributions in this recipe, and crosstabs in subsequent recipes.

Getting ready…

We continue our work with the NLS. We will also be doing a fair bit of column selection using filter methods. It is not necessary to review the recipe in this chapter on column selection, but it might be helpful.

How to do it…

We use pandas tools to generate frequencies, particularly the very handy value_counts:

  1. Load the pandas library and the nls97 file.

    Also, convert the columns with object data type to category data type:

    >>> import pandas as pd
    >>> nls97 = pd.read_csv("data/nls97.csv...
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