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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

Functions for displaying summary statistics and frequencies

During the first few days of working with a DataFrame, we try to get a good sense of the distribution of continuous variables and counts for categorical variables. We also often do counts by selected groups. Although pandas and NumPy have many built-in methods for these purposes – describe, mean, valuecounts, crosstab, and so on – data analysts often have preferences for how they work with these tools. If, for example, an analyst finds that she usually needs to see more percentiles than those generated by describe, she can use her own function instead. We will create user-defined functions for displaying summary statistics and frequencies in this recipe.

Getting ready

We will be working with the basicdescriptives module again in this recipe. All of the functions we will define are saved in that module. We continue to work with the NLS data.

How to do it...

We will use functions we create to generate...

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