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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 getting a first look at our data

The first few steps we take after we import our data into a pandas DataFrame are pretty much the same regardless of the characteristics of the data. We almost always want to know the number of columns and rows and the column data types, and see the first few rows. We also might want to view the index and check whether there is a unique identifier for DataFrame rows. These discrete, easily repeatable tasks are good candidates for a collection of functions we can organize into a module.

In this recipe, we will create a module with functions that give us a good first look at any pandas DataFrame. A module is simply a collection of Python code that we can import into another Python program. Modules are easy to reuse because they can be referenced by any program with access to the folder where the module is saved.

Getting ready...

We create two files in this recipe: one with a function we will use to look at our data and another to...

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