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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data FREE CHAPTER 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

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 to 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...

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