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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 FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 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

Versioning data

There may be times when we want to persist data without overwriting a prior version of the data file. This can be accomplished by appending a time stamp to a filename or a unique identifier. However, there are more elegant solutions available. One such solution is the Delta Lake library, which we will explore in this recipe.

We will work with the land temperature data again in this recipe. We will load the data, save it to a data lake, and then save an altered version to the same data lake.

Getting ready

We will be using the Delta Lake library in this recipe, which can be installed with pip install deltalake. We will also need the os library so that we can make a directory for the data lake.

How to do it...

You can get started with the data and version it as follows:

  1. We start by importing the Delta Lake library. We also create a folder called temps_lake for our data versions:
    import pandas as pd
    from deltalake.writer import write_deltalake...
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