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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

Changing Series values

During the data cleaning process, we often need to change the values in a data Series or create a new one. We can change all the values in a Series, or just the values in a subset of our data. Most of the techniques we have been using to get values from a Series can be used to update Series values, though some minor modifications are necessary.

Getting ready

We will work with the overall high school GPA column from the NLS in this recipe.

How to do it…

We can change the values in a pandas Series for all rows, as well as for selected rows. We can update a Series with scalars by performing arithmetic operations on other Series, and by using summary statistics. Let’s take a look at this:

  1. Import pandas and load the NLS data:
    import pandas as pd
    nls97 = pd.read_csv("data/nls97f.csv",
    low_memory=False)
    nls97.set_index("personid", inplace=True)
    
  2. Edit all the values based on a scalar.
  3. ...
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