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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

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 National Longitudinal Survey 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/nls97b.csv")
    >>> nls97.set_index("personid", inplace=True)
  2. Edit all the values based...
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