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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

Using pivot_table to change the unit of analysis of a DataFrame

We could have used the pandas pivot_table function instead of groupby in the previous recipe. pivot_table can be used to generate summary statistics by the values of a categorical variable, just as we did with groupby. The pivot_table function can also return a DataFrame, as we will see in this recipe.

Getting ready

We will work with the COVID-19 case daily data and the Brazil land temperature data again. The temperature data has one row per month per weather station.

How to do it...

Let’s create a DataFrame from the COVID-19 data that has the total number of cases and deaths for each day across all countries:

  1. We start by loading the COVID-19 and temperature data again:
    import pandas as pd
    coviddaily = pd.read_csv("data/coviddaily.csv", parse_dates=["casedate"])
    ltbrazil = pd.read_csv("data/ltbrazil.csv")
    
  2. Now, we are ready to call the...
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