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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
Languages
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
Author Profile Icon Matthew Harrison
Matthew Harrison
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Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Filling values with unequal indexes

When two Series are added together using the plus operator and one of the index labels does not appear in the other, the resulting value is always missing. pandas has the .add method, which provides an option to fill the missing value. Note that these Series do not include duplicate entries, hence there is no need to worry about a Cartesian product exploding the number of entries.

In this recipe, we add together multiple Series from the baseball dataset with unequal (but unique) indexes using the .add method with the fill_value parameter to ensure that there are no missing values in the result.

How to do it…

  1. Read in the three baseball datasets and set playerID as the index:
    >>> baseball_14 = pd.read_csv(
    ...     "data/baseball14.csv", index_col="playerID"
    ... )
    >>> baseball_15 = pd.read_csv(
    ...     "data/baseball15.csv", index_col="playerID"
    ... )
    >&gt...
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