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Pandas Cookbook

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

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836205876
Length 404 pages
Edition 3rd Edition
Languages
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Authors (2):
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William Ayd William Ayd
Author Profile Icon William Ayd
William Ayd
Matthew Harrison Matthew Harrison
Author Profile Icon Matthew Harrison
Matthew Harrison
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Toc

Table of Contents (13) Chapters Close

Preface 1. pandas Foundations FREE CHAPTER 2. Selection and Assignment 3. Data Types 4. The pandas I/O System 5. Algorithms and How to Apply Them 6. Visualization 7. Reshaping DataFrames 8. Group By 9. Temporal Data Types and Algorithms 10. General Usage and Performance Tips 11. The pandas Ecosystem 12. Index

Position-based selection of a Series

As discussed back in the Basic selection from a DataFrame section, using [] as a selection mechanism does not signal the clearest intent and can sometimes be downright confusing. The fact that ser[42] selects from a label matching the number 42 and not the 42nd row of a pd.Series is a common mistake for new users, and such an ambiguity can grow even more complex as you start trying to select two dimensions with the [] operator from a pd.DataFrame.

To clearly signal that you are trying to select by position instead of by label, you should use pd.Series.iloc.

How to do it

Let’s create a pd.Series where we have an index using integral labels that are also non-unique:

ser = pd.Series(["apple", "banana", "orange"], index=[0, 1, 1])
ser
0     apple
1    banana
1    orange
dtype: object

To select a scalar, you can use pd.Series.iloc with an integer argument:

ser.iloc[1]
banana
...
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