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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
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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
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Matthew Harrison
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Toc

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

Selecting DataFrame rows and columns simultaneously

There are many ways to select rows and columns. The easiest method to select one or more columns from a DataFrame is to index off of the DataFrame. However, this approach has a limitation. Indexing directly on a DataFrame does not allow you to select both rows and columns simultaneously. To select rows and columns, you will need to pass both valid row and column selections separated by a comma to either .iloc or .loc.

The generic form to select rows and columns will look like the following code:

df.iloc[row_idxs, column_idxs]
df.loc[row_names, column_names]

Where row_idxs and column_idxs can be scalar integers, lists of integers, or integer slices. While row_names and column_names can be the scalar names, lists of names, or names slices, row_names can also be a Boolean array.

In this recipe, each step shows a simultaneous row and column selection using both .iloc and .loc.

How to do it…

...
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