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

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839213106

Length 626 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Authors (2):

Matthew Harrison

Theodore Petrou

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Table of Contents (17) Chapters

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 data with both integers and labels

Sometimes, you want the functionality of both .iloc and .loc, to select data by both position and label. In earlier versions of pandas, .ix was available to select data by both position and label. While this conveniently worked for those specific situations, it was ambiguous by nature and was a source of confusion for many pandas users. The .ix indexer has subsequently been deprecated and thus should be avoided.

Before the .ix deprecation, it was possible to select the first five rows and the columns of the college dataset from UGDS_WHITE through UGDS_UNKN using college.ix[:5, 'UGDS_WHITE':'UGDS_UNKN']. This is now impossible to do directly using .loc or .iloc. The following recipe shows how to find the integer location of the columns and then use .iloc to complete the selection.