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Pandas Cookbook

Pandas Cookbook

By : Theodore Petrou, Kuntal Ganguly
4.3 (32)
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Pandas Cookbook

Pandas Cookbook

4.3 (32)
By: Theodore Petrou, Kuntal Ganguly

Overview of this book

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas 0.20 library to generate results.
Table of Contents (12 chapters)
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Selecting DataFrame rows and columns simultaneously

Directly using the indexing operator is the correct method to select one or more columns from a DataFrame. However, it does not allow you to select both rows and columns simultaneously. To select rows and columns simultaneously, you will need to pass both valid row and column selections separated by a comma to either the .iloc or .loc indexers.

Getting ready

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

>>> df.iloc[rows, columns]
>>> df.loc[rows, columns]

The rows and columns variables may be scalar values, lists, slice objects, or boolean sequences.

Passing a boolean sequence to the indexers is covered in Chapter 5, Boolean...
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