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Pandas 1.x Cookbook

Pandas 1.x Cookbook

By : Matthew Harrison, Theodore Petrou
4.5 (28)
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Pandas 1.x Cookbook

Pandas 1.x Cookbook

4.5 (28)
By: Matthew Harrison, Theodore Petrou

Overview of this book

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 as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. 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. Many advanced recipes combine several different features across the pandas library to generate results.
Table of Contents (17 chapters)
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15
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16
Index

Column types

You can glean information about the data in pandas simply by looking at the types of the columns. In this recipe, we will explore the column types.

How to do it…

  1. Inspect the .dtypes attribute:
    >>> fueleco.dtypes
    barrels08     float64
    barrelsA08    float64
    charge120     float64
    charge240     float64
    city08          int64
                   ...    
    modifiedOn     object
    startStop      object
    phevCity        int64
    phevHwy         int64
    phevComb        int64
    Length: 83, dtype: object
    
  2. Summarize the types of columns:
    >>> fueleco.dtypes.value_counts()
    float64    32
    int64      27
    object     23
    bool        1
    dtype: int64
    

How it works…

When you read a CSV file in pandas, it has to infer the types of the columns. The process looks something like this:

  • If all of the values in a column look like whole numeric values, convert them to integers and give the column the type int64
  • ...

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