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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
Other Books You May Enjoy
16
Index

Visualizing the flights dataset

Exploratory data analysis can be guided by visualizations, and pandas provides a great interface for quickly and effortlessly creating them. One strategy when looking at a new dataset is to create some univariate plots. These include bar charts for categorical data (usually strings) and histograms, boxplots, or KDEs for continuous data (always numeric).

In this recipe, we do some basic exploratory data analysis on the flights dataset by creating univariate and multivariate plots with pandas.

How to do it…

  1. Read in the flights dataset:
    >>> flights = pd.read_csv('data/flights.csv')
    >>> flights
           MONTH  DAY  WEEKDAY  ... ARR_DELAY DIVERTED CANCELLED
    0          1    1        4  ...      65.0        0         0
    1          1    1        4  ...     -13.0        0         0
    2          1    1        4  ...      35.0        0         0
    3          1    1        4  ...      -7.0        0        ...

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