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Pandas Cookbook

You're reading from   Pandas Cookbook Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781784393878
Length 532 pages
Edition 1st Edition
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Author (1):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
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Table of Contents (12) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Beginning Data Analysis 4. Selecting Subsets of Data 5. Boolean Indexing 6. Index Alignment 7. Grouping for Aggregation, Filtration, and Transformation 8. Restructuring Data into a Tidy Form 9. Combining Pandas Objects 10. Time Series Analysis 11. Visualization with Matplotlib, Pandas, and Seaborn

Visualizing the flights dataset

Exploratory data analysis is mainly guided by visualizations, and pandas provides a great interface for quickly and effortlessly creating them. A simple strategy when beginning a visualization of any dataset is to focus only on univariate plots. The most popular univariate plots tend to be bar charts for categorical data (usually strings) and histograms, boxplots, or KDEs for continuous data (always numeric). Attempting to analyze multiple variables at the same time, directly at the start of a project, can be quite overwhelming.

Getting ready

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

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