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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
Languages
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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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Toc

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

Time Series Analysis

In this chapter, we will cover the following topics:

  • Understanding the difference between Python and pandas date tools
  • Slicing time series intelligently
  • Using methods that only work with a DatetimeIndex
  • Counting the number of weekly crimes
  • Aggregating weekly crime and traffic accidents separately
  • Measuring crime by weekday and year
  • Grouping with anonymous functions with a DatetimeIndex
  • Grouping by a Timestamp and another column
  • Finding the last time crime was 20% lower with merge_asof
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