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Pandas 1.x Cookbook - Second Edition

You're reading from  Pandas 1.x Cookbook - Second Edition

Product type Book
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Pages 626 pages
Edition 2nd Edition
Languages
Authors (2):
Matt Harrison Matt Harrison
Profile icon Matt Harrison
Theodore Petrou Theodore Petrou
Profile icon Theodore Petrou
View More author details

Table of Contents (17) Chapters

Preface Pandas Foundations Essential DataFrame Operations Creating and Persisting DataFrames Beginning Data Analysis Exploratory Data Analysis Selecting Subsets of Data Filtering Rows Index Alignment Grouping for Aggregation, Filtration, and Transformation Restructuring Data into a Tidy Form Combining Pandas Objects Time Series Analysis Visualization with Matplotlib, Pandas, and Seaborn Debugging and Testing Pandas Other Books You May Enjoy
Index

Measuring crime by weekday and year

Measuring crimes by weekday and by year simultaneously requires the functionality to pull this information from a Timestamp. Thankfully, this functionality is built into any Timestamp column with the .dt attribute.

In this recipe, we will use the .dt attribute to provide us with both the weekday name and year of each crime as a Series. We count all of the crimes by forming groups using both of these Series. Finally, we adjust the data to consider partial years and population before creating a heatmap of the total amount of crime.

How to do it…

  1. Read in the Denver crime hdf5 dataset leaving the REPORTED_DATE as a column:
    >>> crime = pd.read_hdf('data/crime.h5', 'crime')
    >>> crime
                             OFFEN/PE_ID  ... IS_TRAFFIC
    0          traffic-accident-dui-duid  ...          1
    1         vehicular-eluding-no-chase  ...          0
    2               disturbing-the-peace  .....
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