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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. 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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