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

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839213106

Length 626 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Authors (2):

Matthew Harrison

Theodore Petrou

View More author details

Table of Contents (17) Chapters

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

Aggregating weekly crime and traffic accidents separately

The Denver crime dataset has all crime and traffic accidents together in one table, and separates them through the binary columns: IS_CRIME and IS_TRAFFIC. The .resample method allows you to group by a period of time and aggregate specific columns separately.

In this recipe, we will use the .resample method to group by each quarter of the year and then sum up the number of crimes and traffic accidents separately.

How to do it…

Read in the crime hdf5 dataset, set the index as REPORTED_DATE, and then sort it to increase performance for the rest of the recipe:
```
>>> crime = (pd.read_hdf('data/crime.h5', 'crime') 
...     .set_index('REPORTED_DATE') 
...     .sort_index()
... )
```
Use the .resample method to group by each quarter of the year and then sum the IS_CRIME and IS_TRAFFIC columns for each group:
```
>>> (crime
...     .resample...
```