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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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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

Using methods that only work with a DatetimeIndex

There are a number of DataFrame and Series methods that only work with a DatetimeIndex. If the index is of any other type, these methods will fail.

In this recipe, we will first use methods to select rows of data by their time component. We will then learn about the powerful DateOffset objects and their aliases.

How to do it…

  1. Read in the crime hdf5 dataset, set the index as REPORTED_DATE, and ensure that we have a DatetimeIndex:
    >>> crime = (pd.read_hdf('data/crime.h5', 'crime') 
    ...     .set_index('REPORTED_DATE')
    ... )
    >>> type(crime.index)
    <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
    
  2. Use the .between_time method to select all crimes that occurred between 2 A.M. and 5 A.M., regardless of the date:
    >>> crime.between_time('2:00', '5:00', include_end=False)
                  ...
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