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Mastering pandas. - Second Edition

You're reading from  Mastering pandas. - Second Edition

Product type Book
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Pages 674 pages
Edition 2nd Edition
Languages
Author (1):
Ashish Kumar Ashish Kumar
Profile icon Ashish Kumar

Table of Contents (21) Chapters

Preface Section 1: Overview of Data Analysis and pandas
Introduction to pandas and Data Analysis Installation of pandas and Supporting Software Section 2: Data Structures and I/O in pandas
Using NumPy and Data Structures with pandas I/Os of Different Data Formats with pandas Section 3: Mastering Different Data Operations in pandas
Indexing and Selecting in pandas Grouping, Merging, and Reshaping Data in pandas Special Data Operations in pandas Time Series and Plotting Using Matplotlib Section 4: Going a Step Beyond with pandas
Making Powerful Reports In Jupyter Using pandas A Tour of Statistics with pandas and NumPy A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates Data Case Studies Using pandas The pandas Library Architecture pandas Compared with Other Tools A Brief Tour of Machine Learning Other Books You May Enjoy

A summary of time series-related objects

There are many time series-related objects in pandas that are used for manipulating, creating, and processing timestamp data. The following table gives a summary of time series-related objects:

Object

Summary

datetime.datetime

This is a standard Python datetime class.

Timestamp

This is a pandas class derived from. datetime.datetime.

DatetimeIndex

This is a pandas class and is implemented as an immutable numpy.ndarray of the Timestamp/datetime object type.

Period

This is a pandas class representing a time period.

PeriodIndex

This is a pandas class and is implemented as an immutable numpy.ndarray of the Period object type.

DateOffset

DataOffset is used to move forward a date by a given number of valid dates (days, weeks, months, and so on).

timedelta

Timedelta calculates the difference in time...

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