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You're reading from  The Pandas Workshop

Product typeBook
Published inJun 2022
Reading LevelBeginner
PublisherPackt
ISBN-139781800208933
Edition1st Edition
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Authors (4):
Blaine Bateman
Blaine Bateman
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Blaine Bateman

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.
Read more about Blaine Bateman

Saikat Basak
Saikat Basak
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Saikat Basak

Saikat Basak is a data scientist and a passionate programmer. Having worked with multiple industry leaders, he has a good understanding of problem areas that can potentially be solved using data. Apart from being a data guy, he is also a science geek and loves to explore new ideas in the frontiers of science and technology.
Read more about Saikat Basak

Thomas V. Joseph
Thomas V. Joseph
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Thomas V. Joseph

Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.
Read more about Thomas V. Joseph

William So
William So
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William So

William So is a Data Scientist with both a strong academic background and extensive professional experience. He is currently the Head of Data Science at Douugh and also a Lecturer for Master of Data Science and Innovation at the University of Technology Sydney. During his career, he successfully covered the end-end spectrum of data analytics from ML to Business Intelligence helping stakeholders derive valuable insights and achieve amazing results that benefits the business. William is a co-author of the "The Applied Artificial Intelligence Workshop" published by Packt.
Read more about William So

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Datetime math operations

We have shown already some operations that can be applied to datetime/Timestamp objects. In this section, we will go a little deeper and show the use of the origin parameter, which is useful if converting dates in some integer formats (such as might come from Excel).

Date ranges

Suppose you were provided with some data that is daily temperatures from 1/1/2019 to 6/30/2020 but it is provided as simply a series of values without the corresponding dates. You would like to be able to work with the data corresponding to actual dates, for example, to look for repeating patterns or seasonal variations. You can add the dates easily using the pandas date_range method:

  1. Here, you create a temperatures series beginning with just an integer series, using the NumPy sin() function and a period of 180 days to generate variation over time, and adding noise to represent the hypothetical data. The first step is to create the integer series:
    x_values = pd.Series...
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The Pandas Workshop
Published in: Jun 2022Publisher: PacktISBN-13: 9781800208933

Authors (4)

author image
Blaine Bateman

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.
Read more about Blaine Bateman

author image
Saikat Basak

Saikat Basak is a data scientist and a passionate programmer. Having worked with multiple industry leaders, he has a good understanding of problem areas that can potentially be solved using data. Apart from being a data guy, he is also a science geek and loves to explore new ideas in the frontiers of science and technology.
Read more about Saikat Basak

author image
Thomas V. Joseph

Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.
Read more about Thomas V. Joseph

author image
William So

William So is a Data Scientist with both a strong academic background and extensive professional experience. He is currently the Head of Data Science at Douugh and also a Lecturer for Master of Data Science and Innovation at the University of Technology Sydney. During his career, he successfully covered the end-end spectrum of data analytics from ML to Business Intelligence helping stakeholders derive valuable insights and achieve amazing results that benefits the business. William is a co-author of the "The Applied Artificial Intelligence Workshop" published by Packt.
Read more about William So