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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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Introduction to the world of pandas

Tess's latest project has turned out to be much more time-consuming than she initially anticipated. Her client, who develops and provides content for schools, wants her to find insights into their students' needs by analyzing data that's been collected through various sources. Things would have been much easier had this data been in a single format, but unfortunately, that's not the case. The client has sent her data in multiple formats, including HTML, JSON, Excel, and CSV. She has to extract the relevant information from all these files. These are not the only data sources she'll be working with, though. She also has to access the records of the top-performing and struggling students from a SQLite database so that she can analyze their performance patterns. All these disparate data elements differ in their data types, velocities, frequencies, and volumes. She must now extract different elements from these data sources by slicing, subsetting, grouping, merging, and reshaping the data to get a comprehensive list of features for further analysis. Since the volumes are large, she must also optimize her methods for efficient processing.

Does this scenario sound familiar to you? Are you overwhelmed by the data wrangling tasks that must be performed before the analytics processes? Well, you do not have to struggle anymore. pandas is a Python library that is capable of carrying out all these tasks and more. Over the years, pandas has become the go-to tool for all the preprocessing tasks involved in the life cycle of data analytics.

In this chapter, you will begin to explore and have fun with pandas, an amazing library that's used extensively by the data science and machine learning community. As you work through the exercises and activities in this chapter and the ones that follow, you will understand why pandas is considered the de facto standard when working with data. But first, let's take a short trip through time to understand the evolution of the library and get a glimpse into all the functionalities you will be learning about in this chapter.

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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