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You're reading from  Big Data Analysis with Python

Product typeBook
Published inApr 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781789955286
Edition1st Edition
Languages
Python
Tools
MatplotlibHadoop
Concepts
Data Visualization
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Authors (3):
Ivan Marin
Ivan Marin
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Ivan Marin

Ivan Marin is a systems architect and data scientist working at Daitan Group, a Campinas-based software company. He designs big data systems for large volumes of data and implements machine learning pipelines end to end using Python and Spark. He is also an active organizer of data science, machine learning, and Python in So Paulo, and has given Python for data science courses at university level.
Read more about Ivan Marin

Ankit Shukla
Ankit Shukla
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Ankit Shukla

Ankit Shukla is a data scientist working with World Wide Technology, a leading US-based technology solution provider, where he develops and deploys machine learning and artificial intelligence solutions to solve business problems and create actual dollar value for clients. He is also part of the company's R&D initiative, which is responsible for producing intellectual property, building capabilities in new areas, and publishing cutting-edge research in corporate white papers. Besides tinkering with AI/ML models, he likes to read and is a big-time foodie.
Read more about Ankit Shukla

Sarang VK
Sarang VK
author image
Sarang VK

Sarang VK is a lead data scientist at StraitsBridge Advisors, where his responsibilities include requirement gathering, solutioning, development, and productization of scalable machine learning, artificial intelligence, and analytical solutions using open source technologies. Alongside this, he supports pre-sales and competency.
Read more about Sarang VK

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Missing Values

The data entries with no value assigned to them are called missing values. In the real world, encountering missing values in data is common. Values may be missing for a wide variety of reasons, such as non-responsiveness of the system/responder, data corruption, and partial deletion.

Some fields are more likely than other fields to contain missing values. For example, income data collected from surveys is likely to contain missing values, because of people not wanting to disclose their income.

Nevertheless, it is one of the major problems plaguing the data analytics world. Depending on the percentage of missing data, missing values may prove to be a significant challenge in data preparation and exploratory analysis. So, it's important to calculate the missing data percentage before getting started with data analysis.

In the following exercise, we will learn how to detect and calculate the number of missing value entries in PySpark DataFrames.

Exercise 38: Counting Missing Values...

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Big Data Analysis with Python
Published in: Apr 2019Publisher: PacktISBN-13: 9781789955286
© 2019 Packt Publishing Limited All Rights Reserved

Authors (3)

author image
Ivan Marin

Ivan Marin is a systems architect and data scientist working at Daitan Group, a Campinas-based software company. He designs big data systems for large volumes of data and implements machine learning pipelines end to end using Python and Spark. He is also an active organizer of data science, machine learning, and Python in So Paulo, and has given Python for data science courses at university level.
Read more about Ivan Marin

author image
Ankit Shukla

Ankit Shukla is a data scientist working with World Wide Technology, a leading US-based technology solution provider, where he develops and deploys machine learning and artificial intelligence solutions to solve business problems and create actual dollar value for clients. He is also part of the company's R&D initiative, which is responsible for producing intellectual property, building capabilities in new areas, and publishing cutting-edge research in corporate white papers. Besides tinkering with AI/ML models, he likes to read and is a big-time foodie.
Read more about Ankit Shukla

author image
Sarang VK

Sarang VK is a lead data scientist at StraitsBridge Advisors, where his responsibilities include requirement gathering, solutioning, development, and productization of scalable machine learning, artificial intelligence, and analytical solutions using open source technologies. Alongside this, he supports pre-sales and competency.
Read more about Sarang VK

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