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Big Data Analysis with Python

You're reading from   Big Data Analysis with Python Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Length 276 pages
Edition 1st Edition
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Authors (3):
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Ivan Marin Ivan Marin
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Ivan Marin
Sarang VK Sarang VK
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Sarang VK
Ankit Shukla Ankit Shukla
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Ankit Shukla
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Table of Contents (11) Chapters Close

Big Data Analysis with Python
Preface
1. The Python Data Science Stack FREE CHAPTER 2. Statistical Visualizations 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

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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