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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

Generating Statistical Measurements


Python is a general-purpose language with statistical modules. A lot of statistical analysis, such as carrying out descriptive analysis, which includes identifying the distribution of data for numeric variables, generating a correlation matrix, the frequency of levels in categorical variables with identifying mode and so on, can be carried out in Python. The following is an example of correlation:

Figure 8.12: Segment numeric data and correlation matrix output

Identifying the distribution of data and normalizing it is important for parametric models such as linear regression and support vector machines. These algorithms assume the data to be normally distributed. If data is not normally distributed, it can lead to bias in the data. In the following example, we will identify the distribution of data through a normality test and then apply a transformation using the yeo-johnson method to normalize the data:

Figure 8.13: Identifying the distribution of the data...

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