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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

Gathering Data in a Reproducible Way


Once the problem is defined, the first step in an analysis task is gathering data. Data can be extracted from multiple sources: databases, legacy systems, real-time data, external data, and so on. Data sources and the way data can be ingested into the model needs to be documented.

Let's understand how to use markdown and code block functionalities in the Jupyter notebook. Text can be added to Jupyter notebooks using markdown cells. These texts can be changed to bold or italic, like in any text editor. To change the cell type to markdown, you can use the Cell menu. We will look at the ways you can use various functionalities in markdown and code cells in Jupyter.

Functionalities in Markdown and Code Cells

  • Markdown in Jupyter: To select the markdown option in Jupyter, click on Widgets and Markdown from the drop-down menu:

    Figure 7.1: The markdown option in the Jupyter notebook

  • Heading in Jupyter: There are two types of headings in the Jupyter notebook. The syntax...

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