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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

Writing Output from Spark DataFrames


Spark gives us the ability to write the data stored in Spark DataFrames into a local pandas DataFrame, or write them into external structured file formats such as CSV. However, before converting a Spark DataFrame into a local pandas DataFrame, make sure that the data would fit in the local driver memory.

In the following exercise, we will explore how to convert the Spark DataFrame to a pandas DataFrame.

Exercise 27: Converting a Spark DataFrame to a Pandas DataFrame

In this exercise, we will use the pre-created Spark DataFrame of the Iris dataset in the previous exercise, and convert it into a local pandas DataFrame. We will then store this DataFrame into a CSV file. Perform the following steps:

  1. Convert the Spark DataFrame into a pandas DataFrame using the following command:

    import pandas as pd
    df.toPandas()
  2. Now use the following command to write the pandas DataFrame to a CSV file:

    df.toPandas().to_csv('iris.csv')

    Note

    Writing the contents of a Spark DataFrame...

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