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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 2. Statistical Visualizations FREE CHAPTER 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

Exporting Graphs


After generating our visualizations and configuring the details, we can export our graphs to a hard copy format, such as PNG, JPEG, or SVG. If we are using the interactive API in the notebook, we can just call the savefig function over the pyplot interface, and the last generated graph will be exported to the file:

df.plot(kind='scatter', x='weight', y='horsepower', figsize=(20,10))
plt.savefig('horsepower_weight_scatter.png')

Figure 2.26: Exporting the graphs

All plot configurations will be carried to the plot. To export a graph when using the object-oriented API, we can call savefig from the figure:

fig, ax = plt.subplots()
df.plot(kind='scatter', x='weight', y='horsepower', figsize=(20,10), ax=ax)
fig.savefig('horsepower_weight_scatter.jpg')

Figure 2.27: Saving the graph

We can change some parameters for the saved image:

  • dpi: Adjust the saved image resolution.

  • facecolor: The face color of the figure.

  • edgecolor: The edge color of the figure, around the graph.

  • format: Usually PNG...

You have been reading a chapter from
Big Data Analysis with Python
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781789955286
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