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Analyzing and Visualizing
Data with F#
Tomas Petricek
Analyzing and Visualizing Data with F#
by Tomas Petricek
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Editor:
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Nicholas Adams
Copyeditor:
Sonia Saruba
Proofreader:
Nicholas Adams
October 2015:
First Edition
Interior Designer:
David Futato
Cover Designer:
Ellie Volckhausen
Illustrator:
Rebecca Demarest
Revision History for the First Edition
2015-10-15:
First Release
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information and instructions contained in this work are accurate, the publisher and
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978-1-491-93953-6
[LSI]
Table of Contents
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1. Accessing Data with Type Providers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Data Science Workflow
Why Choose F# for Data Science?
Getting Data from the World Bank
Calling the Open Weather Map REST API
Plotting Temperatures Around the World
Conclusions
2
3
4
7
10
13
16
18
20
21
24
26
30
31
33
35
36
39
41
2. Analyzing Data Using F# and Deedle. . . . . . . . . . . . . . . . . . . . . . . . . . 15
Downloading Data Using an XML Provider
Visualizing CO
2
Emissions Change
Aligning and Summarizing Data with Frames
Summarizing Data Using the R Provider
Normalizing the World Data Set
Conclusions
How k-Means Clustering Works
Clustering 2D Points
Initializing Centroids and Clusters
Updating Clusters Recursively
Writing a Reusable Clustering Function
Clustering Countries
Scaling to the Cloud with MBrace
3. Implementing Machine Learning Algorithms. . . . . . . . . . . . . . . . . . . 29
vii

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