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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Statistical analysis

We can easily find information, such as the number of friends and individual data of each one, from our Facebook graph. However, there are many answers that we can't get directly from the site, for example, male to female ratio or How many of my friends are Republicans?, or who is my best friend?; these questions can be easily answered with a few lines of code and some basic statistical analysis. In this chapter, we will start with the male to female ratio, because we already have the gender value in the .gdf file.

Tip

For simplicity in the code examples, we will split the friends.gdf file into two CSV files—one for the nodes (nodes.csv) and one for the links (links.csv).

Male to female ratio

In this example, we will use the gender value of the nodes.csv file and get the male to female ratio in a pie chart visualization.

The nodes.csv file will look like this:

nodedef>name VARCHAR,label VARCHAR,gender VARCHAR,locale VARCHAR,agerank INT 
23917067,Jorge,male...
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