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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (17) Chapters Close

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Draw a histogram of social data


There are a wide variety of social sites that produce datasets. In this example, we will gather one of the datasets and produce a histogram from the data. The specific dataset is the voting behavior on WIKI from https://snap.stanford.edu/data/wiki-Vote.html. Each data item shows user number N voted for user number X. So, we produce some statistics in a histogram to analyze voting behavior by:

  • Gathering all of the voting that took place
  • For each vote:
    • Increment a counter that says who voted
    • Increment a counter that says who was voted for
    • Massage the data so we can display it in two histograms

The coding is as follows:

%matplotlib inline
# import all packages being used
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib

# load voting data drawn from https://snap.stanford.edu/data/wiki-Vote.html
df = pd.read_table('wiki-Vote.txt', sep=r"\s+", index_col=0)

# produce standard summary info to validate
print(df.head())
print(df.describe...
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