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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
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

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

Analyzing 2016 voter registration and voting


Similarly, we can look at voter registration versus actual voting (using census data from https://www.census.gov/data/tables/time-series/demo/voting-and-registration/p20-580.html).

First, we load our dataset and display head information to visually check for accurate loading:

df <- read.csv("Documents/B05238_05_registration.csv")summary(df)

So, we have some registration and voting information by state. Use R to automatically plot all the data in x and y format using the plot command:

plot(df)

We are specifically looking at the relationship between registering to vote and actually voting. We can see in the following graphic that most of the data is highly correlated (as evidenced by the 45 degree angles of most of the relationships):

We can produce somewhat similar results using Python, but the graphic display is not even close.

Import all of the packages we are using for the example:

from numpy import corrcoef, sum, log, arange
from numpy.random import...
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