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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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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

Reading another CSV file


We can look at another CSV in the same dataset to see what kind of issues we run across. Using the yearly batting records for all Major League Baseball players that we previously downloaded from the same site, we can use coding like the following to start analyzing the data:

players <- read.csv(file="Documents/baseball.csv", header=TRUE, sep=",")head(players)

This produces the following head display:

There are many statistics for baseball players in this dataset. There are also many NA values. R is pretty good at ignoring NA values. Let us first look at the statistics for the data using:

summary(players)

This generates statistics on all the fields involved (there are several more that are not in this display):

A number of interesting points are visible in the preceding display that are worth noting:

  • We have about 30 data points per player
  • It is interesting that the player data goes back to 1871
  • There are about 1,000 data points per team
  • American League and National League...
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