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Jupyter for Data Science

You're reading from  Jupyter for Data Science

Product type Book
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Pages 242 pages
Edition 1st Edition
Languages
Author (1):
Dan Toomey Dan Toomey
Profile icon Dan Toomey

Table of Contents (17) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Jupyter and Data Science Working with Analytical Data on Jupyter Data Visualization and Prediction Data Mining and SQL Queries R with Jupyter Data Wrangling Jupyter Dashboards Statistical Modeling Machine Learning Using Jupyter Optimizing Jupyter Notebooks

Sampling a dataset


The dplyr package has a function to gather a sample from your dataset, sample(). You pass in the dataset to operate against and how many samples you want drawn, sample_n(), and the fraction percentage, sample_frac(), as in this example:

data <- sample_n(players, 30)glimpse(data)

We see the results as shown in the following screenshot:

Note that there are 30 observations in the results set, as requested.

Filtering rows in a data frame

Another function we can use is the filter function. The filter function takes a data frame as an argument and a filtering statement. The function passes over each row of the data frame and returns those rows that meet the filtering statement:

#filter only players with over 200 hits in a season
over200 <- filter(players, h > 200)
head(over200)
nrow(over200)

it looks like many players were capable of 200 hits a season. How about if we look at those players that could also get over 40 home runs in a season?

over200and40hr <- filter(players...
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