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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

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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