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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

Manipulating data with dplyr


The dplyr package for R is described as a package providing a grammar for data manipulation. It has the entry points you would expect for wrangling your data frame in one package. We will use the dplyr package against the baseball player statistics we used earlier in this chapter.

We read in the player data and show the first few rows:

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

We will be using the dplyr package, so we need to pull the package into our notebook:

library(dplyr)

Converting a data frame to a dplyr table

The dplyr package has functions to convert your data object into a dplyr table. A dplyr table stores data in a compact format using much less memory. Most of the other dplyr functions can operate directly on the table as well.

We can convert our data frame to a table using:

playerst <- tbl_df(players)playerst

This results in a very similar display pattern:

Getting a quick overview of the data value ranges

Another...

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