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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 a CSV file


One of the standards for file formats is CSV. In this section, we will walk through the process of reading a CSV and adjusting the dataset to arrive at some conclusions about the data. The data I am using is from the Heating System Choice in California Houses dataset, found at https://vincentarelbundock.github.io/Rdatasets/datasets.html:

#read in the CSV file as available on the siteheating <- read.csv(file="Documents/heating.csv", header=TRUE, sep=",")# make sure the data is laid out the way we expecthead(heating)

The data appears to be as expected; however, a number of the columns have acronym names and are somewhat duplicated. Let us change the names of interest that we want to be more readable and remove the extras we are not going to use:

# change the column names to be more readablecolnames(heating)[colnames(heating)=="depvar"] <- "system"colnames(heating)[colnames(heating)=="ic.gc"] <- "install_cost"colnames(heating)[colnames(heating)=="oc.gc"] <- "annual_cost...
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