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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 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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Toc

Table of Contents (12) Chapters Close

Preface 1. Installation and Setting up the Environment FREE CHAPTER 2. Adding an Engine 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs

Reading CSV files

The most common file format for datasets is a comma separated value (CSV) file. A CSV may have a header record followed by a variable number of data records.

The header record may be the first record in the file. In that record, the separated values are headings or column names for each of the columns of data in the file. The column names are all character string values. We can use these column names for variable names in our scripts, corresponding to column names in a dataset.

Each subsequent data record will have a separated value in that record for every column. The value may be an empty string or no value, but the comma separation of the record will correspond to the columns in the header record. 

If there is no header record, you may have to find out what the column layout is for the file. There is normally a descriptor in the same location as...

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