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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Exporting data to a CSV file

Sometimes, it's more convenient to view data using a spreadsheet program such as LibreOffice, Microsoft Office Excel, or Apple Numbers. A standard way to export and import simple spreadsheet tables is through Comma Separated Values (CSVs).

In this recipe, we will use the cassava package to easily encode a CSV file out of a data structure.

Getting ready

Install the Cassava CSV package from cabal, using the following command line:

$ cabal install cassava

How to do it…

  1. Import the relevant packages using the following code:
    import Data.Csv
    import qualified Data.ByteString.Lazy as BSL
  2. Define an association list of data that will be exported as CSV. For this recipe, we will pair letters and numbers together, as shown in the following code:
    myData :: [(Char, Int)]
    myData = zip ['A'..'Z'] [1..]
  3. Run the encode function to convert the data structure into a lazy ByteString CSV representation, as shown in the following code:
    main = BSL.writeFile &quot...
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