Clean Data - Data Science Strategies for Tackling Dirty Data by Megan Squire

By Megan Squire

Key Features

  • Grow your facts technological know-how services by way of filling your toolbox with confirmed options for a large choice of cleansing challenges
  • Familiarize your self with the an important information cleansing procedures, and proportion your individual fresh facts units with others
  • Complete real-world initiatives utilizing information from Twitter and Stack Overflow

Book Description

Is a lot of some time spent doing tedious projects corresponding to cleansing soiled info, accounting for misplaced facts, and getting ready info for use by means of others? if this is the case, then having the precise instruments makes a serious distinction, and may be an excellent funding as you develop your info technology expertise.

The e-book starts off by way of highlighting the significance of information cleansing in facts technology, and should allow you to acquire rewards from reforming your cleansing technique. subsequent, you'll cement your wisdom of the elemental techniques that the remainder of the e-book depends upon: dossier codecs, information kinds, and personality encodings. additionally, you will extract and fresh facts saved in RDBMS, net records, and PDF files, via functional examples.

At the top of the publication, you may be given an opportunity to take on a few real-world projects.

What you are going to learn

  • Understand the function of knowledge cleansing within the total info technological know-how process
  • Learn the fundamentals of dossier codecs, facts varieties, and personality encodings to wash information properly
  • Master serious gains of the spreadsheet and textual content editor for organizing and manipulating data
  • Convert facts from one universal layout to a different, together with JSON, CSV, and a few special-purpose formats
  • Implement 3 diversified options for parsing and cleansing facts present in HTML documents at the Web
  • Reveal the mysteries of PDF records and find out how to pull out simply the information you want
  • Develop a number options for detecting and cleansing undesirable facts kept in an RDBMS
  • Create your individual fresh info units that may be packaged, authorized, and shared with others
  • Use the instruments from this ebook to accomplish real-world tasks utilizing info from Twitter and Stack Overflow

About the Author

Megan Squire is a professor of computing sciences at Elon collage. She has been accumulating and cleansing soiled facts for 2 a long time. She can also be the chief of FLOSSmole.org, a study undertaking to assemble facts and research it on the way to find out how loose, libre, and open resource software program is made.

Table of Contents

  1. Why do you want fresh Data?
  2. Fundamentals codecs, kinds, and Encodings
  3. Workhorses of fresh info Spreadsheets and textual content Editors
  4. Speaking the Lingua Franca facts Conversions
  5. Collecting and cleansing facts from the Web
  6. Cleaning facts in Pdf Files
  7. RDBMS cleansing Techniques
  8. Best Practices for Sharing Your fresh Data
  9. Stack Overflow Project
  10. Twitter Project

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Additional resources for Clean Data - Data Science Strategies for Tackling Dirty Data

Example text

How can you figure out which compression method is the best choice? Archive files An archive file is simply a single file that contains many files inside it. The files inside can be either text or binary or a mixture of both. Archive files are created by a special program that takes a list of files and changes them into a single file. Of course, the archive files are created in such a way that they can be expanded back into many files. tar extension. Their original purpose was to create archives of magnetic tapes.

M. m". html. Other database management systems will have similar versions of these functions. • Option 2: Use the more sophisticated date_format() MySQL function. This function takes a series of string specifiers for how you want the date to be formatted. There is a very long list of these specifiers in the MySQL documentation. A completed example to convert the date into our desired format is shown in the following code: SELECT date_format(date, '%l:%i%p, %W, %M %e, %Y') FROM message WHERE mid=52; Result: 4:51AM, Friday, January 21, 2000 This is pretty close to what we said we wanted, and it is much shorter than Option 1.

Here is a working example that will look for the word Sent: and extract the following characters up to the > symbol and then turn these characters into a real MySQL datetime data type: SELECT str_to_date( substring_index( substring_index(reference,'>',3), 'Sent: ', -1 ), '%W,%M %e, %Y %h:%i %p' ) FROM referenceinfo WHERE mid=79; Result: 2000-08-17 18:29:00 Now we have a datetime value that is ready for inserting into a new MySQL column or for performing more date functions or calculations on it.

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