Clean Data by Megan Squire

By Megan Squire

Save time by means of gaining knowledge of easy innovations for cleansing, organizing, and manipulating your data

About This Book

  • Grow your info 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 the most important facts cleansing methods, and percentage your personal fresh info units with others
  • Complete real-world initiatives utilizing information from Twitter and Stack Overflow

Who This booklet Is For

If you're a facts scientist of any point, novices integrated, and attracted to cleansing up your facts, this is often the ebook for you! event with Python or personal home page is believed, yet no past wisdom of knowledge cleansing is needed.

In Detail

Is a lot of a while spent doing tedious projects reminiscent of cleansing soiled info, accounting for misplaced information, and getting ready facts for use via others? if that is so, then having the suitable instruments makes a serious distinction, and should be an excellent funding as you develop your facts technology expertise.

The booklet starts off via highlighting the significance of knowledge cleansing in info technology, and should assist you to obtain rewards from reforming your cleansing technique. subsequent, you'll cement your wisdom of the fundamental innovations that the remainder of the publication depends on: dossier codecs, information kinds, and personality encodings. additionally, you will the best way to extract and fresh facts kept in RDBMS, net documents, and PDF records, via useful examples.

At the tip of the e-book, you'll be given an opportunity to take on a number of real-world projects.

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Extra resources for Clean Data

Sample text

Throw out the affected e-mails: We can just make an informed decision to discard any e-mail that has a date that falls outside a predetermined window. In order to decide between options 2 and 3, we will need to count how many messages will be affected using only a 1999-2002 window. We can use the following SQL: SELECT count(*) FROM message WHERE year(date) < 1998 or year(date) > 2002; Result: 325 325 messages with bad dates may initially seem like a lot, but then again, they are only about 1 percent of the entire dataset.

Is a senior consultant with Numb3rs and has over 30 years of experience providing services to major companies in the areas of data analytics, data science, optimization, process improvement, and information technology. He has been a member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE) for over 25 years. He is also a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the American Society for Quality (ASQ).

Cleaning Data in PDF Files Why is cleaning PDF files difficult? Try simple solutions first – copying Our experimental file Step one – try copying out the data we want Step two – try pasting the copied data into a text editor Step three – make a smaller version of the file Another technique to try – pdfMiner Step one – install pdfMiner Step two – pull text from the PDF file Third choice – Tabula Step one – download Tabula Step two – run Tabula Step three – direct Tabula to extract the data Step four – copy the data out Step five – more cleaning When all else fails – the fourth technique Summary 7.

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