Data Wrangling and Analysis with Python
Learning Pandas and How to Import, Export, Clean, Analyze and Visualize Data
By Katharine Jarmul
Publisher: O'Reilly Media
Final Release Date: June 2016
Run time: 3 hours 17 minutes

Discover the data analysis capabilities of the Python Pandas software library in this introduction to data wrangling and data analytics. Designed for learners with some core knowledge of Python, you'll explore the basics of importing, exporting, parsing, cleaning, analyzing, and visualizing data.

This course is an introduction to Pandas, where you'll learn to filter, group, match, and join data and then move on to advanced functions like analyzing trends and normalizing your data. There is also an introduction to some nifty skills like web scraping, working with API data, fuzzy matching, multiprocessing, and analyzing code performance.

  • Master the basics of Python data wrangling and data analysis
  • Discover the Pandas software library and its use as a data analysis tool
  • Learn to pull data from disparate sources (Excel, CSV, PDF, APIs, etc.)
  • Explore web scraping and how to handle encoding and decoding
  • Understand how to identify and clean data using RegEx and fuzzy matching
  • Sample other data analysis tools like natural language processing and Numpy
  • Learn the data visualization capabilities of Matplotlib and Bokeh
Katharine Jarmul runs kjamistan UG, a Python consulting, training and competitive analysis company based in Berlin, Germany. She learned Python in 2008 while working at the Washington Post and is co-author of the O'Reilly title Data Wrangling with Python: Tips and Tools to Make Your Life Easier. Originally from Los Angeles, Jarmul earned an M.A. from American University and an M.S. from Pace University.
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oreillyData Wrangling and Analysis with Python
 
2.3

(based on 3 reviews)

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(1 of 2 customers found this review helpful)

 
1.0

An overview, but not very useful

By Collin

from Baltimore, MD

Pros

    Cons

    • Not comprehensive enough

    Best Uses

      Comments about oreilly Data Wrangling and Analysis with Python:

      She hits on a bunch of topics, but does not show how to actually apply the techniques. It's more of an overview like- Here's this cool technique and you should learn how to use it. Not that what she covers is bad, and her enunciation is good, but I bought it so I could do the particular topics she covered, and this didn't get me closer to being able to do those things.

      (2 of 3 customers found this review helpful)

       
      4.0

      Lots of good hands on examples so far

      By Stuart I

      from Seattle WA

      About Me Developer, Educator, Hacker, Hobbyist, Maker

      Pros

      • Accurate
      • Concise
      • Easy to understand
      • Helpful examples
      • Well-written

      Cons

        Best Uses

          Comments about oreilly Data Wrangling and Analysis with Python:

          I haven't finished this yet and that is because its good and I am taking my time.

          I have a big data set to wrangle and I love using the ideas I find here as a jumpoff point into my project.

          Nice balance of hands on, warnings on Gotchas, and pointers to further material.

          I'm trying to do everything in Python 3, this offers both 2 and 3.

          Thank you and will post again further on.

          Videos are clear, concise, sound good, and well edited. Nice thing is you can always hit pause.

          (2 of 3 customers found this review helpful)

           
          2.0

          This is not a data wrangling tutorial

          By AB

          from San Francisco

          About Me Data Scientist

          Verified Reviewer

          Pros

            Cons

            • Difficult to understand
            • Not comprehensive enough
            • Too Fast To Follow

            Best Uses

              Comments about oreilly Data Wrangling and Analysis with Python:

              There are no data wrangling in this video. It's just a FAST and high level overview reading of different python libraries. Thanks for Safaribooksonline I didn't have to spend more money.

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