Python For Data Analysis

What Hacker News says

1. boisterousnessMay 2026
   These are good ...

   "Python for Data Analysis" by McKinney (2018)

   "The Go Programming Language" by Donovan and Kernighan (2016)

   "Hacker's Delight" by Warren (2013)

   "Algorithm Design Manual" by Skiena (2008)

   "Purely Functional Data Structures" by Okasaki (1998)

   "Structure and Interpretation of Computer Programs" by Abelson and Sussman (1985)

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2. sanderjdJan 2024
   I really think this is a misunderstanding of the purpose of different kinds of documentation. The documentation of a new tool for a mature technique is just not the primary place to focus on writing a beginners' tutorial / course on using that technique. Certainly, "the more the merrier" is a good mantra for documentation, so if they do add such material, all the better. But it is very sensible for it to not be the focus. The focus should be, "how can you use this specific iteration of a tool for this technique to do the things you already know how to do".

   Nobody is suggesting that you should be an expert on data frames "through divine intervention". But the place to expect to learn about those things is the many articles, tutorials, courses, and books on the subject, not the website of one specific new tool in the space.

   If you're really interested in learning about this, a fairly canonical place to start would be "Python for Data Analysis"[0] by Wes McKinney, the creator of pandas and one of the creators of the arrow in-memory columnar data format that most of these projects build atop now.

   This is a (multiple-) book length topic, not a project landing page length topic.

   0: https://wesmckinney.com/book/

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3. photochemsynFeb 2023
   LibreOffice Calc combined with python's pandas and numpy modules meet all needs I have for spreadsheets, with matplotlib and seaborn for visualization. The Ipython shell is the optimal IDE for this approach IMO. My desktop reference is:

   "Python for Data Analysis 2nd ed" (Wes McKinney, 2018)

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4. photochemsynAug 2022
   Python's pandas makes working with large numbers of Excel / LibreOfficeCalc files a lot easier. Excel is very convenient for the non-programming user of course, for example making graphic visualization is far easier. With pandas you need to interface programmatically with matplotlib or similar.

   If you want to up your skills in this area, Wes McKinney's "Python for Data Analysis" is pretty great.

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5. proamdev123Aug 2022
   +1 for “Python for Data Analysis”.

   And Wes (the creator of the Pandas library) has made the book available for free on his website!

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6. Tarq0nMar 2019
   Hands-on Machine Learning with Scikit-Learn and TensorFlow [1] is more ML focused, but highly recommended. Out of the three books (Python for Data Analysis and Python Data Science Handbook) I learned the most from this one by far.

   [1] https://github.com/ageron/handson-ml

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