Python For Data Analysis
by Wes McKinney · first published 2012
55% recommend32% neutral13% critical
Why HN recommends it
Practical, readable tutorial on pandas and NumPy by pandas' creator, with IPython and matplotlib covered too. Suits programmers, Excel or R users, and beginners moving to Python for data work.
Summarised by Claude from recommending comments
Serves as an introduction to Python for data-intensive applications.
Affiliate links. As an Amazon Associate I earn from qualifying purchases.
Mentions per year
What Hacker News says
Related books
2012–2017The art of R programming
Norman S. Matloff
Recommended by 8 · mentioned by 118 recommend · 11 mention8 recommend · 118 recommend
2010–2022Data Analysis with Open Source Tools
Philipp K. Janert
Recommended by 7 · mentioned by 127 recommend · 12 mention7 recommend · 127 recommend
2015–2023Data science from scratch
Joel Grus
Recommended by 22 · mentioned by 3322 recommend · 33 mention22 recommend · 3322 recommend
2008–2013Programming Python
Mark Lutz
Recommended by 4 · mentioned by 64 recommend · 6 mention4 recommend · 64 recommend
2014–2024Python for Finance
Yves Hilpisch
Recommended by 4 · mentioned by 64 recommend · 6 mention4 recommend · 64 recommend
2016–2024R for Data Science
Hadley Wickham, Garrett Grolemund
Recommended by 40 · mentioned by 4340 recommend · 43 mention40 recommend · 4340 recommend
2011–2023Mining of massive datasets
Anand Rajaraman, Jeffrey D. Ullman, Jure Leskovec
Recommended by 17 · mentioned by 2017 recommend · 20 mention17 recommend · 2017 recommend
2016–2023Hands-On Machine Learning with Scikit-Learn and TensorFlow
Aurélien Géron
Recommended by 16 · mentioned by 1816 recommend · 18 mention16 recommend · 1816 recommend
…for web development, but I think python is gaining momentum rather than losing it for data analysis and scientific programming. I expect that user base will increase even faster after Wes McKinney's "Python for Data Analysis" book is published.
on “Python is doing just fine” ↗…(http://pandas.pydata.org/) for data analysis. It offers a lot of the basic functionality of R, but everything is in python. The original author of pandas, Wes McKinney, even wrote a book about it: Python for Data Analysis (http://shop.oreilly.com/product/0636920023784.do). One caveat I would mention about data analysis would be that statistics is not just number crunching. It is really a bit of an art to making sure…
on “Founders, teach your employees statistics” ↗So, this article appears to be plagiarized directly (as in, word for word) from Wes McKinney's Python for Data Analysis (which is an excellent, excellent resource): http://books.google.com/books?id=JtJAkfzds4wC&lpg=PA46&ots=p... Seeing as the 'author' of this post was the submitter, I'd love to hear an explanation of…
For people who are not satisfied with knowing just Numpy and want to delve into its applications, I would recommend Wes McKinney's book "Python for data analysis": http://shop.oreilly.com/product/0636920023784.do I have the book and its been great reading so far. Ch 4 gives a nice introduction to Numpy (about 30 pages). Concise but also useful for immediate…
on “Introduction to NumPy” ↗…1. _Stats: Data and Models_ by De Veaux, Velleman & Bock 2. _Fifty Challenging Problems in Probability with Solutions_ by Mosteller 3. http://yudkowsky.net/rational/bayes Basic data analysis: 1. _Python for Data Analysis_ by Wes McKinney 2. http://camdavidsonpilon.github.io/Probabilistic-Programming-... 3. _Exploratory Data Analysis_ by Tukey 4. _The Visual Display of Quantitative Information_ by Tufte Tools: - R…
on “Ask HN: Breaking into Scientific Programming?” ↗I use this along with Chris Albons similar repo of recipes (http://shop.oreilly.com/product/0636920023784.do). It is a great compliment to Wear McKinney's "Python for Data Analysis" it is more like a recipe book than the internals as Wes' book is. Also, JVP includes more than just Pandas and NumPy goodies. Highly Recommend, and fork to create your own curated handbook.
on “Python Data Science Handbook” ↗…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.
on “Excel is pretty dang cool” ↗I mostly enjoyed Python for Data Analysis, but do agree that there was too much focus on pandas (though that was to be expected with Wes McKinney writing it). I would have loved if the book had included sections on the statsmodels and scikit…
on “Python data tools just keep getting better” ↗…(Abelman, Sussman - Lisp oriented, classic) Algorithms in a Nutshell (OReilly) Clever Algorithms (Jason Brownlee. Uses Ruby for examples) Data Mining Analysis - Fundamental Algorithms (Mo. Zaki - ebook) Python for Data Analysis (Wes McKinney) Mining of Massive Datasets (Rajaraman, Leskovec, Ullman - ebook) Enjoy.
on “Good ebook for learning data structures & algorithm” ↗It depends on what kind of analysis you're doing. If you're doing pure exploratory stuff on large datasets, you'll mostly be using NumPy as a tool to help with Pandas. See McKinney's Python for Data Analysis (I believe it's from O'Reilly books) if you'd like a fairly good intro. If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending…
on “GNU Octave” ↗…story about the rise of algorithmic trading -"Flash Boys' (Michael Lewis) offers a nice follow up (HFT), but considered a bit sensationalist EDIT: If you're planning on using Python (a solid bet)... -Python for Data Analysis (Wes McKinney) - Great, quick book for Pandas by former AQR (and now Two Sigma?) guy. -Yves Hilpisch books: "Python for Finance" is introductory while "Derivative Analytics in Python" is quite math heavy.
on “Ask HN: What are the best resources for learning about algorithmic trading?” ↗…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)
on “Launch HN: Neptyne (YC W23) – A programmable spreadsheet that runs Python” ↗…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…
on “Polars” ↗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"…
on “Nobody cracks open a programming book anymore” ↗…basics that no one in industry uses any more". It may be suitable as a beginner's guide if the quality of the exposition is good enough, but my suspicion is that you would be better off getting a copy of "Python for Data Analysis" and a decent quant finance textbook.
on “Python for Finance” ↗