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Pandas Illustrated: Visual Guide to Pandas
- jcq3 4y agoYet another pandas tutorial. Got chatgpt now, thx.
- timdellinger 4y agoThis seems to be getting the Hug of Death, but this looks like the content: https://betterprogramming.pub/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43 https://betterprogramming.pub/pandas-illustrated-the-definit...
- neonate 4y agohttps://web.archive.org/web/20230127194856/https://scribe.citizen4.eu/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43 https://web.archive.org/web/20230127194856/https://scribe.ci...
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- oneoff786 4y agoI do almost all of my day job in pandas. I consider myself very good at it. My number one recommendation to new data scientists learning the ropes is to just not use NumPy almost at all. I’m not sure where people learn it but they do all of this complicated nonsense. Just map simple Python lambda funcs with pd.Series.map and that’s most of what you need. Memorize your pd.DataFrame methods. If your code feels like it dealing with a matrix and not a table, it’s probably doing something funny.
- _Wintermute 4y agoYou lose a lot of performance not using vectorised functions. Maybe not an issue if you're only dealing with small amounts of data.
- oneoff786 4y agoSeries.map is vectorized. Pretty much everything you need in pandas is as performant as you ought to need for doing tabular data manipulation in Python. Except dataframe.apply
- _Wintermute 4y agoIt is not. df = pd.DataFrame({"foo": np.random.randn(100000)}) pandas map: df["foo"].map(lambda x: x * 2) 18.1 ms ± 109 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) pandas apply: df["foo"].apply(lambda x: x * 2) 17.9 ms ± 46.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) Vectorised function, using underlying numpy operations: df["foo"] * 2 267 µs ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
- lmeyerov 4y agoUse numba and it is, including on GPU :)
- axi1 4y agoThe proper (free) link is https://betterprogramming.pub/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43?sk=50184a8a8b46ffca16664f6529741abc https://betterprogramming.pub/pandas-illustrated-the-definit...
- dang 4y agoOk, changed from https://scribe.citizen4.eu/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43 https://scribe.citizen4.eu/pandas-illustrated-the-definitive.... Thanks!
- throwaway_75369 4y agoSo, given the title and how stressful the last couple of weeks have been, I was sadly disappointed when this wasn't about drawing cute black and white bears. I mean, data analysis is useful and all, but not what the heart wanted at the moment.
- 867-5309 4y agoasking DALL-E for some Python Pandas might relieve our disappointment
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- kretaceous 4y agoHang in there! I hope you find a little relief from the stress eventually. Take care and don't forget to take breaks in possible situations.
- rbanffy 4y ago@dang can you replace the link with the original? https://betterprogramming.pub/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43 https://betterprogramming.pub/pandas-illustrated-the-definit...
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- irrational 4y agoLOL. Those were not the kind of pandas I was expecting. One of my daughters is a panda bear fanatic and I thought this would be a resource I could share with her.
- tomcam 4y agoSame! Although at first glance it appears to be an excellent example of clear, well-illustrated documentation.
- gms7777 4y agoBrains are funny things. I saw the link for the first time a few hours ago, while I was on a lunch break, and immediately thought about the animal. I didn't click the link but did have a thought of "okay, that's kind of a strange topic". I went back to work, did some data analysis using pandas. Opened up HN again, and I saw the link and my brain didn't even register that this was a link I'd seen before -- it immediately assumed that it was about the python library. I didn't even think about it until I saw some comments about the animal.
- axi1 4y agoTo her, I can recommend the book "Big Panda and Little Dragon." Nice illustrations and simple wisdom.
- matsemann 4y agoCan recommend taking a look at Polars. Kinda a successor to pandas. https://www.pola.rs/ https://www.pola.rs/
- z3c0 4y agoInteresting. Seems to also take quite a few leaves from PySpark's book.
- 89vision 4y agoNeat. I love that there's a rust implementation. Types make everything better
- malshe 4y agoThanks. This is so clean and also fast! I just can't get used to pandas so I do most data wrangling in R and then bring the dataframe over to Python for analysis. But looks like Polars will streamline my workflow
- ryzvonusef 4y agoon polars vs pandas: https://news.ycombinator.com/item?id=34452526 https://news.ycombinator.com/item?id=34452526
- dark-star 4y agoThese are not the Pandas I was looking for waves hand
- binkHN 4y agoFor those who were expecting pretty panda bears, like me: > Pandas is an industry standard for analyzing data in Python.
- teleforce 4y agoThere is a new 2nd edition of the very popular Pandas book, Pandas for Everyone by Daniel Chen: https://www.pearson.com/en-us/subject-catalog/p/pandas-for-everyone-python-data-analysis/P200000007264/9780137891153 https://www.pearson.com/en-us/subject-catalog/p/pandas-for-e...
- RhysU 4y agoIt'd be funny if this were a sequence of photos of people beside themselves with frustration while they sat in front of a computer.
- iancmceachern 4y agoI thought this was going to be about panda bears ;(
- vict1378 4y agoInteresting info