Python Data Science Handbook
(jakevdp.github.io)285 points by cl3misch a day ago
285 points by cl3misch a day ago
Amazing Thank you for sharing.
Reminds me of how thinking using frequencies rather than computing probabilities is easier and can avoid errors (e.g. a 99% accurate test being positive does not mean 99% likelihood of having disease for a disease with a 1/10,000 prevalence in population).
These types of books are always interesting to me because they tackle so many different things. They cover a range of topics at a high level (data manipulation, visualization, machine learning) and each could have its own book. They balance teaching programming while introducing concepts (and sometimes theory).
In short I think it's hard to strike an appropriate balance between these but this seems to be a good intro level book.
Pandas is cancer. Please stop teaching it to people.
Everything it does can be done reasonable well with list comprehensions and objects that support type annotations and runtime type checking (if needed).
Pandas code is untestable, unreadable, hard to refactor and impossible to reuse.
Trillions of dollars are wasted every year by people having to rewrite pandas code.
Code using pandas is testable and reusable in much the same way as any other code, make functions that take and return data.
That said, the polars/narwals style API is better than pandas API for sure. More readable and composable, simpler (no index) and a bit less weird overall.
I've recently had to migrate over to Python from Matlab. Pandas has been doing my head in. The syntax is so unintuitive. In Matlab, everything begins with a `for` loop. Inelegant and slow, yes, but easy to reason about. Easy to see the scope and domain of the problem, to visualise the data wrangling.
Pandas insist you never use a for loop. So, I feel guilty if I ever need a throwaway variable on the way to creating a new column. Sometimes methods are attached to objects, other times they aren't. And if you need to use a function that isn't vectorised, you've got to do df.apply anyway. You have to remember to change the 'axis' too. Plotting is another thing that I can't get my head around. Am I supposed to use Pandas' helpers like df.plot() all the time? Or ditch it and use the low level matplotlib directly? What is idiomatic? I cannot find answers to much of it, even with ChatGPT. Worse, I can't seem to create a mental model of what Pandas expects me to do in a given situation.
Pandas has disabused me of the notion that Python syntax is self-explanatory and executable-pseudocode. I find it terrible to look at. Matlab was infinitely more enjoyable.
> Everything it does can be done reasonable well with list comprehensions and objects that support type annotations and runtime type checking (if needed).
I see this take somewhat often, and usually with similar lack of nuance. How do you come to this? In other cases where I've seen this it's from people who haven't worked in any context where performance or scientific computing ecosystem interoperability matters - missing a massive part of the picture. I've struggled to get through to them before. Genuine question.
I found Pandera quite good for wrapping input/output expectations over Pandas. At the end of the day the vectorisation of operations in it and other table based formats mean they’re not easy to replace performantly.
Can you write more about this? A lot of people use pandas where I work, whereas I'm completely fluent in list comprehensions and dataclasses etc. I had the impression it was doing something "more" like using numpy arrays/matrices for columns.
I honestly don't get why you'd hate pandas more than anything else in the Python ecosystem. It's probably not the best tool in the world, and sure, like everybody else I'd rewrite the universe in Rust if I could start over, and had infinite time to catch up.
But the code base I work on has thousands and THOUSANDS of lines of Pandas churning through big data, and I can't remember the last time it lead to a bug or error in production.
We use pandas + static schema wrapper + type checker, so you'll have to get exotic to break things.
I used the Kernel Density Estimation (KDE) page/blog at my very first job. It was immensely useful and I've loved his work ever since.
Interesting choice of Pandas in this day and age. Maybe he’s after imparting general concepts that you could apply to any tabular data manipulator rather than selecting for the latest shiny tool.
Looks like it. From https://jakevdp.github.io/PythonDataScienceHandbook/00.00-pr...:
> Copyright 2016
why? It's the industry standard as far as my reach goes.
What other framework would you replace it with?
No, polars or spark is not a good answer, those are optimized for data engineering performance, not a holistic approach to data science.
You can assert whatever you want, but Polars is a great answer. The performance improvements are secondary to me compared to the dramatic improvement in interface.
Today all serious DS work will ultimately become data engineering work anyway. The time when DS can just fiddle around in notebooks all day has passed.
Pandas is widely adopted and deeply integrated into the Python ecosystem. Meanwhile, Polars remains a small niche, and it's one of those hype technologies that will likely be dead in 3 years once most of its users realise that it offers them no actual practical advantages over Pandas.
If you are dealing with huge data sets, you are probably using Spark or something like Dask already where jobs can run in the cloud. If you need speed and efficiency on your local machine, you use NumPy outright. And if you really, really need speed, you rewrite it in C/C++.
Polars is trying to solve an issue that just doesn't exist for the vast majority of users.
>Today DS work will ultimately become data engineering work anyway.
Oh yeah? Well in my ivory tower the work stops being serious once it becomes engineering, how do you like that elitism?!
"Data Science" has never been related to academic research, it has always emerged in a business context. I wouldn't say that researchers at Deep Mind are "data scientists", they are academic researchers who focus on shipping papers. If you're in a pure research environment, nobody cares if you write everything in Matlab.
But the last startup I was at tried to take a similar approach to research was unable to ship a functioning product and will likely disappear in a year from now. FAIR has been largely disbanded in favor of the way more shipping-centric MSL, and the people I know at Deep Mind are increasingly finding themselves under pressure to actually produce things.
Since you've been hanging out in an ivory tower then you might be unaware that during the peek DS frenzy (2016-2019) there were companies where data scientists were allowed to live entirely in notebooks and it was someone else's problem to ship their notebooks. Today if you have that expectation you won't last long at most companies, if you can even find a job in the first place.
On top of that, I know quite a few people at the major LLM teams and, based on my conversations, all of them are doing pretty serious data engineering work to get things shipped even if they were hired for there modeling expertise. It's honestly hard to even run serious experiments at the scale of modern day LLMs without being pretty proficient at data engineering related tasks.
I have not work with Polars, but I would imagine any incompatibility with existing libraries (e.g. plotting libraries like plotnine, bokeh) would quickly put me off.
It is a curse I know. I would also choose a better interface. Performance is meh to me, I use SQL if i want to do something at scale that involves row/column data.
I probably wouldn’t rewrite an entire data science stack that used pandas, but most people would use polars if starting a new project today.
Pandas is generally awful unless you're just living in a notebook (and even then it's probably least favorite implementation of the 'data frame' concept).
Since Pandas lacks Polars' concept of an Expression, it's actually quite challenging to programmatically interact with non-trivial Pandas queries. In Polars the query logic can be entirely independent of the data frame while still referencing specific columns of the data frame. This makes Polars data frames work much more naturally with typical programming abstractions.
Pandas multi-index is a bad idea in nearly all contexts other than it's original use case: financial time series (and I'll admit, if you're working with purely financial time series, then Pandas feels much better). Sufficiently large Pandas code bases are littered with seemingly arbitrary uses of 'reset_index', there are many times where multi-index will create bugs, and, most important, I've never seen any non-financial scenario where anyone has ever used Multi-index to their advantage.
Finally Pandas is slow, which is honestly the least priority for me personally, but using Polars is so refreshing.
What other data frames have you used? Having used R's native dataframes extensively (the way they make use of indexing is so much nicer) in addition to Polars both are drastically preferable to Pandas. My experience is that most people use Pandas because it has been the only data frame implementation in Python. But personally I'd rather just not use data frames if I'm forced to used Pandas. Could you expand on what you like about Pandas over other data frames models you've worked with?
He is also the creator of the Altair visualization library (Vega-Lite in Python https://altair-viz.github.io/). I really like using it.
The linked Github seems to have the 2nd edition in the form of notebooks, https://github.com/jakevdp/PythonDataScienceHandbook/blob/ma..., under the Using Code Examples section, "attribution usually includes the title, author, publisher, and ISBN. For example: "Python Data Science Handbook, 2nd edition, by Jake VanderPlas (O’Reilly). Copyright 2023..." compared to the OP's link which has "The Python Data Science Handbook by Jake VanderPlas (O’Reilly). Copyright 2016..."
VanderPlas' handbook remains remarkably relevant despite rapid ecosystem changes. His focus on fundamentals - NumPy, Pandas, Matplotlib - rather than trendy libraries is why. The tools change, but understanding data structures, vectorization, and visualization principles doesn't age.
I loved his Statistics for Hackers talk: https://speakerdeck.com/pycon2016/jake-vanderplas-statistics...