DuckDb even goes so far as to include a clone of the pyspark dataframe API, so somebody there must like it too.

There are some frustrations in spark however, I remember getting stuck on Winsorizing over groups. Hilariously there are identical functions called `percentile_approx` and `approx_percentile` and it wasn't clear from the docs they were the same or at least did the same thing.

Given all that, the ergonomics of Julia for general purpose data handling is really unmatched IMO. I've got a lot of clean and readable data pipeline and shaping code that I revisited a couple years later and could easily understand. And making updates with new more type-generic functions is a breeze. Very enjoyable.

For me I don't really understand the hype around Polars, other than that it fixes some annoying issues with the Pandas API by sacrificing backwards compatibility.

With a single node engine you have a ceiling how good it can get.

With Spark/Athena/BigQuery the sky is the limit. It is such a freedom to not be limited by available RAM or CPU. They just scale to what they need. Some queryies squeeze in CPU-days in just a few minutes.

Spark is great if you have large datasets since you can easily scale as you said. But if the dataset is small-ish (<50 million rows) you hit a lower bound in Spark in terms of how fast the job can run. Even if the job is super simple it take 1-2 minutes. Polars on the other hand is almost instantaneous (< 1 second). Doesn't sound like much but to me makes a huge difference when iterating on solutions.

Well, you are a lot closer to that with Polars than with Pandas at least.

And I say this as someone who makes much of their living from Pandas.

With that acknowledged, it'll make life a lot easier on everyone if the "fix the API" Pandas 3 had a different package name. Polars and others seem like exactly that solution, even if not literally Pandas.

On a tangent, i wonder what this op would look like in SQL? Probably would need support for filtering in a window function, which I'm not sure is standardized?

But also props to Wes McKinney for giving us a dataframe library during a time when we had none. Java still doesn’t have a decent dataframe library so we mustn’t take these things for granted.

The Pandas API is no longer the way things should be done today nor should it be in new tutorials. Pandas was the jquery of its time —- great but no longer the state of the art. But I have much gratitude for it being around when it was needed.

I disagree that Pandas is no longer state of the art: its interface is optimized for a different use case compared to Polars.

I'm not a big fan of the pandas API but it's a super useful tool nonetheless.

  select id, max(views) from <tbl>
  where sales > avg(sales) over (partition by id) group by 1

  df |> group_by(id) |>
    filter(sales > mean(sales)) |>
    summarize(max(views))

  df |> filter(.by=id, sales>mean(sales)) |> summarize(.by=id,max(views))

  df |> summarize(.by=id, max(views[sales>mean(sales)]))

https://news.ycombinator.com/item?id=30067406

And here in the docs:

https://dplyr.tidyverse.org/reference/dplyr_by.html

And maybe also:

https://www.tidyverse.org/blog/2023/02/dplyr-1-1-0-per-opera...

I guess part of it is that there’s some ‘non-locality’ in the pipeline where the grouping could be relatively distant from the operation acting on the grouped data. Similarly, you get to worry about eg grouping data that is already grouped.

I quite like the prql solution which is to have a ‘structured grouping’ where you have to delimit the pipeline that operates on grouped data, but maybe it can still lead to bad edits for complex queries.

No need to filter within the window function if you use subquery or CTE, which is supported everywhere.

  -- "find the maximum value of 'views',
  -- where 'sales' is greater than its mean, per 'id'".

  select max(views), id  -- "find the maximum value of   'views',
  from example_table as et 
  where exists 
  (
    SELECT *
      FROM 
    ( 
      SELECT id, avg(sales) as mean_sales 
      FROM example_table
      GROUP by id 
    ) as f --  
    where et.sales  > f.mean_sales -- where 'sales' is greater than its mean
    and et.id = f.id 
  )
  group by id; -- per 'id'".

If you mean filtering the rows in the window, you can do 'sum(case when condition then value else null end) over (window)'; if you mean selecting rows based on the value of a window function you use 'qualify' where supported or a trivial subquery or CTE and 'where' (which qualify is just shorthand for)

According to wikipedia, windowing was standardized back in 2003.

My book, Effective Polars, has a whole chapter devoted to the question of whether porting from pandas makes sense.

However there are subtle differences between Pandas and Polars behaviors so regression testing is your friend. It’s not 1:1 mapping.

    df = duckdb.query("from tbl1 join tbl2 using (id) where id is not null ").pl()

I go back and forth between DuckDB and Polars functions in the same scope because it's so cheap to convert between the two.

(software people might beg to differ about the intuitive bit because they are more used to an imperative style, and to my surprise, even the best software engineers struggle with SQL because it requires one to think in set and relation operations rather than function calls, which many software folks are not used to)

I actually don't use the Polars dataframe APIs much except for some operations which are easier to do in dataframe form, like applying a Python function as UDF, or transposing (not pivoting) a dataframe.

Also Polars is good for materializing the query into a dataframe rapidly, which can then be passed into methods/functions. It's also a lot easier to unit test dataframes than SQL tables. There's a lot more tooling for that.

Copilot is still suggesting out of date polars code to me. ChatGPT and Claude regularly suggest operations that are valid in pandas and not in polars.

What could go wrong?

Awesome! Didn't expect such a vast difference in usability at first.

  import numpy as np
  
  def scatter_mean(index, value):
      sums = np.zeros(max(index)+1)
      counts = np.zeros(max(index)+1)
      for i in range(len(index)):
          j = index[i]
          sums[j] += value[i]
          counts[j] += 1
      return sums / counts
  
  def scatter_max(index, value):
      maxs = -np.inf * np.ones(max(index)+1)
      for i in range(len(index)):
          j = index[i]
          maxs[j] = max(maxs[j], value[i])
      return maxs
  
  def scatter_count(index):
      counts = np.zeros(max(index)+1, dtype=np.int32)
      for i in range(len(index)):
          counts[index[i]] += 1
      return counts
  
  id = np.array([1, 1, 1, 2, 2, 2]) - 1
  sales = np.array([4, 1, 2, 7, 6, 7])
  views = np.array([3, 1, 2, 8, 6, 7])
  means = scatter_mean(id, sales).repeat(scatter_count(id))
  print(views[sales > means].max())

Even better is using tools like Narwhals and Ibis which can convert back and forth to any frames you want.

Reason: I can speed things up fairly easily with Cython functions, and do multithreading using the Python module. With polars I would have to learn Rust for that.

I took some data science classes in grad school, but basically haven't had any reason to touch pandas since I graduated. But, did like the ecosystem of tools, learning materials, and other libraries surrounding it when I was working with it. I recently just started a new project and am quickly going through my old notes to refamiliarize myself with pandas, but maybe I should just go and learn Polars?

If you're actually operating on an object, ie. the equivalent to a single row in a dataframe, then yeah, it's silly to use a dataframe library. But if you're operating on N value objects ... yeah you probably want a dataframe with N rows and a column for each field in your object.

Your mileage may vary I guess, but I resisted this for quite awhile and I now think I was the one who was wrong.

Most software devs are used to working 1-dimensional collections like lists, or tree-like abstractions like dicts, or some combination of those. This is why most abstractions and complex data types are built on these. Objects are a natural progression.

But in the data world, high-dimensions are modeled using dataframes (analogously, tables). This is a paradigm shift for most pure software people because manipulating tables typically require manipulating sets and relations. Joining two tables requires knowing how the join-columns relate (inner, full outer, left, right, cross). Aggregation and window functions require thinking in terms of sub-groupings and subsets.

It's not super intuitive unless you have to manipulate and reshape large amounts of data every day, which data scientists and data engineers have to do, and which software engineers typically don't do very often. It's just a kind of muscle memory that gets developed.

I definitely had trouble at first getting software engineers to buy into DuckDB because they were unfamiliar and uncomfortable with SQL. Fortunately some of them had a growth mindset and were willing to learn, and those folks now have now acquired a very powerful tool (DuckDB) and a new way of thinking about large-data manipulation. When data is a certain size, iterative constructs like "for" loops become impractical.

That said, my team moved to polars about a year ago and we haven't looked back.

I have the opposite opinion. In a previous codebase I fought hard to use dataclasses & type hinting where possible over dictionaries, because with dictionaries you'd never know what type anything was, or what keys were present. That worked nicely and it was much easier to understand the codebase.

Now I've been put on a Pandas project and it's full of mysterious

    df = df[df["thing"] == "value"]

Everything's just called "df", you never know what type anything is without going in and checking, the structure of the frames is completely opaque, they change the structure of the dataframe halfway through the program. Type hinting these things is much harder than TypedDict/dataclass, at least doing it correctly & unambiguously is. It's practically a requirement to shove this stuff in a debugger/REPL because you'd have no chance otherwise.

Sure, the argument is that I'm just in a bad Pandas codebase, and it can be done much better. However what I take issue with is that this seems to be the overwhelming "culture" of Pandas. All Pandas code I've ever read is like this. If you look at tutorials, examples online, you see the same stuff. They all call everything the same name and program in the most dynamic & opaque fashion possible. Sure it's quick to write, and if you love Pandas you're used to it, but personally I wince every time I look in a method and see this stuff instead of normal code.

Personally I only use Pandas if I absolutely need it for performance, as a last resort.

Like if we have a dataclass:

    obj.thing == value

    SELECT * FROM table WHERE thing = ‘value’

The fact the dataframe often changes halfway through the program is, I think, more to do with the task at hand, that often pandas is being used to perform data transformation (the T in ETL), where some raw data is read in, and the goal is literally to change the structure to clean it up and normalize it, so the data can be ingested into a SQL table in a consistent form with other data points.

But if transformation is not what you are doing, then yes, that might not be the right use of dataframes.

With all you need to do some work, but I find the Pandas one more involved because you don't have an authoritative "reference", just an initial state then some transformations. With the Pandas example I have to run the program (in my head or actually). The program might need to pull in test data (hopefully some has been provided). The worst is when the structure of the DF is derived from the source data rather than stated in code (e.g. reading in a CSV). It's much more to do than looking at a class definition or declarative database schema; there's a "sequence" to it, there are transformation steps happening to the DF that I need to keep track of.

Here's a really common example where we select a subset of fields:

    df = ...
    df = df[["field1", "field2"]]

This is a totally valid way to transform the data, but the manner in which the transformation is happening, I find, makes the code harder to reason about. And this is the manner of transformation I find most commonplace in Pandas practice. We could instead do the following, but I don't see it much:

    df = ...
    df_limited_fieldset = df[["field1", "field2"]]

None of this is to say that abuse of dynamic types doesn't happen in normal Python, it totally does, but I've found these patterns to be so ingrained in Pandas culture / common practice that I'm comfortable characterising them as part of the nature of the tool.

You put it much better than I could have. Do you know if polars at all solves the problem of having opaque, mutable objects everywhere? I feel like there's a good market for having a dataframe library that's easier to reason about in your editor. It could even be a wrapper around pandas that adds rich typing sort of the way FastAPI does with Pydantic for Starlette.

There is an SO answer[1] by the Polars author which may have some relevance.

[1]: https://stackoverflow.com/questions/73934129/

Editor completion is an extremely low ranking aspect for choosing technologies for data analysis. If SQL is the better tool but you're not using it because it doesn't have editor completion, then you need a better editor. It pains me when people prioritise "developer experience in VS Code" over "actually the correct technological choice".

I ask this sincerely, as I’ve seen many scenarios over the years where a tool like Django is used to manage a Postgres database, solely that the schema migrations are captured in version control. A .sql file can be in version control, but rolling back a git commit is not the same as being able to roll back a schema migration in the actual database.

In the context of comparing SQL and Pandas, we're mostly talking about DQL, so version control for this is exactly the same as any other query code.

For the DDL side that you're asking about, indeed just version controlling SQL files won't work, you need to use a proper migration tool. There are many available that do pure SQL migrations in the same way Django works (a sequence of up/down operations to perform or revert a migration). Personally I use migrate[1].

So you really can achieve the same thing. Personally I like this approach because it can do anything Django can, but it can also do more complicated migrations that can't be expressed by an ORM's DB-agnostic abstract layer. Pure SQL migrations also decouple your database schema from your ORM/framework, which I think is sensible anyway; databases & data tend to be much stickier than apps & frameworks.

A downside here is that you need to do some extra work to keep models in sync with the schema. But the point I was making with my original post is that you can totally use ORMs for CRUD and auto-generate away all that boilerplate, _in addition to_ using raw SQL for complicated data analysis. My point is it's not binary, you can mix them, and there's nothing forcing you to generate _all_ your queries if you use an ORM for most of them. Use the ORM to manage schema, then SQL for a one-off query - still works.

[1]: https://github.com/golang-migrate/migrate

You don't rollback a schema migration to a production database, because that is impossible. What has been done cannot be undone.

"Correct technological choice": I think relational algebra style APIs (a la Polars) are the "correct technological choice" here. SQL is just a tool to express relational algebra, and I'm not sure it's a good one.

For strictly analysis, I’d recommend DataSpell as it’s more oriented towards that. Additionally, it has built in functionality to do sql queries in notebook cells and will save to a variable which is a data frame.

Personally, I see a dataframe library as a last resort. I prefer to improve the algorithm, or push more things into the database, or even scale up the hardware in some cases. If I've exhausted all other options and it's still too slow, then I use a dataframe library.

But then I'm not a data scientist. I've found that data scientists have a hammer that they really really like, so they'll use it everywhere.

(I spend a good deal of my time and helping client use pandas and Polars.)

Sad that Julia is unusable for my workflows, and will likely remain so.

Oh yeah, toss numba or cython on top and you are back to numpy speed...

(Admittedly, there are more features, but in my book, I demonstrate pure Python code that runs as fast as numpy and cython with this decorator added.)