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Comment by bobson_dugnutt5 | Hacker News Reader

I love polars. Did a lot of evangelizing in work to get people to give up pandas in favor of it.

I gave up pandas in favor of polars after someone at work did the same and I am very happy with it. Pandas API is just so much worse and much slower.

Have Polars' inconsistent versioning policy caused you any problems?

What's inconsistent about their versioning?

Taken out of context, your post looks like a conservationist who got fed up with pandas being a flagship species and made it their lifelong mission to replace them with polar bears.

This is not a criticism. As someone who doesn’t use Python, I simply found it amusing.

You should learn Boa constrictor instead of Python.

Thank you, your the only person to correctly interpret my comment :)

I guess I am a casual pandas user only. Reading a guide on migrating/differences, it's hard to see why polars would be obviously better.

Here are a couple reasons:

- much faster, multithreaded by default. Read in a big csv with it and see how it feels.

- no index/MultiIndex. Pandas special treatment of index always felt like more trouble than it was worth, so no need to reset_index() everywhere.

- expressions are very portable. At first using pl.col everywhere feels like a bit much, but you can define them anywhere and then apply them to a dataframe whenever you want.

- once internalized, the syntax makes much more sense and is far more consistent compared to pandas.

Of course all depends on what your use cases are. If performance is important then I'd strongly recommend trying it out. If you just use it to have a look at the odd dataframe, maybe not worth your time as much

End of the day, they both get the job done.

Pandas is more ergonomic in that some ideas can be more tersely represented. The downside is that this results in more dynamism which can change if the underlying data gets updated. Polars is more strict in that it will not silently flip a data type on you. However this strictness does come at the cost of being a bit slower to type and some data idioms not having a good Polars equivalent.

People like to note the speed improvements, but that is the least interesting thing about the library. Rarely have I ever had a problem where I was bottlenecked by Pandas throughout.

Polars is very much a Pandas 2.0 with a bunch of lessons learned. I do not think it is earth shattering changes, but it is worth migrating when you can.

Both have terrible syntax that make SQL look like the most readable thing ever.

I tend to agree. SQL may have been harder to write in the past (worse autocomplete than pandas/polars), but now that AI is writing the code, SQL is usually much easier to read. So DuckDB is another interesting alternative to pandas.

The cool thing about polars is that you can conditionally collect expressions over many layers of business logic, and then compute the result at the end. Doing this in SQL ends up in a hodgepodge of strings and trimmed ends to please the syntax. You can also pretty effortlessly write quite complex conditionals directly in polars, and bridge it easily to the surrounding python.

I find that SQL is only easier to read with minimal abstraction, but as soon as the project gets bigger SQL becomes an unwieldy island of different that has served its purpose after we’re done with reading/writing the data.

This sounds interesting! Do you have a specific example by any chance or blog post/doc references?

It’s just the lazy/expression part of the API, which is really the bread and butter of polars, rather than just being “replacement syntax” for pandas. This allows you to tap into abstraction that SQL can’t keep up with:

  import polars as pl

  # 1. Base Dataset
  lazy_df = pl.LazyFrame(
    {
      "store_id": ["S01", "S02", "S03", "S04", "S05"],
      "revenue": [5000.0, 2400.0, 15000.0, 900.0, 3200.0],
      "margin": [0.45, 0.30, 0.60, 0.15, 0.50],
      "tx_count": [120, 45, 300, 20, 85],
      "returns": [5, 12, 45, 2, 8],
    }
  )

  # 2. Define Layer Abstractions
  def get_kpi_layer() -> list[pl.Expr]:
    return [
      (pl.col("returns") / pl.col("tx_count")).alias("return_rate"),
      (pl.col("revenue") / pl.col("tx_count")).alias("avg_order_value"),
    ]

  def get_threshold_layer(thresholds: dict[str, list[float]]) -> list[pl.Expr]:
    return [
      (pl.col(col) > limit).alias(f"is_{col}above{int(limit)}")
      for col, limits in thresholds.items()
      for limit in limits
    ]

  def get_interaction_layer(numeric_cols: list[str]) -> list[pl.Expr]:
    return [
      (pl.col(a) / (pl.col(b) + 1e-5)).alias(f"ratio_{a}per{b}")
      for i, a in enumerate(numeric_cols)
      for b in numeric_cols[i + 1 :]
    ]

  def get_segmentation_layer() -> list[pl.Expr]:
    return [
      pl.when(pl.col("margin") > 0.4)
      .then(pl.literal("High"))
      .otherwise(pl.literal("Low"))
      .alias("margin_profile")
    ]

  # 3. Consolidate and Execute Single Graph Pass
  thresholds = {"revenue": [1000.0, 5000.0, 10000.0], "tx_count": [50, 100, 200]}
  numeric_cols = ["revenue", "margin", "tx_count", "returns"]

  expr_pool = [
    *get_kpi_layer(),
    *get_threshold_layer(thresholds),
    *get_interaction_layer(numeric_cols),
    *get_segmentation_layer(),
  ]

  final_df = lazy_df.with_columns(expr_pool).collect()

I'm sorry but this looks much better:

  WITH raw_data AS (

    SELECT * FROM (
        VALUES 
            ('S01', 5000.0, 0.45, 120, 5),
            ('S02', 2400.0, 0.30,  45, 12),
            ('S03', 15000.0, 0.60, 300, 45),
            ('S04',  900.0, 0.15,  20, 2),
            ('S05', 3200.0, 0.50,  85, 8)
    ) AS t(store_id, revenue, margin, tx_count, returns)),

  base_data AS (
    SELECT
        store_id,
        revenue,
        margin,
        CAST(tx_count AS DOUBLE) AS tx_count,
        CAST(returns AS DOUBLE) AS returns
    FROM raw_data
  )

  SELECT

    store_id,
    revenue,
    margin,
    CAST(tx_count AS BIGINT) AS tx_count,
    CAST(returns AS BIGINT) AS returns,

    -- KPI Layer
    returns / tx_count AS return_rate,
    revenue / tx_count AS avg_order_value,

    -- Threshold Layer (matching original alias names)
    revenue > 1000.0 AS is_revenueabove1000,
    revenue > 5000.0 AS is_revenueabove5000,
    revenue > 10000.0 AS is_revenueabove10000,
    tx_count > 50 AS is_tx_countabove50,
    tx_count > 100 AS is_tx_countabove100,
    tx_count > 200 AS is_tx_countabove200,

    -- Interaction Layer (preserving exact numeric formula & aliases)
    revenue / (margin + 1e-5) AS ratio_revenuepermargin,
    revenue / (tx_count + 1e-5) AS ratio_revenuepertx_count,
    revenue / (returns + 1e-5) AS ratio_revenueperreturns,
    margin / (tx_count + 1e-5) AS ratio_marginpertx_count,
    margin / (returns + 1e-5) AS ratio_marginperreturns,
    tx_count / (returns + 1e-5) AS ratio_tx_countperreturns,

    -- Segmentation Layer
    CASE WHEN margin > 0.4 THEN 'High' ELSE 'Low' END AS margin_profile

  FROM base_data;

... does it? I don't think it does, even in this form.

And now write it such that all the conditions and transformations are injected into the string (somehow) rather than written in explicitly. Much worse.

Really? You've written out all the ratios and thresholds manually. If a user wanted to change the set of thresholds the polars way is far superior. In what way do you consider this better?

Well, I prefer the polars version. And now if I want to reuse the CTEs elsewhere, I have to reach out to another tool like DBT or hand roll something to do string manipulation.

awesome, thanks!

Coming from an R/dplyr background, I agree. Compare

df.select(

  pl.col("x"),
  (pl.col("w")/pl.col("z")).alias("y")

)

with

df |> select(x, y = w/z)

Fair point, but you can do something like

`df.select("x", y=pl.col.w/pl.col.z)`

    from polars import col as C

    df.select(C.x, y = C.w / C.z)

Still, it’s a very good approximation but still an approximation to the more ergonomic and expressive tidyverse syntax

One person's "ergonomic and expressive" is another person's "wait what in the world is actually going on here".

This is the way. Favor keyword arguments to alias.

R really is/was the superior traditional data science language. Python ecosystem is slowly catching up though.

ggplot vs matplotlib

dplyr vs pandas

And I loved that everything in RStudio was so easily inspectable. Have a huge dataframe? Just look at it right in your IDE.

Altair and Positron should be just as good for your Polars @ Python needs. With software like Marimo notebooks and VegaFusion, Polars/Python experience starts beating R by quite a substantial margin.

Polars is a world away from pandas, but I feel that dplyr still offers the most simple and understandable introduction to data analysis for the beginner. The above is a good example of this.

To me, I immediately wonder whether w, x, y, and z here are variables or column names. It would indeed be nice if python could more tersely represent the distinction between a name and a literal string (or worse, as in your R example, a variable reference), but alas. But I think trading some verbosity for explicitness about this distinction is a pretty good trade, and very in keeping with python style.

Could not agree less. Ive always found SQL an unreadable mess but tools like polars and dplyr are such elegant ways to manipulate data.

Pandas is a mess though.

There's no way SQL is more unreadable than polars. IMO it's the other way around.

> There's no way SQL is more unreadable than polars. IMO it's the other way around.

I think on basic queries, SQL is really nice, but when stuff gets more complex, with a bunch of CTEs, let alone functions requiring loops, it becomes pretty obtuse.

I would say that it is easier to decompose polars (and all dataframe api) queries and to build them up from pieces than it is to do the same with sql. Any time I find myself writing more than five or so lines of sql, or especially building a sql string in parts with logic, I wish I had a dataframe api instead. But the reverse is also somewhat true, that simpler and explicit expressions are nicer with sql.

What is it about polars syntax you don't like? The fact that is very verbose? At first I wasn't a fan, but over time I've grown to really like it. That never happened to me with pandas, always felt the syntax was messy

The verbosity takes a bit to get used too, but it sure beats the anything-goes feeling - messy as you put it - of pandas.

You can query polars data frames with SQL: https://docs.pola.rs/api/python/stable/reference/expressions...

Unfortunately, polars does not support parameterized queries, so the risk of SQL injection is extremely high.

I agree sql is more elegant. The problems arise when you have to add logic on top of sql. Often I end up constructing queries via string manipulation and that is not very ergonomic. Polars api is more verbose and complex than sql but at least it's not meta-programming.

The duckdb python api is okay, but it is a bit limited, no ctes, no as of join, and it can be slow at bind/interpretation time when you do stuff like unioning multiple relations in a loop (I think that becomes O(N^2), but I might be wrong). Most issues can be worked around, but Polars is designed from the ground up to be used from python.

You should be using dbt instead of string manipulation for serious query building.

It's never quite been clear to me what the advantage of dbt over a python program using sqlalchemy / duckdb / polars to transform data is. Can you enlighten me?

At the minimum, it's just Jinja2 templates in your SQL queries - meaning, you can do pure SQL transformations with conditional logic in your templates. In addition to being able to run tests, specify custom macros, having version control and having some constrained way to organize your tables, you're turning SQL into a proper programming language with just one library.

My issue with DBT is it is a mix of SQL, yaml, jinja2 flow controls (and metrics is whole another thing). SQL with jinja2 if/else can get really unmaintainable quickly. It's perhaps better than homegrown sql based transformers.

polars is code and can be version controlled too. Dataframes in my opinion are more elegant, and with the right backends and some lineage enhancements, could serve a much wider set of use cases than what DBT does

Different tools for different tasks.

But is that ... good?

It's definitely better than raw 500+ lines of SQL composed with 300+ lines of Python for some bespoke business transformation without clean versioning, which is what SQL transformations tend to converge towards without something like dbt.

Versioning seems like a separate problem, no? I mean, you can version control your python program generating SQL queries

Yeah that's fair. In practice, the projects I've worked on have always seemed to end up with a mix of all of dbt, SQL strings in python, and dataframe manipulation. I've always felt like we should be able to minimize one of these in favor of the others, but in practice it seems like people find each of these things useful for different things.

I've been looking at dbt for exactly this reason but don't quite get the advantages if you're not interacting with a data warehouse of some sort.

Embedded databases like DuckDB are just as suited for dbt work.

Spark/PySpark/polars/dbt/sqlmesh are all data engineering frameworks for data transformation (and some data scientists).

If you don't have a data warehouse / OLAP system you are generally not in the niche for those tools.

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