> 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.
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.
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.
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.
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
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.
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?
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.
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.
Coming from an R/dplyr background, I agree. Compare
df.select(
)
with
df |> select(x, y = w/z)
This is the way. Favor keyword arguments to alias.
Still, it’s a very good approximation but still an approximation to the more ergonomic and expressive tidyverse syntax
1 reply →
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.
Fair point, but you can do something like
`df.select("x", y=pl.col.w/pl.col.z)`
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.
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.
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.
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?
7 replies →
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.
2 replies →
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 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?
6 replies →