Comments (4)
Aliasing works fine, but we don't (currently) support the old-style "implicit join" syntax; using the more modern/standard JOIN … ON
and/or JOIN … USING
syntax will execute without any trouble:
ldf = pl.DataFrame({
"a": [1, 2, 3, 4, 5, 6, 7, 1, 1],
"b": [1, 1, 1, 1, 1, 1, 1, 10, 20],
})
rdf = pl.DataFrame({
"a": [1, 2, 3, 4, 5, 6, 7],
"b": [1, 1, 1, 1, 1, 1, 1],
})
pl.sql(
"""
SELECT
SUM(l.a) AS la_total,
SUM(l.b) AS lb_total,
SUM(r.b) as rb_total
FROM
ldf l JOIN rdf r USING (a, b)
"""
).collect()
# shape: (1, 3)
# ┌──────────┬──────────┬──────────┐
# │ la_total ┆ lb_total ┆ rb_total │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞══════════╪══════════╪══════════╡
# │ 28 ┆ 7 ┆ 7 │
# └──────────┴──────────┴──────────┘
Now, I am looking at adding support for implicit joins, but I'd recommend use of JOIN … ON
and/or JOIN … USING
for SQL queries generally (and for the time-being Polars requires it) 👍
We have an open issue for implicit join support here: #16662.
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Thanks for the quick reponse @alexander-beedie . In the example above there is no join just a select
from polars.
Thanks for the quick reponse @alexander-beedie . In the example above there is no join just a select
There is - what you have written is the old-school syntax for an implicit Cartesian join of the two tables (which is possibly not what you intended?) ;)
Do you actually want to union the two tables? (You have to relate them somehow)
from polars.
I don't actually want the union. I was mostly playing around with sql and was trying to naively read/select. Thank you
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