After reading Rasmus Bååth’s blog Why pandas feels clunky when coming from R,
I decided to practice more polars and translate his dplyr examples to polars.
Reading the data is pretty straightforward:
import polars as pl
purchases = pl.read_csv("purchases.csv")
get the sum of amount:
# dplyr:
# purchases$amount |> sum()
# actually the "pure" tidy approach would be:
# purchases |> select(amount) |> sum 😉
purchases.select("amount").sum()
same by country:
# dplyr:
# purchases |>
# group_by(country) |>
# summarize(total = sum(amount))
(
purchases
.group_by("country")
.agg(pl.sum("amount").alias("total"))
)
deduct the discount:
# dplyr:
# purchases |>
# group_by(country) |>
# summarize(total = sum(amount - discount))
(
purchases
.group_by("country")
.agg(pl.sum("amount") - pl.sum("discount")
.alias("total"))
)
Note that polars does not automatically sort the country column (dplyr does).
If you want the sorting, add .sort("country")
Now we remove everything 10x larger than the median:
# dplyr:
# purchases |>
# filter(amount <= median(amount) * 10) |>
# group_by(country) |>
# summarize(total = sum(amount - discount))
(
purchases
.filter(pl.col("amount") < pl.median("amount") * 10)
.group_by("country")
.agg(pl.sum("amount") - pl.sum("discount")
.alias("total"))
)
Finally, we should use the median within each country:
# dplyr:
# purchases |>
# group_by(country) |>
# filter(amount <= median(amount) * 10) |>
# summarize(total = sum(amount - discount))
(
purchases
.filter(pl.col("amount") < (pl.median("amount") * 10).over("country"))
.group_by("country")
.agg(pl.sum("amount") - pl.sum("discount")
.alias("total"))
.sort("country")
)
The last one was a bit tricky. Simply switching the group_by and filter lines like in dplyr does not work in polars.