Polars from Python and R

Templates for using Polars in Python and R

This is example templates that use Polars for R and Python. Note that these are short examples. If you want to know more about what they can do, check out this site.

R

library(polars)

nyc = pl$scan_parquet("nyc-taxi/**/*.parquet", hive_partitioning = TRUE)
nyc

q1 = (
    nyc
    $group_by("passenger_count")
    $agg(
        pl$mean("tip_amount")#$alias("mean_tip") ## alias is optional
    )
    $sort("passenger_count")
)
q1 

tic = Sys.time()
dat1 = q1$collect()
toc = Sys.time()

dat1

q2 = (
    nyc
    $filter(pl$col("month") <= 3)
    $group_by("month", "passenger_count")
    $agg(pl$mean("tip_amount")$alias("mean_tip"))
    $sort("passenger_count")
) 

# q2              # naive
cat(q2$explain()) # optimized

tic = Sys.time()
dat2 = q2$collect()
toc = Sys.time()

dat2

q3 = (
    nyc
    $group_by("passenger_count", "trip_distance")
    $agg(
        pl$mean("tip_amount")$alias("mean_tip"),
        pl$mean("fare_amount")$alias("mean_fare")
        )
    $sort("passenger_count", "trip_distance")
)

tic = Sys.time()
dat3 = q3$collect()
toc = Sys.time()
 
dat3

dat3$unpivot(index = c("passenger_count", "trip_distance"))

mean_tips  = nyc$group_by("month")$agg(pl$col("tip_amount")$mean())
mean_fares = nyc$group_by("month")$agg(pl$col("fare_amount")$mean())

(
    mean_tips
    $join(
        mean_fares,
        on = "month",
        how = "left"  # default is inner join
    )
    $collect()
)

# You can also try tidypolars

library(polars) ## Already loaded
library(tidypolars)
library(dplyr, warn.conflicts = FALSE)
library(tidyr, warn.conflicts = FALSE)

nyc = scan_parquet_polars("nyc-taxi/**/*.parquet")

nyc |> 
    summarise(mean_tip = mean(tip_amount), .by = passenger_count) |>
    compute()

# Aside: Use collect() instead of compute() at the end if you would prefer to return a standard R data.frame instead of a Polars DataFrame.

Python

import polars as pl
import time
import matplotlib

nyc = pl.scan_parquet("nyc-taxi/**/*.parquet", hive_partitioning=True)
nyc

q1 = (
    nyc
    .group_by(["passenger_count"])
    .agg([
            pl.mean("tip_amount")#.alias("mean_tip") ## alias is optional
        ])
    .sort("passenger_count")
)
q1

tic = time.time()
dat1 = q1.collect()
toc = time.time()

dat1

q2 = (
    nyc
    .filter(pl.col("month") <= 3)
    .group_by(["month", "passenger_count"])
    .agg([pl.mean("tip_amount").alias("mean_tip")])
    .sort("passenger_count")
)

# q2             # naive
q2.show_graph()  # optimized

tic = time.time()
dat2 = q2.collect()
toc = time.time()

dat2

q3 = (
    nyc
    .group_by(["passenger_count", "trip_distance"])
    .agg([
        pl.mean("tip_amount").alias("mean_tip"),
        pl.mean("fare_amount").alias("mean_fare"),
        ])
    .sort(["passenger_count", "trip_distance"])
)

tic = time.time()
dat3 = q3.collect()
toc = time.time()

dat3

dat3.unpivot(index = ["passenger_count", "trip_distance"])

mean_tips  = nyc.group_by("month").agg(pl.col("tip_amount").mean())
mean_fares = nyc.group_by("month").agg(pl.col("fare_amount").mean())

(
    mean_tips
    .join(
        mean_fares,
        on = "month",
        how = "left" # default is inner join
    )
    .collect()
)