Aggregation
Polars implements a powerful syntax defined not only in its lazy API, but also in its eager API. Let's take a look at what that means.
We can start with the simple US congress dataset.
url = "https://theunitedstates.io/congress-legislators/legislators-historical.csv"
dtypes = {
"first_name": pl.Categorical,
"gender": pl.Categorical,
"type": pl.Categorical,
"state": pl.Categorical,
"party": pl.Categorical,
}
dataset = pl.read_csv(url, dtypes=dtypes).with_columns(
pl.col("birthday").str.strptime(pl.Date, strict=False)
)
DataFrame · Categorical · Available on feature dtype-categorical
use reqwest::blocking::Client;
use std::io::Cursor;
let url = "https://theunitedstates.io/congress-legislators/legislators-historical.csv";
let mut schema = Schema::new();
schema.with_column("first_name".to_string(), DataType::Categorical(None));
schema.with_column("gender".to_string(), DataType::Categorical(None));
schema.with_column("type".to_string(), DataType::Categorical(None));
schema.with_column("state".to_string(), DataType::Categorical(None));
schema.with_column("party".to_string(), DataType::Categorical(None));
schema.with_column("birthday".to_string(), DataType::Date);
let data: Vec<u8> = Client::new().get(url).send()?.text()?.bytes().collect();
let dataset = CsvReader::new(Cursor::new(data))
.has_header(true)
.with_dtypes(Some(&schema))
.with_parse_dates(true)
.finish()?;
println!("{}", &dataset);
Basic aggregations
You can easily combine different aggregations by adding multiple expressions in a
list. There is no upper bound on the number of aggregations you can do, and you can
make any combination you want. In the snippet below we do the following aggregations:
Per GROUP "first_name" we
- count the number of rows in the group:
- short form:
pl.count("party") - full form:
pl.col("party").count() - aggregate the gender values groups:
- full form:
pl.col("gender") - get the first value of column
"last_name"in the group: - short form:
pl.first("last_name")(not available in Rust) - full form:
pl.col("last_name").first()
Besides the aggregation, we immediately sort the result and limit to the top 5 so that
we have a nice summary overview.
q = (
dataset.lazy()
.group_by("first_name")
.agg(
pl.count(),
pl.col("gender"),
pl.first("last_name"),
)
.sort("count", descending=True)
.limit(5)
)
df = q.collect()
print(df)
let df = dataset
.clone()
.lazy()
.group_by(["first_name"])
.agg([count(), col("gender").list(), col("last_name").first()])
.sort(
"count",
SortOptions {
descending: true,
nulls_last: true,
},
)
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 4)
┌────────────┬───────┬───────────────────┬───────────┐
│ first_name ┆ count ┆ gender ┆ last_name │
│ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ u32 ┆ list[cat] ┆ str │
╞════════════╪═══════╪═══════════════════╪═══════════╡
│ John ┆ 1256 ┆ ["M", "M", … "M"] ┆ Walker │
│ William ┆ 1022 ┆ ["M", "M", … "M"] ┆ Few │
│ James ┆ 714 ┆ ["M", "M", … "M"] ┆ Armstrong │
│ Thomas ┆ 454 ┆ ["M", "M", … "M"] ┆ Tucker │
│ Charles ┆ 439 ┆ ["M", "M", … "M"] ┆ Carroll │
└────────────┴───────┴───────────────────┴───────────┘
Conditionals
It's that easy! Let's turn it up a notch. Let's say we want to know how
many delegates of a "state" are "Pro" or "Anti" administration. We could directly query
that in the aggregation without the need of a lambda or grooming the DataFrame.
q = (
dataset.lazy()
.group_by("state")
.agg(
(pl.col("party") == "Anti-Administration").sum().alias("anti"),
(pl.col("party") == "Pro-Administration").sum().alias("pro"),
)
.sort("pro", descending=True)
.limit(5)
)
df = q.collect()
print(df)
let df = dataset
.clone()
.lazy()
.group_by(["state"])
.agg([
(col("party").eq(lit("Anti-Administration")))
.sum()
.alias("anti"),
(col("party").eq(lit("Pro-Administration")))
.sum()
.alias("pro"),
])
.sort(
"pro",
SortOptions {
descending: true,
nulls_last: false,
},
)
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 3)
┌───────┬──────┬─────┐
│ state ┆ anti ┆ pro │
│ --- ┆ --- ┆ --- │
│ cat ┆ u32 ┆ u32 │
╞═══════╪══════╪═════╡
│ CT ┆ 0 ┆ 3 │
│ NJ ┆ 0 ┆ 3 │
│ NC ┆ 1 ┆ 2 │
│ SC ┆ 0 ┆ 1 │
│ VA ┆ 3 ┆ 1 │
└───────┴──────┴─────┘
Similarly, this could also be done with a nested GROUP BY, but that doesn't help show off some of these nice features. 😉
q = (
dataset.lazy()
.group_by("state", "party")
.agg(pl.count("party").alias("count"))
.filter(
(pl.col("party") == "Anti-Administration")
| (pl.col("party") == "Pro-Administration")
)
.sort("count", descending=True)
.limit(5)
)
df = q.collect()
print(df)
let df = dataset
.clone()
.lazy()
.group_by(["state", "party"])
.agg([col("party").count().alias("count")])
.filter(
col("party")
.eq(lit("Anti-Administration"))
.or(col("party").eq(lit("Pro-Administration"))),
)
.sort(
"count",
SortOptions {
descending: true,
nulls_last: true,
},
)
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 3)
┌───────┬─────────────────────┬───────┐
│ state ┆ party ┆ count │
│ --- ┆ --- ┆ --- │
│ cat ┆ cat ┆ u32 │
╞═══════╪═════════════════════╪═══════╡
│ NJ ┆ Pro-Administration ┆ 3 │
│ VA ┆ Anti-Administration ┆ 3 │
│ CT ┆ Pro-Administration ┆ 3 │
│ NC ┆ Pro-Administration ┆ 2 │
│ PA ┆ Pro-Administration ┆ 1 │
└───────┴─────────────────────┴───────┘
Filtering
We can also filter the groups. Let's say we want to compute a mean per group, but we
don't want to include all values from that group, and we also don't want to filter the
rows from the DataFrame (because we need those rows for another aggregation).
In the example below we show how this can be done.
Note
Note that we can make Python functions for clarity. These functions don't cost us anything. That is because we only create Polars expressions, we don't apply a custom function over a Series during runtime of the query. Of course, you can make functions that return expressions in Rust, too.
def compute_age() -> pl.Expr:
return date(2021, 1, 1).year - pl.col("birthday").dt.year()
def avg_birthday(gender: str) -> pl.Expr:
return (
compute_age()
.filter(pl.col("gender") == gender)
.mean()
.alias(f"avg {gender} birthday")
)
q = (
dataset.lazy()
.group_by("state")
.agg(
avg_birthday("M"),
avg_birthday("F"),
(pl.col("gender") == "M").sum().alias("# male"),
(pl.col("gender") == "F").sum().alias("# female"),
)
.limit(5)
)
df = q.collect()
print(df)
fn compute_age() -> Expr {
lit(2022) - col("birthday").dt().year()
}
fn avg_birthday(gender: &str) -> Expr {
compute_age()
.filter(col("gender").eq(lit(gender)))
.mean()
.alias(&format!("avg {} birthday", gender))
}
let df = dataset
.clone()
.lazy()
.group_by(["state"])
.agg([
avg_birthday("M"),
avg_birthday("F"),
(col("gender").eq(lit("M"))).sum().alias("# male"),
(col("gender").eq(lit("F"))).sum().alias("# female"),
])
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 5)
┌───────┬────────────────┬────────────────┬────────┬──────────┐
│ state ┆ avg M birthday ┆ avg F birthday ┆ # male ┆ # female │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ f64 ┆ f64 ┆ u32 ┆ u32 │
╞═══════╪════════════════╪════════════════╪════════╪══════════╡
│ WV ┆ 152.686441 ┆ 126.0 ┆ 120 ┆ 1 │
│ AS ┆ 81.0 ┆ null ┆ 2 ┆ 0 │
│ PA ┆ 179.724846 ┆ 91.857143 ┆ 1050 ┆ 7 │
│ LA ┆ 157.195531 ┆ 97.8 ┆ 194 ┆ 5 │
│ MO ┆ 163.741433 ┆ 81.625 ┆ 329 ┆ 8 │
└───────┴────────────────┴────────────────┴────────┴──────────┘
Sorting
It's common to see a DataFrame being sorted for the sole purpose of managing the ordering during a GROUP BY operation. Let's say that we want to get the names of the oldest and youngest politicians per state. We could SORT and GROUP BY.
def get_person() -> pl.Expr:
return pl.col("first_name") + pl.lit(" ") + pl.col("last_name")
q = (
dataset.lazy()
.sort("birthday", descending=True)
.group_by("state")
.agg(
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
)
.limit(5)
)
df = q.collect()
print(df)
fn get_person() -> Expr {
col("first_name") + lit(" ") + col("last_name")
}
let df = dataset
.clone()
.lazy()
.sort(
"birthday",
SortOptions {
descending: true,
nulls_last: true,
},
)
.group_by(["state"])
.agg([
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
])
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 3)
┌───────┬─────────────────┬───────────────────┐
│ state ┆ youngest ┆ oldest │
│ --- ┆ --- ┆ --- │
│ cat ┆ str ┆ str │
╞═══════╪═════════════════╪═══════════════════╡
│ ID ┆ Raúl Labrador ┆ William Wallace │
│ TN ┆ Stephen Fincher ┆ William Cocke │
│ RI ┆ Patrick Kennedy ┆ James Mason │
│ WI ┆ Sean Duffy ┆ Henry Dodge │
│ MS ┆ Steven Palazzo ┆ Narsworthy Hunter │
└───────┴─────────────────┴───────────────────┘
However, if we also want to sort the names alphabetically, this breaks. Luckily we can sort in a group_by context separate from the DataFrame.
def get_person() -> pl.Expr:
return pl.col("first_name") + pl.lit(" ") + pl.col("last_name")
q = (
dataset.lazy()
.sort("birthday", descending=True)
.group_by("state")
.agg(
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
get_person().sort().first().alias("alphabetical_first"),
)
.limit(5)
)
df = q.collect()
print(df)
let df = dataset
.clone()
.lazy()
.sort(
"birthday",
SortOptions {
descending: true,
nulls_last: true,
},
)
.group_by(["state"])
.agg([
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
get_person().sort(false).first().alias("alphabetical_first"),
])
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 4)
┌───────┬─────────────────┬────────────────┬────────────────────┐
│ state ┆ youngest ┆ oldest ┆ alphabetical_first │
│ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ str ┆ str ┆ str │
╞═══════╪═════════════════╪════════════════╪════════════════════╡
│ NH ┆ Frank Guinta ┆ John Sherburne ┆ Aaron Cragin │
│ IA ┆ Abby Finkenauer ┆ Bernhart Henn ┆ Abby Finkenauer │
│ MO ┆ Vicky Hartzler ┆ Spencer Pettis ┆ Abram Comingo │
│ OL ┆ Julien Poydras ┆ Daniel Clark ┆ Daniel Clark │
│ VT ┆ Peter Smith ┆ Samuel Shaw ┆ Ahiman Miner │
└───────┴─────────────────┴────────────────┴────────────────────┘
We can even sort by another column in the group_by context. If we want to know if the alphabetically sorted name is male or female we could add: pl.col("gender").sort_by("first_name").first().alias("gender")
def get_person() -> pl.Expr:
return pl.col("first_name") + pl.lit(" ") + pl.col("last_name")
q = (
dataset.lazy()
.sort("birthday", descending=True)
.group_by("state")
.agg(
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
get_person().sort().first().alias("alphabetical_first"),
pl.col("gender").sort_by("first_name").first().alias("gender"),
)
.sort("state")
.limit(5)
)
df = q.collect()
print(df)
let df = dataset
.clone()
.lazy()
.sort(
"birthday",
SortOptions {
descending: true,
nulls_last: true,
},
)
.group_by(["state"])
.agg([
get_person().first().alias("youngest"),
get_person().last().alias("oldest"),
get_person().sort(false).first().alias("alphabetical_first"),
col("gender")
.sort_by(["first_name"], [false])
.first()
.alias("gender"),
])
.sort("state", SortOptions::default())
.limit(5)
.collect()?;
println!("{}", df);
shape: (5, 5)
┌───────┬──────────────────┬───────────────────┬────────────────────┬────────┐
│ state ┆ youngest ┆ oldest ┆ alphabetical_first ┆ gender │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ str ┆ str ┆ str ┆ cat │
╞═══════╪══════════════════╪═══════════════════╪════════════════════╪════════╡
│ PA ┆ Conor Lamb ┆ Thomas Fitzsimons ┆ Aaron Kreider ┆ M │
│ KY ┆ Ben Chandler ┆ John Edwards ┆ Aaron Harding ┆ M │
│ MD ┆ Frank Kratovil ┆ Benjamin Contee ┆ Albert Blakeney ┆ M │
│ OH ┆ Anthony Gonzalez ┆ John Smith ┆ Aaron Harlan ┆ M │
│ VA ┆ Scott Taylor ┆ William Grayson ┆ A. McEachin ┆ M │
└───────┴──────────────────┴───────────────────┴────────────────────┴────────┘
Do not kill parallelization
Python Users Only
The following section is specific to Python, and doesn't apply to Rust. Within Rust, blocks and closures (lambdas) can, and will, be executed concurrently.
We have all heard that Python is slow, and does "not scale." Besides the overhead of
running "slow" bytecode, Python has to remain within the constraints of the Global
Interpreter Lock (GIL). This means that if you were to use a lambda or a custom Python
function to apply during a parallelized phase, Polars speed is capped running Python
code preventing any multiple threads from executing the function.
This all feels terribly limiting, especially because we often need those lambda functions in a
.group_by() step, for example. This approach is still supported by Polars, but
keeping in mind bytecode and the GIL costs have to be paid. It is recommended to try to solve your queries using the expression syntax before moving to lambdas. If you want to learn more about using lambdas, go to the user defined functions section.
Conclusion
In the examples above we've seen that we can do a lot by combining expressions. By doing so we delay the use of custom Python functions that slow down the queries (by the slow nature of Python AND the GIL).
If we are missing a type expression let us know by opening a feature request!