Getting started
This chapter is here to help you get started with Polars. It covers all the fundamental features and functionalities of the library, making it easy for new users to familiarise themselves with the basics from initial installation and setup to core functionalities. If you're already an advanced user or familiar with dataframes, feel free to skip ahead to the next chapter about installation options.
Installing Polars
pip install polars
cargo add polars -F lazy
# Or Cargo.toml
[dependencies]
polars = { version = "x", features = ["lazy", ...]}
Reading & writing
Polars supports reading and writing for common file formats (e.g., csv, json, parquet), cloud storage (S3, Azure Blob, BigQuery) and databases (e.g., postgres, mysql). Below, we create a small dataframe and show how to write it to disk and read it back.
import polars as pl
import datetime as dt
df = pl.DataFrame(
{
"name": ["Alice Archer", "Ben Brown", "Chloe Cooper", "Daniel Donovan"],
"birthdate": [
dt.date(1997, 1, 10),
dt.date(1985, 2, 15),
dt.date(1983, 3, 22),
dt.date(1981, 4, 30),
],
"weight": [57.9, 72.5, 53.6, 83.1], # (kg)
"height": [1.56, 1.77, 1.65, 1.75], # (m)
}
)
print(df)
use chrono::prelude::*;
use polars::prelude::*;
let mut df: DataFrame = df!(
"name" => ["Alice Archer", "Ben Brown", "Chloe Cooper", "Daniel Donovan"],
"birthdate" => [
NaiveDate::from_ymd_opt(1997, 1, 10).unwrap(),
NaiveDate::from_ymd_opt(1985, 2, 15).unwrap(),
NaiveDate::from_ymd_opt(1983, 3, 22).unwrap(),
NaiveDate::from_ymd_opt(1981, 4, 30).unwrap(),
],
"weight" => [57.9, 72.5, 53.6, 83.1], // (kg)
"height" => [1.56, 1.77, 1.65, 1.75], // (m)
)
.unwrap();
println!("{}", df);
shape: (4, 4)
┌────────────────┬────────────┬────────┬────────┐
│ name ┆ birthdate ┆ weight ┆ height │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 │
╞════════════════╪════════════╪════════╪════════╡
│ Alice Archer ┆ 1997-01-10 ┆ 57.9 ┆ 1.56 │
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 │
└────────────────┴────────────┴────────┴────────┘
In the example below we write the dataframe to a csv file called output.csv. After that, we read
it back using read_csv and then print the result for inspection.
df.write_csv("docs/assets/data/output.csv")
df_csv = pl.read_csv("docs/assets/data/output.csv", try_parse_dates=True)
print(df_csv)
CsvReader · CsvWriter · Available on feature csv
use std::fs::File;
let mut file = File::create("docs/assets/data/output.csv").expect("could not create file");
CsvWriter::new(&mut file)
.include_header(true)
.with_separator(b',')
.finish(&mut df)?;
let df_csv = CsvReadOptions::default()
.with_infer_schema_length(None)
.with_has_header(true)
.with_parse_options(CsvParseOptions::default().with_try_parse_dates(true))
.try_into_reader_with_file_path(Some("docs/assets/data/output.csv".into()))?
.finish()?;
println!("{}", df_csv);
shape: (4, 4)
┌────────────────┬────────────┬────────┬────────┐
│ name ┆ birthdate ┆ weight ┆ height │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 │
╞════════════════╪════════════╪════════╪════════╡
│ Alice Archer ┆ 1997-01-10 ┆ 57.9 ┆ 1.56 │
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 │
└────────────────┴────────────┴────────┴────────┘
For more examples on the CSV file format and other data formats, see the IO section of the user guide.
Expressions and contexts
Expressions are one of the main strengths of Polars because they provide a modular and flexible way of expressing data transformations.
Here is an example of a Polars expression:
pl.col("weight") / (pl.col("height") ** 2)
As you might be able to guess, this expression takes the column named “weight” and divides its values by the square of the values in the column “height”, computing a person's BMI. Note that the code above expresses an abstract computation: it's only inside a Polars context that the expression materalizes into a series with the results.
Below, we will show examples of Polars expressions inside different contexts:
selectwith_columnsfiltergroup_by
For a more detailed exploration of expressions and contexts see the respective user guide section.
select
The context select allows you to select and manipulate columns from a dataframe. In the simplest
case, each expression you provide will map to a column in the result dataframe:
select · alias · dt namespace
result = df.select(
pl.col("name"),
pl.col("birthdate").dt.year().alias("birth_year"),
(pl.col("weight") / (pl.col("height") ** 2)).alias("bmi"),
)
print(result)
select · alias · dt namespace · Available on feature temporal
let result = df
.clone()
.lazy()
.select([
col("name"),
col("birthdate").dt().year().alias("birth_year"),
(col("weight") / col("height").pow(2)).alias("bmi"),
])
.collect()?;
println!("{}", result);
shape: (4, 3)
┌────────────────┬────────────┬───────────┐
│ name ┆ birth_year ┆ bmi │
│ --- ┆ --- ┆ --- │
│ str ┆ i32 ┆ f64 │
╞════════════════╪════════════╪═══════════╡
│ Alice Archer ┆ 1997 ┆ 23.791913 │
│ Ben Brown ┆ 1985 ┆ 23.141498 │
│ Chloe Cooper ┆ 1983 ┆ 19.687787 │
│ Daniel Donovan ┆ 1981 ┆ 27.134694 │
└────────────────┴────────────┴───────────┘
Polars also supports a feature called “expression expansion”, in which one expression acts as
shorthand for multiple expressions. In the example below, we use expression expansion to manipulate
the columns “weight” and “height” with a single expression. When using expression expansion you can
use .name.suffix to add a suffix to the names of the original columns:
select · alias · name namespace
result = df.select(
pl.col("name"),
(pl.col("weight", "height") * 0.95).round(2).name.suffix("-5%"),
)
print(result)
select · alias · name namespace · Available on feature lazy
let result = df
.clone()
.lazy()
.select([
col("name"),
(cols(["weight", "height"]) * lit(0.95))
.round(2)
.name()
.suffix("-5%"),
])
.collect()?;
println!("{}", result);
shape: (4, 3)
┌────────────────┬───────────┬───────────┐
│ name ┆ weight-5% ┆ height-5% │
│ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 │
╞════════════════╪═══════════╪═══════════╡
│ Alice Archer ┆ 55.01 ┆ 1.48 │
│ Ben Brown ┆ 68.88 ┆ 1.68 │
│ Chloe Cooper ┆ 50.92 ┆ 1.57 │
│ Daniel Donovan ┆ 78.94 ┆ 1.66 │
└────────────────┴───────────┴───────────┘
You can check other sections of the user guide to learn more about basic operations or column selections in expression expansion.
with_columns
The context with_columns is very similar to the context select but with_columns adds columns
to the dataframe instead of selecting them. Notice how the resulting dataframe contains the four
columns of the original dataframe plus the two new columns introduced by the expressions inside
with_columns:
result = df.with_columns(
birth_year=pl.col("birthdate").dt.year(),
bmi=pl.col("weight") / (pl.col("height") ** 2),
)
print(result)
let result = df
.clone()
.lazy()
.with_columns([
col("birthdate").dt().year().alias("birth_year"),
(col("weight") / col("height").pow(2)).alias("bmi"),
])
.collect()?;
println!("{}", result);
shape: (4, 6)
┌────────────────┬────────────┬────────┬────────┬────────────┬───────────┐
│ name ┆ birthdate ┆ weight ┆ height ┆ birth_year ┆ bmi │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 ┆ i32 ┆ f64 │
╞════════════════╪════════════╪════════╪════════╪════════════╪═══════════╡
│ Alice Archer ┆ 1997-01-10 ┆ 57.9 ┆ 1.56 ┆ 1997 ┆ 23.791913 │
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 ┆ 1985 ┆ 23.141498 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 ┆ 1983 ┆ 19.687787 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 ┆ 1981 ┆ 27.134694 │
└────────────────┴────────────┴────────┴────────┴────────────┴───────────┘
In the example above we also decided to use named expressions instead of the method alias to
specify the names of the new columns. Other contexts like select and group_by also accept named
expressions.
filter
The context filter allows us to create a second dataframe with a subset of the rows of the
original one:
result = df.filter(pl.col("birthdate").dt.year() < 1990)
print(result)
filter · dt namespace · Available on feature temporal
let result = df
.clone()
.lazy()
.filter(col("birthdate").dt().year().lt(lit(1990)))
.collect()?;
println!("{}", result);
shape: (3, 4)
┌────────────────┬────────────┬────────┬────────┐
│ name ┆ birthdate ┆ weight ┆ height │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 │
╞════════════════╪════════════╪════════╪════════╡
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 │
└────────────────┴────────────┴────────┴────────┘
You can also provide multiple predicate expressions as separate parameters, which is more convenient
than putting them all together with &:
result = df.filter(
pl.col("birthdate").is_between(dt.date(1982, 12, 31), dt.date(1996, 1, 1)),
pl.col("height") > 1.7,
)
print(result)
filter · is_between · Available on feature is_between
let result = df
.clone()
.lazy()
.filter(
col("birthdate")
.is_between(
lit(NaiveDate::from_ymd_opt(1982, 12, 31).unwrap()),
lit(NaiveDate::from_ymd_opt(1996, 1, 1).unwrap()),
ClosedInterval::Both,
)
.and(col("height").gt(lit(1.7))),
)
.collect()?;
println!("{}", result);
shape: (1, 4)
┌───────────┬────────────┬────────┬────────┐
│ name ┆ birthdate ┆ weight ┆ height │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 │
╞═══════════╪════════════╪════════╪════════╡
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 │
└───────────┴────────────┴────────┴────────┘
group_by
The context group_by can be used to group together the rows of the dataframe that share the same
value across one or more expressions. The example below counts how many people were born in each
decade:
group_by · alias · dt namespace
result = df.group_by(
(pl.col("birthdate").dt.year() // 10 * 10).alias("decade"),
maintain_order=True,
).len()
print(result)
group_by · alias · dt namespace · Available on feature temporal
// Use `group_by_stable` if you want the Python behaviour of `maintain_order=True`.
let result = df
.clone()
.lazy()
.group_by([(col("birthdate").dt().year() / lit(10) * lit(10)).alias("decade")])
.agg([len()])
.collect()?;
println!("{}", result);
shape: (2, 2)
┌────────┬─────┐
│ decade ┆ len │
│ --- ┆ --- │
│ i32 ┆ u32 │
╞════════╪═════╡
│ 1990 ┆ 1 │
│ 1980 ┆ 3 │
└────────┴─────┘
The keyword argument maintain_order forces Polars to present the resulting groups in the same
order as they appear in the original dataframe. This slows down the grouping operation but is used
here to ensure reproducibility of the examples.
After using the context group_by we can use agg to compute aggregations over the resulting
groups:
result = df.group_by(
(pl.col("birthdate").dt.year() // 10 * 10).alias("decade"),
maintain_order=True,
).agg(
pl.len().alias("sample_size"),
pl.col("weight").mean().round(2).alias("avg_weight"),
pl.col("height").max().alias("tallest"),
)
print(result)
shape: (2, 4)
┌────────┬─────────────┬────────────┬─────────┐
│ decade ┆ sample_size ┆ avg_weight ┆ tallest │
│ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ u32 ┆ f64 ┆ f64 │
╞════════╪═════════════╪════════════╪═════════╡
│ 1990 ┆ 1 ┆ 57.9 ┆ 1.56 │
│ 1980 ┆ 3 ┆ 69.73 ┆ 1.77 │
└────────┴─────────────┴────────────┴─────────┘
More complex queries
Contexts and the expressions within can be chained to create more complex queries according to your needs. In the example below we combine some of the contexts we have seen so far to create a more complex query:
group_by · agg · select · with_columns · str namespace · list namespace
result = (
df.with_columns(
(pl.col("birthdate").dt.year() // 10 * 10).alias("decade"),
pl.col("name").str.split(by=" ").list.first(),
)
.select(
pl.all().exclude("birthdate"),
)
.group_by(
pl.col("decade"),
maintain_order=True,
)
.agg(
pl.col("name"),
pl.col("weight", "height").mean().round(2).name.prefix("avg_"),
)
)
print(result)
group_by · agg · select · with_columns · str namespace · list namespace · Available on feature strings
let result = df
.clone()
.lazy()
.with_columns([
(col("birthdate").dt().year() / lit(10) * lit(10)).alias("decade"),
col("name").str().split(lit(" ")).list().first(),
])
.select([all().exclude(["birthdate"])])
.group_by([col("decade")])
.agg([
col("name"),
cols(["weight", "height"])
.mean()
.round(2)
.name()
.prefix("avg_"),
])
.collect()?;
println!("{}", result);
shape: (2, 4)
┌────────┬────────────────────────────┬────────────┬────────────┐
│ decade ┆ name ┆ avg_weight ┆ avg_height │
│ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ list[str] ┆ f64 ┆ f64 │
╞════════╪════════════════════════════╪════════════╪════════════╡
│ 1990 ┆ ["Alice"] ┆ 57.9 ┆ 1.56 │
│ 1980 ┆ ["Ben", "Chloe", "Daniel"] ┆ 69.73 ┆ 1.72 │
└────────┴────────────────────────────┴────────────┴────────────┘
Combining dataframes
Polars provides a number of tools to combine two dataframes. In this section, we show an example of a join and an example of a concatenation.
Joining dataframes
Polars provides many different join algorithms. The example below shows how to use a left outer join to combine two dataframes when a column can be used as a unique identifier to establish a correspondence between rows across the dataframes:
df2 = pl.DataFrame(
{
"name": ["Ben Brown", "Daniel Donovan", "Alice Archer", "Chloe Cooper"],
"parent": [True, False, False, False],
"siblings": [1, 2, 3, 4],
}
)
print(df.join(df2, on="name", how="left"))
let df2: DataFrame = df!(
"name" => ["Ben Brown", "Daniel Donovan", "Alice Archer", "Chloe Cooper"],
"parent" => [true, false, false, false],
"siblings" => [1, 2, 3, 4],
)
.unwrap();
let result = df
.clone()
.lazy()
.join(
df2.clone().lazy(),
[col("name")],
[col("name")],
JoinArgs::new(JoinType::Left),
)
.collect()?;
println!("{}", result);
shape: (4, 6)
┌────────────────┬────────────┬────────┬────────┬────────┬──────────┐
│ name ┆ birthdate ┆ weight ┆ height ┆ parent ┆ siblings │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 ┆ bool ┆ i64 │
╞════════════════╪════════════╪════════╪════════╪════════╪══════════╡
│ Alice Archer ┆ 1997-01-10 ┆ 57.9 ┆ 1.56 ┆ false ┆ 3 │
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 ┆ true ┆ 1 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 ┆ false ┆ 4 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 ┆ false ┆ 2 │
└────────────────┴────────────┴────────┴────────┴────────┴──────────┘
Polars provides many different join algorithms that you can learn about in the joins section of the user guide.
Concatenating dataframes
Concatenating dataframes creates a taller or wider dataframe, depending on the method used. Assuming we have a second dataframe with data from other people, we could use vertical concatenation to create a taller dataframe:
df3 = pl.DataFrame(
{
"name": ["Ethan Edwards", "Fiona Foster", "Grace Gibson", "Henry Harris"],
"birthdate": [
dt.date(1977, 5, 10),
dt.date(1975, 6, 23),
dt.date(1973, 7, 22),
dt.date(1971, 8, 3),
],
"weight": [67.9, 72.5, 57.6, 93.1], # (kg)
"height": [1.76, 1.6, 1.66, 1.8], # (m)
}
)
print(pl.concat([df, df3], how="vertical"))
let df3: DataFrame = df!(
"name" => ["Ethan Edwards", "Fiona Foster", "Grace Gibson", "Henry Harris"],
"birthdate" => [
NaiveDate::from_ymd_opt(1977, 5, 10).unwrap(),
NaiveDate::from_ymd_opt(1975, 6, 23).unwrap(),
NaiveDate::from_ymd_opt(1973, 7, 22).unwrap(),
NaiveDate::from_ymd_opt(1971, 8, 3).unwrap(),
],
"weight" => [67.9, 72.5, 57.6, 93.1], // (kg)
"height" => [1.76, 1.6, 1.66, 1.8], // (m)
)
.unwrap();
let result = concat(
[df.clone().lazy(), df3.clone().lazy()],
UnionArgs::default(),
)?
.collect()?;
println!("{}", result);
shape: (8, 4)
┌────────────────┬────────────┬────────┬────────┐
│ name ┆ birthdate ┆ weight ┆ height │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ date ┆ f64 ┆ f64 │
╞════════════════╪════════════╪════════╪════════╡
│ Alice Archer ┆ 1997-01-10 ┆ 57.9 ┆ 1.56 │
│ Ben Brown ┆ 1985-02-15 ┆ 72.5 ┆ 1.77 │
│ Chloe Cooper ┆ 1983-03-22 ┆ 53.6 ┆ 1.65 │
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1 ┆ 1.75 │
│ Ethan Edwards ┆ 1977-05-10 ┆ 67.9 ┆ 1.76 │
│ Fiona Foster ┆ 1975-06-23 ┆ 72.5 ┆ 1.6 │
│ Grace Gibson ┆ 1973-07-22 ┆ 57.6 ┆ 1.66 │
│ Henry Harris ┆ 1971-08-03 ┆ 93.1 ┆ 1.8 │
└────────────────┴────────────┴────────┴────────┘
Polars provides vertical and horizontal concatenation, as well as diagonal concatenation. You can learn more about these in the concatenations section of the user guide.