polars_ops/frame/join/mod.rs
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mod args;
#[cfg(feature = "asof_join")]
mod asof;
#[cfg(feature = "dtype-categorical")]
mod checks;
mod cross_join;
mod dispatch_left_right;
mod general;
mod hash_join;
#[cfg(feature = "iejoin")]
mod iejoin;
#[cfg(feature = "merge_sorted")]
mod merge_sorted;
use std::borrow::Cow;
use std::fmt::{Debug, Display, Formatter};
use std::hash::Hash;
pub use args::*;
use arrow::trusted_len::TrustedLen;
#[cfg(feature = "asof_join")]
pub use asof::{AsOfOptions, AsofJoin, AsofJoinBy, AsofStrategy};
#[cfg(feature = "dtype-categorical")]
pub(crate) use checks::*;
pub use cross_join::CrossJoin;
#[cfg(feature = "chunked_ids")]
use either::Either;
#[cfg(feature = "chunked_ids")]
use general::create_chunked_index_mapping;
pub use general::{_coalesce_full_join, _finish_join, _join_suffix_name};
pub use hash_join::*;
use hashbrown::hash_map::{Entry, RawEntryMut};
#[cfg(feature = "iejoin")]
pub use iejoin::{IEJoinOptions, InequalityOperator};
#[cfg(feature = "merge_sorted")]
pub use merge_sorted::_merge_sorted_dfs;
#[allow(unused_imports)]
use polars_core::chunked_array::ops::row_encode::{
encode_rows_vertical_par_unordered, encode_rows_vertical_par_unordered_broadcast_nulls,
};
use polars_core::hashing::_HASHMAP_INIT_SIZE;
use polars_core::prelude::*;
pub(super) use polars_core::series::IsSorted;
use polars_core::utils::slice_offsets;
#[allow(unused_imports)]
use polars_core::utils::slice_slice;
use polars_core::POOL;
use polars_utils::hashing::BytesHash;
use rayon::prelude::*;
use super::IntoDf;
pub trait DataFrameJoinOps: IntoDf {
/// Generic join method. Can be used to join on multiple columns.
///
/// # Example
///
/// ```no_run
/// # use polars_core::prelude::*;
/// # use polars_ops::prelude::*;
/// let df1: DataFrame = df!("Fruit" => &["Apple", "Banana", "Pear"],
/// "Phosphorus (mg/100g)" => &[11, 22, 12])?;
/// let df2: DataFrame = df!("Name" => &["Apple", "Banana", "Pear"],
/// "Potassium (mg/100g)" => &[107, 358, 115])?;
///
/// let df3: DataFrame = df1.join(&df2, ["Fruit"], ["Name"], JoinArgs::new(JoinType::Inner))?;
/// assert_eq!(df3.shape(), (3, 3));
/// println!("{}", df3);
/// # Ok::<(), PolarsError>(())
/// ```
///
/// Output:
///
/// ```text
/// shape: (3, 3)
/// +--------+----------------------+---------------------+
/// | Fruit | Phosphorus (mg/100g) | Potassium (mg/100g) |
/// | --- | --- | --- |
/// | str | i32 | i32 |
/// +========+======================+=====================+
/// | Apple | 11 | 107 |
/// +--------+----------------------+---------------------+
/// | Banana | 22 | 358 |
/// +--------+----------------------+---------------------+
/// | Pear | 12 | 115 |
/// +--------+----------------------+---------------------+
/// ```
fn join(
&self,
other: &DataFrame,
left_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
right_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
args: JoinArgs,
) -> PolarsResult<DataFrame> {
let df_left = self.to_df();
let selected_left = df_left.select_columns(left_on)?;
let selected_right = other.select_columns(right_on)?;
let selected_left = selected_left
.into_iter()
.map(Column::take_materialized_series)
.collect::<Vec<_>>();
let selected_right = selected_right
.into_iter()
.map(Column::take_materialized_series)
.collect::<Vec<_>>();
self._join_impl(other, selected_left, selected_right, args, true, false)
}
#[doc(hidden)]
#[allow(clippy::too_many_arguments)]
#[allow(unused_mut)]
fn _join_impl(
&self,
other: &DataFrame,
mut selected_left: Vec<Series>,
mut selected_right: Vec<Series>,
mut args: JoinArgs,
_check_rechunk: bool,
_verbose: bool,
) -> PolarsResult<DataFrame> {
let left_df = self.to_df();
#[cfg(feature = "cross_join")]
if let JoinType::Cross = args.how {
return left_df.cross_join(other, args.suffix.clone(), args.slice);
}
// Clear literals if a frame is empty. Otherwise we could get an oob
fn clear(s: &mut [Series]) {
for s in s.iter_mut() {
if s.len() == 1 {
*s = s.clear()
}
}
}
if left_df.is_empty() {
clear(&mut selected_left);
}
if other.is_empty() {
clear(&mut selected_right);
}
let should_coalesce = args.should_coalesce();
assert_eq!(selected_left.len(), selected_right.len());
#[cfg(feature = "chunked_ids")]
{
// a left join create chunked-ids
// the others not yet.
// TODO! change this to other join types once they support chunked-id joins
if _check_rechunk
&& !(matches!(args.how, JoinType::Left)
|| std::env::var("POLARS_NO_CHUNKED_JOIN").is_ok())
{
let mut left = Cow::Borrowed(left_df);
let mut right = Cow::Borrowed(other);
if left_df.should_rechunk() {
if _verbose {
eprintln!("{:?} join triggered a rechunk of the left DataFrame: {} columns are affected", args.how, left_df.width());
}
let mut tmp_left = left_df.clone();
tmp_left.as_single_chunk_par();
left = Cow::Owned(tmp_left);
}
if other.should_rechunk() {
if _verbose {
eprintln!("{:?} join triggered a rechunk of the right DataFrame: {} columns are affected", args.how, other.width());
}
let mut tmp_right = other.clone();
tmp_right.as_single_chunk_par();
right = Cow::Owned(tmp_right);
}
return left._join_impl(
&right,
selected_left,
selected_right,
args,
false,
_verbose,
);
}
}
if let Some((l, r)) = selected_left
.iter()
.zip(&selected_right)
.find(|(l, r)| l.dtype() != r.dtype())
{
polars_bail!(
ComputeError:
format!(
"datatypes of join keys don't match - `{}`: {} on left does not match `{}`: {} on right",
l.name(), l.dtype(), r.name(), r.dtype()
)
);
};
#[cfg(feature = "dtype-categorical")]
for (l, r) in selected_left.iter_mut().zip(selected_right.iter_mut()) {
match _check_categorical_src(l.dtype(), r.dtype()) {
Ok(_) => {},
Err(_) => {
let (ca_left, ca_right) =
make_categoricals_compatible(l.categorical()?, r.categorical()?)?;
*l = ca_left.into_series().with_name(l.name().clone());
*r = ca_right.into_series().with_name(r.name().clone());
},
}
}
#[cfg(feature = "iejoin")]
if let JoinType::IEJoin(options) = args.how {
let func = if POOL.current_num_threads() > 1 && !left_df.is_empty() && !other.is_empty()
{
iejoin::iejoin_par
} else {
iejoin::iejoin
};
return func(
left_df,
other,
selected_left,
selected_right,
&options,
args.suffix,
args.slice,
);
}
// Single keys.
if selected_left.len() == 1 {
let s_left = &selected_left[0];
let s_right = &selected_right[0];
let drop_names: Option<Vec<PlSmallStr>> =
if should_coalesce { None } else { Some(vec![]) };
return match args.how {
JoinType::Inner => left_df
._inner_join_from_series(other, s_left, s_right, args, _verbose, drop_names),
JoinType::Left => dispatch_left_right::left_join_from_series(
self.to_df().clone(),
other,
s_left,
s_right,
args,
_verbose,
drop_names,
),
JoinType::Right => dispatch_left_right::right_join_from_series(
self.to_df(),
other.clone(),
s_left,
s_right,
args,
_verbose,
drop_names,
),
JoinType::Full => left_df._full_join_from_series(other, s_left, s_right, args),
#[cfg(feature = "semi_anti_join")]
JoinType::Anti => left_df._semi_anti_join_from_series(
s_left,
s_right,
args.slice,
true,
args.join_nulls,
),
#[cfg(feature = "semi_anti_join")]
JoinType::Semi => left_df._semi_anti_join_from_series(
s_left,
s_right,
args.slice,
false,
args.join_nulls,
),
#[cfg(feature = "asof_join")]
JoinType::AsOf(options) => match (options.left_by, options.right_by) {
(Some(left_by), Some(right_by)) => left_df._join_asof_by(
other,
s_left,
s_right,
left_by,
right_by,
options.strategy,
options.tolerance,
args.suffix.clone(),
args.slice,
should_coalesce,
),
(None, None) => left_df._join_asof(
other,
s_left,
s_right,
options.strategy,
options.tolerance,
args.suffix,
args.slice,
should_coalesce,
),
_ => {
panic!("expected by arguments on both sides")
},
},
#[cfg(feature = "iejoin")]
JoinType::IEJoin(_) => {
unreachable!()
},
JoinType::Cross => {
unreachable!()
},
};
}
let lhs_keys = prepare_keys_multiple(&selected_left, args.join_nulls)?.into_series();
let rhs_keys = prepare_keys_multiple(&selected_right, args.join_nulls)?.into_series();
let drop_names = if should_coalesce {
selected_right
.iter()
.map(|s| s.name().clone())
.collect::<Vec<_>>()
} else {
vec![]
};
// Multiple keys.
match args.how {
#[cfg(feature = "asof_join")]
JoinType::AsOf(_) => polars_bail!(
ComputeError: "asof join not supported for join on multiple keys"
),
#[cfg(feature = "iejoin")]
JoinType::IEJoin(_) => {
unreachable!()
},
JoinType::Cross => {
unreachable!()
},
JoinType::Full => {
let names_left = selected_left
.iter()
.map(|s| s.name().clone())
.collect::<Vec<_>>();
args.coalesce = JoinCoalesce::KeepColumns;
let suffix = args.suffix.clone();
let out = left_df._full_join_from_series(other, &lhs_keys, &rhs_keys, args);
if should_coalesce {
Ok(_coalesce_full_join(
out?,
names_left.as_slice(),
drop_names.as_slice(),
suffix.clone(),
left_df,
))
} else {
out
}
},
JoinType::Inner => left_df._inner_join_from_series(
other,
&lhs_keys,
&rhs_keys,
args,
_verbose,
Some(drop_names),
),
JoinType::Left => dispatch_left_right::left_join_from_series(
left_df.clone(),
other,
&lhs_keys,
&rhs_keys,
args,
_verbose,
Some(drop_names),
),
JoinType::Right => dispatch_left_right::right_join_from_series(
left_df,
other.clone(),
&lhs_keys,
&rhs_keys,
args,
_verbose,
Some(drop_names),
),
#[cfg(feature = "semi_anti_join")]
JoinType::Anti | JoinType::Semi => self._join_impl(
other,
vec![lhs_keys],
vec![rhs_keys],
args,
_check_rechunk,
_verbose,
),
}
}
/// Perform an inner join on two DataFrames.
///
/// # Example
///
/// ```
/// # use polars_core::prelude::*;
/// # use polars_ops::prelude::*;
/// fn join_dfs(left: &DataFrame, right: &DataFrame) -> PolarsResult<DataFrame> {
/// left.inner_join(right, ["join_column_left"], ["join_column_right"])
/// }
/// ```
fn inner_join(
&self,
other: &DataFrame,
left_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
right_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
) -> PolarsResult<DataFrame> {
self.join(other, left_on, right_on, JoinArgs::new(JoinType::Inner))
}
/// Perform a left outer join on two DataFrames
/// # Example
///
/// ```no_run
/// # use polars_core::prelude::*;
/// # use polars_ops::prelude::*;
/// let df1: DataFrame = df!("Wavelength (nm)" => &[480.0, 650.0, 577.0, 1201.0, 100.0])?;
/// let df2: DataFrame = df!("Color" => &["Blue", "Yellow", "Red"],
/// "Wavelength nm" => &[480.0, 577.0, 650.0])?;
///
/// let df3: DataFrame = df1.left_join(&df2, ["Wavelength (nm)"], ["Wavelength nm"])?;
/// println!("{:?}", df3);
/// # Ok::<(), PolarsError>(())
/// ```
///
/// Output:
///
/// ```text
/// shape: (5, 2)
/// +-----------------+--------+
/// | Wavelength (nm) | Color |
/// | --- | --- |
/// | f64 | str |
/// +=================+========+
/// | 480 | Blue |
/// +-----------------+--------+
/// | 650 | Red |
/// +-----------------+--------+
/// | 577 | Yellow |
/// +-----------------+--------+
/// | 1201 | null |
/// +-----------------+--------+
/// | 100 | null |
/// +-----------------+--------+
/// ```
fn left_join(
&self,
other: &DataFrame,
left_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
right_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
) -> PolarsResult<DataFrame> {
self.join(other, left_on, right_on, JoinArgs::new(JoinType::Left))
}
/// Perform a full outer join on two DataFrames
/// # Example
///
/// ```
/// # use polars_core::prelude::*;
/// # use polars_ops::prelude::*;
/// fn join_dfs(left: &DataFrame, right: &DataFrame) -> PolarsResult<DataFrame> {
/// left.full_join(right, ["join_column_left"], ["join_column_right"])
/// }
/// ```
fn full_join(
&self,
other: &DataFrame,
left_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
right_on: impl IntoIterator<Item = impl Into<PlSmallStr>>,
) -> PolarsResult<DataFrame> {
self.join(other, left_on, right_on, JoinArgs::new(JoinType::Full))
}
}
trait DataFrameJoinOpsPrivate: IntoDf {
fn _inner_join_from_series(
&self,
other: &DataFrame,
s_left: &Series,
s_right: &Series,
args: JoinArgs,
verbose: bool,
drop_names: Option<Vec<PlSmallStr>>,
) -> PolarsResult<DataFrame> {
let left_df = self.to_df();
#[cfg(feature = "dtype-categorical")]
_check_categorical_src(s_left.dtype(), s_right.dtype())?;
let ((join_tuples_left, join_tuples_right), sorted) =
_sort_or_hash_inner(s_left, s_right, verbose, args.validation, args.join_nulls)?;
let mut join_tuples_left = &*join_tuples_left;
let mut join_tuples_right = &*join_tuples_right;
if let Some((offset, len)) = args.slice {
join_tuples_left = slice_slice(join_tuples_left, offset, len);
join_tuples_right = slice_slice(join_tuples_right, offset, len);
}
let (df_left, df_right) = POOL.join(
// SAFETY: join indices are known to be in bounds
|| unsafe {
left_df._create_left_df_from_slice(
join_tuples_left,
false,
args.slice.is_some(),
sorted,
)
},
|| unsafe {
if let Some(drop_names) = drop_names {
other.drop_many(drop_names)
} else {
other.drop(s_right.name()).unwrap()
}
._take_unchecked_slice(join_tuples_right, true)
},
);
_finish_join(df_left, df_right, args.suffix.clone())
}
}
impl DataFrameJoinOps for DataFrame {}
impl DataFrameJoinOpsPrivate for DataFrame {}
fn prepare_keys_multiple(s: &[Series], join_nulls: bool) -> PolarsResult<BinaryOffsetChunked> {
let keys = s
.iter()
.map(|s| {
let phys = s.to_physical_repr();
match phys.dtype() {
DataType::Float32 => phys.f32().unwrap().to_canonical().into_series(),
DataType::Float64 => phys.f64().unwrap().to_canonical().into_series(),
_ => phys.into_owned(),
}
})
.collect::<Vec<_>>();
if join_nulls {
encode_rows_vertical_par_unordered(&keys)
} else {
encode_rows_vertical_par_unordered_broadcast_nulls(&keys)
}
}
pub fn private_left_join_multiple_keys(
a: &DataFrame,
b: &DataFrame,
join_nulls: bool,
) -> PolarsResult<LeftJoinIds> {
// @scalar-opt
let a_cols = a
.get_columns()
.iter()
.map(|c| c.as_materialized_series().clone())
.collect::<Vec<_>>();
let b_cols = b
.get_columns()
.iter()
.map(|c| c.as_materialized_series().clone())
.collect::<Vec<_>>();
let a = prepare_keys_multiple(&a_cols, join_nulls)?.into_series();
let b = prepare_keys_multiple(&b_cols, join_nulls)?.into_series();
sort_or_hash_left(&a, &b, false, JoinValidation::ManyToMany, join_nulls)
}