Lazy abstraction over an eager DataFrame. It really is an abstraction over a logical plan. The methods of this struct will incrementally modify a logical plan until output is requested (via collect)

LazyGroupBy

Utility struct for lazy groupby operation.

ListBooleanChunkedBuilder

ListPrimitiveChunkedBuilder

ListTakeRandom

ListTakeRandomSingleChunk

ListType

ListUtf8ChunkedBuilder

Logical

Maps a logical type to a a chunked array implementation of the physical type. This saves a lot of compiler bloat and allows us to reuse functionality.

LogicalPlanUdfOptions

NoEq

Wrapper type that indicates that the inner type is not equal to anything

NoNull

Just a wrapper structure. Useful for certain impl specializations This is for instance use to implement impl<T> FromIterator<T::Native> for NoNull<ChunkedArray<T>> as Option<T::Native> was already implemented: impl<T> FromIterator<Option<T::Native>> for ChunkedArray<T>

Null

The literal Null

NumTakeRandomChunked

NumTakeRandomCont

NumTakeRandomSingleChunk

ObjectTakeRandom

ObjectTakeRandomSingleChunk

ObjectTypeobject

OptState

State of the allowed optimizations

ParquetOptions

ParquetReader

Read Apache parquet format into a DataFrame.

ParquetWriter

Write a DataFrame to parquet format

PhysicalIoHelper

Wrapper struct that allow us to use a PhysicalExpr in polars-io.

PrimitiveChunkedBuilder

This is logical type StructChunked that dispatches most logic to the fields implementations

Utf8TakeRandomSingleChunk

Utf8Type

When

Intermediate state of when(..).then(..).otherwise(..) expr.

WhenThen

Intermediate state of when(..).then(..).otherwise(..) expr.

WhenThenThen

Intermediate state of chain when then exprs.

Window

Represents a window in time

WindowOptions

Enums

The set of supported logical types in this crate.

ArrowTimeUnit

The time units defined in Arrow.

Queries consists of multiple expressions.

Compression codec

QuantileInterpolOptions

One of the three arguments allowed in unchecked_take

Constants

IDX_DTYPE

NULL

Traits

ArgAgg

Argmin/ Argmax

BinaryUdfOutputField

ChunkAgg

Aggregation operations

ChunkAggSeries

Aggregations that return Series of unit length. Those can be used in broadcasting operations.

ChunkAnyValue

ChunkApply

Fastest way to do elementwise operations on a ChunkedArray when the operation is cheaper than branching due to null checking

ChunkApplyKernel

Apply kernels on the arrow array chunks in a ChunkedArray.

ChunkBytes

ChunkCast

Cast ChunkedArray<T> to ChunkedArray<N>

ChunkCompare

Compare Series and ChunkedArray’s and get a boolean mask that can be used to filter rows.

ChunkCumAgg

ChunkExpandAtIndex

Create a new ChunkedArray filled with values at that index.

ChunkExplode

Explode/ flatten a

ChunkFillNull

Replace None values with various strategies

ChunkFillNullValue

Replace None values with a value

ChunkFilter

Filter values by a boolean mask.

ChunkFull

Fill a ChunkedArray with one value.

Find local minima/ maxima

ChunkQuantile

Quantile and median aggregation

ChunkReverse

Reverse a ChunkedArray

ChunkRollApply

This differs from ChunkWindowCustom and ChunkWindow by not using a fold aggregator, but reusing a Series wrapper and calling Series aggregators. This likely is a bit slower than ChunkWindow

ChunkSet

Create a ChunkedArray with new values by index or by boolean mask. Note that these operations clone data. This is however the only way we can modify at mask or index level as the underlying Arrow arrays are immutable.

ChunkShift

ChunkShiftFill

Shift the values of a ChunkedArray by a number of periods.

ChunkSort

Sort operations on ChunkedArray.

ChunkTake

Fast access by index.

ChunkTakeEvery

Traverse and collect every nth element

ChunkUnique

Get unique values in a ChunkedArray

ChunkVar

Variance and standard deviation aggregation.

ChunkZip

Combine 2 ChunkedArrays based on some predicate.

Executors will evaluate physical expressions and collect them in a DataFrame.

FunctionOutputField

IndexOfSchema

This trait exists to be unify the API of polars Schema and arrows Schema

Interpolate

IntoGroupsProxy

Used to create the tuples for a groupby operation.

IntoLazy

IntoSeries

IntoTakeRandom

Create a type that implements a faster TakeRandom.

IntoVec

IsFirstis_first

Mask the first unique values as true

IsInis_in

Check if element is member of list array

IsLastis_first

Mask the last unique values as true

NumOpsDispatchChecked

Take a DataFrame and evaluate the expressions. Implement this for Column, lt, eq, etc

PhysicalPlanner

A type that implements this transforms a LogicalPlan to a physical plan.

A PolarsIterator is an iterator over a ChunkedArray which contains polars types. A PolarsIterator must implement ExactSizeIterator and DoubleEndedIterator.

PolarsNumericType

PolarsObject

Values need to implement this so that they can be stored into a Series and DataFrame

PolarsSingleType

Any type that is not nested

PolarsTemporalGroupby

RepeatByrepeat_by

Repeat the values n times.

SerReader

SerWriter

SeriesBinaryUdf

A wrapper trait for any binary closure Fn(Series, Series) -> Result<Series>

SeriesTrait

SeriesUdf

A wrapper trait for any closure Fn(Vec<Series>) -> Result<Series>

StrConcatconcat_str

Concat the values into a string array.

Random access

Functions

all

Selects all columns

all_exprs

Evaluate all the expressions with a bitwise and

any_exprs

Evaluate all the expressions with a bitwise or

apply_binary

apply_multiple

Apply a function/closure over the groups of multiple columns. This should only be used in a groupby aggregation.

arangearange

Create list entries that are range arrays

argsort_by

Find the indexes that would sort these series in order of appearance. That means that the first Series will be used to determine the ordering until duplicates are found. Once duplicates are found, the next Series will be used and so on.

as_struct

Take several expressions and collect them into a StructChunked.

avg

Find the mean of all the values in this Expression.

binary_expr

cast

Cast expression.

col

Create a Column Expression based on a column name.

collect_all

Collect all LazyFrame computations.

cols

Select multiple columns by name

concat

Concat multiple

concat_lstlist

Concat lists entries.

concat_strconcat_str

Horizontally concat string columns in linear time

count

Count expression

cov

Compute the covariance between two columns.

date_range

Create a DatetimeChunked from a given start and stop date and a given every interval.

date_range_vec

datetime

datetime_to_timestamp_ms

datetime_to_timestamp_ns

datetime_to_timestamp_us

dtype_col

Select multiple columns by dtype.

dtype_cols

Select multiple columns by dtype.

first

First column in DataFrame

fmt_groupby_column

fold_exprs

Accumulate over multiple columns horizontally / row wise.

groupby_values

Different from groupby_windows, where define window buckets and search which values fit that pre-defined bucket, this function defines every window based on the: - timestamp (lower bound) - timestamp + period (upper bound) where timestamps are the individual values in the array time

groupby_windows

Based on the given Window, which has an

in_nanoseconds_window

is_not_null

IsNotNull expression.

is_null

IsNull expression

last

Last column in DataFrame

lit

Create a Literal Expression from L

map_binary