Enum polars_plan::dsl::Expr

pub enum Expr {
    Alias(Box<Expr>, Arc<str>),
    Column(Arc<str>),
    Columns(Vec<String>),
    DtypeColumn(Vec<DataType>),
    Literal(LiteralValue),
    BinaryExpr {
        left: Box<Expr>,
        op: Operator,
        right: Box<Expr>,
    },
    Cast {
        expr: Box<Expr>,
        data_type: DataType,
        strict: bool,
    },
    Sort {
        expr: Box<Expr>,
        options: SortOptions,
    },
    Take {
        expr: Box<Expr>,
        idx: Box<Expr>,
    },
    SortBy {
        expr: Box<Expr>,
        by: Vec<Expr>,
        reverse: Vec<bool>,
    },
    Agg(AggExpr),
    Ternary {
        predicate: Box<Expr>,
        truthy: Box<Expr>,
        falsy: Box<Expr>,
    },
    Function {
        input: Vec<Expr>,
        function: FunctionExpr,
        options: FunctionOptions,
    },
    Explode(Box<Expr>),
    Filter {
        input: Box<Expr>,
        by: Box<Expr>,
    },
    Window {
        function: Box<Expr>,
        partition_by: Vec<Expr>,
        order_by: Option<Box<Expr>>,
        options: WindowOptions,
    },
    Wildcard,
    Slice {
        input: Box<Expr>,
        offset: Box<Expr>,
        length: Box<Expr>,
    },
    Exclude(Box<Expr>, Vec<Excluded>),
    KeepName(Box<Expr>),
    Count,
    Nth(i64),
    RenameAlias {
        function: SpecialEq<Arc<dyn RenameAliasFn>>,
        expr: Box<Expr>,
    },
    AnonymousFunction {
        input: Vec<Expr>,
        function: SpecialEq<Arc<dyn SeriesUdf>>,
        output_type: GetOutput,
        options: FunctionOptions,
    },
}

Queries consists of multiple expressions.

Variants

Alias(Box<Expr>, Arc<str>)

Column(Arc<str>)

Columns(Vec<String>)

DtypeColumn(Vec<DataType>)

Literal(LiteralValue)

BinaryExpr

Fields

left: Box<Expr>

op: Operator

right: Box<Expr>

Cast

Fields

expr: Box<Expr>

data_type: DataType

strict: bool

Sort

Fields

expr: Box<Expr>

options: SortOptions

Take

Fields

expr: Box<Expr>

idx: Box<Expr>

SortBy

Fields

expr: Box<Expr>

by: Vec<Expr>

reverse: Vec<bool>

Agg(AggExpr)

Ternary

Fields

predicate: Box<Expr>

truthy: Box<Expr>

falsy: Box<Expr>

A ternary operation if true then “foo” else “bar”

Function

Fields

input: Vec<Expr>

function arguments

function: FunctionExpr

function to apply

options: FunctionOptions

Explode(Box<Expr>)

Filter

Fields

input: Box<Expr>

by: Box<Expr>

Window

Fields

function: Box<Expr>

Also has the input. i.e. avg(“foo”)

partition_by: Vec<Expr>

order_by: Option<Box<Expr>>

options: WindowOptions

See postgres window functions

Wildcard

Slice

Fields

input: Box<Expr>

offset: Box<Expr>

length is not yet known so we accept negative offsets

length: Box<Expr>

Exclude(Box<Expr>, Vec<Excluded>)

Can be used in a select statement to exclude a column from selection

KeepName(Box<Expr>)

Set root name as Alias

Count

Special case that does not need columns

Nth(i64)

Take the nth column in the DataFrame

RenameAlias

Fields

function: SpecialEq<Arc<dyn RenameAliasFn>>

expr: Box<Expr>

AnonymousFunction

Fields

input: Vec<Expr>

function arguments

function: SpecialEq<Arc<dyn SeriesUdf>>

function to apply

output_type: GetOutput

output dtype of the function

options: FunctionOptions

Implementations

impl Expr

pub fn to_dot(&self) -> PolarsResult<String>

Available on crate feature dot_diagram only.

Get a dot language representation of the Expression.

impl Expr

pub fn to_field(&self, schema: &Schema, ctxt: Context) -> PolarsResult<Field>

Get Field result of the expression. The schema is the input data.

impl Expr

pub fn eq<E: Into<Expr>>(self, other: E) -> Expr

Compare Expr with other Expr on equality

pub fn neq<E: Into<Expr>>(self, other: E) -> Expr

Compare Expr with other Expr on non-equality

pub fn lt<E: Into<Expr>>(self, other: E) -> Expr

Check if Expr < Expr

pub fn gt<E: Into<Expr>>(self, other: E) -> Expr

Check if Expr > Expr

pub fn gt_eq<E: Into<Expr>>(self, other: E) -> Expr

Check if Expr >= Expr

pub fn lt_eq<E: Into<Expr>>(self, other: E) -> Expr

Check if Expr <= Expr

pub fn not(self) -> Expr

Negate Expr

pub fn alias(self, name: &str) -> Expr

Rename Column.

pub fn is_null(self) -> Self

Run is_null operation on Expr.

pub fn is_not_null(self) -> Self

Run is_not_null operation on Expr.

pub fn drop_nulls(self) -> Self

Drop null values

pub fn drop_nans(self) -> Self

Drop NaN values

pub fn min(self) -> Self

Reduce groups to minimal value.

pub fn max(self) -> Self

Reduce groups to maximum value.

pub fn nan_min(self) -> Self

Reduce groups to minimal value.

pub fn nan_max(self) -> Self

Reduce groups to maximum value.

pub fn mean(self) -> Self

Reduce groups to the mean value.

pub fn median(self) -> Self

Reduce groups to the median value.

pub fn sum(self) -> Self

Reduce groups to the sum of all the values.

pub fn n_unique(self) -> Self

Get the number of unique values in the groups.

pub fn first(self) -> Self

Get the first value in the group.

pub fn last(self) -> Self

Get the last value in the group.

pub fn list(self) -> Self

Aggregate the group to a Series

pub fn quantile(self, quantile: Expr, interpol: QuantileInterpolOptions) -> Self

Compute the quantile per group.

pub fn agg_groups(self) -> Self

Get the group indexes of the group by operation.

pub fn flatten(self) -> Self

Alias for explode

pub fn explode(self) -> Self

Explode the utf8/ list column

pub fn slice<E: Into<Expr>, F: Into<Expr>>(self, offset: E, length: F) -> Self

Slice the Series. offset may be negative.

pub fn append<E: Into<Expr>>(self, other: E, upcast: bool) -> Self

Append expressions. This is done by adding the chunks of other to this Series.

pub fn head(self, length: Option<usize>) -> Self

Get the first n elements of the Expr result

pub fn tail(self, length: Option<usize>) -> Self

Get the last n elements of the Expr result

pub fn unique(self) -> Self

Get unique values of this expression.

pub fn unique_stable(self) -> Self

Get unique values of this expression, while maintaining order. This requires more work than Expr::unique.

pub fn arg_unique(self) -> Self

Get the first index of unique values of this expression.

pub fn arg_min(self) -> Self

Get the index value that has the minimum value

pub fn arg_max(self) -> Self

Get the index value that has the maximum value

pub fn arg_sort(self, sort_options: SortOptions) -> Self

Get the index values that would sort this expression.

pub fn search_sorted<E: Into<Expr>>(self, element: E) -> Expr

Find indices where elements should be inserted to maintain order.

pub fn strict_cast(self, data_type: DataType) -> Self

Cast expression to another data type. Throws an error if conversion had overflows

pub fn cast(self, data_type: DataType) -> Self

Cast expression to another data type.

pub fn take<E: Into<Expr>>(self, idx: E) -> Self

Take the values by idx.

pub fn sort(self, reverse: bool) -> Self

Sort in increasing order. See the eager implementation.

pub fn sort_with(self, options: SortOptions) -> Self

Sort with given options.

pub fn top_k(self, k: usize, reverse: bool) -> Self

Returns the k largest elements.

This has time complexity O(n + k log(n)).

pub fn reverse(self) -> Self

Reverse column

pub fn map<F>(self, function: F, output_type: GetOutput) -> Selfwhere
    F: Fn(Series) -> PolarsResult<Series> + 'static + Send + Sync,

Apply a function/closure once the logical plan get executed.

This function is very similar to Expr::apply, but differs in how it handles aggregations.

• map should be used for operations that are independent of groups, e.g. multiply * 2, or raise to the power
• apply should be used for operations that work on a group of data. e.g. sum, count, etc.

It is the responsibility of the caller that the schema is correct by giving the correct output_type. If None given the output type of the input expr is used.

pub fn map_many<F>(
    self,
    function: F,
    arguments: &[Expr],
    output_type: GetOutput
) -> Selfwhere
    F: Fn(&mut [Series]) -> PolarsResult<Series> + 'static + Send + Sync,

Apply a function/closure once the logical plan get executed with many arguments

See the Expr::map function for the differences between map and apply.

pub fn map_list<F>(self, function: F, output_type: GetOutput) -> Selfwhere
    F: Fn(Series) -> PolarsResult<Series> + 'static + Send + Sync,

Apply a function/closure once the logical plan get executed.

This function is very similar to apply, but differs in how it handles aggregations.

• map should be used for operations that are independent of groups, e.g. multiply * 2, or raise to the power
• apply should be used for operations that work on a group of data. e.g. sum, count, etc.
• map_list should be used when the function expects a list aggregated series.

pub fn function_with_options<F>(
    self,
    function: F,
    output_type: GetOutput,
    options: FunctionOptions
) -> Selfwhere
    F: Fn(Series) -> PolarsResult<Series> + 'static + Send + Sync,

A function that cannot be expressed with map or apply and requires extra settings.

pub fn apply<F>(self, function: F, output_type: GetOutput) -> Selfwhere
    F: Fn(Series) -> PolarsResult<Series> + 'static + Send + Sync,

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

It is the responsibility of the caller that the schema is correct by giving the correct output_type. If None given the output type of the input expr is used.

This difference with map is that apply will create a separate Series per group.

• map should be used for operations that are independent of groups, e.g. multiply * 2, or raise to the power
• apply should be used for operations that work on a group of data. e.g. sum, count, etc.

pub fn apply_many<F>(
    self,
    function: F,
    arguments: &[Expr],
    output_type: GetOutput
) -> Selfwhere
    F: Fn(&mut [Series]) -> PolarsResult<Series> + 'static + Send + Sync,

Apply a function/closure over the groups with many arguments. This should only be used in a groupby aggregation.

See the Expr::apply function for the differences between map and apply.

pub fn apply_many_private(
    self,
    function_expr: FunctionExpr,
    arguments: &[Expr],
    auto_explode: bool,
    cast_to_supertypes: bool
) -> Self

pub fn map_many_private(
    self,
    function_expr: FunctionExpr,
    arguments: &[Expr],
    cast_to_supertypes: bool
) -> Self

pub fn is_finite(self) -> Self

Get mask of finite values if dtype is Float

pub fn is_infinite(self) -> Self

Get mask of infinite values if dtype is Float

pub fn is_nan(self) -> Self

Get mask of NaN values if dtype is Float

pub fn is_not_nan(self) -> Self

Get inverse mask of NaN values if dtype is Float

pub fn shift(self, periods: i64) -> Self

Shift the values in the array by some period. See the eager implementation.

pub fn shift_and_fill<E: Into<Expr>>(self, periods: i64, fill_value: E) -> Self

Shift the values in the array by some period and fill the resulting empty values.

pub fn cumsum(self, reverse: bool) -> Self

Available on crate feature cum_agg only.

Get an array with the cumulative sum computed at every element

pub fn cumprod(self, reverse: bool) -> Self

Available on crate feature cum_agg only.

Get an array with the cumulative product computed at every element

pub fn cummin(self, reverse: bool) -> Self

Available on crate feature cum_agg only.

Get an array with the cumulative min computed at every element

pub fn cummax(self, reverse: bool) -> Self

Available on crate feature cum_agg only.

Get an array with the cumulative max computed at every element

pub fn product(self) -> Self

Available on crate feature product only.

Get the product aggregation of an expression

pub fn backward_fill(self, limit: FillNullLimit) -> Self

Fill missing value with next non-null.

pub fn forward_fill(self, limit: FillNullLimit) -> Self

Fill missing value with previous non-null.

pub fn round(self, decimals: u32) -> Self

Available on crate feature round_series only.

Round underlying floating point array to given decimal numbers.

pub fn floor(self) -> Self

Available on crate feature round_series only.

Floor underlying floating point array to the lowest integers smaller or equal to the float value.

pub fn ceil(self) -> Self

Available on crate feature round_series only.

Ceil underlying floating point array to the highest integers smaller or equal to the float value.

pub fn clip(self, min: AnyValue<'_>, max: AnyValue<'_>) -> Self

Available on crate feature round_series only.

Clip underlying values to a set boundary.

pub fn clip_max(self, max: AnyValue<'_>) -> Self

Available on crate feature round_series only.

Clip underlying values to a set boundary.

pub fn clip_min(self, min: AnyValue<'_>) -> Self

Available on crate feature round_series only.

Clip underlying values to a set boundary.

pub fn abs(self) -> Self

Available on crate feature abs only.

Convert all values to their absolute/positive value.

pub fn over<E: AsRef<[IE]>, IE: Into<Expr> + Clone>(
    self,
    partition_by: E
) -> Self

Apply window function over a subgroup. This is similar to a groupby + aggregation + self join. Or similar to window functions in Postgres.

Example

#[macro_use] extern crate polars_core;
use polars_core::prelude::*;
use polars_lazy::prelude::*;

fn example() -> PolarsResult<()> {
    let df = df! {
            "groups" => &[1, 1, 2, 2, 1, 2, 3, 3, 1],
            "values" => &[1, 2, 3, 4, 5, 6, 7, 8, 8]
        }?;

    let out = df
     .lazy()
     .select(&[
         col("groups"),
         sum("values").over([col("groups")]),
     ])
     .collect()?;
    dbg!(&out);
    Ok(())
}

Outputs:

╭────────┬────────╮
│ groups ┆ values │
│ ---    ┆ ---    │
│ i32    ┆ i32    │
╞════════╪════════╡
│ 1      ┆ 16     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1      ┆ 16     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2      ┆ 13     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2      ┆ 13     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ ...    ┆ ...    │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1      ┆ 16     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2      ┆ 13     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3      ┆ 15     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3      ┆ 15     │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1      ┆ 16     │
╰────────┴────────╯

pub fn fill_null<E: Into<Expr>>(self, fill_value: E) -> Self

Replace the null values by a value.

pub fn fill_nan<E: Into<Expr>>(self, fill_value: E) -> Self

Replace the floating point NaN values by a value.

pub fn count(self) -> Self

Count the values of the Series or Get counts of the group by operation.

pub fn std(self, ddof: u8) -> Self

Standard deviation of the values of the Series

pub fn var(self, ddof: u8) -> Self

Variance of the values of the Series

pub fn is_duplicated(self) -> Self

Get a mask of duplicated values

pub fn is_unique(self) -> Self

Get a mask of unique values

pub fn and<E: Into<Expr>>(self, expr: E) -> Self

and operation

pub fn xor<E: Into<Expr>>(self, expr: E) -> Self

pub fn or<E: Into<Expr>>(self, expr: E) -> Self

or operation

pub fn pow<E: Into<Expr>>(self, exponent: E) -> Self

Raise expression to the power exponent

pub fn sin(self) -> Self

Compute the sine of the given expression

pub fn cos(self) -> Self

Compute the cosine of the given expression

pub fn tan(self) -> Self

Compute the tangent of the given expression

pub fn arcsin(self) -> Self

Compute the inverse sine of the given expression

pub fn arccos(self) -> Self

Compute the inverse cosine of the given expression

pub fn arctan(self) -> Self

Compute the inverse tangent of the given expression

pub fn sinh(self) -> Self

Compute the hyperbolic sine of the given expression

pub fn cosh(self) -> Self

Compute the hyperbolic cosine of the given expression

pub fn tanh(self) -> Self

Compute the hyperbolic tangent of the given expression

pub fn arcsinh(self) -> Self

Compute the inverse hyperbolic sine of the given expression

pub fn arccosh(self) -> Self

Compute the inverse hyperbolic cosine of the given expression

pub fn arctanh(self) -> Self

Compute the inverse hyperbolic tangent of the given expression

pub fn sign(self) -> Self

Compute the sign of the given expression

pub fn filter<E: Into<Expr>>(self, predicate: E) -> Self

Filter a single column Should be used in aggregation context. If you want to filter on a DataFrame level, use LazyFrame::filter

pub fn is_in<E: Into<Expr>>(self, other: E) -> Self

Available on crate feature is_in only.

Check if the values of the left expression are in the lists of the right expr.

pub fn sort_by<E: AsRef<[IE]>, IE: Into<Expr> + Clone, R: AsRef<[bool]>>(
    self,
    by: E,
    reverse: R
) -> Expr

Sort this column by the ordering of another column. Can also be used in a groupby context to sort the groups.

pub fn repeat_by<E: Into<Expr>>(self, by: E) -> Expr

Available on crate feature repeat_by only.

Repeat the column n times, where n is determined by the values in by. This yields an Expr of dtype List

pub fn is_first(self) -> Expr

Available on crate feature is_first only.

Get a mask of the first unique value.

pub fn dot<E: Into<Expr>>(self, other: E) -> Expr

Available on crate feature dot_product only.

pub fn mode(self) -> Expr

Available on crate feature mode only.

Compute the mode(s) of this column. This is the most occurring value.

pub fn keep_name(self) -> Expr

Keep the original root name

use polars_core::prelude::*;
use polars_lazy::prelude::*;

fn example(df: LazyFrame) -> LazyFrame {
    df.select([
// even thought the alias yields a different column name,
// `keep_name` will make sure that the original column name is used
        col("*").alias("foo").keep_name()
])
}

pub fn map_alias<F>(self, function: F) -> Exprwhere
    F: Fn(&str) -> PolarsResult<String> + 'static + Send + Sync,

Define an alias by mapping a function over the original root column name.

pub fn suffix(self, suffix: &str) -> Expr

Add a suffix to the root column name.

pub fn prefix(self, prefix: &str) -> Expr

Add a prefix to the root column name.

pub fn exclude(self, columns: impl IntoVec<String>) -> Expr

Exclude a column from a wildcard/regex selection.

You may also use regexes in the exclude as long as they start with ^ and end with $/

Example

use polars_core::prelude::*;
use polars_lazy::prelude::*;

// Select all columns except foo.
fn example(df: DataFrame) -> LazyFrame {
      df.lazy()
        .select(&[
                col("*").exclude(&["foo"])
                ])
}

pub fn exclude_dtype<D: AsRef<[DataType]>>(self, dtypes: D) -> Expr

pub fn interpolate(self, method: InterpolationMethod) -> Expr

Available on crate feature interpolate only.

pub fn rolling_min(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling min See: [ChunkedArray::rolling_min]

pub fn rolling_max(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling max See: [ChunkedArray::rolling_max]

pub fn rolling_mean(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling mean See: [ChunkedArray::rolling_mean]

pub fn rolling_sum(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling sum See: [ChunkedArray::rolling_sum]

pub fn rolling_median(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling median See: [ChunkedArray::rolling_median]

pub fn rolling_quantile(
    self,
    quantile: f64,
    interpolation: QuantileInterpolOptions,
    options: RollingOptions
) -> Expr

Available on crate feature rolling_window only.

Apply a rolling quantile See: [ChunkedArray::rolling_quantile]

pub fn rolling_var(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling variance

pub fn rolling_std(self, options: RollingOptions) -> Expr

Available on crate feature rolling_window only.

Apply a rolling std-dev

pub fn rolling_skew(self, window_size: usize, bias: bool) -> Expr

Available on crate features rolling_window and moment only.

Apply a rolling skew

pub fn rolling_apply(
    self,
    f: Arc<dyn Fn(&Series) -> Series + Send + Sync>,
    output_type: GetOutput,
    options: RollingOptionsFixedWindow
) -> Expr

Available on crate feature rolling_window only.

Apply a custom function over a rolling/ moving window of the array. This has quite some dynamic dispatch, so prefer rolling_min, max, mean, sum over this.

pub fn rolling_apply_float<F>(self, window_size: usize, f: F) -> Exprwhere
    F: 'static + FnMut(&mut Float64Chunked) -> Option<f64> + Send + Sync + Copy,

Available on crate feature rolling_window only.

Apply a custom function over a rolling/ moving window of the array. Prefer this over rolling_apply in case of floating point numbers as this is faster. This has quite some dynamic dispatch, so prefer rolling_min, max, mean, sum over this.

pub fn rank(self, options: RankOptions) -> Expr

Available on crate feature rank only.

pub fn diff(self, n: usize, null_behavior: NullBehavior) -> Expr

Available on crate feature diff only.

pub fn pct_change(self, n: usize) -> Expr

Available on crate feature pct_change only.

pub fn skew(self, bias: bool) -> Expr

Available on crate feature moment only.

Compute the sample skewness of a data set.

For normally distributed data, the skewness should be about zero. For uni-modal continuous distributions, a skewness value greater than zero means that there is more weight in the right tail of the distribution. The function skewtest can be used to determine if the skewness value is close enough to zero, statistically speaking.

see: https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/stats/stats.py#L1024

pub fn kurtosis(self, fisher: bool, bias: bool) -> Expr

Available on crate feature moment only.

pub fn upper_bound(self) -> Expr

Get maximal value that could be hold by this dtype.

pub fn lower_bound(self) -> Expr

Get minimal value that could be hold by this dtype.

pub fn reshape(self, dims: &[i64]) -> Self

pub fn cumcount(self, reverse: bool) -> Self

Cumulatively count values from 0 to len.

pub fn shuffle(self, seed: Option<u64>) -> Self

pub fn sample_n(
    self,
    n: usize,
    with_replacement: bool,
    shuffle: bool,
    seed: Option<u64>
) -> Self

pub fn sample_frac(
    self,
    frac: f64,
    with_replacement: bool,
    shuffle: bool,
    seed: Option<u64>
) -> Self

pub fn ewm_mean(self, options: EWMOptions) -> Self

pub fn ewm_std(self, options: EWMOptions) -> Self

pub fn ewm_var(self, options: EWMOptions) -> Self

pub fn any(self) -> Self

Check if any boolean value is true

pub fn shrink_dtype(self) -> Self

Shrink numeric columns to the minimal required datatype needed to fit the extrema of this Series. This can be used to reduce memory pressure.

pub fn all(self) -> Self

Check if all boolean values are true

pub fn value_counts(self, multithreaded: bool, sorted: bool) -> Self

Available on crate feature dtype-struct only.

Count all unique values and create a struct mapping value to count Note that it is better to turn multithreaded off in the aggregation context

pub fn unique_counts(self) -> Self

Available on crate feature unique_counts only.

Returns a count of the unique values in the order of appearance. This method differs from [Expr::value_counts] in that it does not return the values, only the counts and might be faster

pub fn log(self, base: f64) -> Self

Available on crate feature log only.

Compute the logarithm to a given base

pub fn exp(self) -> Self

Available on crate feature log only.

Calculate the exponential of all elements in the input array

pub fn entropy(self, base: f64, normalize: bool) -> Self

Available on crate feature log only.

Compute the entropy as -sum(pk * log(pk). where pk are discrete probabilities.

pub fn null_count(self) -> Expr

Get the null count of the column/group

pub fn set_sorted(self, sorted: IsSorted) -> Expr

Set this Series as sorted so that downstream code can use fast paths for sorted arrays.

Warning

This can lead to incorrect results if this Series is not sorted!! Use with care!

pub fn hash(self, k0: u64, k1: u64, k2: u64, k3: u64) -> Expr

Compute the hash of every element

pub fn str(self) -> StringNameSpace

pub fn dt(self) -> DateLikeNameSpace

pub fn arr(self) -> ListNameSpace

pub fn cat(self) -> CategoricalNameSpace

pub fn struct_(self) -> StructNameSpace

pub fn meta(self) -> MetaNameSpace

impl Expr

pub fn mutate(&mut self) -> ExprMut<'_>

Expr::mutate().apply(fn())

Trait Implementations

impl Add<Expr> for Expr

type Output = Expr

The resulting type after applying the + operator.

fn add(self, rhs: Self) -> Self::Output

Performs the + operation.

impl AsRef<Expr> for AggExpr

fn as_ref(&self) -> &Expr

Converts this type into a shared reference of the (usually inferred) input type.

impl Clone for Expr

fn clone(&self) -> Expr

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Expr

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Expr

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Expr

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
    __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for Expr

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Div<Expr> for Expr

type Output = Expr

The resulting type after applying the / operator.

fn div(self, rhs: Self) -> Self::Output

Performs the / operation.

impl From<&str> for Expr

fn from(s: &str) -> Self

Converts to this type from the input type.

impl From<AggExpr> for Expr

fn from(agg: AggExpr) -> Self

Converts to this type from the input type.

impl From<bool> for Expr

fn from(val: bool) -> Self

Converts to this type from the input type.

impl From<f32> for Expr

fn from(val: f32) -> Self

Converts to this type from the input type.

impl From<f64> for Expr

fn from(val: f64) -> Self

Converts to this type from the input type.

impl From<i16> for Expr

fn from(val: i16) -> Self

Converts to this type from the input type.

impl From<i32> for Expr

fn from(val: i32) -> Self

Converts to this type from the input type.

impl From<i64> for Expr

fn from(val: i64) -> Self

Converts to this type from the input type.

impl From<i8> for Expr

fn from(val: i8) -> Self

Converts to this type from the input type.

impl From<u16> for Expr

fn from(val: u16) -> Self

Converts to this type from the input type.

impl From<u32> for Expr

fn from(val: u32) -> Self

Converts to this type from the input type.

impl From<u64> for Expr

fn from(val: u64) -> Self

Converts to this type from the input type.

impl From<u8> for Expr

fn from(val: u8) -> Self

Converts to this type from the input type.

impl Hash for Expr

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)where
    H: Hasher,
    Self: Sized,

Feeds a slice of this type into the given Hasher.

impl<'a> IntoIterator for &'a Expr

type Item = &'a Expr

The type of the elements being iterated over.

type IntoIter = ExprIter<'a>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl Mul<Expr> for Expr

type Output = Expr

The resulting type after applying the * operator.

fn mul(self, rhs: Self) -> Self::Output

Performs the * operation.

impl PartialEq<Expr> for Expr

fn eq(&self, other: &Expr) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Rem<Expr> for Expr

type Output = Expr

The resulting type after applying the % operator.

fn rem(self, rhs: Self) -> Self::Output

Performs the % operation.

impl Serialize for Expr

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where
    __S: Serializer,

Serialize this value into the given Serde serializer.

impl Sub<Expr> for Expr

type Output = Expr

The resulting type after applying the - operator.

fn sub(self, rhs: Self) -> Self::Output

Performs the - operation.

impl Eq for Expr

impl StructuralPartialEq for Expr