Expressions#

This page gives an overview of all public polars expressions.

class polars.Expr[source]

Expressions that can be used in various contexts.

Methods:

`abs`	Compute absolute values.
`add`	Method equivalent of addition operator `expr + other`.
`agg_groups`	Get the group indexes of the group by operation.
`alias`	Rename the expression.
`all`	Check if all boolean values in a Boolean column are True.
`and_`	Method equivalent of bitwise "and" operator `expr & other & ...`.
`any`	Check if any boolean value in a Boolean column is True.
`append`	Append expressions.
`apply`	Apply a custom/user-defined function (UDF) in a GroupBy or Projection context.
`approx_n_unique`	Approximate count of unique values.
`arccos`	Compute the element-wise value for the inverse cosine.
`arccosh`	Compute the element-wise value for the inverse hyperbolic cosine.
`arcsin`	Compute the element-wise value for the inverse sine.
`arcsinh`	Compute the element-wise value for the inverse hyperbolic sine.
`arctan`	Compute the element-wise value for the inverse tangent.
`arctanh`	Compute the element-wise value for the inverse hyperbolic tangent.
`arg_max`	Get the index of the maximal value.
`arg_min`	Get the index of the minimal value.
`arg_sort`	Get the index values that would sort this column.
`arg_true`	Return indices where expression evaluates True.
`arg_unique`	Get index of first unique value.
`backward_fill`	Fill missing values with the next to be seen values.
`bottom_k`	Return the k smallest elements.
`cache`	Cache this expression so that it only is executed once per context.
`cast`	Cast between data types.
`cbrt`	Compute the cube root of the elements.
`ceil`	Rounds up to the nearest integer value.
`clip`	Clip (limit) the values in an array to a min and max boundary.
`clip_max`	Clip (limit) the values in an array to a max boundary.
`clip_min`	Clip (limit) the values in an array to a min boundary.
`cos`	Compute the element-wise value for the cosine.
`cosh`	Compute the element-wise value for the hyperbolic cosine.
`count`	Count the number of values in this expression.
`cumcount`	Get an array with the cumulative count computed at every element.
`cummax`	Get an array with the cumulative max computed at every element.
`cummin`	Get an array with the cumulative min computed at every element.
`cumprod`	Get an array with the cumulative product computed at every element.
`cumsum`	Get an array with the cumulative sum computed at every element.
`cumulative_eval`	Run an expression over a sliding window that increases 1 slot every iteration.
`cut`	Bin continuous values into discrete categories.
`degrees`	Convert from radians to degrees.
`diff`	Calculate the n-th discrete difference.
`dot`	Compute the dot/inner product between two Expressions.
`drop_nans`	Drop floating point NaN values.
`drop_nulls`	Drop all null values.
`entropy`	Computes the entropy.
`eq`	Method equivalent of equality operator `expr == other`.
`eq_missing`	Method equivalent of equality operator `expr == other` where None == None`.
`ewm_mean`	Exponentially-weighted moving average.
`ewm_std`	Exponentially-weighted moving standard deviation.
`ewm_var`	Exponentially-weighted moving variance.
`exclude`	Exclude columns from a multi-column expression.
`exp`	Compute the exponential, element-wise.
`explode`	Explode a list expression.
`extend_constant`	Extremely fast method for extending the Series with 'n' copies of a value.
`fill_nan`	Fill floating point NaN value with a fill value.
`fill_null`	Fill null values using the specified value or strategy.
`filter`	Filter a single column.
`first`	Get the first value.
`flatten`	Flatten a list or string column.
`floor`	Rounds down to the nearest integer value.
`floordiv`	Method equivalent of integer division operator `expr // other`.
`forward_fill`	Fill missing values with the latest seen values.
`from_json`	Read an expression from a JSON encoded string to construct an Expression.
`ge`	Method equivalent of "greater than or equal" operator `expr >= other`.
`gt`	Method equivalent of "greater than" operator `expr > other`.
`hash`	Hash the elements in the selection.
`head`	Get the first n rows.
`implode`	Aggregate values into a list.
`inspect`	Print the value that this expression evaluates to and pass on the value.
`interpolate`	Fill null values using interpolation.
`is_between`	Check if this expression is between the given start and end values.
`is_duplicated`	Get mask of duplicated values.
`is_finite`	Returns a boolean Series indicating which values are finite.
`is_first`	Get a mask of the first unique value.
`is_in`	Check if elements of this expression are present in the other Series.
`is_infinite`	Returns a boolean Series indicating which values are infinite.
`is_nan`	Returns a boolean Series indicating which values are NaN.
`is_not`	Negate a boolean expression.
`is_not_nan`	Returns a boolean Series indicating which values are not NaN.
`is_not_null`	Returns a boolean Series indicating which values are not null.
`is_null`	Returns a boolean Series indicating which values are null.
`is_unique`	Get mask of unique values.
`keep_name`	Keep the original root name of the expression.
`kurtosis`	Compute the kurtosis (Fisher or Pearson) of a dataset.
`last`	Get the last value.
`le`	Method equivalent of "less than or equal" operator `expr <= other`.
`len`	Count the number of values in this expression.
`limit`	Get the first n rows (alias for `Expr.head()`).
`log`	Compute the logarithm to a given base.
`log10`	Compute the base 10 logarithm of the input array, element-wise.
`log1p`	Compute the natural logarithm of each element plus one.
`lower_bound`	Calculate the lower bound.
`lt`	Method equivalent of "less than" operator `expr < other`.
`map`	Apply a custom python function to a Series or sequence of Series.
`map_alias`	Rename the output of an expression by mapping a function over the root name.
`map_dict`	Replace values in column according to remapping dictionary.
`max`	Get maximum value.
`mean`	Get mean value.
`median`	Get median value using linear interpolation.
`min`	Get minimum value.
`mod`	Method equivalent of modulus operator `expr % other`.
`mode`	Compute the most occurring value(s).
`mul`	Method equivalent of multiplication operator `expr * other`.
`n_unique`	Count unique values.
`nan_max`	Get maximum value, but propagate/poison encountered NaN values.
`nan_min`	Get minimum value, but propagate/poison encountered NaN values.
`ne`	Method equivalent of inequality operator `expr != other`.
`ne_missing`	Method equivalent of equality operator `expr != other` where None == None`.
`null_count`	Count null values.
`or_`	Method equivalent of bitwise "or" operator `expr \| other \| ...`.
`over`	Compute expressions over the given groups.
`pct_change`	Computes percentage change between values.
`pipe`	Offers a structured way to apply a sequence of user-defined functions (UDFs).
`pow`	Method equivalent of exponentiation operator `expr ** exponent`.
`prefix`	Add a prefix to the root column name of the expression.
`product`	Compute the product of an expression.
`qcut`	Bin continuous values into discrete categories based on their quantiles.
`quantile`	Get quantile value.
`radians`	Convert from degrees to radians.
`rank`	Assign ranks to data, dealing with ties appropriately.
`rechunk`	Create a single chunk of memory for this Series.
`reinterpret`	Reinterpret the underlying bits as a signed/unsigned integer.
`repeat_by`	Repeat the elements in this Series as specified in the given expression.
`reshape`	Reshape this Expr to a flat Series or a Series of Lists.
`reverse`	Reverse the selection.
`rle`	Get the lengths of runs of identical values.
`rle_id`	Map values to run IDs.
`rolling_apply`	Apply a custom rolling window function.
`rolling_max`	Apply a rolling max (moving max) over the values in this array.
`rolling_mean`	Apply a rolling mean (moving mean) over the values in this array.
`rolling_median`	Compute a rolling median.
`rolling_min`	Apply a rolling min (moving min) over the values in this array.
`rolling_quantile`	Compute a rolling quantile.
`rolling_skew`	Compute a rolling skew.
`rolling_std`	Compute a rolling standard deviation.
`rolling_sum`	Apply a rolling sum (moving sum) over the values in this array.
`rolling_var`	Compute a rolling variance.
`round`	Round underlying floating point data by decimals digits.
`sample`	Sample from this expression.
`search_sorted`	Find indices where elements should be inserted to maintain order.
`set_sorted`	Flags the expression as 'sorted'.
`shift`	Shift the values by a given period.
`shift_and_fill`	Shift the values by a given period and fill the resulting null values.
`shrink_dtype`	Shrink numeric columns to the minimal required datatype.
`shuffle`	Shuffle the contents of this expression.
`sign`	Compute the element-wise indication of the sign.
`sin`	Compute the element-wise value for the sine.
`sinh`	Compute the element-wise value for the hyperbolic sine.
`skew`	Compute the sample skewness of a data set.
`slice`	Get a slice of this expression.
`sort`	Sort this column.
`sort_by`	Sort this column by the ordering of other columns.
`sqrt`	Compute the square root of the elements.
`std`	Get standard deviation.
`sub`	Method equivalent of subtraction operator `expr - other`.
`suffix`	Add a suffix to the root column name of the expression.
`sum`	Get sum value.
`tail`	Get the last n rows.
`take`	Take values by index.
`take_every`	Take every nth value in the Series and return as a new Series.
`tan`	Compute the element-wise value for the tangent.
`tanh`	Compute the element-wise value for the hyperbolic tangent.
`to_physical`	Cast to physical representation of the logical dtype.
`top_k`	Return the k largest elements.
`truediv`	Method equivalent of float division operator `expr / other`.
`unique`	Get unique values of this expression.
`unique_counts`	Return a count of the unique values in the order of appearance.
`upper_bound`	Calculate the upper bound.
`value_counts`	Count all unique values and create a struct mapping value to count.
`var`	Get variance.
`where`	Filter a single column.
`xor`	Method equivalent of bitwise exclusive-or operator `expr ^ other`.

abs() → Self[source]

Compute absolute values.

Same as abs(expr).

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [-1.0, 0.0, 1.0, 2.0],
...     }
... )
>>> df.select(pl.col("A").abs())
shape: (4, 1)
┌─────┐
│ A   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
│ 0.0 │
│ 1.0 │
│ 2.0 │
└─────┘

add(other: Any) → Self[source]

Method equivalent of addition operator expr + other.

Parameters:

other: numeric or string value; accepts expression input.

Examples

>>> df = pl.DataFrame({"x": [1, 2, 3, 4, 5]})
>>> df.with_columns(
...     pl.col("x").add(2).alias("x+int"),
...     pl.col("x").add(pl.col("x").cumprod()).alias("x+expr"),
... )
shape: (5, 3)
┌─────┬───────┬────────┐
│ x   ┆ x+int ┆ x+expr │
│ --- ┆ ---   ┆ ---    │
│ i64 ┆ i64   ┆ i64    │
╞═════╪═══════╪════════╡
│ 1   ┆ 3     ┆ 2      │
│ 2   ┆ 4     ┆ 4      │
│ 3   ┆ 5     ┆ 9      │
│ 4   ┆ 6     ┆ 28     │
│ 5   ┆ 7     ┆ 125    │
└─────┴───────┴────────┘

>>> df = pl.DataFrame(
...     {"x": ["a", "d", "g"], "y": ["b", "e", "h"], "z": ["c", "f", "i"]}
... )
>>> df.with_columns(pl.col("x").add(pl.col("y")).add(pl.col("z")).alias("xyz"))
shape: (3, 4)
┌─────┬─────┬─────┬─────┐
│ x   ┆ y   ┆ z   ┆ xyz │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ str │
╞═════╪═════╪═════╪═════╡
│ a   ┆ b   ┆ c   ┆ abc │
│ d   ┆ e   ┆ f   ┆ def │
│ g   ┆ h   ┆ i   ┆ ghi │
└─────┴─────┴─────┴─────┘

agg_groups() → Self[source]

Get the group indexes of the group by operation.

Should be used in aggregation context only.

Examples

>>> df = pl.DataFrame(
...     {
...         "group": [
...             "one",
...             "one",
...             "one",
...             "two",
...             "two",
...             "two",
...         ],
...         "value": [94, 95, 96, 97, 97, 99],
...     }
... )
>>> df.groupby("group", maintain_order=True).agg(pl.col("value").agg_groups())
shape: (2, 2)
┌───────┬───────────┐
│ group ┆ value     │
│ ---   ┆ ---       │
│ str   ┆ list[u32] │
╞═══════╪═══════════╡
│ one   ┆ [0, 1, 2] │
│ two   ┆ [3, 4, 5] │
└───────┴───────────┘

alias(name: str) → Self[source]

Rename the expression.

Parameters:

name: The new name.

See also

map_alias
prefix
suffix

Examples

Rename an expression to avoid overwriting an existing column.

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["x", "y", "z"],
...     }
... )
>>> df.with_columns(
...     pl.col("a") + 10,
...     pl.col("b").str.to_uppercase().alias("c"),
... )
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 11  ┆ x   ┆ X   │
│ 12  ┆ y   ┆ Y   │
│ 13  ┆ z   ┆ Z   │
└─────┴─────┴─────┘

Overwrite the default name of literal columns to prevent errors due to duplicate column names.

>>> df.with_columns(
...     pl.lit(True).alias("c"),
...     pl.lit(4.0).alias("d"),
... )
shape: (3, 4)
┌─────┬─────┬──────┬─────┐
│ a   ┆ b   ┆ c    ┆ d   │
│ --- ┆ --- ┆ ---  ┆ --- │
│ i64 ┆ str ┆ bool ┆ f64 │
╞═════╪═════╪══════╪═════╡
│ 1   ┆ x   ┆ true ┆ 4.0 │
│ 2   ┆ y   ┆ true ┆ 4.0 │
│ 3   ┆ z   ┆ true ┆ 4.0 │
└─────┴─────┴──────┴─────┘

all(drop_nulls: bool = True) → Self[source]

Check if all boolean values in a Boolean column are True.

This method is an expression - not to be confused with polars.all() which is a function to select all columns.

Parameters:

drop_nulls: If False, return None if there are any nulls.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame(
...     {"TT": [True, True], "TF": [True, False], "FF": [False, False]}
... )
>>> df.select(pl.col("*").all())
shape: (1, 3)
┌──────┬───────┬───────┐
│ TT   ┆ TF    ┆ FF    │
│ ---  ┆ ---   ┆ ---   │
│ bool ┆ bool  ┆ bool  │
╞══════╪═══════╪═══════╡
│ true ┆ false ┆ false │
└──────┴───────┴───────┘
>>> df = pl.DataFrame(dict(x=[None, False], y=[None, True]))
>>> df.select(pl.col("x").all(True), pl.col("y").all(True))
shape: (1, 2)
┌───────┬───────┐
│ x     ┆ y     │
│ ---   ┆ ---   │
│ bool  ┆ bool  │
╞═══════╪═══════╡
│ false ┆ false │
└───────┴───────┘
>>> df.select(pl.col("x").all(False), pl.col("y").all(False))
shape: (1, 2)
┌──────┬──────┐
│ x    ┆ y    │
│ ---  ┆ ---  │
│ bool ┆ bool │
╞══════╪══════╡
│ null ┆ null │
└──────┴──────┘

and_(*others: Any) → Self[source]

Method equivalent of bitwise “and” operator expr & other & ....

Parameters:

*others: One or more integer or boolean expressions to evaluate/combine.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [5, 6, 7, 4, 8],
...         "y": [1.5, 2.5, 1.0, 4.0, -5.75],
...         "z": [-9, 2, -1, 4, 8],
...     }
... )
>>> df.select(
...     (pl.col("x") >= pl.col("z"))
...     .and_(
...         pl.col("y") >= pl.col("z"),
...         pl.col("y") == pl.col("y"),
...         pl.col("z") <= pl.col("x"),
...         pl.col("y") != pl.col("x"),
...     )
...     .alias("all")
... )
shape: (5, 1)
┌───────┐
│ all   │
│ ---   │
│ bool  │
╞═══════╡
│ true  │
│ true  │
│ true  │
│ false │
│ false │
└───────┘

any(drop_nulls: bool = True) → Self[source]

Check if any boolean value in a Boolean column is True.

Parameters:

drop_nulls: If False, return None if there are nulls but no Trues.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame({"TF": [True, False], "FF": [False, False]})
>>> df.select(pl.all().any())
shape: (1, 2)
┌──────┬───────┐
│ TF   ┆ FF    │
│ ---  ┆ ---   │
│ bool ┆ bool  │
╞══════╪═══════╡
│ true ┆ false │
└──────┴───────┘
>>> df = pl.DataFrame(dict(x=[None, False], y=[None, True]))
>>> df.select(pl.col("x").any(True), pl.col("y").any(True))
shape: (1, 2)
┌───────┬──────┐
│ x     ┆ y    │
│ ---   ┆ ---  │
│ bool  ┆ bool │
╞═══════╪══════╡
│ false ┆ true │
└───────┴──────┘
>>> df.select(pl.col("x").any(False), pl.col("y").any(False))
shape: (1, 2)
┌──────┬──────┐
│ x    ┆ y    │
│ ---  ┆ ---  │
│ bool ┆ bool │
╞══════╪══════╡
│ null ┆ true │
└──────┴──────┘

append(other: IntoExpr, *, upcast: bool = True) → Self[source]

Append expressions.

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

Parameters:

other: Expression to append.
upcast: Cast both Series to the same supertype.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10],
...         "b": [None, 4, 4],
...     }
... )
>>> df.select(pl.all().head(1).append(pl.all().tail(1)))
shape: (2, 2)
┌─────┬──────┐
│ a   ┆ b    │
│ --- ┆ ---  │
│ i64 ┆ i64  │
╞═════╪══════╡
│ 8   ┆ null │
│ 10  ┆ 4    │
└─────┴──────┘

apply( function: Callable[[Series], Series] | Callable[[Any], Any], return_dtype: PolarsDataType | None = None, *, skip_nulls: bool = True, pass_name: bool = False, strategy: ApplyStrategy = 'thread_local', ) → Self[source]

Apply a custom/user-defined function (UDF) in a GroupBy or Projection context.

Warning

This method is much slower than the native expressions API. Only use it if you cannot implement your logic otherwise.

Depending on the context it has the following behavior:

Selection
Expects f to be of type Callable[[Any], Any]. Applies a python function over each individual value in the column.
GroupBy
Expects f to be of type Callable[[Series], Series]. Applies a python function over each group.

Parameters:

function

Lambda/ function to apply.

return_dtype

Dtype of the output Series. If not set, the dtype will be polars.Unknown.

skip_nulls

Don’t apply the function over values that contain nulls. This is faster.

pass_name

Pass the Series name to the custom function This is more expensive.

strategy{‘thread_local’, ‘threading’}

This functionality is in alpha stage. This may be removed /changed without it being considered a breaking change.

‘thread_local’: run the python function on a single thread.
‘threading’: run the python function on separate threads. Use with
care as this can slow performance. This might only speed up your code if the amount of work per element is significant and the python function releases the GIL (e.g. via calling a c function)

Warning

If return_dtype is not provided, this may lead to unexpected results. We allow this, but it is considered a bug in the user’s query.

Notes

Using apply is strongly discouraged as you will be effectively running python “for” loops. This will be very slow. Wherever possible you should strongly prefer the native expression API to achieve the best performance.
If your function is expensive and you don’t want it to be called more than once for a given input, consider applying an @lru_cache decorator to it. With suitable data you may achieve order-of-magnitude speedups (or more).

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 1],
...         "b": ["a", "b", "c", "c"],
...     }
... )

In a selection context, the function is applied by row.

>>> df.with_columns(  
...     pl.col("a").apply(lambda x: x * 2).alias("a_times_2"),
... )
shape: (4, 3)
┌─────┬─────┬───────────┐
│ a   ┆ b   ┆ a_times_2 │
│ --- ┆ --- ┆ ---       │
│ i64 ┆ str ┆ i64       │
╞═════╪═════╪═══════════╡
│ 1   ┆ a   ┆ 2         │
│ 2   ┆ b   ┆ 4         │
│ 3   ┆ c   ┆ 6         │
│ 1   ┆ c   ┆ 2         │
└─────┴─────┴───────────┘

It is better to implement this with an expression:

>>> df.with_columns(
...     (pl.col("a") * 2).alias("a_times_2"),
... )  

In a GroupBy context the function is applied by group:

>>> df.lazy().groupby("b", maintain_order=True).agg(
...     pl.col("a").apply(lambda x: x.sum())
... ).collect()
shape: (3, 2)
┌─────┬─────┐
│ b   ┆ a   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ a   ┆ 1   │
│ b   ┆ 2   │
│ c   ┆ 4   │
└─────┴─────┘

It is better to implement this with an expression:

>>> df.groupby("b", maintain_order=True).agg(
...     pl.col("a").sum(),
... )  

approx_n_unique() → Self[source]

Approximate count of unique values.

This is done using the HyperLogLog++ algorithm for cardinality estimation.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").approx_n_unique())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 2   │
└─────┘

arccos() → Self[source]

Compute the element-wise value for the inverse cosine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [0.0]})
>>> df.select(pl.col("a").arccos())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.570796 │
└──────────┘

arccosh() → Self[source]

Compute the element-wise value for the inverse hyperbolic cosine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").arccosh())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.0 │
└─────┘

arcsin() → Self[source]

Compute the element-wise value for the inverse sine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").arcsin())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.570796 │
└──────────┘

arcsinh() → Self[source]

Compute the element-wise value for the inverse hyperbolic sine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").arcsinh())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.881374 │
└──────────┘

arctan() → Self[source]

Compute the element-wise value for the inverse tangent.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").arctan())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.785398 │
└──────────┘

arctanh() → Self[source]

Compute the element-wise value for the inverse hyperbolic tangent.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").arctanh())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ inf │
└─────┘

arg_max() → Self[source]

Get the index of the maximal value.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [20, 10, 30],
...     }
... )
>>> df.select(pl.col("a").arg_max())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 2   │
└─────┘

arg_min() → Self[source]

Get the index of the minimal value.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [20, 10, 30],
...     }
... )
>>> df.select(pl.col("a").arg_min())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 1   │
└─────┘

arg_sort(*, descending: bool = False, nulls_last: bool = False) → Self[source]

Get the index values that would sort this column.

Parameters:

descending: Sort in descending (descending) order.
nulls_last: Place null values last instead of first.

Returns:

Expr: Expression of data type UInt32.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [20, 10, 30],
...     }
... )
>>> df.select(pl.col("a").arg_sort())
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 1   │
│ 0   │
│ 2   │
└─────┘

arg_true() → Self[source]

Return indices where expression evaluates True.

Warning

Modifies number of rows returned, so will fail in combination with other expressions. Use as only expression in select / with_columns.

See also

Series.arg_true: Return indices where Series is True
polars.arg_where

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2, 1]})
>>> df.select((pl.col("a") == 1).arg_true())
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 0   │
│ 1   │
│ 3   │
└─────┘

arg_unique() → Self[source]

Get index of first unique value.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10],
...         "b": [None, 4, 4],
...     }
... )
>>> df.select(pl.col("a").arg_unique())
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 0   │
│ 1   │
│ 2   │
└─────┘
>>> df.select(pl.col("b").arg_unique())
shape: (2, 1)
┌─────┐
│ b   │
│ --- │
│ u32 │
╞═════╡
│ 0   │
│ 1   │
└─────┘

backward_fill(limit: int | None = None) → Self[source]

Fill missing values with the next to be seen values.

Parameters:

limit: The number of consecutive null values to backward fill.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None],
...         "b": [4, None, 6],
...         "c": [None, None, 2],
...     }
... )
>>> df.select(pl.all().backward_fill())
shape: (3, 3)
┌──────┬─────┬─────┐
│ a    ┆ b   ┆ c   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ i64 ┆ i64 │
╞══════╪═════╪═════╡
│ 1    ┆ 4   ┆ 2   │
│ 2    ┆ 6   ┆ 2   │
│ null ┆ 6   ┆ 2   │
└──────┴─────┴─────┘
>>> df.select(pl.all().backward_fill(limit=1))
shape: (3, 3)
┌──────┬─────┬──────┐
│ a    ┆ b   ┆ c    │
│ ---  ┆ --- ┆ ---  │
│ i64  ┆ i64 ┆ i64  │
╞══════╪═════╪══════╡
│ 1    ┆ 4   ┆ null │
│ 2    ┆ 6   ┆ 2    │
│ null ┆ 6   ┆ 2    │
└──────┴─────┴──────┘

bottom_k(k: int = 5) → Self[source]

Return the k smallest elements.

This has time complexity:

\[\begin{split}O(n + k \\log{}n - \frac{k}{2})\end{split}\]

Parameters:

k: Number of elements to return.

See also

top_k

Examples

>>> df = pl.DataFrame(
...     {
...         "value": [1, 98, 2, 3, 99, 4],
...     }
... )
>>> df.select(
...     [
...         pl.col("value").top_k().alias("top_k"),
...         pl.col("value").bottom_k().alias("bottom_k"),
...     ]
... )
shape: (5, 2)
┌───────┬──────────┐
│ top_k ┆ bottom_k │
│ ---   ┆ ---      │
│ i64   ┆ i64      │
╞═══════╪══════════╡
│ 99    ┆ 1        │
│ 98    ┆ 2        │
│ 4     ┆ 3        │
│ 3     ┆ 4        │
│ 2     ┆ 98       │
└───────┴──────────┘

cache() → Self[source]: Cache this expression so that it only is executed once per context.

Deprecated since version 0.18.9: This method now does nothing. It has been superseded by the comm_subexpr_elim setting on LazyFrame.collect, which automatically caches expressions that are equal.

cast(dtype: PolarsDataType | type[Any], *, strict: bool = True) → Self[source]

Cast between data types.

Parameters:

dtype: DataType to cast to.
strict: Throw an error if a cast could not be done. For instance, due to an overflow.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["4", "5", "6"],
...     }
... )
>>> df.with_columns(
...     [
...         pl.col("a").cast(pl.Float64),
...         pl.col("b").cast(pl.Int32),
...     ]
... )
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ f64 ┆ i32 │
╞═════╪═════╡
│ 1.0 ┆ 4   │
│ 2.0 ┆ 5   │
│ 3.0 ┆ 6   │
└─────┴─────┘

cbrt() → Self[source]

Compute the cube root of the elements.

Examples

>>> df = pl.DataFrame({"values": [1.0, 2.0, 4.0]})
>>> df.select(pl.col("values").cbrt())
shape: (3, 1)
┌──────────┐
│ values   │
│ ---      │
│ f64      │
╞══════════╡
│ 1.0      │
│ 1.259921 │
│ 1.587401 │
└──────────┘

ceil() → Self[source]

Rounds up to the nearest integer value.

Only works on floating point Series.

Examples

>>> df = pl.DataFrame({"a": [0.3, 0.5, 1.0, 1.1]})
>>> df.select(pl.col("a").ceil())
shape: (4, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
│ 1.0 │
│ 1.0 │
│ 2.0 │
└─────┘

clip(lower_bound: int | float, upper_bound: int | float) → Self[source]

Clip (limit) the values in an array to a min and max boundary.

Only works for numerical types.

If you want to clip other dtypes, consider writing a “when, then, otherwise” expression. See when() for more information.

Parameters:

lower_bound: Lower bound.
upper_bound: Upper bound.

Examples

>>> df = pl.DataFrame({"foo": [-50, 5, None, 50]})
>>> df.with_columns(pl.col("foo").clip(1, 10).alias("foo_clipped"))
shape: (4, 2)
┌──────┬─────────────┐
│ foo  ┆ foo_clipped │
│ ---  ┆ ---         │
│ i64  ┆ i64         │
╞══════╪═════════════╡
│ -50  ┆ 1           │
│ 5    ┆ 5           │
│ null ┆ null        │
│ 50   ┆ 10          │
└──────┴─────────────┘

clip_max(upper_bound: int | float) → Self[source]

Clip (limit) the values in an array to a max boundary.

Only works for numerical types.

If you want to clip other dtypes, consider writing a “when, then, otherwise” expression. See when() for more information.

Parameters:

upper_bound: Upper bound.

Examples

>>> df = pl.DataFrame({"foo": [-50, 5, None, 50]})
>>> df.with_columns(pl.col("foo").clip_max(0).alias("foo_clipped"))
shape: (4, 2)
┌──────┬─────────────┐
│ foo  ┆ foo_clipped │
│ ---  ┆ ---         │
│ i64  ┆ i64         │
╞══════╪═════════════╡
│ -50  ┆ -50         │
│ 5    ┆ 0           │
│ null ┆ null        │
│ 50   ┆ 0           │
└──────┴─────────────┘

clip_min(lower_bound: int | float) → Self[source]

Clip (limit) the values in an array to a min boundary.

Only works for numerical types.

If you want to clip other dtypes, consider writing a “when, then, otherwise” expression. See when() for more information.

Parameters:

lower_bound: Lower bound.

Examples

>>> df = pl.DataFrame({"foo": [-50, 5, None, 50]})
>>> df.with_columns(pl.col("foo").clip_min(0).alias("foo_clipped"))
shape: (4, 2)
┌──────┬─────────────┐
│ foo  ┆ foo_clipped │
│ ---  ┆ ---         │
│ i64  ┆ i64         │
╞══════╪═════════════╡
│ -50  ┆ 0           │
│ 5    ┆ 5           │
│ null ┆ null        │
│ 50   ┆ 50          │
└──────┴─────────────┘

cos() → Self[source]

Compute the element-wise value for the cosine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [0.0]})
>>> df.select(pl.col("a").cos())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘

cosh() → Self[source]

Compute the element-wise value for the hyperbolic cosine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").cosh())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.543081 │
└──────────┘

count() → Self[source]

Count the number of values in this expression.

Warning

null is deemed a value in this context.

Examples

>>> df = pl.DataFrame({"a": [8, 9, 10], "b": [None, 4, 4]})
>>> df.select(pl.all().count())  # counts nulls
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 3   ┆ 3   │
└─────┴─────┘

cumcount(*, reverse: bool = False) → Self[source]

Get an array with the cumulative count computed at every element.

Counting from 0 to len

Parameters:

reverse: Reverse the operation.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4]})
>>> df.select(
...     [
...         pl.col("a").cumcount(),
...         pl.col("a").cumcount(reverse=True).alias("a_reverse"),
...     ]
... )
shape: (4, 2)
┌─────┬───────────┐
│ a   ┆ a_reverse │
│ --- ┆ ---       │
│ u32 ┆ u32       │
╞═════╪═══════════╡
│ 0   ┆ 3         │
│ 1   ┆ 2         │
│ 2   ┆ 1         │
│ 3   ┆ 0         │
└─────┴───────────┘

cummax(*, reverse: bool = False) → Self[source]

Get an array with the cumulative max computed at every element.

Parameters:

reverse: Reverse the operation.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4]})
>>> df.select(
...     [
...         pl.col("a").cummax(),
...         pl.col("a").cummax(reverse=True).alias("a_reverse"),
...     ]
... )
shape: (4, 2)
┌─────┬───────────┐
│ a   ┆ a_reverse │
│ --- ┆ ---       │
│ i64 ┆ i64       │
╞═════╪═══════════╡
│ 1   ┆ 4         │
│ 2   ┆ 4         │
│ 3   ┆ 4         │
│ 4   ┆ 4         │
└─────┴───────────┘

Null values are excluded, but can also be filled by calling forward_fill.

>>> df = pl.DataFrame({"values": [None, 10, None, 8, 9, None, 16, None]})
>>> df.with_columns(
...     [
...         pl.col("values").cummax().alias("value_cummax"),
...         pl.col("values")
...         .cummax()
...         .forward_fill()
...         .alias("value_cummax_all_filled"),
...     ]
... )
shape: (8, 3)
┌────────┬──────────────┬─────────────────────────┐
│ values ┆ value_cummax ┆ value_cummax_all_filled │
│ ---    ┆ ---          ┆ ---                     │
│ i64    ┆ i64          ┆ i64                     │
╞════════╪══════════════╪═════════════════════════╡
│ null   ┆ null         ┆ null                    │
│ 10     ┆ 10           ┆ 10                      │
│ null   ┆ null         ┆ 10                      │
│ 8      ┆ 10           ┆ 10                      │
│ 9      ┆ 10           ┆ 10                      │
│ null   ┆ null         ┆ 10                      │
│ 16     ┆ 16           ┆ 16                      │
│ null   ┆ null         ┆ 16                      │
└────────┴──────────────┴─────────────────────────┘

cummin(*, reverse: bool = False) → Self[source]

Get an array with the cumulative min computed at every element.

Parameters:

reverse: Reverse the operation.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4]})
>>> df.select(
...     [
...         pl.col("a").cummin(),
...         pl.col("a").cummin(reverse=True).alias("a_reverse"),
...     ]
... )
shape: (4, 2)
┌─────┬───────────┐
│ a   ┆ a_reverse │
│ --- ┆ ---       │
│ i64 ┆ i64       │
╞═════╪═══════════╡
│ 1   ┆ 1         │
│ 1   ┆ 2         │
│ 1   ┆ 3         │
│ 1   ┆ 4         │
└─────┴───────────┘

cumprod(*, reverse: bool = False) → Self[source]

Get an array with the cumulative product computed at every element.

Parameters:

reverse: Reverse the operation.

Notes

Dtypes in {Int8, UInt8, Int16, UInt16} are cast to Int64 before summing to prevent overflow issues.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4]})
>>> df.select(
...     [
...         pl.col("a").cumprod(),
...         pl.col("a").cumprod(reverse=True).alias("a_reverse"),
...     ]
... )
shape: (4, 2)
┌─────┬───────────┐
│ a   ┆ a_reverse │
│ --- ┆ ---       │
│ i64 ┆ i64       │
╞═════╪═══════════╡
│ 1   ┆ 24        │
│ 2   ┆ 24        │
│ 6   ┆ 12        │
│ 24  ┆ 4         │
└─────┴───────────┘

cumsum(*, reverse: bool = False) → Self[source]

Get an array with the cumulative sum computed at every element.

Parameters:

reverse: Reverse the operation.

Notes

Dtypes in {Int8, UInt8, Int16, UInt16} are cast to Int64 before summing to prevent overflow issues.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4]})
>>> df.select(
...     [
...         pl.col("a").cumsum(),
...         pl.col("a").cumsum(reverse=True).alias("a_reverse"),
...     ]
... )
shape: (4, 2)
┌─────┬───────────┐
│ a   ┆ a_reverse │
│ --- ┆ ---       │
│ i64 ┆ i64       │
╞═════╪═══════════╡
│ 1   ┆ 10        │
│ 3   ┆ 9         │
│ 6   ┆ 7         │
│ 10  ┆ 4         │
└─────┴───────────┘

Null values are excluded, but can also be filled by calling forward_fill.

>>> df = pl.DataFrame({"values": [None, 10, None, 8, 9, None, 16, None]})
>>> df.with_columns(
...     [
...         pl.col("values").cumsum().alias("value_cumsum"),
...         pl.col("values")
...         .cumsum()
...         .forward_fill()
...         .alias("value_cumsum_all_filled"),
...     ]
... )
shape: (8, 3)
┌────────┬──────────────┬─────────────────────────┐
│ values ┆ value_cumsum ┆ value_cumsum_all_filled │
│ ---    ┆ ---          ┆ ---                     │
│ i64    ┆ i64          ┆ i64                     │
╞════════╪══════════════╪═════════════════════════╡
│ null   ┆ null         ┆ null                    │
│ 10     ┆ 10           ┆ 10                      │
│ null   ┆ null         ┆ 10                      │
│ 8      ┆ 18           ┆ 18                      │
│ 9      ┆ 27           ┆ 27                      │
│ null   ┆ null         ┆ 27                      │
│ 16     ┆ 43           ┆ 43                      │
│ null   ┆ null         ┆ 43                      │
└────────┴──────────────┴─────────────────────────┘

cumulative_eval( expr: Expr, min_periods: int = 1, *, parallel: bool = False, ) → Self[source]

Run an expression over a sliding window that increases 1 slot every iteration.

Parameters:

expr: Expression to evaluate
min_periods: Number of valid values there should be in the window before the expression is evaluated. valid values = length - null_count
parallel: Run in parallel. Don’t do this in a groupby or another operation that already has much parallelization.

Warning

This functionality is experimental and may change without it being considered a breaking change.

This can be really slow as it can have O(n^2) complexity. Don’t use this for operations that visit all elements.

Examples

>>> df = pl.DataFrame({"values": [1, 2, 3, 4, 5]})
>>> df.select(
...     [
...         pl.col("values").cumulative_eval(
...             pl.element().first() - pl.element().last() ** 2
...         )
...     ]
... )
shape: (5, 1)
┌────────┐
│ values │
│ ---    │
│ f64    │
╞════════╡
│ 0.0    │
│ -3.0   │
│ -8.0   │
│ -15.0  │
│ -24.0  │
└────────┘

cut( breaks: Sequence[float], *, labels: Sequence[str] | None = None, left_closed: bool = False, include_breaks: bool = False, ) → Self[source]

Bin continuous values into discrete categories.

Parameters:

breaks: List of unique cut points.
labels: Names of the categories. The number of labels must be equal to the number of cut points plus one.
left_closed: Set the intervals to be left-closed instead of right-closed.
include_breaks: Include a column with the right endpoint of the bin each observation falls in. This will change the data type of the output from a Categorical to a Struct.

Returns:

Expr: Expression of data type Categorical if include_breaks is set to False (default), otherwise an expression of data type Struct.

See also

qcut

Examples

Divide a column into three categories.

>>> df = pl.DataFrame({"foo": [-2, -1, 0, 1, 2]})
>>> df.with_columns(
...     pl.col("foo").cut([-1, 1], labels=["a", "b", "c"]).alias("cut")
... )
shape: (5, 2)
┌─────┬─────┐
│ foo ┆ cut │
│ --- ┆ --- │
│ i64 ┆ cat │
╞═════╪═════╡
│ -2  ┆ a   │
│ -1  ┆ a   │
│ 0   ┆ b   │
│ 1   ┆ b   │
│ 2   ┆ c   │
└─────┴─────┘

Add both the category and the breakpoint.

>>> df.with_columns(
...     pl.col("foo").cut([-1, 1], include_breaks=True).alias("cut")
... ).unnest("cut")
shape: (5, 3)
┌─────┬──────┬────────────┐
│ foo ┆ brk  ┆ foo_bin    │
│ --- ┆ ---  ┆ ---        │
│ i64 ┆ f64  ┆ cat        │
╞═════╪══════╪════════════╡
│ -2  ┆ -1.0 ┆ (-inf, -1] │
│ -1  ┆ -1.0 ┆ (-inf, -1] │
│ 0   ┆ 1.0  ┆ (-1, 1]    │
│ 1   ┆ 1.0  ┆ (-1, 1]    │
│ 2   ┆ inf  ┆ (1, inf]   │
└─────┴──────┴────────────┘

degrees() → Self[source]

Convert from radians to degrees.

Returns:

Expr: Expression of data type Float64.

Examples

>>> import math
>>> df = pl.DataFrame({"a": [x * math.pi for x in range(-4, 5)]})
>>> df.select(pl.col("a").degrees())
shape: (9, 1)
┌────────┐
│ a      │
│ ---    │
│ f64    │
╞════════╡
│ -720.0 │
│ -540.0 │
│ -360.0 │
│ -180.0 │
│ 0.0    │
│ 180.0  │
│ 360.0  │
│ 540.0  │
│ 720.0  │
└────────┘

diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Self[source]

Calculate the n-th discrete difference.

Parameters:

n: Number of slots to shift.
null_behavior{‘ignore’, ‘drop’}: How to handle null values.

Examples

>>> df = pl.DataFrame({"int": [20, 10, 30, 25, 35]})
>>> df.with_columns(change=pl.col("int").diff())
shape: (5, 2)
┌─────┬────────┐
│ int ┆ change │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 20  ┆ null   │
│ 10  ┆ -10    │
│ 30  ┆ 20     │
│ 25  ┆ -5     │
│ 35  ┆ 10     │
└─────┴────────┘

>>> df.with_columns(change=pl.col("int").diff(n=2))
shape: (5, 2)
┌─────┬────────┐
│ int ┆ change │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 20  ┆ null   │
│ 10  ┆ null   │
│ 30  ┆ 10     │
│ 25  ┆ 15     │
│ 35  ┆ 5      │
└─────┴────────┘

>>> df.select(pl.col("int").diff(n=2, null_behavior="drop").alias("diff"))
shape: (3, 1)
┌──────┐
│ diff │
│ ---  │
│ i64  │
╞══════╡
│ 10   │
│ 15   │
│ 5    │
└──────┘

dot(other: Expr | str) → Self[source]

Compute the dot/inner product between two Expressions.

Parameters:

other: Expression to compute dot product with.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> df.select(pl.col("a").dot(pl.col("b")))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 44  │
└─────┘

drop_nans() → Self[source]

Drop floating point NaN values.

Warning

Note that NaN values are not null values! To drop null values, use drop_nulls().

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10, 11],
...         "b": [None, 4.0, 4.0, float("nan")],
...     }
... )
>>> df.select(pl.col("b").drop_nans())
shape: (3, 1)
┌──────┐
│ b    │
│ ---  │
│ f64  │
╞══════╡
│ null │
│ 4.0  │
│ 4.0  │
└──────┘

drop_nulls() → Self[source]

Drop all null values.

Warning

Note that null values are not floating point NaN values! To drop NaN values, use drop_nans().

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10, 11],
...         "b": [None, 4.0, 4.0, float("nan")],
...     }
... )
>>> df.select(pl.col("b").drop_nulls())
shape: (3, 1)
┌─────┐
│ b   │
│ --- │
│ f64 │
╞═════╡
│ 4.0 │
│ 4.0 │
│ NaN │
└─────┘

entropy(base: float = 2.718281828459045, *, normalize: bool = True) → Self[source]

Computes the entropy.

Uses the formula -sum(pk * log(pk) where pk are discrete probabilities.

Parameters:

base: Given base, defaults to e
normalize: Normalize pk if it doesn’t sum to 1.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").entropy(base=2))
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.459148 │
└──────────┘
>>> df.select(pl.col("a").entropy(base=2, normalize=False))
shape: (1, 1)
┌───────────┐
│ a         │
│ ---       │
│ f64       │
╞═══════════╡
│ -6.754888 │
└───────────┘

eq(other: Any) → Self[source]

Method equivalent of equality operator expr == other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [1.0, 2.0, float("nan"), 4.0],
...         "y": [2.0, 2.0, float("nan"), 4.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").eq(pl.col("y")).alias("x == y"),
... )
shape: (4, 3)
┌─────┬─────┬────────┐
│ x   ┆ y   ┆ x == y │
│ --- ┆ --- ┆ ---    │
│ f64 ┆ f64 ┆ bool   │
╞═════╪═════╪════════╡
│ 1.0 ┆ 2.0 ┆ false  │
│ 2.0 ┆ 2.0 ┆ true   │
│ NaN ┆ NaN ┆ false  │
│ 4.0 ┆ 4.0 ┆ true   │
└─────┴─────┴────────┘

eq_missing(other: Any) → Self[source]

Method equivalent of equality operator expr == other where None == None`.

This differs from default eq where null values are propagated.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [1.0, 2.0, float("nan"), 4.0, None, None],
...         "y": [2.0, 2.0, float("nan"), 4.0, 5.0, None],
...     }
... )
>>> df.with_columns(
...     pl.col("x").eq_missing(pl.col("y")).alias("x == y"),
... )
shape: (6, 3)
┌──────┬──────┬────────┐
│ x    ┆ y    ┆ x == y │
│ ---  ┆ ---  ┆ ---    │
│ f64  ┆ f64  ┆ bool   │
╞══════╪══════╪════════╡
│ 1.0  ┆ 2.0  ┆ false  │
│ 2.0  ┆ 2.0  ┆ true   │
│ NaN  ┆ NaN  ┆ false  │
│ 4.0  ┆ 4.0  ┆ true   │
│ null ┆ 5.0  ┆ false  │
│ null ┆ null ┆ true   │
└──────┴──────┴────────┘

ewm_mean( com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, min_periods: int = 1, ignore_nulls: bool = True, ) → Self[source]

Exponentially-weighted moving average.

Parameters:

com

Specify decay in terms of center of mass, $\gamma$, with

\[\alpha = \frac{1}{1 + \gamma} \; \forall \; \gamma \geq 0\]

span

Specify decay in terms of span, $\theta$, with

\[\alpha = \frac{2}{\theta + 1} \; \forall \; \theta \geq 1\]

half_life

Specify decay in terms of half-life, $\lambda$, with

\[\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \lambda } \right\} \; \forall \; \lambda > 0\]

alpha

Specify smoothing factor alpha directly, $0 < \alpha \leq 1$.

adjust

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

When adjust=True the EW function is calculated using weights $w_i = (1 - \alpha)^i$

When adjust=False the EW function is calculated recursively by

\[\begin{split}y_0 &= x_0 \\ y_t &= (1 - \alpha)y_{t - 1} + \alpha x_t\end{split}\]

min_periods

Minimum number of observations in window required to have a value (otherwise result is null).

ignore_nulls

Ignore missing values when calculating weights.

When ignore_nulls=False (default), weights are based on absolute positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $(1-\alpha)^2$ and $1$ if adjust=True, and $(1-\alpha)^2$ and $\alpha$ if adjust=False.

When ignore_nulls=True, weights are based on relative positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $1-\alpha$ and $1$ if adjust=True, and $1-\alpha$ and $\alpha$ if adjust=False.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").ewm_mean(com=1))
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.0      │
│ 1.666667 │
│ 2.428571 │
└──────────┘

ewm_std( com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True, ) → Self[source]

Exponentially-weighted moving standard deviation.

Parameters:

com

Specify decay in terms of center of mass, $\gamma$, with

\[\alpha = \frac{1}{1 + \gamma} \; \forall \; \gamma \geq 0\]

span

Specify decay in terms of span, $\theta$, with

\[\alpha = \frac{2}{\theta + 1} \; \forall \; \theta \geq 1\]

half_life

Specify decay in terms of half-life, $\lambda$, with

\[\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \lambda } \right\} \; \forall \; \lambda > 0\]

alpha

Specify smoothing factor alpha directly, $0 < \alpha \leq 1$.

adjust

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

When adjust=True the EW function is calculated using weights $w_i = (1 - \alpha)^i$

When adjust=False the EW function is calculated recursively by

\[\begin{split}y_0 &= x_0 \\ y_t &= (1 - \alpha)y_{t - 1} + \alpha x_t\end{split}\]

bias

When bias=False, apply a correction to make the estimate statistically unbiased.

min_periods

Minimum number of observations in window required to have a value (otherwise result is null).

ignore_nulls

Ignore missing values when calculating weights.

When ignore_nulls=False (default), weights are based on absolute positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $(1-\alpha)^2$ and $1$ if adjust=True, and $(1-\alpha)^2$ and $\alpha$ if adjust=False.

When ignore_nulls=True, weights are based on relative positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $1-\alpha$ and $1$ if adjust=True, and $1-\alpha$ and $\alpha$ if adjust=False.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").ewm_std(com=1))
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.0      │
│ 0.707107 │
│ 0.963624 │
└──────────┘

ewm_var( com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True, ) → Self[source]

Exponentially-weighted moving variance.

Parameters:

com

Specify decay in terms of center of mass, $\gamma$, with

\[\alpha = \frac{1}{1 + \gamma} \; \forall \; \gamma \geq 0\]

span

Specify decay in terms of span, $\theta$, with

\[\alpha = \frac{2}{\theta + 1} \; \forall \; \theta \geq 1\]

half_life

Specify decay in terms of half-life, $\lambda$, with

\[\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \lambda } \right\} \; \forall \; \lambda > 0\]

alpha

Specify smoothing factor alpha directly, $0 < \alpha \leq 1$.

adjust

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

When adjust=True the EW function is calculated using weights $w_i = (1 - \alpha)^i$

When adjust=False the EW function is calculated recursively by

\[\begin{split}y_0 &= x_0 \\ y_t &= (1 - \alpha)y_{t - 1} + \alpha x_t\end{split}\]

bias

When bias=False, apply a correction to make the estimate statistically unbiased.

min_periods

Minimum number of observations in window required to have a value (otherwise result is null).

ignore_nulls

Ignore missing values when calculating weights.

When ignore_nulls=False (default), weights are based on absolute positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $(1-\alpha)^2$ and $1$ if adjust=True, and $(1-\alpha)^2$ and $\alpha$ if adjust=False.

When ignore_nulls=True, weights are based on relative positions. For example, the weights of $x_0$ and $x_2$ used in calculating the final weighted average of [$x_0$, None, $x_2$] are $1-\alpha$ and $1$ if adjust=True, and $1-\alpha$ and $\alpha$ if adjust=False.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").ewm_var(com=1))
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.0      │
│ 0.5      │
│ 0.928571 │
└──────────┘

exclude( columns: str | PolarsDataType | Collection[str] | Collection[PolarsDataType], *more_columns: str | PolarsDataType, ) → Self[source]

Exclude columns from a multi-column expression.

Only works after a wildcard or regex column selection, and you cannot provide both string column names and dtypes (you may prefer to use selectors instead).

Parameters:

columns: The name or datatype of the column(s) to exclude. Accepts regular expression input. Regular expressions should start with ^ and end with $.
*more_columns: Additional names or datatypes of columns to exclude, specified as positional arguments.

Examples

>>> df = pl.DataFrame(
...     {
...         "aa": [1, 2, 3],
...         "ba": ["a", "b", None],
...         "cc": [None, 2.5, 1.5],
...     }
... )
>>> df
shape: (3, 3)
┌─────┬──────┬──────┐
│ aa  ┆ ba   ┆ cc   │
│ --- ┆ ---  ┆ ---  │
│ i64 ┆ str  ┆ f64  │
╞═════╪══════╪══════╡
│ 1   ┆ a    ┆ null │
│ 2   ┆ b    ┆ 2.5  │
│ 3   ┆ null ┆ 1.5  │
└─────┴──────┴──────┘

Exclude by column name(s):

>>> df.select(pl.all().exclude("ba"))
shape: (3, 2)
┌─────┬──────┐
│ aa  ┆ cc   │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ null │
│ 2   ┆ 2.5  │
│ 3   ┆ 1.5  │
└─────┴──────┘

Exclude by regex, e.g. removing all columns whose names end with the letter “a”:

>>> df.select(pl.all().exclude("^.*a$"))
shape: (3, 1)
┌──────┐
│ cc   │
│ ---  │
│ f64  │
╞══════╡
│ null │
│ 2.5  │
│ 1.5  │
└──────┘

Exclude by dtype(s), e.g. removing all columns of type Int64 or Float64:

>>> df.select(pl.all().exclude([pl.Int64, pl.Float64]))
shape: (3, 1)
┌──────┐
│ ba   │
│ ---  │
│ str  │
╞══════╡
│ a    │
│ b    │
│ null │
└──────┘

exp() → Self[source]

Compute the exponential, element-wise.

Examples

>>> df = pl.DataFrame({"values": [1.0, 2.0, 4.0]})
>>> df.select(pl.col("values").exp())
shape: (3, 1)
┌──────────┐
│ values   │
│ ---      │
│ f64      │
╞══════════╡
│ 2.718282 │
│ 7.389056 │
│ 54.59815 │
└──────────┘

explode() → Self[source]

Explode a list expression.

This means that every item is expanded to a new row.

Returns:

Expr: Expression with the data type of the list elements.

See also

Expr.list.explode: Explode a list column.
Expr.str.explode: Explode a string column.

Examples

>>> df = pl.DataFrame(
...     {
...         "group": ["a", "b"],
...         "values": [
...             [1, 2],
...             [3, 4],
...         ],
...     }
... )
>>> df.select(pl.col("values").explode())
shape: (4, 1)
┌────────┐
│ values │
│ ---    │
│ i64    │
╞════════╡
│ 1      │
│ 2      │
│ 3      │
│ 4      │
└────────┘

extend_constant(value: PythonLiteral | None, n: int) → Self[source]

Extremely fast method for extending the Series with ‘n’ copies of a value.

Parameters:

value: A constant literal value (not an expression) with which to extend the expression result Series; can pass None to extend with nulls.
n: The number of additional values that will be added.

Examples

>>> df = pl.DataFrame({"values": [1, 2, 3]})
>>> df.select((pl.col("values") - 1).extend_constant(99, n=2))
shape: (5, 1)
┌────────┐
│ values │
│ ---    │
│ i64    │
╞════════╡
│ 0      │
│ 1      │
│ 2      │
│ 99     │
│ 99     │
└────────┘

fill_nan(value: int | float | Expr | None) → Self[source]

Fill floating point NaN value with a fill value.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1.0, None, float("nan")],
...         "b": [4.0, float("nan"), 6],
...     }
... )
>>> df.with_columns(pl.col("b").fill_nan(0))
shape: (3, 2)
┌──────┬─────┐
│ a    ┆ b   │
│ ---  ┆ --- │
│ f64  ┆ f64 │
╞══════╪═════╡
│ 1.0  ┆ 4.0 │
│ null ┆ 0.0 │
│ NaN  ┆ 6.0 │
└──────┴─────┘

fill_null( value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, ) → Self[source]

Fill null values using the specified value or strategy.

To interpolate over null values see interpolate. See the examples below to fill nulls with an expression.

Parameters:

value: Value used to fill null values.
strategy{None, ‘forward’, ‘backward’, ‘min’, ‘max’, ‘mean’, ‘zero’, ‘one’}: Strategy used to fill null values.
limit: Number of consecutive null values to fill when using the ‘forward’ or ‘backward’ strategy.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None],
...         "b": [4, None, 6],
...     }
... )
>>> df.with_columns(pl.col("b").fill_null(strategy="zero"))
shape: (3, 2)
┌──────┬─────┐
│ a    ┆ b   │
│ ---  ┆ --- │
│ i64  ┆ i64 │
╞══════╪═════╡
│ 1    ┆ 4   │
│ 2    ┆ 0   │
│ null ┆ 6   │
└──────┴─────┘
>>> df.with_columns(pl.col("b").fill_null(99))
shape: (3, 2)
┌──────┬─────┐
│ a    ┆ b   │
│ ---  ┆ --- │
│ i64  ┆ i64 │
╞══════╪═════╡
│ 1    ┆ 4   │
│ 2    ┆ 99  │
│ null ┆ 6   │
└──────┴─────┘
>>> df.with_columns(pl.col("b").fill_null(strategy="forward"))
shape: (3, 2)
┌──────┬─────┐
│ a    ┆ b   │
│ ---  ┆ --- │
│ i64  ┆ i64 │
╞══════╪═════╡
│ 1    ┆ 4   │
│ 2    ┆ 4   │
│ null ┆ 6   │
└──────┴─────┘
>>> df.with_columns(pl.col("b").fill_null(pl.col("b").median()))
shape: (3, 2)
┌──────┬─────┐
│ a    ┆ b   │
│ ---  ┆ --- │
│ i64  ┆ f64 │
╞══════╪═════╡
│ 1    ┆ 4.0 │
│ 2    ┆ 5.0 │
│ null ┆ 6.0 │
└──────┴─────┘
>>> df.with_columns(pl.all().fill_null(pl.all().median()))
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞═════╪═════╡
│ 1.0 ┆ 4.0 │
│ 2.0 ┆ 5.0 │
│ 1.5 ┆ 6.0 │
└─────┴─────┘

filter(predicate: Expr) → Self[source]

Filter a single column.

Mostly useful in an aggregation context. If you want to filter on a DataFrame level, use LazyFrame.filter.

Parameters:

predicate: Boolean expression.

Examples

>>> df = pl.DataFrame(
...     {
...         "group_col": ["g1", "g1", "g2"],
...         "b": [1, 2, 3],
...     }
... )
>>> df.groupby("group_col").agg(
...     [
...         pl.col("b").filter(pl.col("b") < 2).sum().alias("lt"),
...         pl.col("b").filter(pl.col("b") >= 2).sum().alias("gte"),
...     ]
... ).sort("group_col")
shape: (2, 3)
┌───────────┬─────┬─────┐
│ group_col ┆ lt  ┆ gte │
│ ---       ┆ --- ┆ --- │
│ str       ┆ i64 ┆ i64 │
╞═══════════╪═════╪═════╡
│ g1        ┆ 1   ┆ 2   │
│ g2        ┆ 0   ┆ 3   │
└───────────┴─────┴─────┘

first() → Self[source]

Get the first value.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").first())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 1   │
└─────┘

flatten() → Self[source]

Flatten a list or string column.

Alias for polars.expr.list.ExprListNameSpace.explode().

Examples

>>> df = pl.DataFrame(
...     {
...         "group": ["a", "b", "b"],
...         "values": [[1, 2], [2, 3], [4]],
...     }
... )
>>> df.groupby("group").agg(pl.col("values").flatten())  
shape: (2, 2)
┌───────┬───────────┐
│ group ┆ values    │
│ ---   ┆ ---       │
│ str   ┆ list[i64] │
╞═══════╪═══════════╡
│ a     ┆ [1, 2]    │
│ b     ┆ [2, 3, 4] │
└───────┴───────────┘

floor() → Self[source]

Rounds down to the nearest integer value.

Only works on floating point Series.

Examples

>>> df = pl.DataFrame({"a": [0.3, 0.5, 1.0, 1.1]})
>>> df.select(pl.col("a").floor())
shape: (4, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.0 │
│ 0.0 │
│ 1.0 │
│ 1.0 │
└─────┘

floordiv(other: Any) → Self[source]

Method equivalent of integer division operator expr // other.

Parameters:

other: Numeric literal or expression value.

See also

truediv

Examples

>>> df = pl.DataFrame({"x": [1, 2, 3, 4, 5]})
>>> df.with_columns(
...     pl.col("x").truediv(2).alias("x/2"),
...     pl.col("x").floordiv(2).alias("x//2"),
... )
shape: (5, 3)
┌─────┬─────┬──────┐
│ x   ┆ x/2 ┆ x//2 │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ f64 ┆ i64  │
╞═════╪═════╪══════╡
│ 1   ┆ 0.5 ┆ 0    │
│ 2   ┆ 1.0 ┆ 1    │
│ 3   ┆ 1.5 ┆ 1    │
│ 4   ┆ 2.0 ┆ 2    │
│ 5   ┆ 2.5 ┆ 2    │
└─────┴─────┴──────┘

forward_fill(limit: int | None = None) → Self[source]

Fill missing values with the latest seen values.

Parameters:

limit: The number of consecutive null values to forward fill.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None],
...         "b": [4, None, 6],
...     }
... )
>>> df.select(pl.all().forward_fill())
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 4   │
│ 2   ┆ 4   │
│ 2   ┆ 6   │
└─────┴─────┘

classmethod from_json(value: str) → Self[source]

Read an expression from a JSON encoded string to construct an Expression.

Parameters:

value: JSON encoded string value

ge(other: Any) → Self[source]

Method equivalent of “greater than or equal” operator expr >= other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [5.0, 4.0, float("nan"), 2.0],
...         "y": [5.0, 3.0, float("nan"), 1.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").ge(pl.col("y")).alias("x >= y"),
... )
shape: (4, 3)
┌─────┬─────┬────────┐
│ x   ┆ y   ┆ x >= y │
│ --- ┆ --- ┆ ---    │
│ f64 ┆ f64 ┆ bool   │
╞═════╪═════╪════════╡
│ 5.0 ┆ 5.0 ┆ true   │
│ 4.0 ┆ 3.0 ┆ true   │
│ NaN ┆ NaN ┆ false  │
│ 2.0 ┆ 1.0 ┆ true   │
└─────┴─────┴────────┘

gt(other: Any) → Self[source]

Method equivalent of “greater than” operator expr > other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [5.0, 4.0, float("nan"), 2.0],
...         "y": [5.0, 3.0, float("nan"), 1.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").gt(pl.col("y")).alias("x > y"),
... )
shape: (4, 3)
┌─────┬─────┬───────┐
│ x   ┆ y   ┆ x > y │
│ --- ┆ --- ┆ ---   │
│ f64 ┆ f64 ┆ bool  │
╞═════╪═════╪═══════╡
│ 5.0 ┆ 5.0 ┆ false │
│ 4.0 ┆ 3.0 ┆ true  │
│ NaN ┆ NaN ┆ false │
│ 2.0 ┆ 1.0 ┆ true  │
└─────┴─────┴───────┘

hash( seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None, ) → Self[source]

Hash the elements in the selection.

The hash value is of type UInt64.

Parameters:

seed: Random seed parameter. Defaults to 0.
seed_1: Random seed parameter. Defaults to seed if not set.
seed_2: Random seed parameter. Defaults to seed if not set.
seed_3: Random seed parameter. Defaults to seed if not set.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None],
...         "b": ["x", None, "z"],
...     }
... )
>>> df.with_columns(pl.all().hash(10, 20, 30, 40))  
shape: (3, 2)
┌──────────────────────┬──────────────────────┐
│ a                    ┆ b                    │
│ ---                  ┆ ---                  │
│ u64                  ┆ u64                  │
╞══════════════════════╪══════════════════════╡
│ 9774092659964970114  ┆ 13614470193936745724 │
│ 1101441246220388612  ┆ 11638928888656214026 │
│ 11638928888656214026 ┆ 13382926553367784577 │
└──────────────────────┴──────────────────────┘

head(n: int | Expr = 10) → Self[source]

Get the first n rows.

Parameters:

n: Number of rows to return.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7]})
>>> df.head(3)
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

implode() → Self[source]

Aggregate values into a list.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [4, 5, 6],
...     }
... )
>>> df.select(pl.all().implode())
shape: (1, 2)
┌───────────┬───────────┐
│ a         ┆ b         │
│ ---       ┆ ---       │
│ list[i64] ┆ list[i64] │
╞═══════════╪═══════════╡
│ [1, 2, 3] ┆ [4, 5, 6] │
└───────────┴───────────┘

inspect(fmt: str = '{}') → Self[source]

Print the value that this expression evaluates to and pass on the value.

Examples

>>> df = pl.DataFrame({"foo": [1, 1, 2]})
>>> df.select(pl.col("foo").cumsum().inspect("value is: {}").alias("bar"))
value is: shape: (3,)
Series: 'foo' [i64]
[
    1
    2
    4
]
shape: (3, 1)
┌─────┐
│ bar │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 4   │
└─────┘

interpolate(method: InterpolationMethod = 'linear') → Self[source]

Fill null values using interpolation.

Parameters:

method{‘linear’, ‘nearest’}: Interpolation method.

Examples

Fill null values using linear interpolation.

>>> df = pl.DataFrame(
...     {
...         "a": [1, None, 3],
...         "b": [1.0, float("nan"), 3.0],
...     }
... )
>>> df.select(pl.all().interpolate())
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 1   ┆ 1.0 │
│ 2   ┆ NaN │
│ 3   ┆ 3.0 │
└─────┴─────┘

Fill null values using nearest interpolation.

>>> df.select(pl.all().interpolate("nearest"))
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 1   ┆ 1.0 │
│ 3   ┆ NaN │
│ 3   ┆ 3.0 │
└─────┴─────┘

Regrid data to a new grid.

>>> df_original_grid = pl.DataFrame(
...     {
...         "grid_points": [1, 3, 10],
...         "values": [2.0, 6.0, 20.0],
...     }
... )  # Interpolate from this to the new grid
>>> df_new_grid = pl.DataFrame({"grid_points": range(1, 11)})
>>> df_new_grid.join(
...     df_original_grid, on="grid_points", how="left"
... ).with_columns(pl.col("values").interpolate())
shape: (10, 2)
┌─────────────┬────────┐
│ grid_points ┆ values │
│ ---         ┆ ---    │
│ i64         ┆ f64    │
╞═════════════╪════════╡
│ 1           ┆ 2.0    │
│ 2           ┆ 4.0    │
│ 3           ┆ 6.0    │
│ 4           ┆ 8.0    │
│ …           ┆ …      │
│ 7           ┆ 14.0   │
│ 8           ┆ 16.0   │
│ 9           ┆ 18.0   │
│ 10          ┆ 20.0   │
└─────────────┴────────┘

is_between( lower_bound: IntoExpr, upper_bound: IntoExpr, closed: ClosedInterval = 'both', ) → Self[source]

Check if this expression is between the given start and end values.

Parameters:

lower_bound: Lower bound value. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.
upper_bound: Upper bound value. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.
closed{‘both’, ‘left’, ‘right’, ‘none’}: Define which sides of the interval are closed (inclusive).

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame({"num": [1, 2, 3, 4, 5]})
>>> df.with_columns(pl.col("num").is_between(2, 4).alias("is_between"))
shape: (5, 2)
┌─────┬────────────┐
│ num ┆ is_between │
│ --- ┆ ---        │
│ i64 ┆ bool       │
╞═════╪════════════╡
│ 1   ┆ false      │
│ 2   ┆ true       │
│ 3   ┆ true       │
│ 4   ┆ true       │
│ 5   ┆ false      │
└─────┴────────────┘

Use the closed argument to include or exclude the values at the bounds:

>>> df.with_columns(
...     pl.col("num").is_between(2, 4, closed="left").alias("is_between")
... )
shape: (5, 2)
┌─────┬────────────┐
│ num ┆ is_between │
│ --- ┆ ---        │
│ i64 ┆ bool       │
╞═════╪════════════╡
│ 1   ┆ false      │
│ 2   ┆ true       │
│ 3   ┆ true       │
│ 4   ┆ false      │
│ 5   ┆ false      │
└─────┴────────────┘

You can also use strings as well as numeric/temporal values (note: ensure that string literals are wrapped with lit so as not to conflate them with column names):

>>> df = pl.DataFrame({"a": ["a", "b", "c", "d", "e"]})
>>> df.with_columns(
...     pl.col("a")
...     .is_between(pl.lit("a"), pl.lit("c"), closed="both")
...     .alias("is_between")
... )
shape: (5, 2)
┌─────┬────────────┐
│ a   ┆ is_between │
│ --- ┆ ---        │
│ str ┆ bool       │
╞═════╪════════════╡
│ a   ┆ true       │
│ b   ┆ true       │
│ c   ┆ true       │
│ d   ┆ false      │
│ e   ┆ false      │
└─────┴────────────┘

is_duplicated() → Self[source]

Get mask of duplicated values.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").is_duplicated())
shape: (3, 1)
┌───────┐
│ a     │
│ ---   │
│ bool  │
╞═══════╡
│ true  │
│ true  │
│ false │
└───────┘

is_finite() → Self[source]

Returns a boolean Series indicating which values are finite.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1.0, 2],
...         "B": [3.0, float("inf")],
...     }
... )
>>> df.select(pl.all().is_finite())
shape: (2, 2)
┌──────┬───────┐
│ A    ┆ B     │
│ ---  ┆ ---   │
│ bool ┆ bool  │
╞══════╪═══════╡
│ true ┆ true  │
│ true ┆ false │
└──────┴───────┘

is_first() → Self[source]

Get a mask of the first unique value.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame(
...     {
...         "num": [1, 2, 3, 1, 5],
...     }
... )
>>> df.with_columns(pl.col("num").is_first().alias("is_first"))
shape: (5, 2)
┌─────┬──────────┐
│ num ┆ is_first │
│ --- ┆ ---      │
│ i64 ┆ bool     │
╞═════╪══════════╡
│ 1   ┆ true     │
│ 2   ┆ true     │
│ 3   ┆ true     │
│ 1   ┆ false    │
│ 5   ┆ true     │
└─────┴──────────┘

is_in(other: Expr | Collection[Any] | Series) → Self[source]

Check if elements of this expression are present in the other Series.

Parameters:

other: Series or sequence of primitive type.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame(
...     {"sets": [[1, 2, 3], [1, 2], [9, 10]], "optional_members": [1, 2, 3]}
... )
>>> df.select([pl.col("optional_members").is_in("sets").alias("contains")])
shape: (3, 1)
┌──────────┐
│ contains │
│ ---      │
│ bool     │
╞══════════╡
│ true     │
│ true     │
│ false    │
└──────────┘

is_infinite() → Self[source]

Returns a boolean Series indicating which values are infinite.

Returns:

Expr: Expression of data type Boolean.

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1.0, 2],
...         "B": [3.0, float("inf")],
...     }
... )
>>> df.select(pl.all().is_infinite())
shape: (2, 2)
┌───────┬───────┐
│ A     ┆ B     │
│ ---   ┆ ---   │
│ bool  ┆ bool  │
╞═══════╪═══════╡
│ false ┆ false │
│ false ┆ true  │
└───────┴───────┘

is_nan() → Self[source]

Returns a boolean Series indicating which values are NaN.

Notes

Floating point `NaN (Not A Number) should not be confused with missing data represented as Null/None.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 1, 5],
...         "b": [1.0, 2.0, float("nan"), 1.0, 5.0],
...     }
... )
>>> df.with_columns(pl.col(pl.Float64).is_nan().suffix("_isnan"))
shape: (5, 3)
┌──────┬─────┬─────────┐
│ a    ┆ b   ┆ b_isnan │
│ ---  ┆ --- ┆ ---     │
│ i64  ┆ f64 ┆ bool    │
╞══════╪═════╪═════════╡
│ 1    ┆ 1.0 ┆ false   │
│ 2    ┆ 2.0 ┆ false   │
│ null ┆ NaN ┆ true    │
│ 1    ┆ 1.0 ┆ false   │
│ 5    ┆ 5.0 ┆ false   │
└──────┴─────┴─────────┘

is_not() → Self[source]

Negate a boolean expression.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [True, False, False],
...         "b": ["a", "b", None],
...     }
... )
>>> df
shape: (3, 2)
┌───────┬──────┐
│ a     ┆ b    │
│ ---   ┆ ---  │
│ bool  ┆ str  │
╞═══════╪══════╡
│ true  ┆ a    │
│ false ┆ b    │
│ false ┆ null │
└───────┴──────┘
>>> df.select(pl.col("a").is_not())
shape: (3, 1)
┌───────┐
│ a     │
│ ---   │
│ bool  │
╞═══════╡
│ false │
│ true  │
│ true  │
└───────┘

is_not_nan() → Self[source]

Returns a boolean Series indicating which values are not NaN.

Notes

Floating point `NaN (Not A Number) should not be confused with missing data represented as Null/None.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 1, 5],
...         "b": [1.0, 2.0, float("nan"), 1.0, 5.0],
...     }
... )
>>> df.with_columns(pl.col(pl.Float64).is_not_nan().suffix("_is_not_nan"))
shape: (5, 3)
┌──────┬─────┬──────────────┐
│ a    ┆ b   ┆ b_is_not_nan │
│ ---  ┆ --- ┆ ---          │
│ i64  ┆ f64 ┆ bool         │
╞══════╪═════╪══════════════╡
│ 1    ┆ 1.0 ┆ true         │
│ 2    ┆ 2.0 ┆ true         │
│ null ┆ NaN ┆ false        │
│ 1    ┆ 1.0 ┆ true         │
│ 5    ┆ 5.0 ┆ true         │
└──────┴─────┴──────────────┘

is_not_null() → Self[source]

Returns a boolean Series indicating which values are not null.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 1, 5],
...         "b": [1.0, 2.0, float("nan"), 1.0, 5.0],
...     }
... )
>>> df.with_columns(pl.all().is_not_null().suffix("_not_null"))  # nan != null
shape: (5, 4)
┌──────┬─────┬────────────┬────────────┐
│ a    ┆ b   ┆ a_not_null ┆ b_not_null │
│ ---  ┆ --- ┆ ---        ┆ ---        │
│ i64  ┆ f64 ┆ bool       ┆ bool       │
╞══════╪═════╪════════════╪════════════╡
│ 1    ┆ 1.0 ┆ true       ┆ true       │
│ 2    ┆ 2.0 ┆ true       ┆ true       │
│ null ┆ NaN ┆ false      ┆ true       │
│ 1    ┆ 1.0 ┆ true       ┆ true       │
│ 5    ┆ 5.0 ┆ true       ┆ true       │
└──────┴─────┴────────────┴────────────┘

is_null() → Self[source]

Returns a boolean Series indicating which values are null.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 1, 5],
...         "b": [1.0, 2.0, float("nan"), 1.0, 5.0],
...     }
... )
>>> df.with_columns(pl.all().is_null().suffix("_isnull"))  # nan != null
shape: (5, 4)
┌──────┬─────┬──────────┬──────────┐
│ a    ┆ b   ┆ a_isnull ┆ b_isnull │
│ ---  ┆ --- ┆ ---      ┆ ---      │
│ i64  ┆ f64 ┆ bool     ┆ bool     │
╞══════╪═════╪══════════╪══════════╡
│ 1    ┆ 1.0 ┆ false    ┆ false    │
│ 2    ┆ 2.0 ┆ false    ┆ false    │
│ null ┆ NaN ┆ true     ┆ false    │
│ 1    ┆ 1.0 ┆ false    ┆ false    │
│ 5    ┆ 5.0 ┆ false    ┆ false    │
└──────┴─────┴──────────┴──────────┘

is_unique() → Self[source]

Get mask of unique values.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").is_unique())
shape: (3, 1)
┌───────┐
│ a     │
│ ---   │
│ bool  │
╞═══════╡
│ false │
│ false │
│ true  │
└───────┘

keep_name() → Self[source]

Keep the original root name of the expression.

See also

alias

Notes

Due to implementation constraints, this method can only be called as the last expression in a chain.

Examples

Undo an alias operation.

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2],
...         "b": [3, 4],
...     }
... )
>>> df.with_columns((pl.col("a") * 9).alias("c").keep_name())
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 9   ┆ 3   │
│ 18  ┆ 4   │
└─────┴─────┘

Prevent errors due to duplicate column names.

>>> df.select((pl.lit(10) / pl.all()).keep_name())
shape: (2, 2)
┌──────┬──────────┐
│ a    ┆ b        │
│ ---  ┆ ---      │
│ f64  ┆ f64      │
╞══════╪══════════╡
│ 10.0 ┆ 3.333333 │
│ 5.0  ┆ 2.5      │
└──────┴──────────┘

kurtosis(*, fisher: bool = True, bias: bool = True) → Self[source]

Compute the kurtosis (Fisher or Pearson) of a dataset.

Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution. If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators

See scipy.stats for more information

Parameters:

fisherbool, optional: If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0).
biasbool, optional: If False, the calculations are corrected for statistical bias.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 2, 1]})
>>> df.select(pl.col("a").kurtosis())
shape: (1, 1)
┌───────────┐
│ a         │
│ ---       │
│ f64       │
╞═══════════╡
│ -1.153061 │
└───────────┘

last() → Self[source]

Get the last value.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").last())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 2   │
└─────┘

le(other: Any) → Self[source]

Method equivalent of “less than or equal” operator expr <= other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [5.0, 4.0, float("nan"), 0.5],
...         "y": [5.0, 3.5, float("nan"), 2.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").le(pl.col("y")).alias("x <= y"),
... )
shape: (4, 3)
┌─────┬─────┬────────┐
│ x   ┆ y   ┆ x <= y │
│ --- ┆ --- ┆ ---    │
│ f64 ┆ f64 ┆ bool   │
╞═════╪═════╪════════╡
│ 5.0 ┆ 5.0 ┆ true   │
│ 4.0 ┆ 3.5 ┆ false  │
│ NaN ┆ NaN ┆ false  │
│ 0.5 ┆ 2.0 ┆ true   │
└─────┴─────┴────────┘

len() → Self[source]

Count the number of values in this expression.

Alias for count().

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10],
...         "b": [None, 4, 4],
...     }
... )
>>> df.select(pl.all().len())  # counts nulls
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 3   ┆ 3   │
└─────┴─────┘

limit(n: int | Expr = 10) → Self[source]

Get the first n rows (alias for Expr.head()).

Parameters:

n: Number of rows to return.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7]})
>>> df.limit(3)
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

log(base: float = 2.718281828459045) → Self[source]

Compute the logarithm to a given base.

Parameters:

base: Given base, defaults to e

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").log(base=2))
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.0      │
│ 1.0      │
│ 1.584963 │
└──────────┘

log10() → Self[source]

Compute the base 10 logarithm of the input array, element-wise.

Examples

>>> df = pl.DataFrame({"values": [1.0, 2.0, 4.0]})
>>> df.select(pl.col("values").log10())
shape: (3, 1)
┌─────────┐
│ values  │
│ ---     │
│ f64     │
╞═════════╡
│ 0.0     │
│ 0.30103 │
│ 0.60206 │
└─────────┘

log1p() → Self[source]

Compute the natural logarithm of each element plus one.

This computes log(1 + x) but is more numerically stable for x close to zero.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").log1p())
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.693147 │
│ 1.098612 │
│ 1.386294 │
└──────────┘

lower_bound() → Self[source]

Calculate the lower bound.

Returns a unit Series with the lowest value possible for the dtype of this expression.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 2, 1]})
>>> df.select(pl.col("a").lower_bound())
shape: (1, 1)
┌──────────────────────┐
│ a                    │
│ ---                  │
│ i64                  │
╞══════════════════════╡
│ -9223372036854775808 │
└──────────────────────┘

lt(other: Any) → Self[source]

Method equivalent of “less than” operator expr < other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [1.0, 2.0, float("nan"), 3.0],
...         "y": [2.0, 2.0, float("nan"), 4.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").lt(pl.col("y")).alias("x < y"),
... )
shape: (4, 3)
┌─────┬─────┬───────┐
│ x   ┆ y   ┆ x < y │
│ --- ┆ --- ┆ ---   │
│ f64 ┆ f64 ┆ bool  │
╞═════╪═════╪═══════╡
│ 1.0 ┆ 2.0 ┆ true  │
│ 2.0 ┆ 2.0 ┆ false │
│ NaN ┆ NaN ┆ false │
│ 3.0 ┆ 4.0 ┆ true  │
└─────┴─────┴───────┘

map( function: Callable[[Series], Series | Any], return_dtype: PolarsDataType | None = None, *, agg_list: bool = False, ) → Self[source]

Apply a custom python function to a Series or sequence of Series.

The output of this custom function must be a Series. If you want to apply a custom function elementwise over single values, see apply(). A use case for map is when you want to transform an expression with a third-party library.

See also

map_dict

Examples

>>> df = pl.DataFrame(
...     {
...         "sine": [0.0, 1.0, 0.0, -1.0],
...         "cosine": [1.0, 0.0, -1.0, 0.0],
...     }
... )
>>> df.select(pl.all().map(lambda x: x.to_numpy().argmax()))
shape: (1, 2)
┌──────┬────────┐
│ sine ┆ cosine │
│ ---  ┆ ---    │
│ i64  ┆ i64    │
╞══════╪════════╡
│ 1    ┆ 0      │
└──────┴────────┘

map_alias(function: Callable[[str], str]) → Self[source]

Rename the output of an expression by mapping a function over the root name.

Parameters:

function: Function that maps a root name to a new name.

See also

alias
prefix
suffix

Examples

Remove a common suffix and convert to lower case.

>>> df = pl.DataFrame(
...     {
...         "A_reverse": [3, 2, 1],
...         "B_reverse": ["z", "y", "x"],
...     }
... )
>>> df.with_columns(
...     pl.all().reverse().map_alias(lambda c: c.rstrip("_reverse").lower())
... )
shape: (3, 4)
┌───────────┬───────────┬─────┬─────┐
│ A_reverse ┆ B_reverse ┆ a   ┆ b   │
│ ---       ┆ ---       ┆ --- ┆ --- │
│ i64       ┆ str       ┆ i64 ┆ str │
╞═══════════╪═══════════╪═════╪═════╡
│ 3         ┆ z         ┆ 1   ┆ x   │
│ 2         ┆ y         ┆ 2   ┆ y   │
│ 1         ┆ x         ┆ 3   ┆ z   │
└───────────┴───────────┴─────┴─────┘

map_dict( remapping: dict[Any, Any], *, default: Any = None, return_dtype: PolarsDataType | None = None, ) → Self[source]

Replace values in column according to remapping dictionary.

Needs a global string cache for lazily evaluated queries on columns of type pl.Categorical.

Parameters:

remapping: Dictionary containing the before/after values to map.
default: Value to use when the remapping dict does not contain the lookup value. Accepts expression input. Non-expression inputs are parsed as literals. Use pl.first(), to keep the original value.
return_dtype: Set return dtype to override automatic return dtype determination.

See also

map

Examples

>>> country_code_dict = {
...     "CA": "Canada",
...     "DE": "Germany",
...     "FR": "France",
...     None: "Not specified",
... }
>>> df = pl.DataFrame(
...     {
...         "country_code": ["FR", None, "ES", "DE"],
...     }
... ).with_row_count()
>>> df
shape: (4, 2)
┌────────┬──────────────┐
│ row_nr ┆ country_code │
│ ---    ┆ ---          │
│ u32    ┆ str          │
╞════════╪══════════════╡
│ 0      ┆ FR           │
│ 1      ┆ null         │
│ 2      ┆ ES           │
│ 3      ┆ DE           │
└────────┴──────────────┘

>>> df.with_columns(
...     pl.col("country_code").map_dict(country_code_dict).alias("remapped")
... )
shape: (4, 3)
┌────────┬──────────────┬───────────────┐
│ row_nr ┆ country_code ┆ remapped      │
│ ---    ┆ ---          ┆ ---           │
│ u32    ┆ str          ┆ str           │
╞════════╪══════════════╪═══════════════╡
│ 0      ┆ FR           ┆ France        │
│ 1      ┆ null         ┆ Not specified │
│ 2      ┆ ES           ┆ null          │
│ 3      ┆ DE           ┆ Germany       │
└────────┴──────────────┴───────────────┘

Set a default value for values that cannot be mapped…

>>> df.with_columns(
...     pl.col("country_code")
...     .map_dict(country_code_dict, default="unknown")
...     .alias("remapped")
... )
shape: (4, 3)
┌────────┬──────────────┬───────────────┐
│ row_nr ┆ country_code ┆ remapped      │
│ ---    ┆ ---          ┆ ---           │
│ u32    ┆ str          ┆ str           │
╞════════╪══════════════╪═══════════════╡
│ 0      ┆ FR           ┆ France        │
│ 1      ┆ null         ┆ Not specified │
│ 2      ┆ ES           ┆ unknown       │
│ 3      ┆ DE           ┆ Germany       │
└────────┴──────────────┴───────────────┘

…or keep the original value, by making use of pl.first():

>>> df.with_columns(
...     pl.col("country_code")
...     .map_dict(country_code_dict, default=pl.first())
...     .alias("remapped")
... )
shape: (4, 3)
┌────────┬──────────────┬───────────────┐
│ row_nr ┆ country_code ┆ remapped      │
│ ---    ┆ ---          ┆ ---           │
│ u32    ┆ str          ┆ str           │
╞════════╪══════════════╪═══════════════╡
│ 0      ┆ FR           ┆ France        │
│ 1      ┆ null         ┆ Not specified │
│ 2      ┆ ES           ┆ ES            │
│ 3      ┆ DE           ┆ Germany       │
└────────┴──────────────┴───────────────┘

…or keep the original value, by explicitly referring to the column:

>>> df.with_columns(
...     pl.col("country_code")
...     .map_dict(country_code_dict, default=pl.col("country_code"))
...     .alias("remapped")
... )
shape: (4, 3)
┌────────┬──────────────┬───────────────┐
│ row_nr ┆ country_code ┆ remapped      │
│ ---    ┆ ---          ┆ ---           │
│ u32    ┆ str          ┆ str           │
╞════════╪══════════════╪═══════════════╡
│ 0      ┆ FR           ┆ France        │
│ 1      ┆ null         ┆ Not specified │
│ 2      ┆ ES           ┆ ES            │
│ 3      ┆ DE           ┆ Germany       │
└────────┴──────────────┴───────────────┘

If you need to access different columns to set a default value, a struct needs to be constructed; in the first field is the column that you want to remap and the rest of the fields are the other columns used in the default expression.

>>> df.with_columns(
...     pl.struct(pl.col(["country_code", "row_nr"])).map_dict(
...         remapping=country_code_dict,
...         default=pl.col("row_nr").cast(pl.Utf8),
...     )
... )
shape: (4, 2)
┌────────┬───────────────┐
│ row_nr ┆ country_code  │
│ ---    ┆ ---           │
│ u32    ┆ str           │
╞════════╪═══════════════╡
│ 0      ┆ France        │
│ 1      ┆ Not specified │
│ 2      ┆ 2             │
│ 3      ┆ Germany       │
└────────┴───────────────┘

Override return dtype:

>>> df.with_columns(
...     pl.col("row_nr")
...     .map_dict({1: 7, 3: 4}, default=3, return_dtype=pl.UInt8)
...     .alias("remapped")
... )
shape: (4, 3)
┌────────┬──────────────┬──────────┐
│ row_nr ┆ country_code ┆ remapped │
│ ---    ┆ ---          ┆ ---      │
│ u32    ┆ str          ┆ u8       │
╞════════╪══════════════╪══════════╡
│ 0      ┆ FR           ┆ 3        │
│ 1      ┆ null         ┆ 7        │
│ 2      ┆ ES           ┆ 3        │
│ 3      ┆ DE           ┆ 4        │
└────────┴──────────────┴──────────┘

max() → Self[source]

Get maximum value.

Examples

>>> df = pl.DataFrame({"a": [-1, float("nan"), 1]})
>>> df.select(pl.col("a").max())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘

mean() → Self[source]

Get mean value.

Examples

>>> df = pl.DataFrame({"a": [-1, 0, 1]})
>>> df.select(pl.col("a").mean())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.0 │
└─────┘

median() → Self[source]

Get median value using linear interpolation.

Examples

>>> df = pl.DataFrame({"a": [-1, 0, 1]})
>>> df.select(pl.col("a").median())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.0 │
└─────┘

min() → Self[source]

Get minimum value.

Examples

>>> df = pl.DataFrame({"a": [-1, float("nan"), 1]})
>>> df.select(pl.col("a").min())
shape: (1, 1)
┌──────┐
│ a    │
│ ---  │
│ f64  │
╞══════╡
│ -1.0 │
└──────┘

mod(other: Any) → Self[source]

Method equivalent of modulus operator expr % other.

Parameters:

other: Numeric literal or expression value.

Examples

>>> df = pl.DataFrame({"x": [0, 1, 2, 3, 4]})
>>> df.with_columns(pl.col("x").mod(2).alias("x%2"))
shape: (5, 2)
┌─────┬─────┐
│ x   ┆ x%2 │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 0   ┆ 0   │
│ 1   ┆ 1   │
│ 2   ┆ 0   │
│ 3   ┆ 1   │
│ 4   ┆ 0   │
└─────┴─────┘

mode() → Self[source]

Compute the most occurring value(s).

Can return multiple Values.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 1, 2, 3],
...         "b": [1, 1, 2, 2],
...     }
... )
>>> df.select(pl.all().mode())  
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 1   │
│ 1   ┆ 2   │
└─────┴─────┘

mul(other: Any) → Self[source]

Method equivalent of multiplication operator expr * other.

Parameters:

other: Numeric literal or expression value.

Examples

>>> df = pl.DataFrame({"x": [1, 2, 4, 8, 16]})
>>> df.with_columns(
...     pl.col("x").mul(2).alias("x*2"),
...     pl.col("x").mul(pl.col("x").log(2)).alias("x * xlog2"),
... )
shape: (5, 3)
┌─────┬─────┬───────────┐
│ x   ┆ x*2 ┆ x * xlog2 │
│ --- ┆ --- ┆ ---       │
│ i64 ┆ i64 ┆ f64       │
╞═════╪═════╪═══════════╡
│ 1   ┆ 2   ┆ 0.0       │
│ 2   ┆ 4   ┆ 2.0       │
│ 4   ┆ 8   ┆ 8.0       │
│ 8   ┆ 16  ┆ 24.0      │
│ 16  ┆ 32  ┆ 64.0      │
└─────┴─────┴───────────┘

n_unique() → Self[source]

Count unique values.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").n_unique())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 2   │
└─────┘

nan_max() → Self[source]

Get maximum value, but propagate/poison encountered NaN values.

This differs from numpy’s nanmax as numpy defaults to propagating NaN values, whereas polars defaults to ignoring them.

Examples

>>> df = pl.DataFrame({"a": [0, float("nan")]})
>>> df.select(pl.col("a").nan_max())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ NaN │
└─────┘

nan_min() → Self[source]

Get minimum value, but propagate/poison encountered NaN values.

This differs from numpy’s nanmax as numpy defaults to propagating NaN values, whereas polars defaults to ignoring them.

Examples

>>> df = pl.DataFrame({"a": [0, float("nan")]})
>>> df.select(pl.col("a").nan_min())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ NaN │
└─────┘

ne(other: Any) → Self[source]

Method equivalent of inequality operator expr != other.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [1.0, 2.0, float("nan"), 4.0],
...         "y": [2.0, 2.0, float("nan"), 4.0],
...     }
... )
>>> df.with_columns(
...     pl.col("x").ne(pl.col("y")).alias("x != y"),
... )
shape: (4, 3)
┌─────┬─────┬────────┐
│ x   ┆ y   ┆ x != y │
│ --- ┆ --- ┆ ---    │
│ f64 ┆ f64 ┆ bool   │
╞═════╪═════╪════════╡
│ 1.0 ┆ 2.0 ┆ true   │
│ 2.0 ┆ 2.0 ┆ false  │
│ NaN ┆ NaN ┆ true   │
│ 4.0 ┆ 4.0 ┆ false  │
└─────┴─────┴────────┘

ne_missing(other: Any) → Self[source]

Method equivalent of equality operator expr != other where None == None`.

This differs from default ne where null values are propagated.

Parameters:

other: A literal or expression value to compare with.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [1.0, 2.0, float("nan"), 4.0, None, None],
...         "y": [2.0, 2.0, float("nan"), 4.0, 5.0, None],
...     }
... )
>>> df.with_columns(
...     pl.col("x").ne_missing(pl.col("y")).alias("x != y"),
... )
shape: (6, 3)
┌──────┬──────┬────────┐
│ x    ┆ y    ┆ x != y │
│ ---  ┆ ---  ┆ ---    │
│ f64  ┆ f64  ┆ bool   │
╞══════╪══════╪════════╡
│ 1.0  ┆ 2.0  ┆ true   │
│ 2.0  ┆ 2.0  ┆ false  │
│ NaN  ┆ NaN  ┆ true   │
│ 4.0  ┆ 4.0  ┆ false  │
│ null ┆ 5.0  ┆ true   │
│ null ┆ null ┆ false  │
└──────┴──────┴────────┘

null_count() → Self[source]

Count null values.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [None, 1, None],
...         "b": [1, 2, 3],
...     }
... )
>>> df.select(pl.all().null_count())
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 2   ┆ 0   │
└─────┴─────┘

or_(*others: Any) → Self[source]

Method equivalent of bitwise “or” operator expr | other | ....

Parameters:

*others: One or more integer or boolean expressions to evaluate/combine.

Examples

>>> df = pl.DataFrame(
...     data={
...         "x": [5, 6, 7, 4, 8],
...         "y": [1.5, 2.5, 1.0, 4.0, -5.75],
...         "z": [-9, 2, -1, 4, 8],
...     }
... )
>>> df.select(
...     (pl.col("x") == pl.col("y"))
...     .or_(
...         pl.col("x") == pl.col("y"),
...         pl.col("y") == pl.col("z"),
...         pl.col("y").cast(int) == pl.col("z"),
...     )
...     .alias("any")
... )
shape: (5, 1)
┌───────┐
│ any   │
│ ---   │
│ bool  │
╞═══════╡
│ false │
│ true  │
│ false │
│ true  │
│ false │
└───────┘

over( expr: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, mapping_strategy: WindowMappingStrategy = 'group_to_rows', ) → Self[source]

Compute expressions over the given groups.

This expression is similar to performing a groupby aggregation and joining the result back into the original dataframe.

The outcome is similar to how window functions work in PostgreSQL.

Parameters:

expr

Column(s) to group by. Accepts expression input. Strings are parsed as column names.

*more_exprs

Additional columns to group by, specified as positional arguments.

mapping_strategy: {‘group_to_rows’, ‘join’, ‘explode’}

group_to_rows
If the aggregation results in multiple values, assign them back to their position in the DataFrame. This can only be done if the group yields the same elements before aggregation as after.
join
Join the groups as ‘List<group_dtype>’ to the row positions. warning: this can be memory intensive.
explode
Don’t do any mapping, but simply flatten the group. This only makes sense if the input data is sorted.

Examples

Pass the name of a column to compute the expression over that column.

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "a", "b", "b", "b"],
...         "b": [1, 2, 3, 5, 3],
...         "c": [5, 4, 3, 2, 1],
...     }
... )
>>> df.with_columns(pl.col("c").max().over("a").suffix("_max"))
shape: (5, 4)
┌─────┬─────┬─────┬───────┐
│ a   ┆ b   ┆ c   ┆ c_max │
│ --- ┆ --- ┆ --- ┆ ---   │
│ str ┆ i64 ┆ i64 ┆ i64   │
╞═════╪═════╪═════╪═══════╡
│ a   ┆ 1   ┆ 5   ┆ 5     │
│ a   ┆ 2   ┆ 4   ┆ 5     │
│ b   ┆ 3   ┆ 3   ┆ 3     │
│ b   ┆ 5   ┆ 2   ┆ 3     │
│ b   ┆ 3   ┆ 1   ┆ 3     │
└─────┴─────┴─────┴───────┘

Expression input is supported.

>>> df.with_columns(pl.col("c").max().over(pl.col("b") // 2).suffix("_max"))
shape: (5, 4)
┌─────┬─────┬─────┬───────┐
│ a   ┆ b   ┆ c   ┆ c_max │
│ --- ┆ --- ┆ --- ┆ ---   │
│ str ┆ i64 ┆ i64 ┆ i64   │
╞═════╪═════╪═════╪═══════╡
│ a   ┆ 1   ┆ 5   ┆ 5     │
│ a   ┆ 2   ┆ 4   ┆ 4     │
│ b   ┆ 3   ┆ 3   ┆ 4     │
│ b   ┆ 5   ┆ 2   ┆ 2     │
│ b   ┆ 3   ┆ 1   ┆ 4     │
└─────┴─────┴─────┴───────┘

Group by multiple columns by passing a list of column names or expressions.

>>> df.with_columns(pl.col("c").min().over(["a", "b"]).suffix("_min"))
shape: (5, 4)
┌─────┬─────┬─────┬───────┐
│ a   ┆ b   ┆ c   ┆ c_min │
│ --- ┆ --- ┆ --- ┆ ---   │
│ str ┆ i64 ┆ i64 ┆ i64   │
╞═════╪═════╪═════╪═══════╡
│ a   ┆ 1   ┆ 5   ┆ 5     │
│ a   ┆ 2   ┆ 4   ┆ 4     │
│ b   ┆ 3   ┆ 3   ┆ 1     │
│ b   ┆ 5   ┆ 2   ┆ 2     │
│ b   ┆ 3   ┆ 1   ┆ 1     │
└─────┴─────┴─────┴───────┘

Or use positional arguments to group by multiple columns in the same way.

>>> df.with_columns(pl.col("c").min().over("a", pl.col("b") % 2).suffix("_min"))
shape: (5, 4)
┌─────┬─────┬─────┬───────┐
│ a   ┆ b   ┆ c   ┆ c_min │
│ --- ┆ --- ┆ --- ┆ ---   │
│ str ┆ i64 ┆ i64 ┆ i64   │
╞═════╪═════╪═════╪═══════╡
│ a   ┆ 1   ┆ 5   ┆ 5     │
│ a   ┆ 2   ┆ 4   ┆ 4     │
│ b   ┆ 3   ┆ 3   ┆ 1     │
│ b   ┆ 5   ┆ 2   ┆ 1     │
│ b   ┆ 3   ┆ 1   ┆ 1     │
└─────┴─────┴─────┴───────┘

pct_change(n: int = 1) → Self[source]

Computes percentage change between values.

Percentage change (as fraction) between current element and most-recent non-null element at least n period(s) before the current element.

Computes the change from the previous row by default.

Parameters:

n: periods to shift for forming percent change.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [10, 11, 12, None, 12],
...     }
... )
>>> df.with_columns(pl.col("a").pct_change().alias("pct_change"))
shape: (5, 2)
┌──────┬────────────┐
│ a    ┆ pct_change │
│ ---  ┆ ---        │
│ i64  ┆ f64        │
╞══════╪════════════╡
│ 10   ┆ null       │
│ 11   ┆ 0.1        │
│ 12   ┆ 0.090909   │
│ null ┆ 0.0        │
│ 12   ┆ 0.0        │
└──────┴────────────┘

pipe(

function: Callable[Concatenate[Expr, P], T],

*args: P.args,

**kwargs: P.kwargs,

) → T[source]

Offers a structured way to apply a sequence of user-defined functions (UDFs).

Parameters:

function: Callable; will receive the expression as the first parameter, followed by any given args/kwargs.
*args: Arguments to pass to the UDF.
**kwargs: Keyword arguments to pass to the UDF.

Examples

>>> def extract_number(expr: pl.Expr) -> pl.Expr:
...     """Extract the digits from a string."""
...     return expr.str.extract(r"\d+", 0).cast(pl.Int64)
>>>
>>> def scale_negative_even(expr: pl.Expr, *, n: int = 1) -> pl.Expr:
...     """Set even numbers negative, and scale by a user-supplied value."""
...     expr = pl.when(expr % 2 == 0).then(-expr).otherwise(expr)
...     return expr * n
>>>
>>> df = pl.DataFrame({"val": ["a: 1", "b: 2", "c: 3", "d: 4"]})
>>> df.with_columns(
...     udfs=(
...         pl.col("val").pipe(extract_number).pipe(scale_negative_even, n=5)
...     ),
... )
shape: (4, 2)
┌──────┬──────┐
│ val  ┆ udfs │
│ ---  ┆ ---  │
│ str  ┆ i64  │
╞══════╪══════╡
│ a: 1 ┆ 5    │
│ b: 2 ┆ -10  │
│ c: 3 ┆ 15   │
│ d: 4 ┆ -20  │
└──────┴──────┘

pow(exponent: int | float | None | Series | Expr) → Self[source]

Method equivalent of exponentiation operator expr ** exponent.

Parameters:

exponent: Numeric literal or expression exponent value.

Examples

>>> df = pl.DataFrame({"x": [1, 2, 4, 8]})
>>> df.with_columns(
...     pl.col("x").pow(3).alias("cube"),
...     pl.col("x").pow(pl.col("x").log(2)).alias("x ** xlog2"),
... )
shape: (4, 3)
┌─────┬───────┬────────────┐
│ x   ┆ cube  ┆ x ** xlog2 │
│ --- ┆ ---   ┆ ---        │
│ i64 ┆ f64   ┆ f64        │
╞═════╪═══════╪════════════╡
│ 1   ┆ 1.0   ┆ 1.0        │
│ 2   ┆ 8.0   ┆ 2.0        │
│ 4   ┆ 64.0  ┆ 16.0       │
│ 8   ┆ 512.0 ┆ 512.0      │
└─────┴───────┴────────────┘

prefix(prefix: str) → Self[source]

Add a prefix to the root column name of the expression.

Parameters:

prefix: Prefix to add to the root column name.

See also

suffix

Notes

This will undo any previous renaming operations on the expression.

Due to implementation constraints, this method can only be called as the last expression in a chain.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["x", "y", "z"],
...     }
... )
>>> df.with_columns(pl.all().reverse().prefix("reverse_"))
shape: (3, 4)
┌─────┬─────┬───────────┬───────────┐
│ a   ┆ b   ┆ reverse_a ┆ reverse_b │
│ --- ┆ --- ┆ ---       ┆ ---       │
│ i64 ┆ str ┆ i64       ┆ str       │
╞═════╪═════╪═══════════╪═══════════╡
│ 1   ┆ x   ┆ 3         ┆ z         │
│ 2   ┆ y   ┆ 2         ┆ y         │
│ 3   ┆ z   ┆ 1         ┆ x         │
└─────┴─────┴───────────┴───────────┘

product() → Self[source]

Compute the product of an expression.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").product())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 6   │
└─────┘

qcut( quantiles: Sequence[float] | int, *, labels: Sequence[str] | None = None, left_closed: bool = False, allow_duplicates: bool = False, include_breaks: bool = False, ) → Self[source]

Bin continuous values into discrete categories based on their quantiles.

Parameters:

quantiles: Either a list of quantile probabilities between 0 and 1 or a positive integer determining the number of bins with uniform probability.
labels: Names of the categories. The number of labels must be equal to the number of categories.
left_closed: Set the intervals to be left-closed instead of right-closed.
allow_duplicates: If set to True, duplicates in the resulting quantiles are dropped, rather than raising a DuplicateError. This can happen even with unique probabilities, depending on the data.
include_breaks: Include a column with the right endpoint of the bin each observation falls in. This will change the data type of the output from a Categorical to a Struct.

Returns:

Expr: Expression of data type Categorical if include_breaks is set to False (default), otherwise an expression of data type Struct.

See also

cut

Examples

Divide a column into three categories according to pre-defined quantile probabilities.

>>> df = pl.DataFrame({"foo": [-2, -1, 0, 1, 2]})
>>> df.with_columns(
...     pl.col("foo").qcut([0.25, 0.75], labels=["a", "b", "c"]).alias("qcut")
... )
shape: (5, 2)
┌─────┬──────┐
│ foo ┆ qcut │
│ --- ┆ ---  │
│ i64 ┆ cat  │
╞═════╪══════╡
│ -2  ┆ a    │
│ -1  ┆ a    │
│ 0   ┆ b    │
│ 1   ┆ b    │
│ 2   ┆ c    │
└─────┴──────┘

Divide a column into two categories using uniform quantile probabilities.

>>> df.with_columns(
...     pl.col("foo")
...     .qcut(2, labels=["low", "high"], left_closed=True)
...     .alias("qcut")
... )
shape: (5, 2)
┌─────┬──────┐
│ foo ┆ qcut │
│ --- ┆ ---  │
│ i64 ┆ cat  │
╞═════╪══════╡
│ -2  ┆ low  │
│ -1  ┆ low  │
│ 0   ┆ high │
│ 1   ┆ high │
│ 2   ┆ high │
└─────┴──────┘

Add both the category and the breakpoint.

>>> df.with_columns(
...     pl.col("foo").qcut([0.25, 0.75], include_breaks=True).alias("qcut")
... ).unnest("qcut")
shape: (5, 3)
┌─────┬──────┬────────────┐
│ foo ┆ brk  ┆ foo_bin    │
│ --- ┆ ---  ┆ ---        │
│ i64 ┆ f64  ┆ cat        │
╞═════╪══════╪════════════╡
│ -2  ┆ -1.0 ┆ (-inf, -1] │
│ -1  ┆ -1.0 ┆ (-inf, -1] │
│ 0   ┆ 1.0  ┆ (-1, 1]    │
│ 1   ┆ 1.0  ┆ (-1, 1]    │
│ 2   ┆ inf  ┆ (1, inf]   │
└─────┴──────┴────────────┘

quantile( quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest', ) → Self[source]

Get quantile value.

Parameters:

quantile: Quantile between 0.0 and 1.0.
interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}: Interpolation method.

Examples

>>> df = pl.DataFrame({"a": [0, 1, 2, 3, 4, 5]})
>>> df.select(pl.col("a").quantile(0.3))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="higher"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 2.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="lower"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="midpoint"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.5 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="linear"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.5 │
└─────┘

radians() → Self[source]

Convert from degrees to radians.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [-720, -540, -360, -180, 0, 180, 360, 540, 720]})
>>> df.select(pl.col("a").radians())
shape: (9, 1)
┌────────────┐
│ a          │
│ ---        │
│ f64        │
╞════════════╡
│ -12.566371 │
│ -9.424778  │
│ -6.283185  │
│ -3.141593  │
│ 0.0        │
│ 3.141593   │
│ 6.283185   │
│ 9.424778   │
│ 12.566371  │
└────────────┘

rank( method: RankMethod = 'average', *, descending: bool = False, seed: int | None = None, ) → Self[source]

Assign ranks to data, dealing with ties appropriately.

Parameters:

method{‘average’, ‘min’, ‘max’, ‘dense’, ‘ordinal’, ‘random’}

The method used to assign ranks to tied elements. The following methods are available (default is ‘average’):

‘average’ : The average of the ranks that would have been assigned to all the tied values is assigned to each value.
‘min’ : The minimum of the ranks that would have been assigned to all the tied values is assigned to each value. (This is also referred to as “competition” ranking.)
‘max’ : The maximum of the ranks that would have been assigned to all the tied values is assigned to each value.
‘dense’ : Like ‘min’, but the rank of the next highest element is assigned the rank immediately after those assigned to the tied elements.
‘ordinal’ : All values are given a distinct rank, corresponding to the order that the values occur in the Series.
‘random’ : Like ‘ordinal’, but the rank for ties is not dependent on the order that the values occur in the Series.

descending

Rank in descending order.

seed

If method=”random”, use this as seed.

Examples

The ‘average’ method:

>>> df = pl.DataFrame({"a": [3, 6, 1, 1, 6]})
>>> df.select(pl.col("a").rank())
shape: (5, 1)
┌─────┐
│ a   │
│ --- │
│ f32 │
╞═════╡
│ 3.0 │
│ 4.5 │
│ 1.5 │
│ 1.5 │
│ 4.5 │
└─────┘

The ‘ordinal’ method:

>>> df = pl.DataFrame({"a": [3, 6, 1, 1, 6]})
>>> df.select(pl.col("a").rank("ordinal"))
shape: (5, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 3   │
│ 4   │
│ 1   │
│ 2   │
│ 5   │
└─────┘

Use ‘rank’ with ‘over’ to rank within groups:

>>> df = pl.DataFrame({"a": [1, 1, 2, 2, 2], "b": [6, 7, 5, 14, 11]})
>>> df.with_columns(pl.col("b").rank().over("a").alias("rank"))
shape: (5, 3)
┌─────┬─────┬──────┐
│ a   ┆ b   ┆ rank │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ i64 ┆ f32  │
╞═════╪═════╪══════╡
│ 1   ┆ 6   ┆ 1.0  │
│ 1   ┆ 7   ┆ 2.0  │
│ 2   ┆ 5   ┆ 1.0  │
│ 2   ┆ 14  ┆ 3.0  │
│ 2   ┆ 11  ┆ 2.0  │
└─────┴─────┴──────┘

rechunk() → Self[source]

Create a single chunk of memory for this Series.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})

Create a Series with 3 nulls, append column a then rechunk

>>> df.select(pl.repeat(None, 3).append(pl.col("a")).rechunk())
shape: (6, 1)
┌────────┐
│ repeat │
│ ---    │
│ i64    │
╞════════╡
│ null   │
│ null   │
│ null   │
│ 1      │
│ 1      │
│ 2      │
└────────┘

reinterpret(*, signed: bool = True) → Self[source]

Reinterpret the underlying bits as a signed/unsigned integer.

This operation is only allowed for 64bit integers. For lower bits integers, you can safely use that cast operation.

Parameters:

signed: If True, reinterpret as pl.Int64. Otherwise, reinterpret as pl.UInt64.

Examples

>>> s = pl.Series("a", [1, 1, 2], dtype=pl.UInt64)
>>> df = pl.DataFrame([s])
>>> df.select(
...     [
...         pl.col("a").reinterpret(signed=True).alias("reinterpreted"),
...         pl.col("a").alias("original"),
...     ]
... )
shape: (3, 2)
┌───────────────┬──────────┐
│ reinterpreted ┆ original │
│ ---           ┆ ---      │
│ i64           ┆ u64      │
╞═══════════════╪══════════╡
│ 1             ┆ 1        │
│ 1             ┆ 1        │
│ 2             ┆ 2        │
└───────────────┴──────────┘

repeat_by(by: Series | Expr | str | int) → Self[source]

Repeat the elements in this Series as specified in the given expression.

The repeated elements are expanded into a List.

Parameters:

by: Numeric column that determines how often the values will be repeated. The column will be coerced to UInt32. Give this dtype to make the coercion a no-op.

Returns:

Expr: Expression of data type List, where the inner data type is equal to the original data type.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": ["x", "y", "z"],
...         "n": [1, 2, 3],
...     }
... )
>>> df.select(pl.col("a").repeat_by("n"))
shape: (3, 1)
┌─────────────────┐
│ a               │
│ ---             │
│ list[str]       │
╞═════════════════╡
│ ["x"]           │
│ ["y", "y"]      │
│ ["z", "z", "z"] │
└─────────────────┘

reshape(dimensions: tuple[int, ...]) → Self[source]

Reshape this Expr to a flat Series or a Series of Lists.

Parameters:

dimensions: Tuple of the dimension sizes. If a -1 is used in any of the dimensions, that dimension is inferred.

Returns:

Expr: If a single dimension is given, results in an expression of the original data type. If a multiple dimensions are given, results in an expression of data type List with shape (rows, cols).

See also

Expr.list.explode: Explode a list column.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7, 8, 9]})
>>> df.select(pl.col("foo").reshape((3, 3)))
shape: (3, 1)
┌───────────┐
│ foo       │
│ ---       │
│ list[i64] │
╞═══════════╡
│ [1, 2, 3] │
│ [4, 5, 6] │
│ [7, 8, 9] │
└───────────┘

reverse() → Self[source]

Reverse the selection.

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4, 5],
...         "fruits": ["banana", "banana", "apple", "apple", "banana"],
...         "B": [5, 4, 3, 2, 1],
...         "cars": ["beetle", "audi", "beetle", "beetle", "beetle"],
...     }
... )
>>> df.select(
...     [
...         pl.all(),
...         pl.all().reverse().suffix("_reverse"),
...     ]
... )
shape: (5, 8)
┌─────┬────────┬─────┬────────┬───────────┬────────────────┬───────────┬──────────────┐
│ A   ┆ fruits ┆ B   ┆ cars   ┆ A_reverse ┆ fruits_reverse ┆ B_reverse ┆ cars_reverse │
│ --- ┆ ---    ┆ --- ┆ ---    ┆ ---       ┆ ---            ┆ ---       ┆ ---          │
│ i64 ┆ str    ┆ i64 ┆ str    ┆ i64       ┆ str            ┆ i64       ┆ str          │
╞═════╪════════╪═════╪════════╪═══════════╪════════════════╪═══════════╪══════════════╡
│ 1   ┆ banana ┆ 5   ┆ beetle ┆ 5         ┆ banana         ┆ 1         ┆ beetle       │
│ 2   ┆ banana ┆ 4   ┆ audi   ┆ 4         ┆ apple          ┆ 2         ┆ beetle       │
│ 3   ┆ apple  ┆ 3   ┆ beetle ┆ 3         ┆ apple          ┆ 3         ┆ beetle       │
│ 4   ┆ apple  ┆ 2   ┆ beetle ┆ 2         ┆ banana         ┆ 4         ┆ audi         │
│ 5   ┆ banana ┆ 1   ┆ beetle ┆ 1         ┆ banana         ┆ 5         ┆ beetle       │
└─────┴────────┴─────┴────────┴───────────┴────────────────┴───────────┴──────────────┘

rle() → Self[source]

Get the lengths of runs of identical values.

Returns:

Expr: Expression of data type Struct with Fields “lengths” and “values”.

Examples

>>> df = pl.DataFrame(pl.Series("s", [1, 1, 2, 1, None, 1, 3, 3]))
>>> df.select(pl.col("s").rle()).unnest("s")
shape: (6, 2)
┌─────────┬────────┐
│ lengths ┆ values │
│ ---     ┆ ---    │
│ i32     ┆ i64    │
╞═════════╪════════╡
│ 2       ┆ 1      │
│ 1       ┆ 2      │
│ 1       ┆ 1      │
│ 1       ┆ null   │
│ 1       ┆ 1      │
│ 2       ┆ 3      │
└─────────┴────────┘

rle_id() → Self[source]

Map values to run IDs.

Similar to RLE, but it maps each value to an ID corresponding to the run into which it falls. This is especially useful when you want to define groups by runs of identical values rather than the values themselves.

Examples

>>> df = pl.DataFrame(dict(a=[1, 2, 1, 1, 1], b=["x", "x", None, "y", "y"]))
>>> # It works on structs of multiple values too!
>>> df.with_columns(a_r=pl.col("a").rle_id(), ab_r=pl.struct("a", "b").rle_id())
shape: (5, 4)
┌─────┬──────┬─────┬──────┐
│ a   ┆ b    ┆ a_r ┆ ab_r │
│ --- ┆ ---  ┆ --- ┆ ---  │
│ i64 ┆ str  ┆ u32 ┆ u32  │
╞═════╪══════╪═════╪══════╡
│ 1   ┆ x    ┆ 0   ┆ 0    │
│ 2   ┆ x    ┆ 1   ┆ 1    │
│ 1   ┆ null ┆ 2   ┆ 2    │
│ 1   ┆ y    ┆ 2   ┆ 3    │
│ 1   ┆ y    ┆ 2   ┆ 3    │
└─────┴──────┴─────┴──────┘

rolling_apply( function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ) → Self[source]

Apply a custom rolling window function.

Prefer the specific rolling window functions over this one, as they are faster.

Prefer:

rolling_min

rolling_max

rolling_mean

rolling_sum

The window at a given row will include the row itself and the window_size - 1 elements before it.

Parameters:

function: Aggregation function
window_size: The length of the window.
weights: An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.
min_periods: The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.
center: Set the labels at the center of the window

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1.0, 2.0, 9.0, 2.0, 13.0],
...     }
... )
>>> df.select(
...     [
...         pl.col("A").rolling_apply(lambda s: s.std(), window_size=3),
...     ]
... )
 shape: (5, 1)
┌──────────┐
│ A        │
│ ---      │
│ f64      │
╞══════════╡
│ null     │
│ null     │
│ 4.358899 │
│ 4.041452 │
│ 5.567764 │
└──────────┘

Apply a rolling max (moving max) over the values in this array.

A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weight vector. The resulting values will be aggregated to their sum.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal, for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_max=pl.col("A").rolling_max(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_max │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 2.0         │
│ 3.0 ┆ 3.0         │
│ 4.0 ┆ 4.0         │
│ 5.0 ┆ 5.0         │
│ 6.0 ┆ 6.0         │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_max=pl.col("A").rolling_max(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_max │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.5         │
│ 3.0 ┆ 2.25        │
│ 4.0 ┆ 3.0         │
│ 5.0 ┆ 3.75        │
│ 6.0 ┆ 4.5         │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_max=pl.col("A").rolling_max(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_max │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 3.0         │
│ 3.0 ┆ 4.0         │
│ 4.0 ┆ 5.0         │
│ 5.0 ┆ 6.0         │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling max with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_max=pl.col("row_nr").rolling_max(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_max │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 2               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 20              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 21              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 22              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 23              │
└────────┴─────────────────────┴─────────────────┘

Compute the rolling max with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_max=pl.col("row_nr").rolling_max(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_max │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0               │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 1               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 2               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 3               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 21              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 22              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 23              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 24              │
└────────┴─────────────────────┴─────────────────┘

Apply a rolling mean (moving mean) over the values in this array.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_mean=pl.col("A").rolling_mean(window_size=2),
... )
shape: (6, 2)
┌─────┬──────────────┐
│ A   ┆ rolling_mean │
│ --- ┆ ---          │
│ f64 ┆ f64          │
╞═════╪══════════════╡
│ 1.0 ┆ null         │
│ 2.0 ┆ 1.5          │
│ 3.0 ┆ 2.5          │
│ 4.0 ┆ 3.5          │
│ 5.0 ┆ 4.5          │
│ 6.0 ┆ 5.5          │
└─────┴──────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_mean=pl.col("A").rolling_mean(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬──────────────┐
│ A   ┆ rolling_mean │
│ --- ┆ ---          │
│ f64 ┆ f64          │
╞═════╪══════════════╡
│ 1.0 ┆ null         │
│ 2.0 ┆ 1.75         │
│ 3.0 ┆ 2.75         │
│ 4.0 ┆ 3.75         │
│ 5.0 ┆ 4.75         │
│ 6.0 ┆ 5.75         │
└─────┴──────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_mean=pl.col("A").rolling_mean(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬──────────────┐
│ A   ┆ rolling_mean │
│ --- ┆ ---          │
│ f64 ┆ f64          │
╞═════╪══════════════╡
│ 1.0 ┆ null         │
│ 2.0 ┆ 2.0          │
│ 3.0 ┆ 3.0          │
│ 4.0 ┆ 4.0          │
│ 5.0 ┆ 5.0          │
│ 6.0 ┆ null         │
└─────┴──────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling mean with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_mean=pl.col("row_nr").rolling_mean(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬──────────────────┐
│ row_nr ┆ date                ┆ rolling_row_mean │
│ ---    ┆ ---                 ┆ ---              │
│ u32    ┆ datetime[μs]        ┆ f64              │
╞════════╪═════════════════════╪══════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null             │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.0              │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 0.5              │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 1.5              │
│ …      ┆ …                   ┆ …                │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 19.5             │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 20.5             │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 21.5             │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 22.5             │
└────────┴─────────────────────┴──────────────────┘

Compute the rolling mean with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_mean=pl.col("row_nr").rolling_mean(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬──────────────────┐
│ row_nr ┆ date                ┆ rolling_row_mean │
│ ---    ┆ ---                 ┆ ---              │
│ u32    ┆ datetime[μs]        ┆ f64              │
╞════════╪═════════════════════╪══════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0.0              │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.5              │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1.0              │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 2.0              │
│ …      ┆ …                   ┆ …                │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 20.0             │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 21.0             │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 22.0             │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 23.0             │
└────────┴─────────────────────┴──────────────────┘

Compute a rolling median.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that determines the relative contribution of each value in a window to the output.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_median=pl.col("A").rolling_median(window_size=2),
... )
shape: (6, 2)
┌─────┬────────────────┐
│ A   ┆ rolling_median │
│ --- ┆ ---            │
│ f64 ┆ f64            │
╞═════╪════════════════╡
│ 1.0 ┆ null           │
│ 2.0 ┆ 1.5            │
│ 3.0 ┆ 2.5            │
│ 4.0 ┆ 3.5            │
│ 5.0 ┆ 4.5            │
│ 6.0 ┆ 5.5            │
└─────┴────────────────┘

Specify weights for the values in each window:

>>> df.with_columns(
...     rolling_median=pl.col("A").rolling_median(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬────────────────┐
│ A   ┆ rolling_median │
│ --- ┆ ---            │
│ f64 ┆ f64            │
╞═════╪════════════════╡
│ 1.0 ┆ null           │
│ 2.0 ┆ 1.5            │
│ 3.0 ┆ 2.5            │
│ 4.0 ┆ 3.5            │
│ 5.0 ┆ 4.5            │
│ 6.0 ┆ 5.5            │
└─────┴────────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_median=pl.col("A").rolling_median(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬────────────────┐
│ A   ┆ rolling_median │
│ --- ┆ ---            │
│ f64 ┆ f64            │
╞═════╪════════════════╡
│ 1.0 ┆ null           │
│ 2.0 ┆ 2.0            │
│ 3.0 ┆ 3.0            │
│ 4.0 ┆ 4.0            │
│ 5.0 ┆ 5.0            │
│ 6.0 ┆ null           │
└─────┴────────────────┘

Apply a rolling min (moving min) over the values in this array.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_min=pl.col("A").rolling_min(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_min │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.0         │
│ 3.0 ┆ 2.0         │
│ 4.0 ┆ 3.0         │
│ 5.0 ┆ 4.0         │
│ 6.0 ┆ 5.0         │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_min=pl.col("A").rolling_min(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_min │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 0.25        │
│ 3.0 ┆ 0.5         │
│ 4.0 ┆ 0.75        │
│ 5.0 ┆ 1.0         │
│ 6.0 ┆ 1.25        │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_min=pl.col("A").rolling_min(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_min │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.0         │
│ 3.0 ┆ 2.0         │
│ 4.0 ┆ 3.0         │
│ 5.0 ┆ 4.0         │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘
>>> df_temporal.with_columns(
...     rolling_row_min=pl.col("row_nr").rolling_min(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_min │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 0               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 1               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 19              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 20              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 21              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 22              │
└────────┴─────────────────────┴─────────────────┘

rolling_quantile( quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int | timedelta | str = 2, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, by: str | None = None, closed: ClosedInterval = 'left', ) → Self[source]

Compute a rolling quantile.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

quantile

Quantile between 0.0 and 1.0.

interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}

Interpolation method.

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that determines the relative contribution of each value in a window to the output.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25, window_size=4
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 2.0              │
│ 5.0 ┆ 3.0              │
│ 6.0 ┆ 4.0              │
└─────┴──────────────────┘

Specify weights for the values in each window:

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25, window_size=4, weights=[0.2, 0.4, 0.4, 0.2]
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 2.0              │
│ 5.0 ┆ 3.0              │
│ 6.0 ┆ 4.0              │
└─────┴──────────────────┘

Specify weights and interpolation method

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25,
...         window_size=4,
...         weights=[0.2, 0.4, 0.4, 0.2],
...         interpolation="linear",
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 1.625            │
│ 5.0 ┆ 2.625            │
│ 6.0 ┆ 3.625            │
└─────┴──────────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.2, window_size=5, center=True
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ 2.0              │
│ 4.0 ┆ 3.0              │
│ 5.0 ┆ null             │
│ 6.0 ┆ null             │
└─────┴──────────────────┘

rolling_skew(window_size: int, *, bias: bool = True) → Self[source]

Compute a rolling skew.

The window at a given row includes the row itself and the window_size - 1 elements before it.

Parameters:

window_size: Integer size of the rolling window.
bias: If False, the calculations are corrected for statistical bias.

Examples

>>> df = pl.DataFrame({"a": [1, 4, 2, 9]})
>>> df.select(pl.col("a").rolling_skew(3))
shape: (4, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ null     │
│ null     │
│ 0.381802 │
│ 0.47033  │
└──────────┘

Note how the values match the following:

>>> pl.Series([1, 4, 2]).skew(), pl.Series([4, 2, 9]).skew()
(0.38180177416060584, 0.47033046033698594)

Compute a rolling standard deviation.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that determines the relative contribution of each value in a window to the output.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

ddof

“Delta Degrees of Freedom”: The divisor for a length N window is N - ddof

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_std=pl.col("A").rolling_std(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_std │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 0.707107    │
│ 3.0 ┆ 0.707107    │
│ 4.0 ┆ 0.707107    │
│ 5.0 ┆ 0.707107    │
│ 6.0 ┆ 0.707107    │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_std=pl.col("A").rolling_std(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_std │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 0.433013    │
│ 3.0 ┆ 0.433013    │
│ 4.0 ┆ 0.433013    │
│ 5.0 ┆ 0.433013    │
│ 6.0 ┆ 0.433013    │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_std=pl.col("A").rolling_std(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_std │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.0         │
│ 3.0 ┆ 1.0         │
│ 4.0 ┆ 1.0         │
│ 5.0 ┆ 1.0         │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling std with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_std=pl.col("row_nr").rolling_std(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_std │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ f64             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.0             │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 0.707107        │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 0.707107        │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 0.707107        │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 0.707107        │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 0.707107        │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 0.707107        │
└────────┴─────────────────────┴─────────────────┘

Compute the rolling std with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_std=pl.col("row_nr").rolling_std(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_std │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ f64             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0.0             │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.707107        │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1.0             │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 1.0             │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 1.0             │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 1.0             │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 1.0             │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 1.0             │
└────────┴─────────────────────┴─────────────────┘

Apply a rolling sum (moving sum) over the values in this array.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must of dtype {Date, Datetime}

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 3.0         │
│ 3.0 ┆ 5.0         │
│ 4.0 ┆ 7.0         │
│ 5.0 ┆ 9.0         │
│ 6.0 ┆ 11.0        │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.75        │
│ 3.0 ┆ 2.75        │
│ 4.0 ┆ 3.75        │
│ 5.0 ┆ 4.75        │
│ 6.0 ┆ 5.75        │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_sum=pl.col("A").rolling_sum(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_sum │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 6.0         │
│ 3.0 ┆ 9.0         │
│ 4.0 ┆ 12.0        │
│ 5.0 ┆ 15.0        │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling sum with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_sum=pl.col("row_nr").rolling_sum(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_sum │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 3               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 39              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 41              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 43              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 45              │
└────────┴─────────────────────┴─────────────────┘

Compute the rolling sum with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_sum=pl.col("row_nr").rolling_sum(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_sum │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ u32             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0               │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 1               │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 3               │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 6               │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 60              │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 63              │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 66              │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 69              │
└────────┴─────────────────────┴─────────────────┘

Compute a rolling variance.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed=”left” means the windows will be:

[t_0 - window_size, t_0)

[t_1 - window_size, t_1)

…

[t_n - window_size, t_n)

With closed=”right”, the left endpoint is not included and the right endpoint is included.

Parameters:

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 calendar day)
1w (1 calendar week)
1mo (1 calendar month)
1q (1 calendar quarter)
1y (1 calendar year)
1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that determines the relative contribution of each value in a window to the output.

min_periods

The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype Datetime.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

ddof

“Delta Degrees of Freedom”: The divisor for a length N window is N - ddof

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.with_columns(
...     rolling_var=pl.col("A").rolling_var(window_size=2),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_var │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 0.5         │
│ 3.0 ┆ 0.5         │
│ 4.0 ┆ 0.5         │
│ 5.0 ┆ 0.5         │
│ 6.0 ┆ 0.5         │
└─────┴─────────────┘

Specify weights to multiply the values in the window with:

>>> df.with_columns(
...     rolling_var=pl.col("A").rolling_var(
...         window_size=2, weights=[0.25, 0.75]
...     ),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_var │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 0.1875      │
│ 3.0 ┆ 0.1875      │
│ 4.0 ┆ 0.1875      │
│ 5.0 ┆ 0.1875      │
│ 6.0 ┆ 0.1875      │
└─────┴─────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_var=pl.col("A").rolling_var(window_size=3, center=True),
... )
shape: (6, 2)
┌─────┬─────────────┐
│ A   ┆ rolling_var │
│ --- ┆ ---         │
│ f64 ┆ f64         │
╞═════╪═════════════╡
│ 1.0 ┆ null        │
│ 2.0 ┆ 1.0         │
│ 3.0 ┆ 1.0         │
│ 4.0 ┆ 1.0         │
│ 5.0 ┆ 1.0         │
│ 6.0 ┆ null        │
└─────┴─────────────┘

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.date_range(start, stop, "1h", eager=True)}
... ).with_row_count()
>>> df_temporal
shape: (25, 2)
┌────────┬─────────────────────┐
│ row_nr ┆ date                │
│ ---    ┆ ---                 │
│ u32    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 │
│ 1      ┆ 2001-01-01 01:00:00 │
│ 2      ┆ 2001-01-01 02:00:00 │
│ 3      ┆ 2001-01-01 03:00:00 │
│ …      ┆ …                   │
│ 21     ┆ 2001-01-01 21:00:00 │
│ 22     ┆ 2001-01-01 22:00:00 │
│ 23     ┆ 2001-01-01 23:00:00 │
│ 24     ┆ 2001-01-02 00:00:00 │
└────────┴─────────────────────┘

Compute the rolling var with the default left closure of temporal windows

>>> df_temporal.with_columns(
...     rolling_row_var=pl.col("row_nr").rolling_var(
...         window_size="2h", by="date", closed="left"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_var │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ f64             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.0             │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 0.5             │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 0.5             │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 0.5             │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 0.5             │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 0.5             │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 0.5             │
└────────┴─────────────────────┴─────────────────┘

Compute the rolling var with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_var=pl.col("row_nr").rolling_var(
...         window_size="2h", by="date", closed="both"
...     )
... )
shape: (25, 3)
┌────────┬─────────────────────┬─────────────────┐
│ row_nr ┆ date                ┆ rolling_row_var │
│ ---    ┆ ---                 ┆ ---             │
│ u32    ┆ datetime[μs]        ┆ f64             │
╞════════╪═════════════════════╪═════════════════╡
│ 0      ┆ 2001-01-01 00:00:00 ┆ 0.0             │
│ 1      ┆ 2001-01-01 01:00:00 ┆ 0.5             │
│ 2      ┆ 2001-01-01 02:00:00 ┆ 1.0             │
│ 3      ┆ 2001-01-01 03:00:00 ┆ 1.0             │
│ …      ┆ …                   ┆ …               │
│ 21     ┆ 2001-01-01 21:00:00 ┆ 1.0             │
│ 22     ┆ 2001-01-01 22:00:00 ┆ 1.0             │
│ 23     ┆ 2001-01-01 23:00:00 ┆ 1.0             │
│ 24     ┆ 2001-01-02 00:00:00 ┆ 1.0             │
└────────┴─────────────────────┴─────────────────┘

round(decimals: int = 0) → Self[source]

Round underlying floating point data by decimals digits.

Parameters:

decimals: Number of decimals to round by.

Examples

>>> df = pl.DataFrame({"a": [0.33, 0.52, 1.02, 1.17]})
>>> df.select(pl.col("a").round(1))
shape: (4, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.3 │
│ 0.5 │
│ 1.0 │
│ 1.2 │
└─────┘

sample( n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None, fixed_seed: bool = False, ) → Self[source]

Sample from this expression.

Parameters:

n: Number of items to return. Cannot be used with fraction. Defaults to 1 if fraction is None.
fraction: Fraction of items to return. Cannot be used with n.
with_replacement: Allow values to be sampled more than once.
shuffle: Shuffle the order of sampled data points.
seed: Seed for the random number generator. If set to None (default), a random seed is generated using the random module.
fixed_seed: If True, The seed will not be incremented between draws. This can make output predictable because draw ordering can change due to threads being scheduled in a different order.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").sample(fraction=1.0, with_replacement=True, seed=1))
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 3   │
│ 1   │
│ 1   │
└─────┘

search_sorted( element: Expr | int | float | Series, side: SearchSortedSide = 'any', ) → Self[source]

Find indices where elements should be inserted to maintain order.

\[a[i-1] < v <= a[i]\]

Parameters:

element: Expression or scalar value.
side{‘any’, ‘left’, ‘right’}: If ‘any’, the index of the first suitable location found is given. If ‘left’, the index of the leftmost suitable location found is given. If ‘right’, return the rightmost suitable location found is given.

Examples

>>> df = pl.DataFrame(
...     {
...         "values": [1, 2, 3, 5],
...     }
... )
>>> df.select(
...     [
...         pl.col("values").search_sorted(0).alias("zero"),
...         pl.col("values").search_sorted(3).alias("three"),
...         pl.col("values").search_sorted(6).alias("six"),
...     ]
... )
shape: (1, 3)
┌──────┬───────┬─────┐
│ zero ┆ three ┆ six │
│ ---  ┆ ---   ┆ --- │
│ u32  ┆ u32   ┆ u32 │
╞══════╪═══════╪═════╡
│ 0    ┆ 2     ┆ 4   │
└──────┴───────┴─────┘

set_sorted(*, descending: bool = False) → Self[source]

Flags the expression as ‘sorted’.

Enables downstream code to user fast paths for sorted arrays.

Parameters:

descending: Whether the Series order is descending.

Warning

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

Examples

>>> df = pl.DataFrame({"values": [1, 2, 3]})
>>> df.select(pl.col("values").set_sorted().max())
shape: (1, 1)
┌────────┐
│ values │
│ ---    │
│ i64    │
╞════════╡
│ 3      │
└────────┘

shift(periods: int = 1) → Self[source]

Shift the values by a given period.

Parameters:

periods: Number of places to shift (may be negative).

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4]})
>>> df.select(pl.col("foo").shift(1))
shape: (4, 1)
┌──────┐
│ foo  │
│ ---  │
│ i64  │
╞══════╡
│ null │
│ 1    │
│ 2    │
│ 3    │
└──────┘

shift_and_fill(fill_value: IntoExpr, *, periods: int = 1) → Self[source]

Shift the values by a given period and fill the resulting null values.

Parameters:

fill_value: Fill None values with the result of this expression.
periods: Number of places to shift (may be negative).

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4]})
>>> df.select(pl.col("foo").shift_and_fill("a", periods=1))
shape: (4, 1)
┌─────┐
│ foo │
│ --- │
│ str │
╞═════╡
│ a   │
│ 1   │
│ 2   │
│ 3   │
└─────┘

shrink_dtype() → Self[source]

Shrink numeric columns to the minimal required datatype.

Shrink to the dtype needed to fit the extrema of this [Series]. This can be used to reduce memory pressure.

Examples

>>> pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [1, 2, 2 << 32],
...         "c": [-1, 2, 1 << 30],
...         "d": [-112, 2, 112],
...         "e": [-112, 2, 129],
...         "f": ["a", "b", "c"],
...         "g": [0.1, 1.32, 0.12],
...         "h": [True, None, False],
...     }
... ).select(pl.all().shrink_dtype())
shape: (3, 8)
┌─────┬────────────┬────────────┬──────┬──────┬─────┬──────┬───────┐
│ a   ┆ b          ┆ c          ┆ d    ┆ e    ┆ f   ┆ g    ┆ h     │
│ --- ┆ ---        ┆ ---        ┆ ---  ┆ ---  ┆ --- ┆ ---  ┆ ---   │
│ i8  ┆ i64        ┆ i32        ┆ i8   ┆ i16  ┆ str ┆ f32  ┆ bool  │
╞═════╪════════════╪════════════╪══════╪══════╪═════╪══════╪═══════╡
│ 1   ┆ 1          ┆ -1         ┆ -112 ┆ -112 ┆ a   ┆ 0.1  ┆ true  │
│ 2   ┆ 2          ┆ 2          ┆ 2    ┆ 2    ┆ b   ┆ 1.32 ┆ null  │
│ 3   ┆ 8589934592 ┆ 1073741824 ┆ 112  ┆ 129  ┆ c   ┆ 0.12 ┆ false │
└─────┴────────────┴────────────┴──────┴──────┴─────┴──────┴───────┘

shuffle(seed: int | None = None, fixed_seed: bool = False) → Self[source]

Shuffle the contents of this expression.

Parameters:

seed: Seed for the random number generator. If set to None (default), a random seed is generated using the random module.
fixed_seed: If True, The seed will not be incremented between draws. This can make output predictable because draw ordering can change due to threads being scheduled in a different order.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").shuffle(seed=1))
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 2   │
│ 1   │
│ 3   │
└─────┘

sign() → Self[source]

Compute the element-wise indication of the sign.

The returned values can be -1, 0, or 1:

-1 if x < 0.
0 if x == 0.
1 if x > 0.

(null values are preserved as-is).

Examples

>>> df = pl.DataFrame({"a": [-9.0, -0.0, 0.0, 4.0, None]})
>>> df.select(pl.col("a").sign())
shape: (5, 1)
┌──────┐
│ a    │
│ ---  │
│ i64  │
╞══════╡
│ -1   │
│ 0    │
│ 0    │
│ 1    │
│ null │
└──────┘

sin() → Self[source]

Compute the element-wise value for the sine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [0.0]})
>>> df.select(pl.col("a").sin())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.0 │
└─────┘

sinh() → Self[source]

Compute the element-wise value for the hyperbolic sine.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").sinh())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 1.175201 │
└──────────┘

skew(*, bias: bool = True) → Self[source]

Compute the sample skewness of a data set.

For normally distributed data, the skewness should be about zero. For unimodal 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 scipy.stats for more information.

Parameters:

biasbool, optional: If False, the calculations are corrected for statistical bias.

Notes

The sample skewness is computed as the Fisher-Pearson coefficient of skewness, i.e.

\[g_1=\frac{m_3}{m_2^{3/2}}\]

where

\[m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i\]

is the biased sample $i\texttt{th}$ central moment, and $\bar{x}$ is the sample mean. If bias is False, the calculations are corrected for bias and the value computed is the adjusted Fisher-Pearson standardized moment coefficient, i.e.

\[G_1 = \frac{k_3}{k_2^{3/2}} = \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}\]

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 2, 1]})
>>> df.select(pl.col("a").skew())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.343622 │
└──────────┘

slice(offset: int | Expr, length: int | Expr | None = None) → Self[source]

Get a slice of this expression.

Parameters:

offset: Start index. Negative indexing is supported.
length: Length of the slice. If set to None, all rows starting at the offset will be selected.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [8, 9, 10, 11],
...         "b": [None, 4, 4, 4],
...     }
... )
>>> df.select(pl.all().slice(1, 2))
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 9   ┆ 4   │
│ 10  ┆ 4   │
└─────┴─────┘

sort(*, descending: bool = False, nulls_last: bool = False) → Self[source]

Sort this column.

When used in a projection/selection context, the whole column is sorted. When used in a groupby context, the groups are sorted.

Parameters:

descending: Sort in descending order.
nulls_last: Place null values last.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, None, 3, 2],
...     }
... )
>>> df.select(pl.col("a").sort())
shape: (4, 1)
┌──────┐
│ a    │
│ ---  │
│ i64  │
╞══════╡
│ null │
│ 1    │
│ 2    │
│ 3    │
└──────┘
>>> df.select(pl.col("a").sort(descending=True))
shape: (4, 1)
┌──────┐
│ a    │
│ ---  │
│ i64  │
╞══════╡
│ null │
│ 3    │
│ 2    │
│ 1    │
└──────┘
>>> df.select(pl.col("a").sort(nulls_last=True))
shape: (4, 1)
┌──────┐
│ a    │
│ ---  │
│ i64  │
╞══════╡
│ 1    │
│ 2    │
│ 3    │
│ null │
└──────┘

When sorting in a groupby context, the groups are sorted.

>>> df = pl.DataFrame(
...     {
...         "group": ["one", "one", "one", "two", "two", "two"],
...         "value": [1, 98, 2, 3, 99, 4],
...     }
... )
>>> df.groupby("group").agg(pl.col("value").sort())  
shape: (2, 2)
┌───────┬────────────┐
│ group ┆ value      │
│ ---   ┆ ---        │
│ str   ┆ list[i64]  │
╞═══════╪════════════╡
│ two   ┆ [3, 4, 99] │
│ one   ┆ [1, 2, 98] │
└───────┴────────────┘

sort_by( by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, ) → Self[source]

Sort this column by the ordering of other columns.

When used in a projection/selection context, the whole column is sorted. When used in a groupby context, the groups are sorted.

Parameters:

by: Column(s) to sort by. Accepts expression input. Strings are parsed as column names.
*more_by: Additional columns to sort by, specified as positional arguments.
descending: Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.

Examples

Pass a single column name to sort by that column.

>>> df = pl.DataFrame(
...     {
...         "group": ["a", "a", "b", "b"],
...         "value1": [1, 3, 4, 2],
...         "value2": [8, 7, 6, 5],
...     }
... )
>>> df.select(pl.col("group").sort_by("value1"))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ a     │
│ b     │
│ a     │
│ b     │
└───────┘

Sorting by expressions is also supported.

>>> df.select(pl.col("group").sort_by(pl.col("value1") + pl.col("value2")))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ b     │
│ a     │
│ a     │
│ b     │
└───────┘

Sort by multiple columns by passing a list of columns.

>>> df.select(pl.col("group").sort_by(["value1", "value2"], descending=True))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ b     │
│ a     │
│ b     │
│ a     │
└───────┘

Or use positional arguments to sort by multiple columns in the same way.

>>> df.select(pl.col("group").sort_by("value1", "value2"))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ a     │
│ b     │
│ a     │
│ b     │
└───────┘

When sorting in a groupby context, the groups are sorted.

>>> df.groupby("group").agg(
...     pl.col("value1").sort_by("value2")
... )  
shape: (2, 2)
┌───────┬───────────┐
│ group ┆ value1    │
│ ---   ┆ ---       │
│ str   ┆ list[i64] │
╞═══════╪═══════════╡
│ a     ┆ [3, 1]    │
│ b     ┆ [2, 4]    │
└───────┴───────────┘

Take a single row from each group where a column attains its minimal value within that group.

>>> df.groupby("group").agg(
...     pl.all().sort_by("value2").first()
... )  
shape: (2, 3)
┌───────┬────────┬────────┐
│ group ┆ value1 ┆ value2 |
│ ---   ┆ ---    ┆ ---    │
│ str   ┆ i64    ┆ i64    |
╞═══════╪════════╪════════╡
│ a     ┆ 3      ┆ 7      |
│ b     ┆ 2      ┆ 5      |
└───────┴────────┴────────┘

sqrt() → Self[source]

Compute the square root of the elements.

Examples

>>> df = pl.DataFrame({"values": [1.0, 2.0, 4.0]})
>>> df.select(pl.col("values").sqrt())
shape: (3, 1)
┌──────────┐
│ values   │
│ ---      │
│ f64      │
╞══════════╡
│ 1.0      │
│ 1.414214 │
│ 2.0      │
└──────────┘

std(ddof: int = 1) → Self[source]

Get standard deviation.

Parameters:

ddof: “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

Examples

>>> df = pl.DataFrame({"a": [-1, 0, 1]})
>>> df.select(pl.col("a").std())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘

sub(other: Any) → Self[source]

Method equivalent of subtraction operator expr - other.

Parameters:

other: Numeric literal or expression value.

Examples

>>> df = pl.DataFrame({"x": [0, 1, 2, 3, 4]})
>>> df.with_columns(
...     pl.col("x").sub(2).alias("x-2"),
...     pl.col("x").sub(pl.col("x").cumsum()).alias("x-expr"),
... )
shape: (5, 3)
┌─────┬─────┬────────┐
│ x   ┆ x-2 ┆ x-expr │
│ --- ┆ --- ┆ ---    │
│ i64 ┆ i64 ┆ i64    │
╞═════╪═════╪════════╡
│ 0   ┆ -2  ┆ 0      │
│ 1   ┆ -1  ┆ 0      │
│ 2   ┆ 0   ┆ -1     │
│ 3   ┆ 1   ┆ -3     │
│ 4   ┆ 2   ┆ -6     │
└─────┴─────┴────────┘

suffix(suffix: str) → Self[source]

Add a suffix to the root column name of the expression.

Parameters:

suffix: Suffix to add to the root column name.

See also

prefix

Notes

This will undo any previous renaming operations on the expression.

Due to implementation constraints, this method can only be called as the last expression in a chain.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["x", "y", "z"],
...     }
... )
>>> df.with_columns(pl.all().reverse().suffix("_reverse"))
shape: (3, 4)
┌─────┬─────┬───────────┬───────────┐
│ a   ┆ b   ┆ a_reverse ┆ b_reverse │
│ --- ┆ --- ┆ ---       ┆ ---       │
│ i64 ┆ str ┆ i64       ┆ str       │
╞═════╪═════╪═══════════╪═══════════╡
│ 1   ┆ x   ┆ 3         ┆ z         │
│ 2   ┆ y   ┆ 2         ┆ y         │
│ 3   ┆ z   ┆ 1         ┆ x         │
└─────┴─────┴───────────┴───────────┘

sum() → Self[source]

Get sum value.

Notes

Dtypes in {Int8, UInt8, Int16, UInt16} are cast to Int64 before summing to prevent overflow issues.

Examples

>>> df = pl.DataFrame({"a": [-1, 0, 1]})
>>> df.select(pl.col("a").sum())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│  0  │
└─────┘

tail(n: int | Expr = 10) → Self[source]

Get the last n rows.

Parameters:

n: Number of rows to return.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7]})
>>> df.tail(3)
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 5   │
│ 6   │
│ 7   │
└─────┘

take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) → Self[source]

Take values by index.

Parameters:

indices: An expression that leads to a UInt32 dtyped Series.

Returns:

Expr: Expression of the same data type.

Examples

>>> df = pl.DataFrame(
...     {
...         "group": [
...             "one",
...             "one",
...             "one",
...             "two",
...             "two",
...             "two",
...         ],
...         "value": [1, 98, 2, 3, 99, 4],
...     }
... )
>>> df.groupby("group", maintain_order=True).agg(pl.col("value").take(1))
shape: (2, 2)
┌───────┬───────┐
│ group ┆ value │
│ ---   ┆ ---   │
│ str   ┆ i64   │
╞═══════╪═══════╡
│ one   ┆ 98    │
│ two   ┆ 99    │
└───────┴───────┘

take_every(n: int) → Self[source]

Take every nth value in the Series and return as a new Series.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7, 8, 9]})
>>> df.select(pl.col("foo").take_every(3))
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 4   │
│ 7   │
└─────┘

tan() → Self[source]

Compute the element-wise value for the tangent.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").tan().round(2))
shape: (1, 1)
┌──────┐
│ a    │
│ ---  │
│ f64  │
╞══════╡
│ 1.56 │
└──────┘

tanh() → Self[source]

Compute the element-wise value for the hyperbolic tangent.

Returns:

Expr: Expression of data type Float64.

Examples

>>> df = pl.DataFrame({"a": [1.0]})
>>> df.select(pl.col("a").tanh())
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.761594 │
└──────────┘

to_physical() → Self[source]

Cast to physical representation of the logical dtype.

polars.datatypes.Date() -> polars.datatypes.Int32()
polars.datatypes.Datetime() -> polars.datatypes.Int64()
polars.datatypes.Time() -> polars.datatypes.Int64()
polars.datatypes.Duration() -> polars.datatypes.Int64()
polars.datatypes.Categorical() -> polars.datatypes.UInt32()
List(inner) -> List(physical of inner)

Other data types will be left unchanged.

Examples

Replicating the pandas pd.factorize function.

>>> pl.DataFrame({"vals": ["a", "x", None, "a"]}).with_columns(
...     [
...         pl.col("vals").cast(pl.Categorical),
...         pl.col("vals")
...         .cast(pl.Categorical)
...         .to_physical()
...         .alias("vals_physical"),
...     ]
... )
shape: (4, 2)
┌──────┬───────────────┐
│ vals ┆ vals_physical │
│ ---  ┆ ---           │
│ cat  ┆ u32           │
╞══════╪═══════════════╡
│ a    ┆ 0             │
│ x    ┆ 1             │
│ null ┆ null          │
│ a    ┆ 0             │
└──────┴───────────────┘

top_k(k: int = 5) → Self[source]

Return the k largest elements.

This has time complexity:

\[\begin{split}O(n + k \\log{}n - \frac{k}{2})\end{split}\]

Parameters:

k: Number of elements to return.

See also

bottom_k

Examples

>>> df = pl.DataFrame(
...     {
...         "value": [1, 98, 2, 3, 99, 4],
...     }
... )
>>> df.select(
...     [
...         pl.col("value").top_k().alias("top_k"),
...         pl.col("value").bottom_k().alias("bottom_k"),
...     ]
... )
shape: (5, 2)
┌───────┬──────────┐
│ top_k ┆ bottom_k │
│ ---   ┆ ---      │
│ i64   ┆ i64      │
╞═══════╪══════════╡
│ 99    ┆ 1        │
│ 98    ┆ 2        │
│ 4     ┆ 3        │
│ 3     ┆ 4        │
│ 2     ┆ 98       │
└───────┴──────────┘

truediv(other: Any) → Self[source]

Method equivalent of float division operator expr / other.

Parameters:

other: Numeric literal or expression value.

See also

floordiv

Notes

Zero-division behaviour follows IEEE-754:

0/0: Invalid operation - mathematically undefined, returns NaN. n/0: On finite operands gives an exact infinite result, eg: ±infinity.

Examples

>>> df = pl.DataFrame(
...     data={"x": [-2, -1, 0, 1, 2], "y": [0.5, 0.0, 0.0, -4.0, -0.5]}
... )
>>> df.with_columns(
...     pl.col("x").truediv(2).alias("x/2"),
...     pl.col("x").truediv(pl.col("y")).alias("x/y"),
... )
shape: (5, 4)
┌─────┬──────┬──────┬───────┐
│ x   ┆ y    ┆ x/2  ┆ x/y   │
│ --- ┆ ---  ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ f64  ┆ f64   │
╞═════╪══════╪══════╪═══════╡
│ -2  ┆ 0.5  ┆ -1.0 ┆ -4.0  │
│ -1  ┆ 0.0  ┆ -0.5 ┆ -inf  │
│ 0   ┆ 0.0  ┆ 0.0  ┆ NaN   │
│ 1   ┆ -4.0 ┆ 0.5  ┆ -0.25 │
│ 2   ┆ -0.5 ┆ 1.0  ┆ -4.0  │
└─────┴──────┴──────┴───────┘

unique(*, maintain_order: bool = False) → Self[source]

Get unique values of this expression.

Parameters:

maintain_order: Maintain order of data. This requires more work.

Examples

>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.select(pl.col("a").unique())  
shape: (2, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 2   │
│ 1   │
└─────┘
>>> df.select(pl.col("a").unique(maintain_order=True))
shape: (2, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
└─────┘

unique_counts() → Self[source]

Return a count of the unique values in the order of appearance.

This method differs from value_counts in that it does not return the values, only the counts and might be faster

Examples

>>> df = pl.DataFrame(
...     {
...         "id": ["a", "b", "b", "c", "c", "c"],
...     }
... )
>>> df.select(
...     [
...         pl.col("id").unique_counts(),
...     ]
... )
shape: (3, 1)
┌─────┐
│ id  │
│ --- │
│ u32 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

upper_bound() → Self[source]

Calculate the upper bound.

Returns a unit Series with the highest value possible for the dtype of this expression.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 2, 1]})
>>> df.select(pl.col("a").upper_bound())
shape: (1, 1)
┌─────────────────────┐
│ a                   │
│ ---                 │
│ i64                 │
╞═════════════════════╡
│ 9223372036854775807 │
└─────────────────────┘

value_counts(*, multithreaded: bool = False, sort: bool = False) → Self[source]

Count all unique values and create a struct mapping value to count.

Parameters:

multithreaded:: Better to turn this off in the aggregation context, as it can lead to contention.
sort:: Ensure the output is sorted from most values to least.

Returns:

Expr: Expression of data type Struct.

Examples

>>> df = pl.DataFrame(
...     {
...         "id": ["a", "b", "b", "c", "c", "c"],
...     }
... )
>>> df.select(
...     [
...         pl.col("id").value_counts(sort=True),
...     ]
... )
shape: (3, 1)
┌───────────┐
│ id        │
│ ---       │
│ struct[2] │
╞═══════════╡
│ {"c",3}   │
│ {"b",2}   │
│ {"a",1}   │
└───────────┘

var(ddof: int = 1) → Self[source]

Get variance.

Parameters:

ddof: “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

Examples

>>> df = pl.DataFrame({"a": [-1, 0, 1]})
>>> df.select(pl.col("a").var())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘

where(predicate: Expr) → Self[source]

Filter a single column.

Alias for filter().

Parameters:

predicate: Boolean expression.

Examples

>>> df = pl.DataFrame(
...     {
...         "group_col": ["g1", "g1", "g2"],
...         "b": [1, 2, 3],
...     }
... )
>>> df.groupby("group_col").agg(
...     [
...         pl.col("b").where(pl.col("b") < 2).sum().alias("lt"),
...         pl.col("b").where(pl.col("b") >= 2).sum().alias("gte"),
...     ]
... ).sort("group_col")
shape: (2, 3)
┌───────────┬─────┬─────┐
│ group_col ┆ lt  ┆ gte │
│ ---       ┆ --- ┆ --- │
│ str       ┆ i64 ┆ i64 │
╞═══════════╪═════╪═════╡
│ g1        ┆ 1   ┆ 2   │
│ g2        ┆ 0   ┆ 3   │
└───────────┴─────┴─────┘

xor(other: Any) → Self[source]

Method equivalent of bitwise exclusive-or operator expr ^ other.

Parameters:

other: Integer or boolean value; accepts expression input.

Examples

>>> df = pl.DataFrame(
...     {"x": [True, False, True, False], "y": [True, True, False, False]}
... )
>>> df.with_columns(pl.col("x").xor(pl.col("y")).alias("x ^ y"))
shape: (4, 3)
┌───────┬───────┬───────┐
│ x     ┆ y     ┆ x ^ y │
│ ---   ┆ ---   ┆ ---   │
│ bool  ┆ bool  ┆ bool  │
╞═══════╪═══════╪═══════╡
│ true  ┆ true  ┆ false │
│ false ┆ true  ┆ true  │
│ true  ┆ false ┆ true  │
│ false ┆ false ┆ false │
└───────┴───────┴───────┘

>>> def binary_string(n: int) -> str:
...     return bin(n)[2:].zfill(8)
>>>
>>> df = pl.DataFrame(
...     data={"x": [10, 8, 250, 66], "y": [1, 2, 3, 4]},
...     schema={"x": pl.UInt8, "y": pl.UInt8},
... )
>>> df.with_columns(
...     pl.col("x").apply(binary_string).alias("bin_x"),
...     pl.col("y").apply(binary_string).alias("bin_y"),
...     pl.col("x").xor(pl.col("y")).alias("xor_xy"),
...     pl.col("x").xor(pl.col("y")).apply(binary_string).alias("bin_xor_xy"),
... )
shape: (4, 6)
┌─────┬─────┬──────────┬──────────┬────────┬────────────┐
│ x   ┆ y   ┆ bin_x    ┆ bin_y    ┆ xor_xy ┆ bin_xor_xy │
│ --- ┆ --- ┆ ---      ┆ ---      ┆ ---    ┆ ---        │
│ u8  ┆ u8  ┆ str      ┆ str      ┆ u8     ┆ str        │
╞═════╪═════╪══════════╪══════════╪════════╪════════════╡
│ 10  ┆ 1   ┆ 00001010 ┆ 00000001 ┆ 11     ┆ 00001011   │
│ 8   ┆ 2   ┆ 00001000 ┆ 00000010 ┆ 10     ┆ 00001010   │
│ 250 ┆ 3   ┆ 11111010 ┆ 00000011 ┆ 249    ┆ 11111001   │
│ 66  ┆ 4   ┆ 01000010 ┆ 00000100 ┆ 70     ┆ 01000110   │
└─────┴─────┴──────────┴──────────┴────────┴────────────┘