Series#

This page gives an overview of all public Series methods.

class polars.Series(
name: str | ArrayLike | None = None,
values: ArrayLike | None = None,
dtype: PolarsDataType | None = None,
*,
strict: bool = True,
nan_to_null: bool = False,
dtype_if_empty: PolarsDataType = Null,
)[source]

A Series represents a single column in a polars DataFrame.

Parameters:
namestr, default None

Name of the Series. Will be used as a column name when used in a DataFrame. When not specified, name is set to an empty string.

valuesArrayLike, default None

One-dimensional data in various forms. Supported are: Sequence, Series, pyarrow Array, and numpy ndarray.

dtypeDataType, default None

Data type of the resulting Series. If set to None (default), the data type is inferred from the values input. The strategy for data type inference depends on the strict parameter:

  • If strict is set to True (default), the inferred data type is equal to the first non-null value, or Null if all values are null.

  • If strict is set to False, the inferred data type is the supertype of the values, or Object if no supertype can be found. WARNING: A full pass over the values is required to determine the supertype.

  • If no values were passed, the resulting data type is Null.

strictbool, default True

Throw an error if any value does not exactly match the given or inferred data type. If set to False, values that do not match the data type are cast to that data type or, if casting is not possible, set to null instead.

nan_to_nullbool, default False

In case a numpy array is used to create this Series, indicate how to deal with np.nan values. (This parameter is a no-op on non-numpy data).

dtype_if_emptyDataType, default Null

Data type of the Series if values contains no non-null data.

Deprecated since version 0.20.6: The data type for empty Series will always be Null, unless dtype is specified. To preserve behavior, check if the resulting Series has data type Null and cast to the desired data type. This parameter will be removed in the next breaking release.

Examples

Constructing a Series by specifying name and values positionally:

>>> s = pl.Series("a", [1, 2, 3])
>>> s
shape: (3,)
Series: 'a' [i64]
[
        1
        2
        3
]

Notice that the dtype is automatically inferred as a polars Int64:

>>> s.dtype
Int64

Constructing a Series with a specific dtype:

>>> s2 = pl.Series("a", [1, 2, 3], dtype=pl.Float32)
>>> s2
shape: (3,)
Series: 'a' [f32]
[
    1.0
    2.0
    3.0
]

It is possible to construct a Series with values as the first positional argument. This syntax considered an anti-pattern, but it can be useful in certain scenarios. You must specify any other arguments through keywords.

>>> s3 = pl.Series([1, 2, 3])
>>> s3
shape: (3,)
Series: '' [i64]
[
        1
        2
        3
]

Methods:

abs

Compute absolute values.

alias

Rename the series.

all

Return whether all values in the column are True.

any

Return whether any of the values in the column are True.

append

Append a Series to this one.

apply

Apply a custom/user-defined function (UDF) over elements in this Series.

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 Series.

arg_true

Get index values where Boolean Series evaluate True.

arg_unique

Get unique index as Series.

bottom_k

Return the k smallest elements.

cast

Cast between data types.

cbrt

Compute the cube root of the elements.

ceil

Rounds up to the nearest integer value.

chunk_lengths

Get the length of each individual chunk.

clear

Create an empty copy of the current Series, with zero to 'n' elements.

clip

Set values outside the given boundaries to the boundary value.

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.

clone

Create a copy of this Series.

cos

Compute the element-wise value for the cosine.

cosh

Compute the element-wise value for the hyperbolic cosine.

cot

Compute the element-wise value for the cotangent.

count

Return the number of non-null elements in the column.

cum_count

Return the cumulative count of the non-null values in the column.

cum_max

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

cum_min

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

cum_prod

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

cum_sum

Get an array with the cumulative sum 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.

describe

Quick summary statistics of a Series.

diff

Calculate the first discrete difference between shifted items.

dot

Compute the dot/inner product between two Series.

drop_nans

Drop all floating point NaN values.

drop_nulls

Drop all null values.

entropy

Computes the entropy.

eq

Method equivalent of operator expression series == other.

eq_missing

Method equivalent of equality operator series == other where None == None.

equals

Check whether the Series is equal to another Series.

estimated_size

Return an estimation of the total (heap) allocated size of the Series.

ewm_mean

Exponentially-weighted moving average.

ewm_mean_by

Calculate time-based exponentially weighted moving average.

ewm_std

Exponentially-weighted moving standard deviation.

ewm_var

Exponentially-weighted moving variance.

exp

Compute the exponential, element-wise.

explode

Explode a list Series.

extend

Extend the memory backed by this Series with the values from another.

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 elements by a boolean mask.

floor

Rounds down to the nearest integer value.

gather

Take values by index.

gather_every

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

ge

Method equivalent of operator expression series >= other.

get_chunks

Get the chunks of this Series as a list of Series.

gt

Method equivalent of operator expression series > other.

has_nulls

Check whether the Series contains one or more null values.

has_validity

Return True if the Series has a validity bitmask.

hash

Hash the Series.

head

Get the first n elements.

hist

Bin values into buckets and count their occurrences.

implode

Aggregate values into a list.

interpolate

Fill null values using interpolation.

interpolate_by

Fill null values using interpolation based on another column.

is_between

Get a boolean mask of the values that are between the given lower/upper bounds.

is_boolean

Check if this Series is a Boolean.

is_duplicated

Get mask of all duplicated values.

is_empty

Check if the Series is empty.

is_finite

Returns a boolean Series indicating which values are finite.

is_first

Return a boolean mask indicating the first occurrence of each distinct value.

is_first_distinct

Return a boolean mask indicating the first occurrence of each distinct value.

is_float

Check if this Series has floating point numbers.

is_in

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

is_infinite

Returns a boolean Series indicating which values are infinite.

is_integer

Check if this Series datatype is an integer (signed or unsigned).

is_last

Return a boolean mask indicating the last occurrence of each distinct value.

is_last_distinct

Return a boolean mask indicating the last occurrence of each distinct value.

is_nan

Returns a boolean Series indicating which values are not NaN.

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_numeric

Check if this Series datatype is numeric.

is_sorted

Check if the Series is sorted.

is_temporal

Check if this Series datatype is temporal.

is_unique

Get mask of all unique values.

is_utf8

Check if this Series datatype is a String.

item

Return the Series as a scalar, or return the element at the given index.

kurtosis

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

le

Method equivalent of operator expression series <= other.

len

Return the number of elements in the Series.

limit

Get the first n elements.

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 the input array plus one, element-wise.

lower_bound

Return the lower bound of this Series' dtype as a unit Series.

lt

Method equivalent of operator expression series < other.

map_dict

Replace values in the Series using a remapping dictionary.

map_elements

Map a custom/user-defined function (UDF) over elements in this Series.

max

Get the maximum value in this Series.

mean

Reduce this Series to the mean value.

median

Get the median of this Series.

min

Get the minimal value in this Series.

mode

Compute the most occurring value(s).

n_chunks

Get the number of chunks that this Series contains.

n_unique

Count the number of unique values in this Series.

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 operator expression series != other.

ne_missing

Method equivalent of equality operator series != other where None == None.

new_from_index

Create a new Series filled with values from the given index.

not_

Negate a boolean Series.

null_count

Count the null values in this Series.

pct_change

Computes percentage change between values.

peak_max

Get a boolean mask of the local maximum peaks.

peak_min

Get a boolean mask of the local minimum peaks.

pow

Raise to the power of the given exponent.

product

Reduce this Series to the product value.

qcut

Bin continuous values into discrete categories based on their quantiles.

quantile

Get the quantile value of this Series.

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.

rename

Rename this Series.

replace

Replace values by different values.

reshape

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

reverse

Return Series in reverse order.

rle

Compress the Series data using run-length encoding.

rle_id

Get a distinct integer ID for each run of identical values.

rolling_apply

Apply a custom rolling window function.

rolling_map

Compute 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 std dev.

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.

round_sig_figs

Round to a number of significant figures.

sample

Sample from this Series.

scatter

Set values at the index locations.

search_sorted

Find indices where elements should be inserted to maintain order.

series_equal

Check whether the Series is equal to another Series.

set

Set masked values.

set_at_idx

Set values at the index locations.

set_sorted

Flags the Series as 'sorted'.

shift

Shift values by the given number of indices.

shift_and_fill

Shift values by the given number of places and fill the resulting null values.

shrink_dtype

Shrink numeric columns to the minimal required datatype.

shrink_to_fit

Shrink Series memory usage.

shuffle

Shuffle the contents of this Series.

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 Series.

sort

Sort this Series.

sqrt

Compute the square root of the elements.

std

Get the standard deviation of this Series.

sum

Reduce this Series to the sum value.

tail

Get the last n elements.

take

Take values by index.

take_every

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

tan

Compute the element-wise value for the tangent.

tanh

Compute the element-wise value for the hyperbolic tangent.

to_arrow

Return the underlying Arrow array.

to_dummies

Get dummy/indicator variables.

to_frame

Cast this Series to a DataFrame.

to_init_repr

Convert Series to instantiatable string representation.

to_jax

Convert this Series to a Jax Array.

to_list

Convert this Series to a Python list.

to_numpy

Convert this Series to a NumPy ndarray.

to_pandas

Convert this Series to a pandas Series.

to_physical

Cast to physical representation of the logical dtype.

to_torch

Convert this Series to a PyTorch Tensor.

top_k

Return the k largest elements.

unique

Get unique elements in series.

unique_counts

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

upper_bound

Return the upper bound of this Series' dtype as a unit Series.

value_counts

Count the occurrences of unique values.

var

Get variance of this Series.

view

Get a view into this Series data with a numpy array.

zip_with

Take values from self or other based on the given mask.

Attributes:

dtype

Get the data type of this Series.

flags

Get flags that are set on the Series.

name

Get the name of this Series.

plot

Create a plot namespace.

shape

Shape of this Series.

abs() Series[source]

Compute absolute values.

Same as abs(series).

Examples

>>> s = pl.Series([1, -2, -3])
>>> s.abs()
shape: (3,)
Series: '' [i64]
[
    1
    2
    3
]
alias(name: str) Series[source]

Rename the series.

Parameters:
name

The new name.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.alias("b")
shape: (3,)
Series: 'b' [i64]
[
        1
        2
        3
]
all(*, ignore_nulls: bool = True) bool | None[source]

Return whether all values in the column are True.

Only works on columns of data type Boolean.

Parameters:
ignore_nulls

Ignore null values (default).

If set to False, Kleene logic is used to deal with nulls: if the column contains any null values and no False values, the output is None.

Returns:
bool or None

Examples

>>> pl.Series([True, True]).all()
True
>>> pl.Series([False, True]).all()
False
>>> pl.Series([None, True]).all()
True

Enable Kleene logic by setting ignore_nulls=False.

>>> pl.Series([None, True]).all(ignore_nulls=False)  # Returns None
any(*, ignore_nulls: bool = True) bool | None[source]

Return whether any of the values in the column are True.

Only works on columns of data type Boolean.

Parameters:
ignore_nulls

Ignore null values (default).

If set to False, Kleene logic is used to deal with nulls: if the column contains any null values and no True values, the output is None.

Returns:
bool or None

Examples

>>> pl.Series([True, False]).any()
True
>>> pl.Series([False, False]).any()
False
>>> pl.Series([None, False]).any()
False

Enable Kleene logic by setting ignore_nulls=False.

>>> pl.Series([None, False]).any(ignore_nulls=False)  # Returns None
append(other: Series) Self[source]

Append a Series to this one.

The resulting series will consist of multiple chunks.

Parameters:
other

Series to append.

Warning

This method modifies the series in-place. The series is returned for convenience only.

See also

extend

Examples

>>> a = pl.Series("a", [1, 2, 3])
>>> b = pl.Series("b", [4, 5])
>>> a.append(b)
shape: (5,)
Series: 'a' [i64]
[
    1
    2
    3
    4
    5
]

The resulting series will consist of multiple chunks.

>>> a.n_chunks()
2
apply(
function: Callable[[Any], Any],
return_dtype: PolarsDataType | None = None,
*,
skip_nulls: bool = True,
) Self[source]

Apply a custom/user-defined function (UDF) over elements in this Series.

Deprecated since version 0.19.0: This method has been renamed to Series.map_elements().

Parameters:
function

Custom function or lambda.

return_dtype

Output datatype. If none is given, the same datatype as this Series will be used.

skip_nulls

Nulls will be skipped and not passed to the python function. This is faster because python can be skipped and because we call more specialized functions.

arccos() Series[source]

Compute the element-wise value for the inverse cosine.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.arccos()
shape: (3,)
Series: 'a' [f64]
[
    0.0
    1.570796
    3.141593
]
arccosh() Series[source]

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

Examples

>>> s = pl.Series("a", [5.0, 1.0, 0.0, -1.0])
>>> s.arccosh()
shape: (4,)
Series: 'a' [f64]
[
    2.292432
    0.0
    NaN
    NaN
]
arcsin() Series[source]

Compute the element-wise value for the inverse sine.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.arcsin()
shape: (3,)
Series: 'a' [f64]
[
    1.570796
    0.0
    -1.570796
]
arcsinh() Series[source]

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

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.arcsinh()
shape: (3,)
Series: 'a' [f64]
[
    0.881374
    0.0
    -0.881374
]
arctan() Series[source]

Compute the element-wise value for the inverse tangent.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.arctan()
shape: (3,)
Series: 'a' [f64]
[
    0.785398
    0.0
    -0.785398
]
arctanh() Series[source]

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

Examples

>>> s = pl.Series("a", [2.0, 1.0, 0.5, 0.0, -0.5, -1.0, -1.1])
>>> s.arctanh()
shape: (7,)
Series: 'a' [f64]
[
    NaN
    inf
    0.549306
    0.0
    -0.549306
    -inf
    NaN
]
arg_max() int | None[source]

Get the index of the maximal value.

Returns:
int

Examples

>>> s = pl.Series("a", [3, 2, 1])
>>> s.arg_max()
0
arg_min() int | None[source]

Get the index of the minimal value.

Returns:
int

Examples

>>> s = pl.Series("a", [3, 2, 1])
>>> s.arg_min()
2
arg_sort(
*,
descending: bool = False,
nulls_last: bool = False,
) Series[source]

Get the index values that would sort this Series.

Parameters:
descending

Sort in descending order.

nulls_last

Place null values last instead of first.

See also

Series.gather

Take values by index.

Series.rank

Get the rank of each row.

Examples

>>> s = pl.Series("a", [5, 3, 4, 1, 2])
>>> s.arg_sort()
shape: (5,)
Series: 'a' [u32]
[
    3
    4
    1
    2
    0
]
arg_true() Series[source]

Get index values where Boolean Series evaluate True.

Returns:
Series

Series of data type UInt32.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> (s == 2).arg_true()
shape: (1,)
Series: 'a' [u32]
[
        1
]
arg_unique() Series[source]

Get unique index as Series.

Returns:
Series

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.arg_unique()
shape: (3,)
Series: 'a' [u32]
[
        0
        1
        3
]
bottom_k(k: int = 5) Series[source]

Return the k smallest elements.

This has time complexity:

\[O(n + k \log{n})\]
Parameters:
k

Number of elements to return.

See also

top_k

Examples

>>> s = pl.Series("a", [2, 5, 1, 4, 3])
>>> s.bottom_k(3)
shape: (3,)
Series: 'a' [i64]
[
    1
    2
    3
]
cast(
dtype: PolarsDataType | type[int] | type[float] | type[str] | type[bool],
*,
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

>>> s = pl.Series("a", [True, False, True])
>>> s
shape: (3,)
Series: 'a' [bool]
[
    true
    false
    true
]
>>> s.cast(pl.UInt32)
shape: (3,)
Series: 'a' [u32]
[
    1
    0
    1
]
cbrt() Series[source]

Compute the cube root of the elements.

Optimization for

>>> pl.Series([1, 2]) ** (1.0 / 3)
shape: (2,)
Series: '' [f64]
[
    1.0
    1.259921
]

Examples

>>> s = pl.Series([1, 2, 3])
>>> s.cbrt()
shape: (3,)
Series: '' [f64]
[
    1.0
    1.259921
    1.44225
]
ceil() Series[source]

Rounds up to the nearest integer value.

Only works on floating point Series.

Examples

>>> s = pl.Series("a", [1.12345, 2.56789, 3.901234])
>>> s.ceil()
shape: (3,)
Series: 'a' [f64]
[
        2.0
        3.0
        4.0
]
chunk_lengths() list[int][source]

Get the length of each individual chunk.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s2 = pl.Series("a", [4, 5, 6])

Concatenate Series with rechunk = True

>>> pl.concat([s, s2], rechunk=True).chunk_lengths()
[6]

Concatenate Series with rechunk = False

>>> pl.concat([s, s2], rechunk=False).chunk_lengths()
[3, 3]
clear(n: int = 0) Series[source]

Create an empty copy of the current Series, with zero to ‘n’ elements.

The copy has an identical name/dtype, but no data.

Parameters:
n

Number of (empty) elements to return in the cleared frame.

See also

clone

Cheap deepcopy/clone.

Examples

>>> s = pl.Series("a", [None, True, False])
>>> s.clear()
shape: (0,)
Series: 'a' [bool]
[
]
>>> s.clear(n=2)
shape: (2,)
Series: 'a' [bool]
[
    null
    null
]
clip(
lower_bound: NumericLiteral | TemporalLiteral | IntoExprColumn | None = None,
upper_bound: NumericLiteral | TemporalLiteral | IntoExprColumn | None = None,
) Series[source]

Set values outside the given boundaries to the boundary value.

Parameters:
lower_bound

Lower bound. Accepts expression input. Non-expression inputs are parsed as literals. If set to None (default), no lower bound is applied.

upper_bound

Upper bound. Accepts expression input. Non-expression inputs are parsed as literals. If set to None (default), no upper bound is applied.

See also

when

Notes

This method only works for numeric and temporal columns. To clip other data types, consider writing a when-then-otherwise expression. See when().

Examples

Specifying both a lower and upper bound:

>>> s = pl.Series([-50, 5, 50, None])
>>> s.clip(1, 10)
shape: (4,)
Series: '' [i64]
[
        1
        5
        10
        null
]

Specifying only a single bound:

>>> s.clip(upper_bound=10)
shape: (4,)
Series: '' [i64]
[
        -50
        5
        10
        null
]
clip_max(
upper_bound: NumericLiteral | TemporalLiteral | IntoExprColumn,
) Series[source]

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

Deprecated since version 0.19.12: Use clip() instead.

Parameters:
upper_bound

Upper bound.

clip_min(
lower_bound: NumericLiteral | TemporalLiteral | IntoExprColumn,
) Series[source]

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

Deprecated since version 0.19.12: Use clip() instead.

Parameters:
lower_bound

Lower bound.

clone() Self[source]

Create a copy of this Series.

This is a cheap operation that does not copy data.

See also

clear

Create an empty copy of the current Series, with identical schema but no data.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.clone()
shape: (3,)
Series: 'a' [i64]
[
        1
        2
        3
]
cos() Series[source]

Compute the element-wise value for the cosine.

Examples

>>> import math
>>> s = pl.Series("a", [0.0, math.pi / 2.0, math.pi])
>>> s.cos()
shape: (3,)
Series: 'a' [f64]
[
    1.0
    6.1232e-17
    -1.0
]
cosh() Series[source]

Compute the element-wise value for the hyperbolic cosine.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.cosh()
shape: (3,)
Series: 'a' [f64]
[
    1.543081
    1.0
    1.543081
]
cot() Series[source]

Compute the element-wise value for the cotangent.

Examples

>>> import math
>>> s = pl.Series("a", [0.0, math.pi / 2.0, math.pi])
>>> s.cot()
shape: (3,)
Series: 'a' [f64]
[
    inf
    6.1232e-17
    -8.1656e15
]
count() int[source]

Return the number of non-null elements in the column.

See also

len

Examples

>>> s = pl.Series("a", [1, 2, None])
>>> s.count()
2
cum_count(*, reverse: bool = False) Self[source]

Return the cumulative count of the non-null values in the column.

Parameters:
reverse

Reverse the operation.

Examples

>>> s = pl.Series(["x", "k", None, "d"])
>>> s.cum_count()
shape: (4,)
Series: '' [u32]
[
        1
        2
        2
        3
]
cum_max(*, reverse: bool = False) Series[source]

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

Parameters:
reverse

reverse the operation.

Examples

>>> s = pl.Series("s", [3, 5, 1])
>>> s.cum_max()
shape: (3,)
Series: 's' [i64]
[
    3
    5
    5
]
cum_min(*, reverse: bool = False) Series[source]

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

Parameters:
reverse

reverse the operation.

Examples

>>> s = pl.Series("s", [1, 2, 3])
>>> s.cum_min()
shape: (3,)
Series: 's' [i64]
[
    1
    1
    1
]
cum_prod(*, reverse: bool = False) Series[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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.cum_prod()
shape: (3,)
Series: 'a' [i64]
[
    1
    2
    6
]
cum_sum(*, reverse: bool = False) Series[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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.cum_sum()
shape: (3,)
Series: 'a' [i64]
[
    1
    3
    6
]
cummax(*, reverse: bool = False) Series[source]

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

Deprecated since version 0.19.14: This method has been renamed to cum_max().

Parameters:
reverse

reverse the operation.

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

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

Deprecated since version 0.19.14: This method has been renamed to cum_min().

Parameters:
reverse

reverse the operation.

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

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

Deprecated since version 0.19.14: This method has been renamed to cum_prod().

Parameters:
reverse

reverse the operation.

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

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

Deprecated since version 0.19.14: This method has been renamed to cum_sum().

Parameters:
reverse

reverse the operation.

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

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

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 group by or another operation that already has much parallelization.

Warning

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

Examples

>>> s = pl.Series("values", [1, 2, 3, 4, 5])
>>> s.cumulative_eval(pl.element().first() - pl.element().last() ** 2)
shape: (5,)
Series: 'values' [i64]
[
    0
    -3
    -8
    -15
    -24
]
cut(
breaks: Sequence[float],
*,
labels: Sequence[str] | None = None,
break_point_label: str = 'break_point',
category_label: str = 'category',
left_closed: bool = False,
include_breaks: bool = False,
as_series: bool = True,
) Series | DataFrame[source]

Bin continuous values into discrete categories.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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.

break_point_label

Name of the breakpoint column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.

category_label

Name of the category column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.

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.

as_series

If set to False, return a DataFrame containing the original values, the breakpoints, and the categories.

Deprecated since version 0.19.0: This parameter will be removed. The same behavior can be achieved by setting include_breaks=True, unnesting the resulting struct Series, and adding the result to the original Series.

Returns:
Series

Series of data type Categorical if include_breaks is set to False (default), otherwise a Series of data type Struct.

See also

qcut

Examples

Divide the column into three categories.

>>> s = pl.Series("foo", [-2, -1, 0, 1, 2])
>>> s.cut([-1, 1], labels=["a", "b", "c"])
shape: (5,)
Series: 'foo' [cat]
[
        "a"
        "a"
        "b"
        "b"
        "c"
]

Create a DataFrame with the breakpoint and category for each value.

>>> cut = s.cut([-1, 1], include_breaks=True).alias("cut")
>>> s.to_frame().with_columns(cut).unnest("cut")
shape: (5, 3)
┌─────┬─────────────┬────────────┐
│ foo ┆ break_point ┆ category   │
│ --- ┆ ---         ┆ ---        │
│ 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]   │
└─────┴─────────────┴────────────┘
describe(
percentiles: Sequence[float] | float | None = (0.25, 0.5, 0.75),
interpolation: RollingInterpolationMethod = 'nearest',
) DataFrame[source]

Quick summary statistics of a Series.

Series with mixed datatypes will return summary statistics for the datatype of the first value.

Parameters:
percentiles

One or more percentiles to include in the summary statistics (if the Series has a numeric dtype). All values must be in the range [0, 1].

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

Interpolation method used when calculating percentiles.

Returns:
DataFrame

Mapping with summary statistics of a Series.

Notes

The median is included by default as the 50% percentile.

Examples

>>> s = pl.Series([1, 2, 3, 4, 5])
>>> s.describe()
shape: (9, 2)
┌────────────┬──────────┐
│ statistic  ┆ value    │
│ ---        ┆ ---      │
│ str        ┆ f64      │
╞════════════╪══════════╡
│ count      ┆ 5.0      │
│ null_count ┆ 0.0      │
│ mean       ┆ 3.0      │
│ std        ┆ 1.581139 │
│ min        ┆ 1.0      │
│ 25%        ┆ 2.0      │
│ 50%        ┆ 3.0      │
│ 75%        ┆ 4.0      │
│ max        ┆ 5.0      │
└────────────┴──────────┘

Non-numeric data types may not have all statistics available.

>>> s = pl.Series(["aa", "aa", None, "bb", "cc"])
>>> s.describe()
shape: (4, 2)
┌────────────┬───────┐
│ statistic  ┆ value │
│ ---        ┆ ---   │
│ str        ┆ str   │
╞════════════╪═══════╡
│ count      ┆ 4     │
│ null_count ┆ 1     │
│ min        ┆ aa    │
│ max        ┆ cc    │
└────────────┴───────┘
diff(n: int = 1, null_behavior: NullBehavior = 'ignore') Series[source]

Calculate the first discrete difference between shifted items.

Parameters:
n

Number of slots to shift.

null_behavior{‘ignore’, ‘drop’}

How to handle null values.

Examples

>>> s = pl.Series("s", values=[20, 10, 30, 25, 35], dtype=pl.Int8)
>>> s.diff()
shape: (5,)
Series: 's' [i8]
[
    null
    -10
    20
    -5
    10
]
>>> s.diff(n=2)
shape: (5,)
Series: 's' [i8]
[
    null
    null
    10
    15
    5
]
>>> s.diff(n=2, null_behavior="drop")
shape: (3,)
Series: 's' [i8]
[
    10
    15
    5
]
dot(other: Series | ArrayLike) int | float | None[source]

Compute the dot/inner product between two Series.

Parameters:
other

Series (or array) to compute dot product with.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s2 = pl.Series("b", [4.0, 5.0, 6.0])
>>> s.dot(s2)
32.0
drop_nans() Series[source]

Drop all floating point NaN values.

The original order of the remaining elements is preserved.

See also

drop_nulls

Notes

A NaN value is not the same as a null value. To drop null values, use drop_nulls().

Examples

>>> s = pl.Series([1.0, None, 3.0, float("nan")])
>>> s.drop_nans()
shape: (3,)
Series: '' [f64]
[
        1.0
        null
        3.0
]
drop_nulls() Series[source]

Drop all null values.

The original order of the remaining elements is preserved.

See also

drop_nans

Notes

A null value is not the same as a NaN value. To drop NaN values, use drop_nans().

Examples

>>> s = pl.Series([1.0, None, 3.0, float("nan")])
>>> s.drop_nulls()
shape: (3,)
Series: '' [f64]
[
        1.0
        3.0
        NaN
]
property dtype: DataType[source]

Get the data type of this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.dtype
Int64
entropy(
base: float = 2.718281828459045,
*,
normalize: bool = False,
) float | None[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

>>> a = pl.Series([0.99, 0.005, 0.005])
>>> a.entropy(normalize=True)
0.06293300616044681
>>> b = pl.Series([0.65, 0.10, 0.25])
>>> b.entropy(normalize=True)
0.8568409950394724
eq(other: Any) Series | Expr[source]

Method equivalent of operator expression series == other.

eq_missing(other: Any) Series | Expr[source]

Method equivalent of equality operator series == other where None == None.

This differs from the standard ne where null values are propagated.

Parameters:
other

A literal or expression value to compare with.

See also

ne_missing
eq

Examples

>>> s1 = pl.Series("a", [333, 200, None])
>>> s2 = pl.Series("a", [100, 200, None])
>>> s1.eq(s2)
shape: (3,)
Series: 'a' [bool]
[
    false
    true
    null
]
>>> s1.eq_missing(s2)
shape: (3,)
Series: 'a' [bool]
[
    false
    true
    true
]
equals(
other: Series,
*,
check_dtypes: bool = False,
null_equal: bool = True,
) bool[source]

Check whether the Series is equal to another Series.

Parameters:
other

Series to compare with.

check_dtypes

Require data types to match.

null_equal

Consider null values as equal.

See also

assert_series_equal

Examples

>>> s1 = pl.Series("a", [1, 2, 3])
>>> s2 = pl.Series("b", [4, 5, 6])
>>> s1.equals(s1)
True
>>> s1.equals(s2)
False
estimated_size(unit: SizeUnit = 'b') int | float[source]

Return an estimation of the total (heap) allocated size of the Series.

Estimated size is given in the specified unit (bytes by default).

This estimation is the sum of the size of its buffers, validity, including nested arrays. Multiple arrays may share buffers and bitmaps. Therefore, the size of 2 arrays is not the sum of the sizes computed from this function. In particular, [StructArray]’s size is an upper bound.

When an array is sliced, its allocated size remains constant because the buffer unchanged. However, this function will yield a smaller number. This is because this function returns the visible size of the buffer, not its total capacity.

FFI buffers are included in this estimation.

Parameters:
unit{‘b’, ‘kb’, ‘mb’, ‘gb’, ‘tb’}

Scale the returned size to the given unit.

Examples

>>> s = pl.Series("values", list(range(1_000_000)), dtype=pl.UInt32)
>>> s.estimated_size()
4000000
>>> s.estimated_size("mb")
3.814697265625
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 | None = None,
) Series[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 default) 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, 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 (current default), 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

>>> s = pl.Series([1, 2, 3])
>>> s.ewm_mean(com=1, ignore_nulls=False)
shape: (3,)
Series: '' [f64]
[
        1.0
        1.666667
        2.428571
]
ewm_mean_by(by: IntoExpr, *, half_life: str | timedelta) Series[source]

Calculate time-based exponentially weighted moving average.

Given observations \(x_1, x_2, \ldots, x_n\) at times \(t_1, t_2, \ldots, t_n\), the EWMA is calculated as

\[ \begin{align}\begin{aligned}y_0 &= x_0\\\alpha_i &= \exp(-\lambda(t_i - t_{i-1}))\\y_i &= \alpha_i x_i + (1 - \alpha_i) y_{i-1}; \quad i > 0\end{aligned}\end{align} \]

where \(\lambda\) equals \(\ln(2) / \text{half_life}\).

Parameters:
by

Times to calculate average by. Should be DateTime, Date, UInt64, UInt32, Int64, or Int32 data type.

half_life

Unit over which observation decays to half its value.

Can be created either from a timedelta, or by using the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 day)

  • 1w (1 week)

  • 1i (1 index count)

Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

Note that half_life is treated as a constant duration - calendar durations such as months (or even days in the time-zone-aware case) are not supported, please express your duration in an approximately equivalent number of hours (e.g. ‘370h’ instead of ‘1mo’).

check_sorted

Check whether by column is sorted. Incorrectly setting this to False will lead to incorrect output.

Returns:
Expr

Float32 if input is Float32, otherwise Float64.

Examples

>>> from datetime import date, timedelta
>>> df = pl.DataFrame(
...     {
...         "values": [0, 1, 2, None, 4],
...         "times": [
...             date(2020, 1, 1),
...             date(2020, 1, 3),
...             date(2020, 1, 10),
...             date(2020, 1, 15),
...             date(2020, 1, 17),
...         ],
...     }
... ).sort("times")
>>> df["values"].ewm_mean_by(df["times"], half_life="4d")
shape: (5,)
Series: 'values' [f64]
[
        0.0
        0.292893
        1.492474
        null
        3.254508
]
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 | None = None,
) Series[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 default) 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, 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 (current default), 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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.ewm_std(com=1, ignore_nulls=False)
shape: (3,)
Series: '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 | None = None,
) Series[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 default) 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, 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 (current default), 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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.ewm_var(com=1, ignore_nulls=False)
shape: (3,)
Series: 'a' [f64]
[
    0.0
    0.5
    0.928571
]
exp() Series[source]

Compute the exponential, element-wise.

Examples

>>> s = pl.Series([1, 2, 3])
>>> s.exp()
shape: (3,)
Series: '' [f64]
[
    2.718282
    7.389056
    20.085537
]
explode() Series[source]

Explode a list Series.

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

Returns:
Series

Series with the data type of the list elements.

See also

Series.list.explode

Explode a list column.

Examples

>>> s = pl.Series("a", [[1, 2, 3], [4, 5, 6]])
>>> s
shape: (2,)
Series: 'a' [list[i64]]
[
        [1, 2, 3]
        [4, 5, 6]
]
>>> s.explode()
shape: (6,)
Series: 'a' [i64]
[
        1
        2
        3
        4
        5
        6
]
extend(other: Series) Self[source]

Extend the memory backed by this Series with the values from another.

Different from append, which adds the chunks from other to the chunks of this series, extend appends the data from other to the underlying memory locations and thus may cause a reallocation (which is expensive).

If this does not cause a reallocation, the resulting data structure will not have any extra chunks and thus will yield faster queries.

Prefer extend over append when you want to do a query after a single append. For instance, during online operations where you add n rows and rerun a query.

Prefer append over extend when you want to append many times before doing a query. For instance, when you read in multiple files and want to store them in a single Series. In the latter case, finish the sequence of append operations with a rechunk.

Parameters:
other

Series to extend the series with.

Warning

This method modifies the series in-place. The series is returned for convenience only.

See also

append

Examples

>>> a = pl.Series("a", [1, 2, 3])
>>> b = pl.Series("b", [4, 5])
>>> a.extend(b)
shape: (5,)
Series: 'a' [i64]
[
    1
    2
    3
    4
    5
]

The resulting series will consist of a single chunk.

>>> a.n_chunks()
1
extend_constant(value: IntoExpr, n: int | IntoExprColumn) Series[source]

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

Parameters:
value

A constant literal value or a unit expressioin 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

>>> s = pl.Series([1, 2, 3])
>>> s.extend_constant(99, n=2)
shape: (5,)
Series: '' [i64]
[
        1
        2
        3
        99
        99
]
fill_nan(value: int | float | Expr | None) Series[source]

Fill floating point NaN value with a fill value.

Parameters:
value

Value used to fill NaN values.

Warning

Note that floating point NaNs (Not a Number) are not missing values. To replace missing values, use fill_null().

See also

fill_null

Examples

>>> s = pl.Series("a", [1, 2, 3, float("nan")])
>>> s.fill_nan(0)
shape: (4,)
Series: 'a' [f64]
[
        1.0
        2.0
        3.0
        0.0
]
fill_null(
value: Any | Expr | None = None,
strategy: FillNullStrategy | None = None,
limit: int | None = None,
) Series[source]

Fill null values using the specified value or strategy.

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.

See also

fill_nan

Examples

>>> s = pl.Series("a", [1, 2, 3, None])
>>> s.fill_null(strategy="forward")
shape: (4,)
Series: 'a' [i64]
[
    1
    2
    3
    3
]
>>> s.fill_null(strategy="min")
shape: (4,)
Series: 'a' [i64]
[
    1
    2
    3
    1
]
>>> s = pl.Series("b", ["x", None, "z"])
>>> s.fill_null(pl.lit(""))
shape: (3,)
Series: 'b' [str]
[
    "x"
    ""
    "z"
]
filter(predicate: Series | list[bool]) Self[source]

Filter elements by a boolean mask.

The original order of the remaining elements is preserved.

Elements where the filter does not evaluate to True are discarded, including nulls.

Parameters:
predicate

Boolean mask.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> mask = pl.Series("", [True, False, True])
>>> s.filter(mask)
shape: (2,)
Series: 'a' [i64]
[
        1
        3
]
property flags: dict[str, bool][source]

Get flags that are set on the Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.flags
{'SORTED_ASC': False, 'SORTED_DESC': False}
floor() Series[source]

Rounds down to the nearest integer value.

Only works on floating point Series.

Examples

>>> s = pl.Series("a", [1.12345, 2.56789, 3.901234])
>>> s.floor()
shape: (3,)
Series: 'a' [f64]
[
        1.0
        2.0
        3.0
]
gather(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) Series[source]

Take values by index.

Parameters:
indices

Index location used for selection.

Examples

>>> s = pl.Series("a", [1, 2, 3, 4])
>>> s.gather([1, 3])
shape: (2,)
Series: 'a' [i64]
[
        2
        4
]
gather_every(n: int, offset: int = 0) Series[source]

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

Parameters:
n

Gather every n-th row.

offset

Start the row index at this offset.

Examples

>>> s = pl.Series("a", [1, 2, 3, 4])
>>> s.gather_every(2)
shape: (2,)
Series: 'a' [i64]
[
    1
    3
]
>>> s.gather_every(2, offset=1)
shape: (2,)
Series: 'a' [i64]
[
    2
    4
]
ge(other: Any) Series | Expr[source]

Method equivalent of operator expression series >= other.

get_chunks() list[Series][source]

Get the chunks of this Series as a list of Series.

Examples

>>> s1 = pl.Series("a", [1, 2, 3])
>>> s2 = pl.Series("a", [4, 5, 6])
>>> s = pl.concat([s1, s2], rechunk=False)
>>> s.get_chunks()
[shape: (3,)
Series: 'a' [i64]
[
        1
        2
        3
], shape: (3,)
Series: 'a' [i64]
[
        4
        5
        6
]]
gt(other: Any) Series | Expr[source]

Method equivalent of operator expression series > other.

has_nulls() bool[source]

Check whether the Series contains one or more null values.

Examples

>>> s = pl.Series([1, 2, None])
>>> s.has_nulls()
True
>>> s[:2].has_nulls()
False
has_validity() bool[source]

Return True if the Series has a validity bitmask.

Deprecated since version 0.20.30: Use has_nulls() instead.

If there is no mask, it means that there are no null values.

Notes

While the absence of a validity bitmask guarantees that a Series does not have null values, the converse is not true, eg: the presence of a bitmask does not mean that there are null values, as every value of the bitmask could be false.

To confirm that a column has null values use has_nulls().

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

Hash the Series.

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.

Notes

This implementation of hash does not guarantee stable results across different Polars versions. Its stability is only guaranteed within a single version.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.hash(seed=42)
shape: (3,)
Series: 'a' [u64]
[
    10734580197236529959
    3022416320763508302
    13756996518000038261
]
head(n: int = 10) Series[source]

Get the first n elements.

Parameters:
n

Number of elements to return. If a negative value is passed, return all elements except the last abs(n).

See also

tail, slice

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.head(3)
shape: (3,)
Series: 'a' [i64]
[
        1
        2
        3
]

Pass a negative value to get all rows except the last abs(n).

>>> s.head(-3)
shape: (2,)
Series: 'a' [i64]
[
        1
        2
]
hist(
bins: list[float] | None = None,
*,
bin_count: int | None = None,
include_category: bool = True,
include_breakpoint: bool = True,
) DataFrame[source]

Bin values into buckets and count their occurrences.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Parameters:
bins

Discretizations to make. If None given, we determine the boundaries based on the data.

bin_count

If no bins provided, this will be used to determine the distance of the bins

include_breakpoint

Include a column that indicates the upper breakpoint.

include_category

Include a column that shows the intervals as categories.

Returns:
DataFrame

Examples

>>> a = pl.Series("a", [1, 3, 8, 8, 2, 1, 3])
>>> a.hist(bin_count=4)
shape: (5, 3)
┌─────────────┬─────────────┬───────┐
│ break_point ┆ category    ┆ count │
│ ---         ┆ ---         ┆ ---   │
│ f64         ┆ cat         ┆ u32   │
╞═════════════╪═════════════╪═══════╡
│ 0.0         ┆ (-inf, 0.0] ┆ 0     │
│ 2.25        ┆ (0.0, 2.25] ┆ 3     │
│ 4.5         ┆ (2.25, 4.5] ┆ 2     │
│ 6.75        ┆ (4.5, 6.75] ┆ 0     │
│ inf         ┆ (6.75, inf] ┆ 2     │
└─────────────┴─────────────┴───────┘
implode() Self[source]

Aggregate values into a list.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.implode()
shape: (1,)
Series: 'a' [list[i64]]
[
    [1, 2, 3]
]
interpolate(method: InterpolationMethod = 'linear') Series[source]

Fill null values using interpolation.

Parameters:
method{‘linear’, ‘nearest’}

Interpolation method.

Examples

>>> s = pl.Series("a", [1, 2, None, None, 5])
>>> s.interpolate()
shape: (5,)
Series: 'a' [f64]
[
    1.0
    2.0
    3.0
    4.0
    5.0
]
interpolate_by(by: IntoExpr) Series[source]

Fill null values using interpolation based on another column.

Parameters:
by

Column to interpolate values based on.

Examples

Fill null values using linear interpolation.

>>> s = pl.Series([1, None, None, 3])
>>> by = pl.Series([1, 2, 7, 8])
>>> s.interpolate_by(by)
shape: (4,)
Series: '' [f64]
[
    1.0
    1.285714
    2.714286
    3.0
]
is_between(
lower_bound: IntoExpr,
upper_bound: IntoExpr,
closed: ClosedInterval = 'both',
) Series[source]

Get a boolean mask of the values that are between the given lower/upper bounds.

Parameters:
lower_bound

Lower bound value. Accepts expression input. Non-expression inputs (including strings) are parsed as literals.

upper_bound

Upper bound value. Accepts expression input. Non-expression inputs (including strings) are parsed as literals.

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

Define which sides of the interval are closed (inclusive).

Notes

If the value of the lower_bound is greater than that of the upper_bound then the result will be False, as no value can satisfy the condition.

Examples

>>> s = pl.Series("num", [1, 2, 3, 4, 5])
>>> s.is_between(2, 4)
shape: (5,)
Series: 'num' [bool]
[
    false
    true
    true
    true
    false
]

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

>>> s.is_between(2, 4, closed="left")
shape: (5,)
Series: 'num' [bool]
[
    false
    true
    true
    false
    false
]

You can also use strings as well as numeric/temporal values:

>>> s = pl.Series("s", ["a", "b", "c", "d", "e"])
>>> s.is_between("b", "d", closed="both")
shape: (5,)
Series: 's' [bool]
[
    false
    true
    true
    true
    false
]
is_boolean() bool[source]

Check if this Series is a Boolean.

Deprecated since version 0.19.14: Use Series.dtype == pl.Boolean instead.

Examples

>>> s = pl.Series("a", [True, False, True])
>>> s.is_boolean()
True
is_duplicated() Series[source]

Get mask of all duplicated values.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.is_duplicated()
shape: (4,)
Series: 'a' [bool]
[
        false
        true
        true
        false
]
is_empty() bool[source]

Check if the Series is empty.

Examples

>>> s = pl.Series("a", [], dtype=pl.Float32)
>>> s.is_empty()
True
is_finite() Series[source]

Returns a boolean Series indicating which values are finite.

Returns:
Series

Series of data type Boolean.

Examples

>>> import numpy as np
>>> s = pl.Series("a", [1.0, 2.0, np.inf])
>>> s.is_finite()
shape: (3,)
Series: 'a' [bool]
[
        true
        true
        false
]
is_first() Series[source]

Return a boolean mask indicating the first occurrence of each distinct value.

Deprecated since version 0.19.3: This method has been renamed to Series.is_first_distinct().

Returns:
Series

Series of data type Boolean.

is_first_distinct() Series[source]

Return a boolean mask indicating the first occurrence of each distinct value.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series([1, 1, 2, 3, 2])
>>> s.is_first_distinct()
shape: (5,)
Series: '' [bool]
[
        true
        false
        true
        true
        false
]
is_float() bool[source]

Check if this Series has floating point numbers.

Deprecated since version 0.19.13: Use Series.dtype.is_float() instead.

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0])
>>> s.is_float()
True
is_in(
other: Series | Collection[Any],
) Series[source]

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

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s2 = pl.Series("b", [2, 4])
>>> s2.is_in(s)
shape: (2,)
Series: 'b' [bool]
[
        true
        false
]
>>> # check if some values are a member of sublists
>>> sets = pl.Series("sets", [[1, 2, 3], [1, 2], [9, 10]])
>>> optional_members = pl.Series("optional_members", [1, 2, 3])
>>> print(sets)
shape: (3,)
Series: 'sets' [list[i64]]
[
    [1, 2, 3]
    [1, 2]
    [9, 10]
]
>>> print(optional_members)
shape: (3,)
Series: 'optional_members' [i64]
[
    1
    2
    3
]
>>> optional_members.is_in(sets)
shape: (3,)
Series: 'optional_members' [bool]
[
    true
    true
    false
]
is_infinite() Series[source]

Returns a boolean Series indicating which values are infinite.

Returns:
Series

Series of data type Boolean.

Examples

>>> import numpy as np
>>> s = pl.Series("a", [1.0, 2.0, np.inf])
>>> s.is_infinite()
shape: (3,)
Series: 'a' [bool]
[
        false
        false
        true
]
is_integer(signed: bool | None = None) bool[source]

Check if this Series datatype is an integer (signed or unsigned).

Deprecated since version 0.19.13: Use Series.dtype.is_integer() instead. For signed/unsigned variants, use Series.dtype.is_signed_integer() or Series.dtype.is_unsigned_integer().

Parameters:
signed
  • if None, both signed and unsigned integer dtypes will match.

  • if True, only signed integer dtypes will be considered a match.

  • if False, only unsigned integer dtypes will be considered a match.

Examples

>>> s = pl.Series("a", [1, 2, 3], dtype=pl.UInt32)
>>> s.is_integer()
True
>>> s.is_integer(signed=False)
True
>>> s.is_integer(signed=True)
False
is_last() Series[source]

Return a boolean mask indicating the last occurrence of each distinct value.

Deprecated since version 0.19.3: This method has been renamed to Series.is_last_distinct().

Returns:
Series

Series of data type Boolean.

is_last_distinct() Series[source]

Return a boolean mask indicating the last occurrence of each distinct value.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series([1, 1, 2, 3, 2])
>>> s.is_last_distinct()
shape: (5,)
Series: '' [bool]
[
        false
        true
        false
        true
        true
]
is_nan() Series[source]

Returns a boolean Series indicating which values are not NaN.

Returns:
Series

Series of data type Boolean.

Examples

>>> import numpy as np
>>> s = pl.Series("a", [1.0, 2.0, 3.0, np.nan])
>>> s.is_nan()
shape: (4,)
Series: 'a' [bool]
[
        false
        false
        false
        true
]
is_not_nan() Series[source]

Returns a boolean Series indicating which values are not NaN.

Returns:
Series

Series of data type Boolean.

Examples

>>> import numpy as np
>>> s = pl.Series("a", [1.0, 2.0, 3.0, np.nan])
>>> s.is_not_nan()
shape: (4,)
Series: 'a' [bool]
[
        true
        true
        true
        false
]
is_not_null() Series[source]

Returns a boolean Series indicating which values are not null.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, None])
>>> s.is_not_null()
shape: (4,)
Series: 'a' [bool]
[
    true
    true
    true
    false
]
is_null() Series[source]

Returns a boolean Series indicating which values are null.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, None])
>>> s.is_null()
shape: (4,)
Series: 'a' [bool]
[
    false
    false
    false
    true
]
is_numeric() bool[source]

Check if this Series datatype is numeric.

Deprecated since version 0.19.13: Use Series.dtype.is_numeric() instead.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.is_numeric()
True
is_sorted(*, descending: bool = False, nulls_last: bool = False) bool[source]

Check if the Series is sorted.

Parameters:
descending

Check if the Series is sorted in descending order

nulls_last

Set nulls at the end of the Series in sorted check.

Examples

>>> s = pl.Series([1, 3, 2])
>>> s.is_sorted()
False
>>> s = pl.Series([3, 2, 1])
>>> s.is_sorted(descending=True)
True
is_temporal(excluding: OneOrMoreDataTypes | None = None) bool[source]

Check if this Series datatype is temporal.

Deprecated since version 0.19.13: Use Series.dtype.is_temporal() instead.

Parameters:
excluding

Optionally exclude one or more temporal dtypes from matching.

Examples

>>> from datetime import date
>>> s = pl.Series([date(2021, 1, 1), date(2021, 1, 2), date(2021, 1, 3)])
>>> s.is_temporal()
True
>>> s.is_temporal(excluding=[pl.Date])
False
is_unique() Series[source]

Get mask of all unique values.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.is_unique()
shape: (4,)
Series: 'a' [bool]
[
        true
        false
        false
        true
]
is_utf8() bool[source]

Check if this Series datatype is a String.

Deprecated since version 0.19.14: Use Series.dtype == pl.String instead.

Examples

>>> s = pl.Series("x", ["a", "b", "c"])
>>> s.is_utf8()
True
item(index: int | None = None) Any[source]

Return the Series as a scalar, or return the element at the given index.

If no index is provided, this is equivalent to s[0], with a check that the shape is (1,). With an index, this is equivalent to s[index].

Examples

>>> s1 = pl.Series("a", [1])
>>> s1.item()
1
>>> s2 = pl.Series("a", [9, 8, 7])
>>> s2.cum_sum().item(-1)
24
kurtosis(*, fisher: bool = True, bias: bool = True) float | None[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

>>> s = pl.Series("grades", [66, 79, 54, 97, 96, 70, 69, 85, 93, 75])
>>> s.kurtosis()
-1.0522623626787952
>>> s.kurtosis(fisher=False)
1.9477376373212048
>>> s.kurtosis(fisher=False, bias=False)
2.1040361802642726
le(other: Any) Series | Expr[source]

Method equivalent of operator expression series <= other.

len() int[source]

Return the number of elements in the Series.

Null values count towards the total.

See also

count

Examples

>>> s = pl.Series("a", [1, 2, None])
>>> s.len()
3
limit(n: int = 10) Series[source]

Get the first n elements.

Alias for Series.head().

Parameters:
n

Number of elements to return. If a negative value is passed, return all elements except the last abs(n).

See also

head

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.limit(3)
shape: (3,)
Series: 'a' [i64]
[
    1
    2
    3
]

Pass a negative value to get all rows except the last abs(n).

>>> s.limit(-3)
shape: (2,)
Series: 'a' [i64]
[
        1
        2
]
log(base: float = 2.718281828459045) Series[source]

Compute the logarithm to a given base.

Examples

>>> s = pl.Series([1, 2, 3])
>>> s.log()
shape: (3,)
Series: '' [f64]
[
    0.0
    0.693147
    1.098612
]
log10() Series[source]

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

Examples

>>> s = pl.Series([10, 100, 1000])
>>> s.log10()
shape: (3,)
Series: '' [f64]
[
    1.0
    2.0
    3.0
]
log1p() Series[source]

Compute the natural logarithm of the input array plus one, element-wise.

Examples

>>> s = pl.Series([1, 2, 3])
>>> s.log1p()
shape: (3,)
Series: '' [f64]
[
    0.693147
    1.098612
    1.386294
]
lower_bound() Self[source]

Return the lower bound of this Series’ dtype as a unit Series.

See also

upper_bound

return the upper bound of the given Series’ dtype.

Examples

>>> s = pl.Series("s", [-1, 0, 1], dtype=pl.Int32)
>>> s.lower_bound()
shape: (1,)
Series: 's' [i32]
[
    -2147483648
]
>>> s = pl.Series("s", [1.0, 2.5, 3.0], dtype=pl.Float32)
>>> s.lower_bound()
shape: (1,)
Series: 's' [f32]
[
    -inf
]
lt(other: Any) Series | Expr[source]

Method equivalent of operator expression series < other.

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

Replace values in the Series using a remapping dictionary.

Deprecated since version 0.19.16: This method has been renamed to replace(). The default behavior has changed to keep any values not present in the mapping unchanged. Pass default=None to keep existing behavior.

Parameters:
mapping

Dictionary containing the before/after values to map.

default

Value to use when the remapping dict does not contain the lookup value. Use pl.first(), to keep the original value.

return_dtype

Set return dtype to override automatic return dtype determination.

map_elements(
function: Callable[[Any], Any],
return_dtype: PolarsDataType | None = None,
*,
skip_nulls: bool = True,
) Self[source]

Map a custom/user-defined function (UDF) over elements in this Series.

Warning

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

Suppose that the function is: x sqrt(x):

  • For mapping elements of a series, consider: s.sqrt().

  • For mapping inner elements of lists, consider: s.list.eval(pl.element().sqrt()).

If the function returns a different datatype, the return_dtype arg should be set, otherwise the method will fail.

Implementing logic using a Python function is almost always significantly slower and more memory intensive than implementing the same logic using the native expression API because:

  • The native expression engine runs in Rust; UDFs run in Python.

  • Use of Python UDFs forces the DataFrame to be materialized in memory.

  • Polars-native expressions can be parallelised (UDFs typically cannot).

  • Polars-native expressions can be logically optimised (UDFs cannot).

Wherever possible you should strongly prefer the native expression API to achieve the best performance.

Parameters:
function

Custom function or lambda.

return_dtype

Output datatype. If not set, the dtype will be inferred based on the first non-null value that is returned by the function.

skip_nulls

Nulls will be skipped and not passed to the python function. This is faster because python can be skipped and because we call more specialized functions.

Returns:
Series

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

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. If your data is suitable you may achieve significant speedups.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.map_elements(lambda x: x + 10, return_dtype=pl.Int64)
shape: (3,)
Series: 'a' [i64]
[
        11
        12
        13
]
max() PythonLiteral | None[source]

Get the maximum value in this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.max()
3
mean() PythonLiteral | None[source]

Reduce this Series to the mean value.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.mean()
2.0
median() PythonLiteral | None[source]

Get the median of this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.median()
2.0
min() PythonLiteral | None[source]

Get the minimal value in this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.min()
1
mode() Series[source]

Compute the most occurring value(s).

Can return multiple Values.

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.mode()
shape: (1,)
Series: 'a' [i64]
[
        2
]
n_chunks() int[source]

Get the number of chunks that this Series contains.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.n_chunks()
1
>>> s2 = pl.Series("a", [4, 5, 6])

Concatenate Series with rechunk = True

>>> pl.concat([s, s2], rechunk=True).n_chunks()
1

Concatenate Series with rechunk = False

>>> pl.concat([s, s2], rechunk=False).n_chunks()
2
n_unique() int[source]

Count the number of unique values in this Series.

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.n_unique()
3
property name: str[source]

Get the name of this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.name
'a'
nan_max() int | float | date | datetime | timedelta | str[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

>>> s = pl.Series("a", [1, 3, 4])
>>> s.nan_max()
4
>>> s = pl.Series("a", [1, float("nan"), 4])
>>> s.nan_max()
nan
nan_min() int | float | date | datetime | timedelta | str[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

>>> s = pl.Series("a", [1, 3, 4])
>>> s.nan_min()
1
>>> s = pl.Series("a", [1, float("nan"), 4])
>>> s.nan_min()
nan
ne(other: Any) Series | Expr[source]

Method equivalent of operator expression series != other.

ne_missing(other: Any) Series | Expr[source]

Method equivalent of equality operator series != other where None == None.

This differs from the standard ne where null values are propagated.

Parameters:
other

A literal or expression value to compare with.

See also

eq_missing
ne

Examples

>>> s1 = pl.Series("a", [333, 200, None])
>>> s2 = pl.Series("a", [100, 200, None])
>>> s1.ne(s2)
shape: (3,)
Series: 'a' [bool]
[
    true
    false
    null
]
>>> s1.ne_missing(s2)
shape: (3,)
Series: 'a' [bool]
[
    true
    false
    false
]
new_from_index(index: int, length: int) Self[source]

Create a new Series filled with values from the given index.

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.new_from_index(1, 3)
shape: (3,)
Series: 'a' [i64]
[
    2
    2
    2
]
not_() Series[source]

Negate a boolean Series.

Returns:
Series

Series of data type Boolean.

Examples

>>> s = pl.Series("a", [True, False, False])
>>> s.not_()
shape: (3,)
Series: 'a' [bool]
[
    false
    true
    true
]
null_count() int[source]

Count the null values in this Series.

Examples

>>> s = pl.Series([1, None, None])
>>> s.null_count()
2
pct_change(n: int | IntoExprColumn = 1) Series[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

>>> pl.Series(range(10)).pct_change()
shape: (10,)
Series: '' [f64]
[
    null
    inf
    1.0
    0.5
    0.333333
    0.25
    0.2
    0.166667
    0.142857
    0.125
]
>>> pl.Series([1, 2, 4, 8, 16, 32, 64, 128, 256, 512]).pct_change(2)
shape: (10,)
Series: '' [f64]
[
    null
    null
    3.0
    3.0
    3.0
    3.0
    3.0
    3.0
    3.0
    3.0
]
peak_max() Self[source]

Get a boolean mask of the local maximum peaks.

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.peak_max()
shape: (5,)
Series: 'a' [bool]
[
        false
        false
        false
        false
        true
]
peak_min() Self[source]

Get a boolean mask of the local minimum peaks.

Examples

>>> s = pl.Series("a", [4, 1, 3, 2, 5])
>>> s.peak_min()
shape: (5,)
Series: 'a' [bool]
[
    false
    true
    false
    true
    false
]
property plot: hvPlotTabularPolars[source]

Create a plot namespace.

Polars does not implement plotting logic itself, but instead defers to hvplot. Please see the hvplot reference gallery for more information and documentation.

Examples

Histogram:

>>> s = pl.Series("values", [1, 4, 2])
>>> s.plot.hist()

KDE plot (note: in addition to hvplot, this one also requires scipy):

>>> s.plot.kde()

For more info on what you can pass, you can use hvplot.help:

>>> import hvplot
>>> hvplot.help("hist")
pow(
exponent: int | float | Series,
) Series[source]

Raise to the power of the given exponent.

Parameters:
exponent

The exponent. Accepts Series input.

Examples

>>> s = pl.Series("foo", [1, 2, 3, 4])
>>> s.pow(3)
shape: (4,)
Series: 'foo' [i64]
[
    1
    8
    27
    64
]
product() int | float[source]

Reduce this Series to the product value.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.product()
6
qcut(
quantiles: Sequence[float] | int,
*,
labels: Sequence[str] | None = None,
left_closed: bool = False,
allow_duplicates: bool = False,
include_breaks: bool = False,
break_point_label: str = 'break_point',
category_label: str = 'category',
as_series: bool = True,
) Series | DataFrame[source]

Bin continuous values into discrete categories based on their quantiles.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 cut points plus one.

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.

break_point_label

Name of the breakpoint column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.

category_label

Name of the category column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.

as_series

If set to False, return a DataFrame containing the original values, the breakpoints, and the categories.

Deprecated since version 0.19.0: This parameter will be removed. The same behavior can be achieved by setting include_breaks=True, unnesting the resulting struct Series, and adding the result to the original Series.

Returns:
Series

Series of data type Categorical if include_breaks is set to False (default), otherwise a Series of data type Struct.

See also

cut

Examples

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

>>> s = pl.Series("foo", [-2, -1, 0, 1, 2])
>>> s.qcut([0.25, 0.75], labels=["a", "b", "c"])
shape: (5,)
Series: 'foo' [cat]
[
        "a"
        "a"
        "b"
        "b"
        "c"
]

Divide a column into two categories using uniform quantile probabilities.

>>> s.qcut(2, labels=["low", "high"], left_closed=True)
shape: (5,)
Series: 'foo' [cat]
[
        "low"
        "low"
        "high"
        "high"
        "high"
]

Create a DataFrame with the breakpoint and category for each value.

>>> cut = s.qcut([0.25, 0.75], include_breaks=True).alias("cut")
>>> s.to_frame().with_columns(cut).unnest("cut")
shape: (5, 3)
┌─────┬─────────────┬────────────┐
│ foo ┆ break_point ┆ category   │
│ --- ┆ ---         ┆ ---        │
│ 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,
interpolation: RollingInterpolationMethod = 'nearest',
) float | None[source]

Get the quantile value of this Series.

Parameters:
quantile

Quantile between 0.0 and 1.0.

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

Interpolation method.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.quantile(0.5)
2.0
rank(
method: RankMethod = 'average',
*,
descending: bool = False,
seed: int | None = None,
) Series[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:

>>> s = pl.Series("a", [3, 6, 1, 1, 6])
>>> s.rank()
shape: (5,)
Series: 'a' [f64]
[
    3.0
    4.5
    1.5
    1.5
    4.5
]

The ‘ordinal’ method:

>>> s = pl.Series("a", [3, 6, 1, 1, 6])
>>> s.rank("ordinal")
shape: (5,)
Series: 'a' [u32]
[
    3
    4
    1
    2
    5
]
rechunk(*, in_place: bool = False) Self[source]

Create a single chunk of memory for this Series.

Parameters:
in_place

In place or not.

Examples

>>> s1 = pl.Series("a", [1, 2, 3])
>>> s1.n_chunks()
1
>>> s2 = pl.Series("a", [4, 5, 6])
>>> s = pl.concat([s1, s2], rechunk=False)
>>> s.n_chunks()
2
>>> s.rechunk(in_place=True)
shape: (6,)
Series: 'a' [i64]
[
        1
        2
        3
        4
        5
        6
]
>>> s.n_chunks()
1
reinterpret(*, signed: bool = True) Series[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", [-(2**60), -2, 3])
>>> s
shape: (3,)
Series: 'a' [i64]
[
        -1152921504606846976
        -2
        3
]
>>> s.reinterpret(signed=False)
shape: (3,)
Series: 'a' [u64]
[
        17293822569102704640
        18446744073709551614
        3
]
rename(name: str) Series[source]

Rename this Series.

Alias for Series.alias().

Parameters:
name

New name.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.rename("b")
shape: (3,)
Series: 'b' [i64]
[
        1
        2
        3
]
replace(
old: IntoExpr | Sequence[Any] | Mapping[Any, Any],
new: IntoExpr | Sequence[Any] | NoDefault = _NoDefault.no_default,
*,
default: IntoExpr | NoDefault = _NoDefault.no_default,
return_dtype: PolarsDataType | None = None,
) Self[source]

Replace values by different values.

Parameters:
old

Value or sequence of values to replace. Also accepts a mapping of values to their replacement as syntactic sugar for replace(old=Series(mapping.keys()), new=Series(mapping.values())).

new

Value or sequence of values to replace by. Length must match the length of old or have length 1.

default

Set values that were not replaced to this value. Defaults to keeping the original value. Accepts expression input. Non-expression inputs are parsed as literals.

return_dtype

The data type of the resulting Series. If set to None (default), the data type is determined automatically based on the other inputs.

See also

str.replace

Notes

The global string cache must be enabled when replacing categorical values.

Examples

Replace a single value by another value. Values that were not replaced remain unchanged.

>>> s = pl.Series([1, 2, 2, 3])
>>> s.replace(2, 100)
shape: (4,)
Series: '' [i64]
[
        1
        100
        100
        3
]

Replace multiple values by passing sequences to the old and new parameters.

>>> s.replace([2, 3], [100, 200])
shape: (4,)
Series: '' [i64]
[
        1
        100
        100
        200
]

Passing a mapping with replacements is also supported as syntactic sugar. Specify a default to set all values that were not matched.

>>> mapping = {2: 100, 3: 200}
>>> s.replace(mapping, default=-1)
shape: (4,)
Series: '' [i64]
[
        -1
        100
        100
        200
]

The default can be another Series.

>>> default = pl.Series([2.5, 5.0, 7.5, 10.0])
>>> s.replace(2, 100, default=default)
shape: (4,)
Series: '' [f64]
[
        2.5
        100.0
        100.0
        10.0
]

Replacing by values of a different data type sets the return type based on a combination of the new data type and either the original data type or the default data type if it was set.

>>> s = pl.Series(["x", "y", "z"])
>>> mapping = {"x": 1, "y": 2, "z": 3}
>>> s.replace(mapping)
shape: (3,)
Series: '' [str]
[
        "1"
        "2"
        "3"
]
>>> s.replace(mapping, default=None)
shape: (3,)
Series: '' [i64]
[
        1
        2
        3
]

Set the return_dtype parameter to control the resulting data type directly.

>>> s.replace(mapping, return_dtype=pl.UInt8)
shape: (3,)
Series: '' [u8]
[
        1
        2
        3
]
reshape(
dimensions: tuple[int, ...],
nested_type: type[Array] | type[List] = List,
) Series[source]

Reshape this Series 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.

nested_type

The nested data type to create. List only supports 2 dimension, whereas Array supports an arbitrary number of dimensions.

Returns:
Series

If a single dimension is given, results in a Series of the original data type. If a multiple dimensions are given, results in a Series of data type List with shape (rows, cols) or Array with shape dimensions.

See also

Series.list.explode

Explode a list column.

Examples

>>> s = pl.Series("foo", [1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> s.reshape((3, 3))
shape: (3,)
Series: 'foo' [list[i64]]
[
        [1, 2, 3]
        [4, 5, 6]
        [7, 8, 9]
]
reverse() Series[source]

Return Series in reverse order.

Examples

>>> s = pl.Series("a", [1, 2, 3], dtype=pl.Int8)
>>> s.reverse()
shape: (3,)
Series: 'a' [i8]
[
    3
    2
    1
]
rle() Series[source]

Compress the Series data using run-length encoding.

Run-length encoding (RLE) encodes data by storing each run of identical values as a single value and its length.

Returns:
Series

Series of data type Struct with fields lengths of data type Int32 and values of the original data type.

Examples

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

Get a distinct integer ID for each run of identical values.

The ID starts at 0 and increases by one each time the value of the column changes.

Returns:
Series

Series of data type UInt32.

See also

rle

Notes

This functionality is especially useful for defining a new group for every time a column’s value changes, rather than for every distinct value of that column.

Examples

>>> s = pl.Series("s", [1, 1, 2, 1, None, 1, 3, 3])
>>> s.rle_id()
shape: (8,)
Series: 's' [u32]
[
    0
    0
    1
    2
    3
    4
    5
    5
]
rolling_apply(
function: Callable[[Series], Any],
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

Apply a custom rolling window function.

Deprecated since version 0.19.0: This method has been renamed to Series.rolling_map().

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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

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

Compute a custom rolling window function.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Parameters:
function

Custom aggregation function.

window_size

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

weights

A list of weights 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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window.

Warning

Computing custom functions is extremely slow. Use specialized rolling functions such as Series.rolling_sum() if at all possible.

Examples

>>> from numpy import nansum
>>> s = pl.Series([11.0, 2.0, 9.0, float("nan"), 8.0])
>>> s.rolling_map(nansum, window_size=3)
shape: (5,)
Series: '' [f64]
[
        null
        null
        22.0
        11.0
        17.0
]
rolling_max(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

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

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 max.

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

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

Examples

>>> s = pl.Series("a", [100, 200, 300, 400, 500])
>>> s.rolling_max(window_size=2)
shape: (5,)
Series: 'a' [i64]
[
    null
    200
    300
    400
    500
]
rolling_mean(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

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

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 mean.

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

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

Examples

>>> s = pl.Series("a", [100, 200, 300, 400, 500])
>>> s.rolling_mean(window_size=2)
shape: (5,)
Series: 'a' [f64]
[
    null
    150.0
    250.0
    350.0
    450.0
]
rolling_median(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

Compute a rolling median.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

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

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, 4.0, 6.0, 8.0])
>>> s.rolling_median(window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        2.0
        3.0
        4.0
        6.0
]
rolling_min(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

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

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 min.

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

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

Examples

>>> s = pl.Series("a", [100, 200, 300, 400, 500])
>>> s.rolling_min(window_size=3)
shape: (5,)
Series: 'a' [i64]
[
    null
    null
    100
    200
    300
]
rolling_quantile(
quantile: float,
interpolation: RollingInterpolationMethod = 'nearest',
window_size: int = 2,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

Compute a rolling quantile.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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

Parameters:
quantile

Quantile between 0.0 and 1.0.

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

Interpolation method.

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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, 4.0, 6.0, 8.0])
>>> s.rolling_quantile(quantile=0.33, window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        1.0
        2.0
        3.0
        4.0
]
>>> s.rolling_quantile(quantile=0.33, interpolation="linear", window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        1.66
        2.66
        3.66
        5.32
]
rolling_skew(
window_size: int,
*,
bias: bool = True,
) Series[source]

Compute a rolling skew.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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

>>> pl.Series([1, 4, 2, 9]).rolling_skew(3)
shape: (4,)
Series: '' [f64]
[
    null
    null
    0.381802
    0.47033
]

Note how the values match

>>> pl.Series([1, 4, 2]).skew(), pl.Series([4, 2, 9]).skew()
(0.38180177416060584, 0.47033046033698594)
rolling_std(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
ddof: int = 1,
) Series[source]

Compute a rolling std dev.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 std dev.

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

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

ddof

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

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, 4.0, 6.0, 8.0])
>>> s.rolling_std(window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        1.0
        1.0
        1.527525
        2.0
]
rolling_sum(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
) Series[source]

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

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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.

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

Parameters:
window_size

The length of the window.

weights

An optional slice with the same length of 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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.rolling_sum(window_size=2)
shape: (5,)
Series: 'a' [i64]
[
        null
        3
        5
        7
        9
]
rolling_var(
window_size: int,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
ddof: int = 1,
) Series[source]

Compute a rolling variance.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 variance.

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

Parameters:
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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window

ddof

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

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, 4.0, 6.0, 8.0])
>>> s.rolling_var(window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        1.0
        1.0
        2.333333
        4.0
]
round(decimals: int = 0) Series[source]

Round underlying floating point data by decimals digits.

Parameters:
decimals

number of decimals to round by.

Examples

>>> s = pl.Series("a", [1.12345, 2.56789, 3.901234])
>>> s.round(2)
shape: (3,)
Series: 'a' [f64]
[
        1.12
        2.57
        3.9
]
round_sig_figs(digits: int) Series[source]

Round to a number of significant figures.

Parameters:
digits

Number of significant figures to round to.

Examples

>>> s = pl.Series([0.01234, 3.333, 1234.0])
>>> s.round_sig_figs(2)
shape: (3,)
Series: '' [f64]
[
        0.012
        3.3
        1200.0
]
sample(
n: int | None = None,
*,
fraction: float | None = None,
with_replacement: bool = False,
shuffle: bool = False,
seed: int | None = None,
) Series[source]

Sample from this Series.

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 for each sample operation.

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.sample(2, seed=0)
shape: (2,)
Series: 'a' [i64]
[
    1
    5
]
scatter(
indices: Series | Iterable[int] | int | np.ndarray[Any, Any],
values: Series | Iterable[PythonLiteral] | PythonLiteral | None,
) Series[source]

Set values at the index locations.

Parameters:
indices

Integers representing the index locations.

values

Replacement values.

Notes

Use of this function is frequently an anti-pattern, as it can block optimization (predicate pushdown, etc). Consider using pl.when(predicate).then(value).otherwise(self) instead.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.scatter(1, 10)
shape: (3,)
Series: 'a' [i64]
[
        1
        10
        3
]

It is better to implement this as follows:

>>> s.to_frame().with_row_index().select(
...     pl.when(pl.col("index") == 1).then(10).otherwise(pl.col("a"))
... )
shape: (3, 1)
┌─────────┐
│ literal │
│ ---     │
│ i64     │
╞═════════╡
│ 1       │
│ 10      │
│ 3       │
└─────────┘
search_sorted(
element: IntoExpr | np.ndarray[Any, Any] | None,
side: SearchSortedSide = 'any',
) int | Series[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

>>> s = pl.Series("set", [1, 2, 3, 4, 4, 5, 6, 7])
>>> s.search_sorted(4)
3
>>> s.search_sorted(4, "left")
3
>>> s.search_sorted(4, "right")
5
>>> s.search_sorted([1, 4, 5])
shape: (3,)
Series: 'set' [u32]
[
        0
        3
        5
]
>>> s.search_sorted([1, 4, 5], "left")
shape: (3,)
Series: 'set' [u32]
[
        0
        3
        5
]
>>> s.search_sorted([1, 4, 5], "right")
shape: (3,)
Series: 'set' [u32]
[
        1
        5
        6
]
series_equal(
other: Series,
*,
null_equal: bool = True,
strict: bool = False,
) bool[source]

Check whether the Series is equal to another Series.

Deprecated since version 0.19.16: This method has been renamed to equals().

Parameters:
other

Series to compare with.

null_equal

Consider null values as equal.

strict

Don’t allow different numerical dtypes, e.g. comparing pl.UInt32 with a pl.Int64 will return False.

set(
filter: Series,
value: int | float | str | bool | None,
) Series[source]

Set masked values.

Parameters:
filter

Boolean mask.

value

Value with which to replace the masked values.

Notes

Use of this function is frequently an anti-pattern, as it can block optimisation (predicate pushdown, etc). Consider using pl.when(predicate).then(value).otherwise(self) instead.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.set(s == 2, 10)
shape: (3,)
Series: 'a' [i64]
[
        1
        10
        3
]

It is better to implement this as follows:

>>> s.to_frame().select(
...     pl.when(pl.col("a") == 2).then(10).otherwise(pl.col("a"))
... )
shape: (3, 1)
┌─────────┐
│ literal │
│ ---     │
│ i64     │
╞═════════╡
│ 1       │
│ 10      │
│ 3       │
└─────────┘
set_at_idx(
indices: Series | ndarray[Any, Any] | Sequence[int] | int,
values: int | float | str | bool | date | datetime | Sequence[int] | Sequence[float] | Sequence[bool] | Sequence[str] | Sequence[date] | Sequence[datetime] | Series | None,
) Series[source]

Set values at the index locations.

Deprecated since version 0.19.14: This method has been renamed to scatter().

Parameters:
indices

Integers representing the index locations.

values

Replacement values.

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

Flags the Series as ‘sorted’.

Enables downstream code to user fast paths for sorted arrays.

Parameters:
descending

If the Series order is descending.

Warning

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

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.set_sorted().max()
3
property shape: tuple[int][source]

Shape of this Series.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.shape
(3,)
shift(n: int = 1, *, fill_value: IntoExpr | None = None) Series[source]

Shift values by the given number of indices.

Parameters:
n

Number of indices to shift forward. If a negative value is passed, values are shifted in the opposite direction instead.

fill_value

Fill the resulting null values with this value. Accepts expression input. Non-expression inputs are parsed as literals.

Notes

This method is similar to the LAG operation in SQL when the value for n is positive. With a negative value for n, it is similar to LEAD.

Examples

By default, values are shifted forward by one index.

>>> s = pl.Series([1, 2, 3, 4])
>>> s.shift()
shape: (4,)
Series: '' [i64]
[
        null
        1
        2
        3
]

Pass a negative value to shift in the opposite direction instead.

>>> s.shift(-2)
shape: (4,)
Series: '' [i64]
[
        3
        4
        null
        null
]

Specify fill_value to fill the resulting null values.

>>> s.shift(-2, fill_value=100)
shape: (4,)
Series: '' [i64]
[
        3
        4
        100
        100
]
shift_and_fill(fill_value: int | Expr, *, n: int = 1) Series[source]

Shift values by the given number of places and fill the resulting null values.

Deprecated since version 0.19.12: Use shift() instead.

Parameters:
fill_value

Fill None values with the result of this expression.

n

Number of places to shift (may be negative).

shrink_dtype() Series[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

>>> s = pl.Series("a", [1, 2, 3, 4, 5, 6])
>>> s
shape: (6,)
Series: 'a' [i64]
[
        1
        2
        3
        4
        5
        6
]
>>> s.shrink_dtype()
shape: (6,)
Series: 'a' [i8]
[
        1
        2
        3
        4
        5
        6
]
shrink_to_fit(*, in_place: bool = False) Series[source]

Shrink Series memory usage.

Shrinks the underlying array capacity to exactly fit the actual data. (Note that this function does not change the Series data type).

shuffle(seed: int | None = None) Series[source]

Shuffle the contents of this Series.

Parameters:
seed

Seed for the random number generator. If set to None (default), a random seed is generated each time the shuffle is called.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.shuffle(seed=1)
shape: (3,)
Series: 'a' [i64]
[
        2
        1
        3
]
sign() Series[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

>>> s = pl.Series("a", [-9.0, -0.0, 0.0, 4.0, None])
>>> s.sign()
shape: (5,)
Series: 'a' [i64]
[
        -1
        0
        0
        1
        null
]
sin() Series[source]

Compute the element-wise value for the sine.

Examples

>>> import math
>>> s = pl.Series("a", [0.0, math.pi / 2.0, math.pi])
>>> s.sin()
shape: (3,)
Series: 'a' [f64]
[
    0.0
    1.0
    1.2246e-16
]
sinh() Series[source]

Compute the element-wise value for the hyperbolic sine.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.sinh()
shape: (3,)
Series: 'a' [f64]
[
    1.175201
    0.0
    -1.175201
]
skew(*, bias: bool = True) float | None[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

>>> s = pl.Series([1, 2, 2, 4, 5])
>>> s.skew()
0.34776706224699483
slice(offset: int, length: int | None = None) Series[source]

Get a slice of this Series.

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

>>> s = pl.Series("a", [1, 2, 3, 4])
>>> s.slice(1, 2)
shape: (2,)
Series: 'a' [i64]
[
        2
        3
]
sort(
*,
descending: bool = False,
nulls_last: bool = False,
multithreaded: bool = True,
in_place: bool = False,
) Self[source]

Sort this Series.

Parameters:
descending

Sort in descending order.

nulls_last

Place null values last instead of first.

multithreaded

Sort using multiple threads.

in_place

Sort in-place.

Examples

>>> s = pl.Series("a", [1, 3, 4, 2])
>>> s.sort()
shape: (4,)
Series: 'a' [i64]
[
        1
        2
        3
        4
]
>>> s.sort(descending=True)
shape: (4,)
Series: 'a' [i64]
[
        4
        3
        2
        1
]
sqrt() Series[source]

Compute the square root of the elements.

Syntactic sugar for

>>> pl.Series([1, 2]) ** 0.5
shape: (2,)
Series: '' [f64]
[
    1.0
    1.414214
]

Examples

>>> s = pl.Series([1, 2, 3])
>>> s.sqrt()
shape: (3,)
Series: '' [f64]
[
    1.0
    1.414214
    1.732051
]
std(ddof: int = 1) float | timedelta | None[source]

Get the standard deviation of this Series.

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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.std()
1.0
sum() int | float[source]

Reduce this Series to the sum value.

Notes

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

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.sum()
6
tail(n: int = 10) Series[source]

Get the last n elements.

Parameters:
n

Number of elements to return. If a negative value is passed, return all elements except the first abs(n).

See also

head, slice

Examples

>>> s = pl.Series("a", [1, 2, 3, 4, 5])
>>> s.tail(3)
shape: (3,)
Series: 'a' [i64]
[
        3
        4
        5
]

Pass a negative value to get all rows except the first abs(n).

>>> s.tail(-3)
shape: (2,)
Series: 'a' [i64]
[
        4
        5
]
take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) Series[source]

Take values by index.

Deprecated since version 0.19.14: This method has been renamed to gather().

Parameters:
indices

Index location used for selection.

take_every(n: int, offset: int = 0) Series[source]

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

Deprecated since version 0.19.14: This method has been renamed to gather_every().

Parameters:
n

Gather every n-th row.

offset

Starting index.

tan() Series[source]

Compute the element-wise value for the tangent.

Examples

>>> import math
>>> s = pl.Series("a", [0.0, math.pi / 2.0, math.pi])
>>> s.tan()
shape: (3,)
Series: 'a' [f64]
[
    0.0
    1.6331e16
    -1.2246e-16
]
tanh() Series[source]

Compute the element-wise value for the hyperbolic tangent.

Examples

>>> s = pl.Series("a", [1.0, 0.0, -1.0])
>>> s.tanh()
shape: (3,)
Series: 'a' [f64]
[
    0.761594
    0.0
    -0.761594
]
to_arrow() Array[source]

Return the underlying Arrow array.

If the Series contains only a single chunk this operation is zero copy.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s = s.to_arrow()
>>> s
<pyarrow.lib.Int64Array object at ...>
[
  1,
  2,
  3
]
to_dummies(*, separator: str = '_', drop_first: bool = False) DataFrame[source]

Get dummy/indicator variables.

Parameters:
separator

Separator/delimiter used when generating column names.

drop_first

Remove the first category from the variable being encoded.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.to_dummies()
shape: (3, 3)
┌─────┬─────┬─────┐
│ a_1 ┆ a_2 ┆ a_3 │
│ --- ┆ --- ┆ --- │
│ u8  ┆ u8  ┆ u8  │
╞═════╪═════╪═════╡
│ 1   ┆ 0   ┆ 0   │
│ 0   ┆ 1   ┆ 0   │
│ 0   ┆ 0   ┆ 1   │
└─────┴─────┴─────┘
>>> s.to_dummies(drop_first=True)
shape: (3, 2)
┌─────┬─────┐
│ a_2 ┆ a_3 │
│ --- ┆ --- │
│ u8  ┆ u8  │
╞═════╪═════╡
│ 0   ┆ 0   │
│ 1   ┆ 0   │
│ 0   ┆ 1   │
└─────┴─────┘
to_frame(name: str | None = None) DataFrame[source]

Cast this Series to a DataFrame.

Parameters:
name

optionally name/rename the Series column in the new DataFrame.

Examples

>>> s = pl.Series("a", [123, 456])
>>> df = s.to_frame()
>>> df
shape: (2, 1)
┌─────┐
│ a   │
│ --- │
│ i64 │
╞═════╡
│ 123 │
│ 456 │
└─────┘
>>> df = s.to_frame("xyz")
>>> df
shape: (2, 1)
┌─────┐
│ xyz │
│ --- │
│ i64 │
╞═════╡
│ 123 │
│ 456 │
└─────┘
to_init_repr(n: int = 1000) str[source]

Convert Series to instantiatable string representation.

Parameters:
n

Only use first n elements.

Examples

>>> s = pl.Series("a", [1, 2, None, 4], dtype=pl.Int16)
>>> print(s.to_init_repr())
pl.Series("a", [1, 2, None, 4], dtype=pl.Int16)
>>> s_from_str_repr = eval(s.to_init_repr())
>>> s_from_str_repr
shape: (4,)
Series: 'a' [i16]
[
    1
    2
    null
    4
]
to_jax(device: jax.Device | str | None = None) jax.Array[source]

Convert this Series to a Jax Array.

New in version 0.20.27.

Warning

This functionality is currently considered unstable. It may be changed at any point without it being considered a breaking change.

Parameters:
device

Specify the jax Device on which the array will be created; can provide a string (such as “cpu”, “gpu”, or “tpu”) in which case the device is retrieved as jax.devices(string)[0]. For more specific control you can supply the instantiated Device directly. If None, arrays are created on the default device.

Examples

>>> s = pl.Series("x", [10.5, 0.0, -10.0, 5.5])
>>> s.to_jax()
Array([ 10.5,   0. , -10. ,   5.5], dtype=float32)
to_list(*, use_pyarrow: bool | None = None) list[Any][source]

Convert this Series to a Python list.

This operation copies data.

Parameters:
use_pyarrow

Use PyArrow to perform the conversion.

Deprecated since version 0.19.9: This parameter will be removed. The function can safely be called without the parameter - it should give the exact same result.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.to_list()
[1, 2, 3]
>>> type(s.to_list())
<class 'list'>
to_numpy(
*,
writable: bool = False,
allow_copy: bool = True,
use_pyarrow: bool | None = None,
zero_copy_only: bool | None = None,
) ndarray[Any, Any][source]

Convert this Series to a NumPy ndarray.

This operation copies data only when necessary. The conversion is zero copy when all of the following hold:

  • The data type is an integer, float, Datetime, Duration, or Array.

  • The Series contains no null values.

  • The Series consists of a single chunk.

  • The writable parameter is set to False (default).

Parameters:
writable

Ensure the resulting array is writable. This will force a copy of the data if the array was created without copy as the underlying Arrow data is immutable.

allow_copy

Allow memory to be copied to perform the conversion. If set to False, causes conversions that are not zero-copy to fail.

use_pyarrow

First convert to PyArrow, then call pyarrow.Array.to_numpy to convert to NumPy. If set to False, Polars’ own conversion logic is used.

Deprecated since version 0.20.28: Polars now uses its native engine by default for conversion to NumPy. To use PyArrow’s engine, call .to_arrow().to_numpy() instead.

zero_copy_only

Raise an exception if the conversion to a NumPy would require copying the underlying data. Data copy occurs, for example, when the Series contains nulls or non-numeric types.

Deprecated since version 0.20.10: Use the allow_copy parameter instead, which is the inverse of this one.

Examples

Numeric data without nulls can be converted without copying data. The resulting array will not be writable.

>>> s = pl.Series([1, 2, 3], dtype=pl.Int8)
>>> arr = s.to_numpy()
>>> arr
array([1, 2, 3], dtype=int8)
>>> arr.flags.writeable
False

Set writable=True to force data copy to make the array writable.

>>> s.to_numpy(writable=True).flags.writeable
True

Integer Series containing nulls will be cast to a float type with nan representing a null value. This requires data to be copied.

>>> s = pl.Series([1, 2, None], dtype=pl.UInt16)
>>> s.to_numpy()
array([ 1.,  2., nan], dtype=float32)

Set allow_copy=False to raise an error if data would be copied.

>>> s.to_numpy(allow_copy=False)
Traceback (most recent call last):
...
RuntimeError: copy not allowed: cannot convert to a NumPy array without copying data

Series of data type Array and Struct will result in an array with more than one dimension.

>>> s = pl.Series([[1, 2, 3], [4, 5, 6]], dtype=pl.Array(pl.Int64, 3))
>>> s.to_numpy()
array([[1, 2, 3],
       [4, 5, 6]])
to_pandas(
*,
use_pyarrow_extension_array: bool = False,
**kwargs: Any,
) pd.Series[Any][source]

Convert this Series to a pandas Series.

This operation copies data if use_pyarrow_extension_array is not enabled.

Parameters:
use_pyarrow_extension_array

Use a PyArrow-backed extension array instead of a NumPy array for the pandas Series. This allows zero copy operations and preservation of null values. Subsequent operations on the resulting pandas Series may trigger conversion to NumPy if those operations are not supported by PyArrow compute functions.

**kwargs

Additional keyword arguments to be passed to pyarrow.Array.to_pandas().

Returns:
pandas.Series

Notes

This operation requires that both pandas and pyarrow are installed.

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.to_pandas()
0    1
1    2
2    3
Name: a, dtype: int64

Null values are converted to NaN.

>>> s = pl.Series("b", [1, 2, None])
>>> s.to_pandas()
0    1.0
1    2.0
2    NaN
Name: b, dtype: float64

Pass use_pyarrow_extension_array=True to get a pandas Series backed by a PyArrow extension array. This will preserve null values.

>>> s.to_pandas(use_pyarrow_extension_array=True)
0       1
1       2
2    <NA>
Name: b, dtype: int64[pyarrow]
to_physical() Series[source]

Cast to physical representation of the logical dtype.

Examples

Replicating the pandas pd.Series.factorize method.

>>> s = pl.Series("values", ["a", None, "x", "a"])
>>> s.cast(pl.Categorical).to_physical()
shape: (4,)
Series: 'values' [u32]
[
    0
    null
    1
    0
]
to_torch() torch.Tensor[source]

Convert this Series to a PyTorch Tensor.

New in version 0.20.23.

Warning

This functionality is currently considered unstable. It may be changed at any point without it being considered a breaking change.

Notes

PyTorch tensors do not support UInt16, UInt32, or UInt64; these dtypes will be automatically cast to Int32, Int64, and Int64, respectively.

Examples

>>> s = pl.Series("x", [1, 0, 1, 2, 0], dtype=pl.UInt8)
>>> s.to_torch()
tensor([1, 0, 1, 2, 0], dtype=torch.uint8)
>>> s = pl.Series("x", [5.5, -10.0, 2.5], dtype=pl.Float32)
>>> s.to_torch()
tensor([  5.5000, -10.0000,   2.5000])
top_k(k: int = 5) Series[source]

Return the k largest elements.

This has time complexity:

\[O(n + k \log{n})\]
Parameters:
k

Number of elements to return.

See also

bottom_k

Examples

>>> s = pl.Series("a", [2, 5, 1, 4, 3])
>>> s.top_k(3)
shape: (3,)
Series: 'a' [i64]
[
    5
    4
    3
]
unique(*, maintain_order: bool = False) Series[source]

Get unique elements in series.

Parameters:
maintain_order

Maintain order of data. This requires more work.

Examples

>>> s = pl.Series("a", [1, 2, 2, 3])
>>> s.unique().sort()
shape: (3,)
Series: 'a' [i64]
[
    1
    2
    3
]
unique_counts() Series[source]

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

Examples

>>> s = pl.Series("id", ["a", "b", "b", "c", "c", "c"])
>>> s.unique_counts()
shape: (3,)
Series: 'id' [u32]
[
    1
    2
    3
]
upper_bound() Self[source]

Return the upper bound of this Series’ dtype as a unit Series.

See also

lower_bound

return the lower bound of the given Series’ dtype.

Examples

>>> s = pl.Series("s", [-1, 0, 1], dtype=pl.Int8)
>>> s.upper_bound()
shape: (1,)
Series: 's' [i8]
[
    127
]
>>> s = pl.Series("s", [1.0, 2.5, 3.0], dtype=pl.Float64)
>>> s.upper_bound()
shape: (1,)
Series: 's' [f64]
[
    inf
]
value_counts(
*,
sort: bool = False,
parallel: bool = False,
name: str = 'count',
) DataFrame[source]

Count the occurrences of unique values.

Parameters:
sort

Sort the output by count in descending order. If set to False (default), the order of the output is random.

parallel

Execute the computation in parallel.

Note

This option should likely not be enabled in a group by context, as the computation is already parallelized per group.

name

Give the resulting count column a specific name; defaults to “count”.

Returns:
DataFrame

Mapping of unique values to their count.

Examples

>>> s = pl.Series("color", ["red", "blue", "red", "green", "blue", "blue"])
>>> s.value_counts()
shape: (3, 2)
┌───────┬───────┐
│ color ┆ count │
│ ---   ┆ ---   │
│ str   ┆ u32   │
╞═══════╪═══════╡
│ red   ┆ 2     │
│ green ┆ 1     │
│ blue  ┆ 3     │
└───────┴───────┘

Sort the output by count and customize the count column name.

>>> s.value_counts(sort=True, name="n")
shape: (3, 2)
┌───────┬─────┐
│ color ┆ n   │
│ ---   ┆ --- │
│ str   ┆ u32 │
╞═══════╪═════╡
│ blue  ┆ 3   │
│ red   ┆ 2   │
│ green ┆ 1   │
└───────┴─────┘
var(ddof: int = 1) float | timedelta | None[source]

Get variance of this Series.

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

>>> s = pl.Series("a", [1, 2, 3])
>>> s.var()
1.0
view(*, ignore_nulls: bool = False) SeriesView[source]

Get a view into this Series data with a numpy array.

Deprecated since version 0.19.14: This method will be removed in a future version.

This operation doesn’t clone data, but does not include missing values. Don’t use this unless you know what you are doing.

Parameters:
ignore_nulls

If True then nulls are converted to 0. If False then an Exception is raised if nulls are present.

zip_with(mask: Series, other: Series) Self[source]

Take values from self or other based on the given mask.

Where mask evaluates true, take values from self. Where mask evaluates false, take values from other.

Parameters:
mask

Boolean Series.

other

Series of same type.

Returns:
Series

Examples

>>> s1 = pl.Series([1, 2, 3, 4, 5])
>>> s2 = pl.Series([5, 4, 3, 2, 1])
>>> s1.zip_with(s1 < s2, s2)
shape: (5,)
Series: '' [i64]
[
        1
        2
        3
        2
        1
]
>>> mask = pl.Series([True, False, True, False, True])
>>> s1.zip_with(mask, s2)
shape: (5,)
Series: '' [i64]
[
        1
        4
        3
        2
        5
]