Functions

These functions are available from the Polars module root and can be used as expressions, and sometimes also in eager contexts.

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Available in module namespace:

all(*names[, ignore_nulls])	Either return an expression representing all columns, or evaluate a bitwise AND operation.
all_horizontal(*exprs)	Compute the bitwise AND horizontally across columns.
any(*names[, ignore_nulls])	Evaluate a bitwise OR operation.
any_horizontal(*exprs)	Compute the bitwise OR horizontally across columns.
approx_n_unique(*columns)	Approximate count of unique values.
arange([start, end, step, dtype, eager])	Generate a range of integers.
arctan2(y, x)	Compute two argument arctan in radians.
arctan2d(y, x)	Compute two argument arctan in degrees.
arg_sort_by(exprs, *more_exprs[, ...])	Return the row indices that would sort the column(s).
arg_where(condition, *[, eager])	Return indices where condition evaluates True.
business_day_count(start, end[, week_mask, ...])	Count the number of business days between start and end (not including end).
coalesce(exprs, *more_exprs)	Folds the columns from left to right, keeping the first non-null value.
concat_arr(exprs, *more_exprs)	Horizontally concatenate columns into a single array column.
concat_list(exprs, *more_exprs)	Horizontally concatenate columns into a single list column.
concat_str(exprs, *more_exprs[, separator, ...])	Horizontally concatenate columns into a single string column.
corr(a, b, *[, method, ddof, propagate_nans])	Compute the Pearson's or Spearman rank correlation correlation between two columns.
count(*columns)	Return the number of non-null values in the column.
cov(a, b[, ddof])	Compute the covariance between two columns/ expressions.
cum_count(*columns[, reverse])	Return the cumulative count of the non-null values in the column.
cum_fold(acc, function, exprs, *[, include_init])	Cumulatively fold horizontally across columns with a left fold.
cum_reduce(function, exprs)	Cumulatively reduce horizontally across columns with a left fold.
cum_sum(*names)	Cumulatively sum all values.
cum_sum_horizontal(*exprs)	Cumulatively sum all values horizontally across columns.
date(year, month, day)	Create a Polars literal expression of type Date.
date_range(start, end[, interval, closed, eager])	Generate a date range.
date_ranges(start, end[, interval, closed, ...])	Create a column of date ranges.
datetime(year, month, day[, hour, minute, ...])	Create a Polars literal expression of type Datetime.
datetime_range(start, end[, interval, ...])	Generate a datetime range.
datetime_ranges(start, end[, interval, ...])	Create a column of datetime ranges.
duration(*[, weeks, days, hours, minutes, ...])	Create polars Duration from distinct time components.
element()	Alias for an element being evaluated in an eval expression.
exclude(columns, *more_columns)	Represent all columns except for the given columns.
first(*columns)	Get the first column or value.
fold(acc, function, exprs)	Accumulate over multiple columns horizontally/ row wise with a left fold.
format(f_string, *args)	Format expressions as a string.
from_epoch(column[, time_unit])	Utility function that parses an epoch timestamp (or Unix time) to Polars Date(time).
groups(column)	Syntactic sugar for pl.col("foo").agg_groups().
head(column[, n])	Get the first n rows.
implode(*columns)	Aggregate all column values into a list.
int_range([start, end, step, dtype, eager])	Generate a range of integers.
int_ranges([start, end, step, dtype, eager])	Generate a range of integers for each row of the input columns.
last(*columns)	Get the last column or value.
len()	Return the number of rows in the context.
lit(value[, dtype, allow_object])	Return an expression representing a literal value.
map_batches(exprs, function[, return_dtype])	Map a custom function over multiple columns/expressions.
map_groups(exprs, function[, return_dtype, ...])	Apply a custom/user-defined function (UDF) in a GroupBy context.
max(*names)	Get the maximum value.
max_horizontal(*exprs)	Get the maximum value horizontally across columns.
mean(*columns)	Get the mean value.
mean_horizontal(*exprs)	Compute the mean of all values horizontally across columns.
median(*columns)	Get the median value.
min(*names)	Get the minimum value.
min_horizontal(*exprs)	Get the minimum value horizontally across columns.
n_unique(*columns)	Count unique values.
nth(*indices)	Get the nth column(s) of the context.
ones(n[, dtype, eager])	Construct a column of length n filled with ones.
quantile(column, quantile[, interpolation])	Syntactic sugar for pl.col("foo").quantile(..).
reduce(function, exprs)	Accumulate over multiple columns horizontally/ row wise with a left fold.
repeat(value, n, *[, dtype, eager])	Construct a column of length n filled with the given value.
rolling_corr(a, b, *, window_size[, ...])	Compute the rolling correlation between two columns/ expressions.
rolling_cov(a, b, *, window_size[, ...])	Compute the rolling covariance between two columns/ expressions.
select(*exprs, **named_exprs)	Run polars expressions without a context.
sql(query, *[, eager])	Execute a SQL query against frames in the global namespace.
sql_expr(sql)	Parse one or more SQL expressions to Polars expression(s).
std(column[, ddof])	Get the standard deviation.
struct(*exprs[, schema, eager])	Collect columns into a struct column.
sum(*names)	Sum all values.
sum_horizontal(*exprs)	Sum all values horizontally across columns.
tail(column[, n])	Get the last n rows.
time([hour, minute, second, microsecond])	Create a Polars literal expression of type Time.
time_range([start, end, interval, closed, eager])	Generate a time range.
time_ranges([start, end, interval, closed, ...])	Create a column of time ranges.
var(column[, ddof])	Get the variance.
when(*predicates, **constraints)	Start a when-then-otherwise expression.
zeros(n[, dtype, eager])	Construct a column of length n filled with zeros.

Available in expression namespace:

Expr.all(*[, ignore_nulls])	Return whether all values in the column are True.
Expr.any(*[, ignore_nulls])	Return whether any of the values in the column are True.
Expr.approx_n_unique()	Approximate count of unique values.
Expr.count()	Return the number of non-null elements in the column.
Expr.first()	Get the first value.
Expr.head([n])	Get the first n rows.
Expr.implode()	Aggregate values into a list.
Expr.map_batches(function[, return_dtype, ...])	Apply a custom python function to a whole Series or sequence of Series.
Expr.map_elements(function[, return_dtype, ...])	Map a custom/user-defined function (UDF) to each element of a column.
Expr.max()	Get maximum value.
Expr.mean()	Get mean value.
Expr.median()	Get median value using linear interpolation.
Expr.min()	Get minimum value.
Expr.n_unique()	Count unique values.
Expr.quantile(quantile[, interpolation])	Get quantile value.
Expr.std([ddof])	Get standard deviation.
Expr.sum()	Get sum value.
Expr.tail([n])	Get the last n rows.
Expr.var([ddof])	Get variance.