Method: Polars::Expr#cumulative_eval

Defined in:

lib/polars/expr.rb

#cumulative_eval(expr, min_periods: 1, parallel: false) ⇒ Expr

Note:

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

Note:

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

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

Examples:

df = Polars::DataFrame.new({"values" => [1, 2, 3, 4, 5]})
df.select(
  [
    Polars.col("values").cumulative_eval(
      Polars.element.first - Polars.element.last ** 2
    )
  ]
)
# =>
# shape: (5, 1)
# ┌────────┐
# │ values │
# │ ---    │
# │ i64    │
# ╞════════╡
# │ 0      │
# │ -3     │
# │ -8     │
# │ -15    │
# │ -24    │
# └────────┘

Parameters:

• expr (Expr) —

  Expression to evaluate

• min_periods (Integer) (defaults to: 1) —

  Number of valid values there should be in the window before the expression is evaluated. valid values = length - null_count

• parallel (Boolean) (defaults to: false) —

  Run in parallel. Don't do this in a group by or another operation that already has much parallelization.

Returns:

• (Expr)

# File 'lib/polars/expr.rb', line 6822

def cumulative_eval(expr, min_periods: 1, parallel: false)
  _from_rbexpr(
    _rbexpr.cumulative_eval(expr._rbexpr, min_periods, parallel)
  )
end