Method: Polars::Expr#rolling_mean

Defined in:

lib/polars/expr.rb

#rolling_mean(window_size, weights: nil, min_periods: nil, center: false) ⇒ Expr

Note:

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

Note:

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

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

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

Examples:

df = Polars::DataFrame.new({"A" => [1.0, 8.0, 6.0, 2.0, 16.0, 10.0]})
df.select(
  [
    Polars.col("A").rolling_mean(2)
  ]
)
# =>
# shape: (6, 1)
# ┌──────┐
# │ A    │
# │ ---  │
# │ f64  │
# ╞══════╡
# │ null │
# │ 4.5  │
# │ 7.0  │
# │ 4.0  │
# │ 9.0  │
# │ 13.0 │
# └──────┘

Parameters:

• window_size (Integer) —

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

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

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

• weights (Array) (defaults to: nil) —

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

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

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

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

  Set the labels at the center of the window

Returns:

• (Expr)

# File 'lib/polars/expr.rb', line 5214

def rolling_mean(
  window_size,
  weights: nil,
  min_periods: nil,
  center: false
)
  _from_rbexpr(
    _rbexpr.rolling_mean(
      window_size, weights, min_periods, center
    )
  )
end