mean – Replace missing cells with the per-series rolling mean.#

Back to imputation_policy axis | Back to L2 | Browse all options

Operational op under axis imputation_policy, sub-layer l2_d, layer l2. Standalone callable: mf.functions.mean_impute_clean.

Function signature#

mf.functions.mean_impute_clean(
    panel: pd.DataFrame,
) -> pd.DataFrame

Parameters#

name

type

default

constraint

description

panel

pd.DataFrame

—

—

Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

Returns#

pd.DataFrame — scalar result.

Behavior#

Simple, fast, deterministic. No iteration. Useful when the missing pattern is sparse.

When to use

Sparse missingness; quick smoke tests.

In recipe context#

Set params.imputation_policy = "mean" in the relevant layer to activate this op within a recipe:

# Layer L2 recipe fragment
params:
  imputation_policy: mean

References#

  • macroforecast design Part 2, L2: ‘preprocessing is the only layer with a strict A→B→C→D→E execution order; every cell follows the same pipeline.’