zero_fill_leading – Zero-fill leading missing predictor cells; preserve the rest.#

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Operational op under axis frame_edge_policy, sub-layer l2_e, layer l2. Standalone callable: mf.functions.zero_fill_leading_clean.

Function signature#

mf.functions.zero_fill_leading_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#

Useful when leading NaN values block early-sample fits but interior NaN should remain visible to imputation.

When to use

Studies that want the early sample but accept zero-fill on leading edges.

In recipe context#

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

# Layer L2 recipe fragment
params:
  frame_edge_policy: zero_fill_leading

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.’