drop_unbalanced_series – Drop predictor columns that aren’t observed across the full sample.#

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

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

Function signature#

mf.functions.drop_unbalanced_series_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#

Trades predictor count for sample length. Useful when the recipe wants to keep early observations and is willing to lose late-arrival series.

When to use

Long-history studies (1959-) where late-introduction series should be excluded.

In recipe context#

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

# Layer L2 recipe fragment
params:
  frame_edge_policy: drop_unbalanced_series

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.’