Comments (1)
oh yeah, this is tricky 🤔. The "problem" is only at the start of the window
another easy example:
pl.DataFrame({"x": [1, 2, 3, 4]}).with_columns(
without_weight=pl.col("x").rolling_mean(
window_size=2,
min_periods=0,
),
with_weight_50_50=pl.col("x").rolling_mean(
window_size=2,
min_periods=0,
weights=[0.5, 0.5],
),
with_weight_20_80=pl.col("x").rolling_mean(
window_size=2,
min_periods=0,
weights=[0.2, 0.8],
),
)
# shape: (4, 4)
# ┌─────┬────────────────┬───────────────────┬───────────────────┐
# │ x ┆ without_weight ┆ with_weight_50_50 ┆ with_weight_20_80 │
# │ --- ┆ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ f64 ┆ f64 │
# ╞═════╪════════════════╪═══════════════════╪═══════════════════╡
# │ 1 ┆ 1.0 ┆ 0.5 ┆ 0.2 │ 🤔 window [1]
# │ 2 ┆ 1.5 ┆ 1.5 ┆ 1.8 │ ✅ window [1, 2]
# │ 3 ┆ 2.5 ┆ 2.5 ┆ 2.8 │ ✅
# │ 4 ┆ 3.5 ┆ 3.5 ┆ 3.8 │ ✅
# └─────┴────────────────┴───────────────────┴───────────────────┘
First window is only [1]
- without weights: knows that this window has only length of 1 and divides by 1 ✅
- with weights: multiplies by weight without considering missing weights (should always add to 100%?!) 🤔
Not sure if this is a bug or intended behaviour and requires more documentation.
But one can surely argue that the 50/50 weighted mean of 1
and nothing else is still 1
and not 0.5
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