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docs(python): correct default in rolling_* function examples #16000

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May 2, 2024
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docs(python): correct default in rolling_* function examples #16000

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117 changes: 55 additions & 62 deletions py-polars/polars/expr/expr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6238,28 +6238,29 @@ def rolling_min(
│ 23 ┆ 2001-01-01 23:00:00 │
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling min with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_min=pl.col("index").rolling_min(
... window_size="2h", by="date", closed="left"
... )
... rolling_row_min=pl.col("index").rolling_min(window_size="2h", by="date")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
│ index ┆ date ┆ rolling_row_min │
│ --- ┆ --- ┆ --- │
│ u32 ┆ datetime[μs] ┆ u32 │
╞═══════╪═════════════════════╪═════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 0 ┆ 2001-01-01 00:00:00 ┆ 0 │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 0 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 1 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 2 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 1 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 2 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 3 │
│ … ┆ … ┆ … │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 18 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 19 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 20 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 21 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 22 │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 19 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 20 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 21 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 22 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 23 │
└───────┴─────────────────────┴─────────────────┘
"""
window_size = deprecate_saturating(window_size)
Expand Down Expand Up @@ -6447,30 +6448,28 @@ def rolling_max(
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling max with the default left closure of temporal windows
Compute the rolling max with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_max=pl.col("index").rolling_max(
... window_size="2h", by="date", closed="left"
... )
... rolling_row_max=pl.col("index").rolling_max(window_size="2h", by="date")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
│ index ┆ date ┆ rolling_row_max │
│ --- ┆ --- ┆ --- │
│ u32 ┆ datetime[μs] ┆ u32 │
╞═══════╪═════════════════════╪═════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 1 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 2 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 3 │
│ 0 ┆ 2001-01-01 00:00:00 ┆ 0 │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 1 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 2 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 3 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 4 │
│ … ┆ … ┆ … │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 19 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 20 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 21 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 22 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 23 │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 20 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 21 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 22 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 23 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 24 │
└───────┴─────────────────────┴─────────────────┘

Compute the rolling max with the closure of windows on both sides
Expand Down Expand Up @@ -6688,11 +6687,11 @@ def rolling_mean(
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling mean with the default left closure of temporal windows
Compute the rolling mean with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_mean=pl.col("index").rolling_mean(
... window_size="2h", by="date", closed="left"
... window_size="2h", by="date"
... )
... )
shape: (25, 3)
Expand All @@ -6701,17 +6700,17 @@ def rolling_mean(
│ --- ┆ --- ┆ --- │
│ u32 ┆ datetime[μs] ┆ f64 │
╞═══════╪═════════════════════╪══════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0.0 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 0.5 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 1.5 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 2.5 │
│ 0 ┆ 2001-01-01 00:00:00 ┆ 0.0 │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0.5 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 1.5 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 2.5 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 3.5 │
│ … ┆ … ┆ … │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 18.5 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 19.5 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 20.5 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 21.5 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 22.5 │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 19.5 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 20.5 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 21.5 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 22.5 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 23.5 │
└───────┴─────────────────────┴──────────────────┘

Compute the rolling mean with the closure of windows on both sides
Expand Down Expand Up @@ -6931,30 +6930,28 @@ def rolling_sum(
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling sum with the default left closure of temporal windows
Compute the rolling sum with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_sum=pl.col("index").rolling_sum(
... window_size="2h", by="date", closed="left"
... )
... rolling_row_sum=pl.col("index").rolling_sum(window_size="2h", by="date")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
│ index ┆ date ┆ rolling_row_sum │
│ --- ┆ --- ┆ --- │
│ u32 ┆ datetime[μs] ┆ u32 │
╞═══════╪═════════════════════╪═════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 1 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 3 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 5 │
│ 0 ┆ 2001-01-01 00:00:00 ┆ 0 │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 1 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 3 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 5 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 7 │
│ … ┆ … ┆ … │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 37 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 39 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 41 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 43 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 45 │
│ 20 ┆ 2001-01-01 20:00:00 ┆ 39 │
│ 21 ┆ 2001-01-01 21:00:00 ┆ 41 │
│ 22 ┆ 2001-01-01 22:00:00 ┆ 43 │
│ 23 ┆ 2001-01-01 23:00:00 ┆ 45 │
│ 24 ┆ 2001-01-02 00:00:00 ┆ 47 │
└───────┴─────────────────────┴─────────────────┘

Compute the rolling sum with the closure of windows on both sides
Expand Down Expand Up @@ -7172,12 +7169,10 @@ def rolling_std(
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling std with the default left closure of temporal windows
Compute the rolling std with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_std=pl.col("index").rolling_std(
... window_size="2h", by="date", closed="left"
... )
... rolling_row_std=pl.col("index").rolling_std(window_size="2h", by="date")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
Expand All @@ -7186,7 +7181,7 @@ def rolling_std(
│ u32 ┆ datetime[μs] ┆ f64 │
╞═══════╪═════════════════════╪═════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0.707107 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 0.707107 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 0.707107 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 0.707107 │
Expand Down Expand Up @@ -7419,12 +7414,10 @@ def rolling_var(
│ 24 ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling var with the default left closure of temporal windows
Compute the rolling var with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
... rolling_row_var=pl.col("index").rolling_var(
... window_size="2h", by="date", closed="left"
... )
... rolling_row_var=pl.col("index").rolling_var(window_size="2h", by="date")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
Expand All @@ -7433,7 +7426,7 @@ def rolling_var(
│ u32 ┆ datetime[μs] ┆ f64 │
╞═══════╪═════════════════════╪═════════════════╡
│ 0 ┆ 2001-01-01 00:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ null │
│ 1 ┆ 2001-01-01 01:00:00 ┆ 0.5 │
│ 2 ┆ 2001-01-01 02:00:00 ┆ 0.5 │
│ 3 ┆ 2001-01-01 03:00:00 ┆ 0.5 │
│ 4 ┆ 2001-01-01 04:00:00 ┆ 0.5 │
Expand Down