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docs(python): simplify/clarify group_by_dynamic examples #11335

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docs(python): simplify/clarify group_by_dynamic examples #11335

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docs(python): simplify/clarify group_by_dynamic examples
MarcoGorelli Sep 26, 2023
432cd24
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MarcoGorelli Sep 27, 2023
40e652b
Update py-polars/polars/lazyframe/frame.py
MarcoGorelli Sep 27, 2023
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179 changes: 78 additions & 101 deletions py-polars/polars/dataframe/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5379,7 +5379,6 @@ def group_by_dynamic(
Examples
--------
>>> from datetime import datetime
>>> # create an example dataframe
>>> df = pl.DataFrame(
... {
... "time": pl.datetime_range(
Expand Down Expand Up @@ -5409,129 +5408,107 @@ def group_by_dynamic(

Group by windows of 1 hour starting at 2021-12-16 00:00:00.

>>> df.group_by_dynamic("time", every="1h", closed="right").agg(
... [
... pl.col("time").min().alias("time_min"),
... pl.col("time").max().alias("time_max"),
... ]
... )
shape: (4, 3)
┌─────────────────────┬─────────────────────┬─────────────────────┐
│ time ┆ time_min ┆ time_max │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ datetime[μs] ┆ datetime[μs] │
╞═════════════════════╪═════════════════════╪═════════════════════╡
│ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 00:00:00 │
│ 2021-12-16 00:00:00 ┆ 2021-12-16 00:30:00 ┆ 2021-12-16 01:00:00 │
│ 2021-12-16 01:00:00 ┆ 2021-12-16 01:30:00 ┆ 2021-12-16 02:00:00 │
│ 2021-12-16 02:00:00 ┆ 2021-12-16 02:30:00 ┆ 2021-12-16 03:00:00 │
└─────────────────────┴─────────────────────┴─────────────────────┘
>>> df.group_by_dynamic("time", every="1h", closed="right").agg(pl.col("n"))
shape: (4, 2)
┌─────────────────────┬───────────┐
│ time ┆ n │
│ --- ┆ --- │
│ datetime[μs] ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-15 23:00:00 ┆ [0] │
│ 2021-12-16 00:00:00 ┆ [1, 2] │
│ 2021-12-16 01:00:00 ┆ [3, 4] │
│ 2021-12-16 02:00:00 ┆ [5, 6] │
└─────────────────────┴───────────┘

The window boundaries can also be added to the aggregation result

>>> df.group_by_dynamic(
... "time", every="1h", include_boundaries=True, closed="right"
... ).agg([pl.col("time").count().alias("time_count")])
... ).agg(pl.col("n").mean())
shape: (4, 4)
┌─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
│ _lower_boundary ┆ _upper_boundary ┆ time ┆ time_count │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ datetime[μs] ┆ datetime[μs] ┆ u32 │
╞═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
│ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 1 │
│ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 2 │
│ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2 │
│ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2 │
└─────────────────────┴─────────────────────┴─────────────────────┴────────────┘

When closed="left", should not include right end of interval
┌─────────────────────┬─────────────────────┬─────────────────────┬─────┐
│ _lower_boundary ┆ _upper_boundary ┆ time ┆ n │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ datetime[μs] ┆ datetime[μs] ┆ f64 │
╞═════════════════════╪═════════════════════╪═════════════════════╪═════╡
│ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 0.0 │
│ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 1.5 │
│ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 3.5 │
│ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 5.5 │
└─────────────────────┴─────────────────────┴─────────────────────┴─────┘

When closed="left", the window excludes the right end of interval:
[lower_bound, upper_bound)

>>> df.group_by_dynamic("time", every="1h", closed="left").agg(
... [
... pl.col("time").count().alias("time_count"),
... pl.col("time").alias("time_agg_list"),
... ]
... )
shape: (4, 3)
┌─────────────────────┬────────────┬───────────────────────────────────┐
│ time ┆ time_count ┆ time_agg_list │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ u32 ┆ list[datetime[μs]] │
╞═════════════════════╪════════════╪═══════════════════════════════════╡
│ 2021-12-16 00:00:00 ┆ 2 ┆ [2021-12-16 00:00:00, 2021-12-16… │
│ 2021-12-16 01:00:00 ┆ 2 ┆ [2021-12-16 01:00:00, 2021-12-16… │
│ 2021-12-16 02:00:00 ┆ 2 ┆ [2021-12-16 02:00:00, 2021-12-16… │
│ 2021-12-16 03:00:00 ┆ 1 ┆ [2021-12-16 03:00:00] │
└─────────────────────┴────────────┴───────────────────────────────────┘
>>> df.group_by_dynamic("time", every="1h", closed="left").agg(pl.col("n"))
shape: (4, 2)
┌─────────────────────┬───────────┐
│ time ┆ n │
│ --- ┆ --- │
│ datetime[μs] ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-16 00:00:00 ┆ [0, 1] │
│ 2021-12-16 01:00:00 ┆ [2, 3] │
│ 2021-12-16 02:00:00 ┆ [4, 5] │
│ 2021-12-16 03:00:00 ┆ [6] │
└─────────────────────┴───────────┘

When closed="both" the time values at the window boundaries belong to 2 groups.

>>> df.group_by_dynamic("time", every="1h", closed="both").agg(
... [pl.col("time").count().alias("time_count")]
... )
>>> df.group_by_dynamic("time", every="1h", closed="both").agg(pl.col("n"))
shape: (5, 2)
┌─────────────────────┬────────────┐
│ time ┆ time_count │
│ --- ┆ --- │
│ datetime[μs] ┆ u32 │
╞═════════════════════╪════════════╡
│ 2021-12-15 23:00:00 ┆ 1 │
│ 2021-12-16 00:00:00 ┆ 3 │
│ 2021-12-16 01:00:00 ┆ 3 │
│ 2021-12-16 02:00:00 ┆ 3 │
│ 2021-12-16 03:00:00 ┆ 1 │
└─────────────────────┴────────────┘
┌─────────────────────┬───────────┐
│ time ┆ n │
│ --- ┆ --- │
│ datetime[μs] ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-15 23:00:00 ┆ [0] │
│ 2021-12-16 00:00:00 ┆ [0, 1, 2] │
│ 2021-12-16 01:00:00 ┆ [2, 3, 4] │
│ 2021-12-16 02:00:00 ┆ [4, 5, 6] │
│ 2021-12-16 03:00:00 ┆ [6] │
└─────────────────────┴───────────┘

Dynamic group bys can also be combined with grouping on normal keys

>>> df = pl.DataFrame(
... {
... "time": pl.datetime_range(
... start=datetime(2021, 12, 16),
... end=datetime(2021, 12, 16, 3),
... interval="30m",
... eager=True,
... ),
... "groups": ["a", "a", "a", "b", "b", "a", "a"],
... }
... )
>>> df = df.with_columns(groups=pl.Series(["a", "a", "a", "b", "b", "a", "a"]))
>>> df
shape: (7, 2)
┌─────────────────────┬────────┐
│ time ┆ groups │
│ --- ┆ --- │
│ datetime[μs] ┆ str │
╞═════════════════════╪════════╡
│ 2021-12-16 00:00:00 ┆ a │
│ 2021-12-16 00:30:00 ┆ a │
│ 2021-12-16 01:00:00 ┆ a │
│ 2021-12-16 01:30:00 ┆ b │
│ 2021-12-16 02:00:00 ┆ b │
│ 2021-12-16 02:30:00 ┆ a │
│ 2021-12-16 03:00:00 ┆ a │
└─────────────────────┴────────┘
shape: (7, 3)
┌─────────────────────┬─────┬────────┐
│ time ┆ n ┆ groups │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ i64 ┆ str │
╞═════════════════════╪═════╪════════╡
│ 2021-12-16 00:00:00 ┆ 0 ┆ a │
│ 2021-12-16 00:30:00 ┆ 1 ┆ a │
│ 2021-12-16 01:00:00 ┆ 2 ┆ a │
│ 2021-12-16 01:30:00 ┆ 3 ┆ b │
│ 2021-12-16 02:00:00 ┆ 4 ┆ b │
│ 2021-12-16 02:30:00 ┆ 5 ┆ a │
│ 2021-12-16 03:00:00 ┆ 6 ┆ a │
└─────────────────────┴─────┴────────┘
>>> df.group_by_dynamic(
... "time",
... every="1h",
... closed="both",
... by="groups",
... include_boundaries=True,
... ).agg([pl.col("time").count().alias("time_count")])
... ).agg(pl.col("n"))
shape: (7, 5)
┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
│ groups ┆ _lower_boundary ┆ _upper_boundary ┆ time ┆ time_count │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ datetime[μs] ┆ datetime[μs] ┆ datetime[μs] ┆ u32 │
╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
│ a ┆ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 1 │
│ a ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 3 │
│ a ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 1 │
│ a ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2 │
│ a ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 04:00:00 ┆ 2021-12-16 03:00:00 ┆ 1 │
│ b ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2 │
│ b ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 1 │
└────────┴─────────────────────┴─────────────────────┴─────────────────────┴────────────┘
┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬───────────┐
│ groups ┆ _lower_boundary ┆ _upper_boundary ┆ time ┆ n │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ datetime[μs] ┆ datetime[μs] ┆ datetime[μs] ┆ list[i64] │
╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪═══════════╡
│ a ┆ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ [0] │
│ a ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ [0, 1, 2] │
│ a ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ [2] │
│ a ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ [5, 6] │
│ a ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 04:00:00 ┆ 2021-12-16 03:00:00 ┆ [6] │
│ b ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ [3, 4] │
│ b ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ [4] │
└────────┴─────────────────────┴─────────────────────┴─────────────────────┴───────────┘

Dynamic group by on an index column

Expand Down
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