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Verify all non-alias functions have doc string
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python/datafusion/functions.py

Lines changed: 28 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -1859,6 +1859,18 @@ def to_char(arg: Expr, formatter: Expr) -> Expr:
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For usage of ``formatter`` see the rust chrono package ``strftime`` package.
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[Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html)
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Examples:
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>>> ctx = dfn.SessionContext()
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>>> df = ctx.from_pydict({"a": ["2021-01-01T00:00:00"]})
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>>> result = df.select(
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... dfn.functions.to_char(
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... dfn.functions.to_timestamp(dfn.col("a")),
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... dfn.lit("%Y/%m/%d"),
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... ).alias("formatted")
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... )
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>>> result.collect_column("formatted")[0].as_py()
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'2021/01/01'
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"""
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return Expr(f.to_char(arg.expr, formatter.expr))
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@@ -1878,6 +1890,14 @@ def to_date(arg: Expr, *formatters: Expr) -> Expr:
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For usage of ``formatters`` see the rust chrono package ``strftime`` package.
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[Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html)
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Examples:
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>>> ctx = dfn.SessionContext()
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>>> df = ctx.from_pydict({"a": ["2021-07-20"]})
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>>> result = df.select(
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... dfn.functions.to_date(dfn.col("a")).alias("dt"))
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>>> str(result.collect_column("dt")[0].as_py())
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'2021-07-20'
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"""
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return Expr(f.to_date(arg.expr, *_unwrap_exprs(formatters)))
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@@ -1899,6 +1919,14 @@ def to_time(arg: Expr, *formatters: Expr) -> Expr:
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For usage of ``formatters`` see the rust chrono package ``strftime`` package.
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[Documentation here.](https://docs.rs/chrono/latest/chrono/format/strftime/index.html)
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Examples:
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>>> ctx = dfn.SessionContext()
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>>> df = ctx.from_pydict({"a": ["14:30:00"]})
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>>> result = df.select(
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... dfn.functions.to_time(dfn.col("a")).alias("t"))
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>>> str(result.collect_column("t")[0].as_py())
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'14:30:00'
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"""
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return Expr(f.to_time(arg.expr, *_unwrap_exprs(formatters)))
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@@ -3730,15 +3758,6 @@ def var_sample(expression: Expr, filter: Expr | None = None) -> Expr:
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"""Computes the sample variance of the argument.
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This is an alias for :py:func:`var_samp`.
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Examples:
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>>> ctx = dfn.SessionContext()
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>>> df = ctx.from_pydict({"a": [1.0, 2.0, 3.0]})
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>>> result = df.aggregate(
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... [], [dfn.functions.var_sample(dfn.col("a")).alias("v")]
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... )
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>>> result.collect_column("v")[0].as_py()
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1.0
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"""
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return var_samp(expression, filter)
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