[SPARK-40839][CONNECT][PYTHON] Implement DataFrame.sample
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -206,6 +206,33 @@ def sort(self, *cols: "ColumnOrString") -> "DataFrame": | |
| """Sort by a specific column""" | ||
| return DataFrame.withPlan(plan.Sort(self._plan, *cols), session=self._session) | ||
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| def sample( | ||
| self, | ||
| fraction: float, | ||
| *, | ||
| withReplacement: bool = False, | ||
| seed: Optional[int] = None, | ||
|
Member
There was a problem hiding this comment. Maybe we should just leverage keyword-only argument which will make the logic much simpler. Actually we wanted to do it in PySpark API layer in the past. Since this is a new API layer, I think it;s a good chance to replace them. cc @ueshin
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Author
There was a problem hiding this comment. yes, that's a bit confusing at first glance.
Member
There was a problem hiding this comment. Yes, if we can break the signature, it would be: def sample(
self,
fraction: float,
*,
withReplacement: Optional[bool] = None,
seed: Optional[int] = None,
) -> "DataFrame":
...
Member
There was a problem hiding this comment.
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Author
There was a problem hiding this comment. I like this idea |
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| ) -> "DataFrame": | ||
| if not isinstance(fraction, float): | ||
| raise TypeError(f"'fraction' must be float, but got {type(fraction).__name__}") | ||
| if not isinstance(withReplacement, bool): | ||
| raise TypeError( | ||
| f"'withReplacement' must be bool, but got {type(withReplacement).__name__}" | ||
| ) | ||
| if seed is not None and not isinstance(seed, int): | ||
| raise TypeError(f"'seed' must be None or int, but got {type(seed).__name__}") | ||
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| return DataFrame.withPlan( | ||
| plan.Sample( | ||
| child=self._plan, | ||
| lower_bound=0.0, | ||
| upper_bound=fraction, | ||
| with_replacement=withReplacement, | ||
| seed=seed, | ||
| ), | ||
| session=self._session, | ||
| ) | ||
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| def show(self, n: int, truncate: Optional[Union[bool, int]], vertical: Optional[bool]) -> None: | ||
| ... | ||
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The pyspark dataframe API has
Can we match (as easy as copy the API into connect dataframe.py)?
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I guess we can discard those ones ? @HyukjinKwon
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Maybe my real question was, will we have an issue to be compatible with existing pyspark dataframe code (needs different imports, of course) if we discard such API? I see many other similar API existing for pyspark dataframe.
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users may have to change their codes for this emigration, but I think this is also a chance to make some changes.
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Sure. We also can go to that direction.