TheNeuralBit commented on a change in pull request #15827:
URL: https://github.com/apache/beam/pull/15827#discussion_r758802696
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File path: sdks/python/apache_beam/dataframe/frames.py
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@@ -1277,6 +1277,90 @@ def align(self, other, join, axis, level, method,
**kwargs):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def idxmin(self, **kwargs):
+ skipna = kwargs.get('skipna', True)
+
+ def compute_idxmin(s):
+ min_index = s.idxmin(**kwargs)
+ if pd.isna(min_index):
+ return s
+ else:
+ return s.loc[[min_index]]
+
+ # Avoids empty Series error when evaluating proxy
+ index_dtype = self._expr.proxy().index.dtype
+ index = pd.Index([], dtype=index_dtype)
+ proxy = self._expr.proxy().copy()
+ proxy.index = index
+ proxy = proxy.append(
+ pd.Series([np.inf], index=np.asarray(['0']).astype(proxy.index.dtype)))
+
+ if not skipna:
+ proxy = proxy.append(
+ pd.Series([None],
+ index=np.asarray(['1']).astype(proxy.index.dtype)).astype(
+ proxy.dtype))
+
+ idx_min = expressions.ComputedExpression(
+ 'idx_min',
+ compute_idxmin, [self._expr],
+ proxy=proxy,
+ requires_partition_by=partitionings.Index(),
+ preserves_partition_by=partitionings.Singleton())
+
+ with expressions.allow_non_parallel_operations(True):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'idxmin_combine',
+ lambda s: s.idxmin(**kwargs), [idx_min],
+ requires_partition_by=partitionings.Singleton(),
+ preserves_partition_by=partitionings.Singleton()))
+
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def idxmax(self, **kwargs):
+ skipna = kwargs.get('skipna', True)
+
+ def compute_idxmax(s):
+ max_index = s.idxmax(**kwargs)
+ if pd.isna(max_index):
+ return s
+ else:
+ return s.loc[[max_index]]
+
+ # Avoids empty Series error when evaluating proxy
+ index_dtype = self._expr.proxy().index.dtype
+ index = pd.Index([], dtype=index_dtype)
+ proxy = self._expr.proxy().copy()
+ proxy.index = index
+ proxy = proxy.append(
+ pd.Series([-np.inf],
index=np.asarray(['0']).astype(proxy.index.dtype)))
Review comment:
Does the ValueError occur when generating the proxy for idxmax_combine?
I think it might be a better approach to just generate the proxy for the
idxmax_combine expression too
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