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--- a/apache_beam/dataframe/frames.py
+++ b/apache_beam/dataframe/frames.py
@@ -335,35 +335,37 @@
if hasattr(pd.DataFrame, 'pad'):
pad = _fillna_alias('pad')
- @frame_base.with_docs_from(pd.DataFrame)
- def first(self, offset):
- per_partition = expressions.ComputedExpression(
- 'first-per-partition', lambda df: df.sort_index().first(offset=offset),
- [self._expr],
- preserves_partition_by=partitionings.Arbitrary(),
- requires_partition_by=partitionings.Arbitrary())
- with expressions.allow_non_parallel_operations(True):
- return frame_base.DeferredFrame.wrap(
- expressions.ComputedExpression(
- 'first', lambda df: df.sort_index().first(offset=offset),
- [per_partition],
- preserves_partition_by=partitionings.Arbitrary(),
- requires_partition_by=partitionings.Singleton()))
+ if hasattr(pd.DataFrame, 'first'):
+ @frame_base.with_docs_from(pd.DataFrame)
+ def first(self, offset):
+ per_partition = expressions.ComputedExpression(
+ 'first-per-partition', lambda df: df.sort_index().first(offset=offset),
+ [self._expr],
+ preserves_partition_by=partitionings.Arbitrary(),
+ requires_partition_by=partitionings.Arbitrary())
+ with expressions.allow_non_parallel_operations(True):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'first', lambda df: df.sort_index().first(offset=offset),
+ [per_partition],
+ preserves_partition_by=partitionings.Arbitrary(),
+ requires_partition_by=partitionings.Singleton()))
- @frame_base.with_docs_from(pd.DataFrame)
- def last(self, offset):
- per_partition = expressions.ComputedExpression(
- 'last-per-partition', lambda df: df.sort_index().last(offset=offset),
- [self._expr],
- preserves_partition_by=partitionings.Arbitrary(),
- requires_partition_by=partitionings.Arbitrary())
- with expressions.allow_non_parallel_operations(True):
- return frame_base.DeferredFrame.wrap(
- expressions.ComputedExpression(
- 'last', lambda df: df.sort_index().last(offset=offset),
- [per_partition],
- preserves_partition_by=partitionings.Arbitrary(),
- requires_partition_by=partitionings.Singleton()))
+ if hasattr(pd.DataFrame, 'last'):
+ @frame_base.with_docs_from(pd.DataFrame)
+ def last(self, offset):
+ per_partition = expressions.ComputedExpression(
+ 'last-per-partition', lambda df: df.sort_index().last(offset=offset),
+ [self._expr],
+ preserves_partition_by=partitionings.Arbitrary(),
+ requires_partition_by=partitionings.Arbitrary())
+ with expressions.allow_non_parallel_operations(True):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'last', lambda df: df.sort_index().last(offset=offset),
+ [per_partition],
+ preserves_partition_by=partitionings.Arbitrary(),
+ requires_partition_by=partitionings.Singleton()))
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@@ -798,27 +800,28 @@
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
- @frame_base.with_docs_from(pd.DataFrame)
- def bool(self):
- # TODO: Documentation about DeferredScalar
- # Will throw if any partition has >1 element
- bools = expressions.ComputedExpression(
- 'get_bools',
- # Wrap scalar results in a Series for easier concatenation later
- lambda df: pd.Series([], dtype=bool)
- if df.empty else pd.Series([df.bool()]),
- [self._expr],
- requires_partition_by=partitionings.Arbitrary(),
- preserves_partition_by=partitionings.Singleton())
+ if hasattr(pd.DataFrame, 'bool'):
+ @frame_base.with_docs_from(pd.DataFrame)
+ def bool(self):
+ # TODO: Documentation about DeferredScalar
+ # Will throw if any partition has >1 element
+ bools = expressions.ComputedExpression(
+ 'get_bools',
+ # Wrap scalar results in a Series for easier concatenation later
+ lambda df: pd.Series([], dtype=bool)
+ if df.empty else pd.Series([df.bool()]),
+ [self._expr],
+ requires_partition_by=partitionings.Arbitrary(),
+ preserves_partition_by=partitionings.Singleton())
- with expressions.allow_non_parallel_operations(True):
- # Will throw if overall dataset has != 1 element
- return frame_base.DeferredFrame.wrap(
- expressions.ComputedExpression(
- 'combine_all_bools', lambda bools: bools.bool(), [bools],
- proxy=bool(),
- requires_partition_by=partitionings.Singleton(),
- preserves_partition_by=partitionings.Singleton()))
+ with expressions.allow_non_parallel_operations(True):
+ # Will throw if overall dataset has != 1 element
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'combine_all_bools', lambda bools: bools.bool(), [bools],
+ proxy=bool(),
+ requires_partition_by=partitionings.Singleton(),
+ preserves_partition_by=partitionings.Singleton()))
@frame_base.with_docs_from(pd.DataFrame)
def equals(self, other):
@@ -2932,7 +2935,8 @@
agg = aggregate
- applymap = frame_base._elementwise_method('applymap', base=pd.DataFrame)
+ if hasattr(pd.DataFrame, 'applymap'):
+ applymap = frame_base._elementwise_method('applymap', base=pd.DataFrame)
if PD_VERSION >= (2, 1):
map = frame_base._elementwise_method('map', base=pd.DataFrame)
add_prefix = frame_base._elementwise_method('add_prefix', base=pd.DataFrame)
@@ -4414,18 +4418,19 @@
return self.apply(apply_fn).droplevel(self._grouping_columns)
- @property # type: ignore
- @frame_base.with_docs_from(DataFrameGroupBy)
- def dtypes(self):
- return frame_base.DeferredFrame.wrap(
- expressions.ComputedExpression(
- 'dtypes',
- lambda gb: gb.dtypes,
- [self._expr],
- requires_partition_by=partitionings.Arbitrary(),
- preserves_partition_by=partitionings.Arbitrary()
- )
- )
+ if hasattr(DataFrameGroupBy, 'dtypes'):
+ @property # type: ignore
+ @frame_base.with_docs_from(DataFrameGroupBy)
+ def dtypes(self):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'dtypes',
+ lambda gb: gb.dtypes,
+ [self._expr],
+ requires_partition_by=partitionings.Arbitrary(),
+ preserves_partition_by=partitionings.Arbitrary()
+ )
+ )
if hasattr(DataFrameGroupBy, 'value_counts'):
@frame_base.with_docs_from(DataFrameGroupBy)
@@ -4745,7 +4750,8 @@
describe = frame_base.not_implemented_method('describe',
base_type=DataFrameGroupBy)
diff = frame_base._elementwise_method('diff', base=DataFrameGroupBy)
- fillna = frame_base._elementwise_method('fillna', base=DataFrameGroupBy)
+ if hasattr(DataFrameGroupBy, 'fillna'):
+ fillna = frame_base._elementwise_method('fillna', base=DataFrameGroupBy)
filter = frame_base._elementwise_method('filter', base=DataFrameGroupBy)
first = frame_base._elementwise_method('first', base=DataFrameGroupBy)
get_group = frame_base._elementwise_method('get_group', base=DataFrameGroupBy)
|
