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Nix Packages collection & NixOS github.com/NixOS/nixpkgs
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123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170--- 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)