apache_beam.dataframe.partitionings module¶
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class apache_beam.dataframe.partitionings.Partitioning[source]¶
- Bases: - object- A class representing a (consistent) partitioning of dataframe objects. - 
is_subpartitioning_of(other)[source]¶
- Returns whether self is a sub-partition of other. - Specifically, returns whether something partitioned by self is necissarily also partitioned by other. 
 
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class apache_beam.dataframe.partitionings.Index(levels=None)[source]¶
- Bases: - apache_beam.dataframe.partitionings.Partitioning- A partitioning by index (either fully or partially). - If the set of “levels” of the index to consider is not specified, the entire index is used. - These form a partial order, given by Singleton() < Index([i]) < Index([i, j]) < … < Index() < Arbitrary()- The ordering is implemented via the is_subpartitioning_of method, where the examples on the right are subpartitionings of the examples on the left above. 
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class apache_beam.dataframe.partitionings.Singleton(reason=None)[source]¶
- Bases: - apache_beam.dataframe.partitionings.Partitioning- A partitioning of all the data into a single partition. - 
reason¶
 
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class apache_beam.dataframe.partitionings.JoinIndex(ancestor=None)[source]¶
- Bases: - apache_beam.dataframe.partitionings.Partitioning- A partitioning that lets two frames be joined. This can either be a hash partitioning on the full index, or a common ancestor with no intervening re-indexing/re-partitioning. - It fits into the partial ordering as Index() < JoinIndex(x) < JoinIndex() < Arbitrary()- with JoinIndex(x) and JoinIndex(y)- being incomparable for nontrivial x != y. - Expressions desiring to make use of this index should simply declare a requirement of JoinIndex().