pydeephaven

• combo_agg
  • AggBase
  • ComboAggregation
    • ComboAggregation.abs_sum()
    • ComboAggregation.avg()
    • ComboAggregation.count()
    • ComboAggregation.first()
    • ComboAggregation.group()
    • ComboAggregation.last()
    • ComboAggregation.max()
    • ComboAggregation.median()
    • ComboAggregation.min()
    • ComboAggregation.pct()
    • ComboAggregation.std()
    • ComboAggregation.sum()
    • ComboAggregation.var()
    • ComboAggregation.weighted_avg()
  • CommonAgg
  • CountAgg
  • PctAgg
  • WeightedAvgAgg
• constants
  • MatchRule
    • MatchRule.GREATER_THAN
    • MatchRule.GREATER_THAN_EQUAL
    • MatchRule.LESS_THAN
    • MatchRule.LESS_THAN_EQUAL
  • SortDirection
    • SortDirection.ASCENDING
    • SortDirection.DESCENDING
    • SortDirection.REVERSE
    • SortDirection.UNKNOWN
• dherror
  • DHError
    • DHError.with_traceback()
• query
  • Query
    • Query.agg_by()
    • Query.aj()
    • Query.avg_by()
    • Query.count()
    • Query.count_by()
    • Query.drop_columns()
    • Query.exact_join()
    • Query.exec()
    • Query.first_by()
    • Query.group_by()
    • Query.head()
    • Query.head_by()
    • Query.join()
    • Query.last_by()
    • Query.lazy_update()
    • Query.max_by()
    • Query.median_by()
    • Query.min_by()
    • Query.natural_join()
    • Query.raj()
    • Query.select()
    • Query.select_distinct()
    • Query.snapshot()
    • Query.snapshot_when()
    • Query.sort()
    • Query.std_by()
    • Query.sum_by()
    • Query.tail()
    • Query.tail_by()
    • Query.ungroup()
    • Query.update()
    • Query.update_by()
    • Query.update_view()
    • Query.var_by()
    • Query.view()
    • Query.where()
• session
  • Session
    • Session.tables
    • Session.is_alive
    • Session.bind_table()
    • Session.close()
    • Session.empty_table()
    • Session.import_table()
    • Session.merge_tables()
    • Session.open_table()
    • Session.query()
    • Session.run_script()
    • Session.time_table()
• table
  • Table
    • Table.is_closed
    • Table.agg_by()
    • Table.aj()
    • Table.avg_by()
    • Table.close()
    • Table.count()
    • Table.count_by()
    • Table.drop_columns()
    • Table.exact_join()
    • Table.first_by()
    • Table.group_by()
    • Table.head()
    • Table.head_by()
    • Table.join()
    • Table.last_by()
    • Table.lazy_update()
    • Table.max_by()
    • Table.median_by()
    • Table.min_by()
    • Table.natural_join()
    • Table.raj()
    • Table.select()
    • Table.select_distinct()
    • Table.snapshot()
    • Table.snapshot_when()
    • Table.sort()
    • Table.std_by()
    • Table.sum_by()
    • Table.tail()
    • Table.tail_by()
    • Table.to_arrow()
    • Table.ungroup()
    • Table.update()
    • Table.update_by()
    • Table.update_view()
    • Table.var_by()
    • Table.view()
    • Table.where()
• updateby
  • BadDataBehavior
    • BadDataBehavior.POISON
    • BadDataBehavior.RESET
    • BadDataBehavior.SKIP
    • BadDataBehavior.THROW
  • MathContext
    • MathContext.DECIMAL128
    • MathContext.DECIMAL32
    • MathContext.DECIMAL64
    • MathContext.UNLIMITED
  • OperationControl
  • UpdateByOperation
  • cum_max()
  • cum_min()
  • cum_prod()
  • cum_sum()
  • ema_tick_decay()
  • ema_time_decay()
  • forward_fill()
• utils
  • is_deephaven_compatible()

Deephaven Python Client (pydeephaven) is a Python API built on top of Deephaven’s highly efficient Open API which is based on gRPC and Apache Arrow. It allows Python applications to remotely connect to Deephaven data servers, export/import data with the server, run Python scripts on the server, and execute powerful queries on data tables.

Because Deephaven data servers and Deephaven clients including pydeephaven exchange data in the Apache Arrow format, pydeephaven is able to leverage ‘pyarrow’ - the Python bindings of Arrow (ttps://arrow.apache.org/docs/python/) for data representation and integration with other data analytic tools such as NumPy, Pandas, etc.

Examples

>>> from pydeephaven import Session
>>> from pyarrow import csv
>>> session = Session() # assuming Deephaven Community Edition is running locally with the default configuration
>>> table1 = session.import_table(csv.read_csv("data1.csv"))
>>> table2 = session.import_table(csv.read_csv("data2.csv"))
>>> joined_table = table1.join(table2, on=["key_col_1", "key_col_2"], joins=["data_col1"])
>>> df = joined_table.to_arrow().to_pandas()
>>> print(df)
>>> session.close()