Deephaven Python Integration API Documentation

Deephaven Python Integration Package is created by Deephaven Data Labs. It allows Python developers, including data scientists, to access data, run queries, and execute Python scripts directly inside Deephaven data servers to achieve maximum performance. By taking advantage of the unique streaming table capability of Deephaven and its many data ingestion facilities (Kafka, Parquet, CSV, SQL, etc.), Python developers can quickly put together a real-time data processing pipeline that is high performing and easy to consume.

Examples:

>>> from deephaven import read_csv
>>> from deephaven.stream.kafka.consumer import kafka_consumer, TableType
>>> from deephaven.plot import Figure, PlotStyle
>>> csv_table = read_csv("data1.csv")
>>> kafka_table = kafka_consumer.consume({'bootstrap.servers': 'redpanda:29092'}, topic='realtime_feed', table_type=TableType.Append)
>>> joined_table = kafka_table.join(csv_table, on=["key_col_1", "key_col_2"], joins=["data_col1"])
>>> figure = Figure() \
>>>    .axes(plot_style = PlotStyle.STACKED_BAR) \
>>>    .plot_cat(series_name="Categories1", t=joined_table, category="Key_col_1", y = "data_col1") \
>>>    .show()