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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()