Query.to_dask_dataframe

Query.to_dask_dataframe(max_results=None, *, variables=None, progress=True, batch_preprocessor=None, max_parallelization=os.cpu_count()) → dask.DataFrame

Returns a representation of the query results as a dask.DataFrame, which can be used for parallel processing and larger-than-memory analysis. The underlying dask dataframe is backed by a Parquet file on disk, meaning that loading query results in this method will not lead to significant memory consumption. The parquet file is stored in your operating system's temp directory, unless the REDIVIS_TMPDIR environment variable is set.

Parameters:

max_results : int, default None The maximum number of rows to return. If not specified, all rows in the quer results will be read.

progress : bool, default True Whether to show a progress bar.

batch_preprocessor : function, default None Function used to preprocess the data, invoked for each batch of records as they are initially loaded. This can be helpful in reducing the size of the data before being loaded into a dataframe. The function accepts one argument, a pyarrow.RecordBatch, and must return a pyarrow.RecordBatch or None. If you prefer to work with the data solely in a streaming manner, see Query.to_arrow_batch_iterator()

max_parallelization : int, default os.cpu_count() The maximum number of threads utilized when loading the query.

Returns:

dask.DataFrame

See also

• Query.to_arrow_batch_iterator()

• Query.to_arrow_dataset()

• Query.to_arrow_table()

• Query.to_geopandas_dataframe()

• Query.to_pandas_dataframe()

• Query.to_polars_lazyframe()