Table.to_arrow_table
Table.to_arrow_table(max_results=None, *, variables=None, progress=True, batch_preprocessor=None, max_parallelization=os.cpu_count()) → pyarrow.Table
Returns a representation of the table as a PyArrow Table. Since arrow is the underlying transport format for Redivis tables, loading data directly into an arrow table will always be the most performant in-memory option.
Parameters:
max_results : int, default None
The maximum number of rows to return. If not specified, all rows in the table will be read.
variables : list<str>, default None
A list of variable names to read, improving performance when not all variables are needed. If unspecified, all variables will be represented in the returned rows. Variable names are case-insensitive, though the names in the results will reflect the variable's true casing. The order of the columns returned will correspond to the order of names in this list.
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 Table.to_arrow_batch_iterator()
max_parallelization : int, default os.cpu_count()
The maximum number of threads utilized when loading the table.
Returns:
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