read_ipc_polars() imports the data as a Polars DataFrame.
scan_ipc_polars() imports the data as a Polars LazyFrame.
Usage
read_ipc_polars(
source,
...,
n_rows = NULL,
memory_map = TRUE,
row_index_name = NULL,
row_index_offset = 0L,
rechunk = FALSE,
cache = TRUE,
include_file_paths = NULL
)
scan_ipc_polars(
source,
...,
n_rows = NULL,
memory_map = TRUE,
row_index_name = NULL,
row_index_offset = 0L,
rechunk = FALSE,
cache = TRUE,
include_file_paths = NULL
)Arguments
- source
Path to a file. You can use globbing with
*to scan/read multiple files in the same directory (see examples).- ...
Ignored.
- n_rows
Maximum number of rows to read.
- memory_map
A logical. If
TRUE, try to memory map the file. This can greatly improve performance on repeated queries as the OS may cache pages. Only uncompressed Arrow IPC files can be memory mapped.- row_index_name
If not
NULL, this will insert a row index column with the given name into the DataFrame.- row_index_offset
Offset to start the row index column (only used if the name is set).
- rechunk
In case of reading multiple files via a glob pattern, rechunk the final DataFrame into contiguous memory chunks.
- cache
Cache the result after reading.
- include_file_paths
Character value indicating the column name that will include the path of the source file(s).