cudf.read_orc(filepath_or_buffer, engine='cudf', columns=None, filters=None, stripes=None, skiprows=None, num_rows=None, use_index=True, timestamp_type=None, use_python_file_object=True, **kwargs)#

Load an ORC dataset into a DataFrame

filepath_or_bufferstr, path object, bytes, or file-like object

Either a path to a file (a str, pathlib.Path, or py._path.local.LocalPath), URL (including http, ftp, and S3 locations), Python bytes of raw binary data, or any object with a read() method (such as builtin open() file handler function or BytesIO).

engine{ ‘cudf’, ‘pyarrow’ }, default ‘cudf’

Parser engine to use.

columnslist, default None

If not None, only these columns will be read from the file.

filterslist of tuple, list of lists of tuples default None

If not None, specifies a filter predicate used to filter out row groups using statistics stored for each row group as Parquet metadata. Row groups that do not match the given filter predicate are not read. The predicate is expressed in disjunctive normal form (DNF) like [[(‘x’, ‘=’, 0), …], …]. DNF allows arbitrary boolean logical combinations of single column predicates. The innermost tuples each describe a single column predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple column predicate. Finally, the outermost list combines these filters as a disjunction (OR). Predicates may also be passed as a list of tuples. This form is interpreted as a single conjunction. To express OR in predicates, one must use the (preferred) notation of list of lists of tuples.

stripes: list, default None

If not None, only these stripe will be read from the file. Stripes are concatenated with index ignored.

skiprowsint, default None

If not None, the number of rows to skip from the start of the file.

num_rowsint, default None

If not None, the total number of rows to read.

use_indexbool, default True

If True, use row index if available for faster seeking.

use_python_file_objectboolean, default True

If True, Arrow-backed PythonFile objects will be used in place of fsspec AbstractBufferedFile objects at IO time. This option is likely to improve performance when making small reads from larger ORC files.

kwargs are passed to the engine


  • cuDF supports local and remote data stores. See configuration details for available sources here.


>>> import cudf
>>> df = cudf.read_orc(filename)
>>> df
  num1                datetime text
0  123 2018-11-13T12:00:00.000 5451
1  456 2018-11-14T12:35:01.000 5784
2  789 2018-11-15T18:02:59.000 6117