pandas.read_feather#

pandas.read_feather(path, columns=None, use_threads=True, storage_options=None, dtype_backend=<no_default>)[source]#

Load a feather-format object from the file path.

Feather is particularly useful for scenarios that require efficient serialization and deserialization of tabular data. It supports schema preservation, making it a reliable choice for use cases such as sharing data between Python and R, or persisting intermediate results during data processing pipelines. This method provides additional flexibility with options for selective column reading, thread parallelism, and choosing the backend for data types.

Parameters:

pathstr, path object, or file-like object

String, path object (implementing os.PathLike[str]), or file-like object implementing a binary read() function. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.feather.

columnssequence, default None

If not provided, all columns are read.

use_threadsbool, default True

Whether to parallelize reading using multiple threads.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}

Back-end data type applied to the resultant DataFrame (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows:

"numpy_nullable": returns nullable-dtype-backed DataFrame.
"pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame

Added in version 2.0.

Returns:

type of object stored in file: DataFrame object stored in the file.

pandas.read_feather#

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