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 binaryread()
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 tofsspec.open
. Please seefsspec
andurllib
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-backedDataFrame
."pyarrow"
: returns pyarrow-backed nullableArrowDtype
DataFrame
Added in version 2.0.
- Returns:
- type of object stored in file
DataFrame object stored in the file.
See also
read_csv
Read a comma-separated values (csv) file into a pandas DataFrame.
read_excel
Read an Excel file into a pandas DataFrame.
read_spss
Read an SPSS file into a pandas DataFrame.
read_orc
Load an ORC object into a pandas DataFrame.
read_sas
Read SAS file into a pandas DataFrame.
Examples
>>> df = pd.read_feather("path/to/file.feather")