pandas.read_parquet(path, engine='auto', columns=None, storage_options=None, dtype_backend=<no_default>, filesystem=None, filters=None, **kwargs)[source]#

Load a parquet object from the file path, returning a DataFrame.

The function automatically handles reading the data from a parquet file and creates a DataFrame with the appropriate structure.

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, gs, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.parquet. A file URL can also be a path to a directory that contains multiple partitioned parquet files. Both pyarrow and fastparquet support paths to directories as well as file URLs. A directory path could be: file://localhost/path/to/tables or s3://bucket/partition_dir.

engine{‘auto’, ‘pyarrow’, ‘fastparquet’}, default ‘auto’

Parquet library to use. If ‘auto’, then the option io.parquet.engine is used. The default io.parquet.engine behavior is to try ‘pyarrow’, falling back to ‘fastparquet’ if ‘pyarrow’ is unavailable.

When using the 'pyarrow' engine and no storage options are provided and a filesystem is implemented by both pyarrow.fs and fsspec (e.g. “s3://”), then the pyarrow.fs filesystem is attempted first. Use the filesystem keyword with an instantiated fsspec filesystem if you wish to use its implementation.

columnslist, default=None

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

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 Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Added in version 1.3.0.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultant DataFrame (still experimental). Behaviour is as follows:

  • "numpy_nullable": returns nullable-dtype-backed DataFrame (default).

  • "pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame.

Added in version 2.0.

filesystemfsspec or pyarrow filesystem, default None

Filesystem object to use when reading the parquet file. Only implemented for engine="pyarrow".

Added in version 2.1.0.

filtersList[Tuple] or List[List[Tuple]], default None

To filter out data. Filter syntax: [[(column, op, val), …],…] where op is [==, =, >, >=, <, <=, !=, in, not in] The innermost tuples are transposed into a set of filters applied through an AND operation. The outer list combines these sets of filters through an OR operation. A single list of tuples can also be used, meaning that no OR operation between set of filters is to be conducted.

Using this argument will NOT result in row-wise filtering of the final partitions unless engine="pyarrow" is also specified. For other engines, filtering is only performed at the partition level, that is, to prevent the loading of some row-groups and/or files.

Added in version 2.1.0.


Any additional kwargs are passed to the engine.


DataFrame based on parquet file.

See also


Create a parquet object that serializes a DataFrame.


>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> df_parquet_bytes = original_df.to_parquet()
>>> from io import BytesIO
>>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
>>> restored_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> restored_df.equals(original_df)
>>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
>>> restored_bar
0    5
1    6
2    7
3    8
4    9
>>> restored_bar.equals(original_df[["bar"]])

The function uses kwargs that are passed directly to the engine. In the following example, we use the filters argument of the pyarrow engine to filter the rows of the DataFrame.

Since pyarrow is the default engine, we can omit the engine argument. Note that the filters argument is implemented by the pyarrow engine, which can benefit from multithreading and also potentially be more economical in terms of memory.

>>> sel = [("foo", ">", 2)]
>>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
>>> restored_part
    foo  bar
0    3    8
1    4    9