pandas.read_parquet#
- pandas.read_parquet(path, engine='auto', columns=None, storage_options=None, use_nullable_dtypes=<no_default>, dtype_backend=<no_default>, filesystem=None, filters=None, **kwargs)[source]#
Load a parquet object from the file path, returning a DataFrame.
- 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, 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
ors3://bucket/partition_dir
.- engine{‘auto’, ‘pyarrow’, ‘fastparquet’}, default ‘auto’
Parquet library to use. If ‘auto’, then the option
io.parquet.engine
is used. The defaultio.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 bothpyarrow.fs
andfsspec
(e.g. “s3://”), then thepyarrow.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 tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.Added in version 1.3.0.
- use_nullable_dtypesbool, default False
If True, use dtypes that use
pd.NA
as missing value indicator for the resulting DataFrame. (only applicable for thepyarrow
engine) As new dtypes are added that supportpd.NA
in the future, the output with this option will change to use those dtypes. Note: this is an experimental option, and behaviour (e.g. additional support dtypes) may change without notice.Deprecated since version 2.0.
- 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-backedDataFrame
(default)."pyarrow"
: returns pyarrow-backed nullableArrowDtype
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.
- **kwargs
Any additional kwargs are passed to the engine.
- Returns:
- DataFrame
See also
DataFrame.to_parquet
Create a parquet object that serializes a DataFrame.
Examples
>>> 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) True >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"]) >>> restored_bar bar 0 5 1 6 2 7 3 8 4 9 >>> restored_bar.equals(original_df[['bar']]) True
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