# dask.dataframe.read_parquet

dask.dataframe.read_parquet(path, columns=None, filters=None, categories=None, index=None, storage_options=None, engine='auto', use_nullable_dtypes: bool | None = None, dtype_backend=None, calculate_divisions=None, ignore_metadata_file=False, metadata_task_size=None, split_row_groups='infer', blocksize='default', aggregate_files=None, parquet_file_extension=('.parq', '.parquet', '.pq'), filesystem=None, **kwargs)

  Read a Parquet file into a Dask DataFrame

  This reads a directory of Parquet data into a Dask.dataframe, one file per partition. It selects the index among the sorted columns if any exist.

  Parameters:
    path str or list

      Source directory for data, or path(s) to individual parquet files. Prefix with a protocol like `s3://` to read from alternative filesystems. To read from multiple files you can pass a globstring or a list of paths, with the caveat that they must all have the same protocol.

    columns str or list, default None

      Field name(s) to read in as columns in the output. By default all non-index fields will be read (as determined by the pandas parquet metadata, if present). Provide a single field name instead of a list to read in the data as a Series.

    filters Union[List[Tuple[str, str, Any]], List[List[Tuple[str, str, Any]]]], default None

      List of filters to apply, like `[[('col1', '==', 0), ...], ...]`. Using this argument will result in row-wise filtering of the final partitions.

      Predicates can be expressed in disjunctive normal form (DNF). This means that the inner-most tuple describes a single column predicate. These inner predicates are combined with an AND conjunction into a larger predicate. The outer-most list then combines all of the combined filters with an OR disjunction.

      Predicates can also be expressed as a `List[Tuple]`. These are evaluated as an AND conjunction. To express OR in predicates, one must use the (preferred for “pyarrow”) `List[List[Tuple]]` notation.

    index str, list or False, default None

      Field name(s) to use as the output frame index. By default will be inferred from the pandas parquet file metadata, if present. Use `False` to read all fields as columns.

    categories list or dict, default None

      For any fields listed here, if the parquet encoding is Dictionary, the column will be created with dtype category. Use only if it is guaranteed that the column is encoded as dictionary in all row-groups. If a list, assumes up to 2**16-1 labels; if a dict, specify the number of labels expected; if None, will load categories automatically for data written by dask, not otherwise.

    storage_options dict, default None

      Key/value pairs to be passed on to the file-system backend, if any. Note that the default file-system backend can be configured with the `filesystem` argument, described below.

    open_file_options dict, default None

      Key/value arguments to be passed along to `AbstractFileSystem.open` when each parquet data file is open for reading. Experimental (optimized) “precaching” for remote file systems (e.g. S3, GCS) can be enabled by adding `{"method": "parquet"}` under the `"precache_options"` key. Also, a custom file-open function can be used (instead of `AbstractFileSystem.open`), by specifying the desired function under the `"open_file_func"` key.

    engine{‘auto’, ‘pyarrow’}

      Parquet library to use. Defaults to ‘auto’, which uses `pyarrow` if it is installed, and falls back to the deprecated `fastparquet` otherwise. Note that `fastparquet` does not support all functionality offered by `pyarrow`. This is also used by third-party packages (e.g. CuDF) to inject bespoke engines.

    use_nullable_dtypes{False, True}

      Whether to use extension dtypes for the resulting `DataFrame`.

      Note

      This option is deprecated. Use “dtype_backend” instead.

    dtype_backend{‘numpy_nullable’, ‘pyarrow’}, defaults to NumPy backed DataFrames

      Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when ‘numpy_nullable’ is set, pyarrow is used for all dtypes if ‘pyarrow’ is set. `dtype_backend="pyarrow"` requires `pandas` 1.5+.

    calculate_divisions bool, default False

      Whether to use min/max statistics from the footer metadata (or global `_metadata` file) to calculate divisions for the output DataFrame collection. Divisions will not be calculated if statistics are missing. This option will be ignored if `index` is not specified and there is no physical index column specified in the custom “pandas” Parquet metadata. Note that `calculate_divisions=True` may be extremely slow when no global `_metadata` file is present, especially when reading from remote storage. Set this to `True` only when known divisions are needed for your workload (see Partitions).

    ignore_metadata_file bool, default False

      Whether to ignore the global `_metadata` file (when one is present). If `True`, or if the global `_metadata` file is missing, the parquet metadata may be gathered and processed in parallel. Parallel metadata processing is currently supported for `ArrowDatasetEngine` only.

    metadata_task_size int, default configurable

      If parquet metadata is processed in parallel (see `ignore_metadata_file` description above), this argument can be used to specify the number of dataset files to be processed by each task in the Dask graph. If this argument is set to `0`, parallel metadata processing will be disabled. The default values for local and remote filesystems can be specified with the “metadata-task-size-local” and “metadata-task-size-remote” config fields, respectively (see “dataframe.parquet”).

    split_row_groups‘infer’, ‘adaptive’, bool, or int, default ‘infer’

      If True, then each output dataframe partition will correspond to a single parquet-file row-group. If False, each partition will correspond to a complete file. If a positive integer value is given, each dataframe partition will correspond to that number of parquet row-groups (or fewer). If ‘adaptive’, the metadata of each file will be used to ensure that every partition satisfies `blocksize`. If ‘infer’ (the default), the uncompressed storage-size metadata in the first file will be used to automatically set `split_row_groups` to either ‘adaptive’ or `False`.

    blocksize int or str, default ‘default’

      The desired size of each output `DataFrame` partition in terms of total (uncompressed) parquet storage space. This argument is currently used to set the default value of `split_row_groups` (using row-group metadata from a single file), and will be ignored if `split_row_groups` is not set to ‘infer’ or ‘adaptive’. Default is 256 MiB.

    aggregate_files bool or str, default None

      WARNING: Passing a string argument to `aggregate_files` will result in experimental behavior. This behavior may change in the future.

      Whether distinct file paths may be aggregated into the same output partition. This parameter is only used when split_row_groups is set to ‘infer’, ‘adaptive’ or to an integer >1. A setting of True means that any two file paths may be aggregated into the same output partition, while False means that inter-file aggregation is prohibited.

      For “hive-partitioned” datasets, a “partition”-column name can also be specified. In this case, we allow the aggregation of any two files sharing a file path up to, and including, the corresponding directory name. For example, if `aggregate_files` is set to `"section"` for the directory structure below, `03.parquet` and `04.parquet` may be aggregated together, but `01.parquet` and `02.parquet` cannot be. If, however, `aggregate_files` is set to `"region"`, `01.parquet` may be aggregated with `02.parquet`, and `03.parquet` may be aggregated with `04.parquet`:

          dataset-path/
          ├── region=1/
          │   ├── section=a/
          │   │   └── 01.parquet
          │   ├── section=b/
          │   └── └── 02.parquet
          └── region=2/
              ├── section=a/
              │   ├── 03.parquet
              └── └── 04.parquet

      Note that the default behavior of `aggregate_files` is `False`.

    parquet_file_extension: str, tuple[str], or None, default (“.parq”, “.parquet”, “.pq”)

      A file extension or an iterable of extensions to use when discovering parquet files in a directory. Files that don’t match these extensions will be ignored. This argument only applies when `paths` corresponds to a directory and no `_metadata` file is present (or `ignore_metadata_file=True`). Passing in `parquet_file_extension=None` will treat all files in the directory as parquet files.

      The purpose of this argument is to ensure that the engine will ignore unsupported metadata files (like Spark’s ‘_SUCCESS’ and ‘crc’ files). It may be necessary to change this argument if the data files in your parquet dataset do not end in “.parq”, “.parquet”, or “.pq”.

    filesystem: “fsspec”, “arrow”, or fsspec.AbstractFileSystem backend to use.
    dataset: dict, default None

      Dictionary of options to use when creating a `pyarrow.dataset.Dataset` object. These options may include a “filesystem” key to configure the desired file-system backend. However, the top-level `filesystem` argument will always take precedence.

      Note: The `dataset` options may include a “partitioning” key. However, since `pyarrow.dataset.Partitioning` objects cannot be serialized, the value can be a dict of key-word arguments for the `pyarrow.dataset.partitioning` API (e.g. `dataset={"partitioning": {"flavor": "hive", "schema": ...}}`). Note that partitioned columns will not be converted to categorical dtypes when a custom partitioning schema is specified in this way.

    read: dict, default None

      Dictionary of options to pass through to `engine.read_partitions` using the `read` key-word argument.

    arrow_to_pandas: dict, default None

      Dictionary of options to use when converting from `pyarrow.Table` to a pandas `DataFrame` object. Only used by the “arrow” engine.

    **kwargs: dict (of dicts)

      Options to pass through to `engine.read_partitions` as stand-alone key-word arguments. Note that these options will be ignored by the engines defined in `dask.dataframe`, but may be used by other custom implementations.

  See also

  `to_parquet`
  `pyarrow.parquet.ParquetDataset`

  Examples

      >>> df = dd.read_parquet('s3://bucket/my-parquet-data')