nested_dask.io ============== .. py:module:: nested_dask.io Functions --------- .. autoapisummary:: nested_dask.io.read_parquet Module Contents --------------- .. py:function:: 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) -> nested_dask.core.NestedFrame 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. Docstring copied from `dask.dataframe.read_parquet` :param path: 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. :type path: str or list :param columns: 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. :type columns: str or list, default None :param filters: 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. :type filters: Union[List[Tuple[str, str, Any]], List[List[Tuple[str, str, Any]]]], default None :param index: 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. :type index: str, list or False, default None :param categories: 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. :type categories: list or dict, default None :param storage_options: 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. :type storage_options: dict, default None :param open_file_options: 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. :type open_file_options: dict, default None :param engine: 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. :type engine: {'auto', 'pyarrow'} :param use_nullable_dtypes: Whether to use extension dtypes for the resulting ``DataFrame``. .. note:: This option is deprecated. Use "dtype_backend" instead. :type use_nullable_dtypes: {False, True} :param dtype_backend: 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+. :type dtype_backend: {'numpy_nullable', 'pyarrow'}, defaults to NumPy backed DataFrames :param calculate_divisions: 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 :ref:`dataframe-design-partitions`). :type calculate_divisions: bool, default False :param ignore_metadata_file: 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. :type ignore_metadata_file: bool, default False :param metadata_task_size: 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"). :type metadata_task_size: int, default configurable :param split_row_groups: 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``. :type split_row_groups: 'infer', 'adaptive', bool, or int, default 'infer' :param blocksize: 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. :type blocksize: int or str, default 'default' :param aggregate_files: 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``. :type aggregate_files: bool or str, default None :param parquet_file_extension: 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". :type parquet_file_extension: str, tuple[str], or None, default (".parq", ".parquet", ".pq") :param filesystem: Specifies the backend to use. :type filesystem: "fsspec", "arrow", or fsspec.AbstractFileSystem backend to use. :param dataset: 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. :type dataset: dict, default None :param read: Dictionary of options to pass through to ``engine.read_partitions`` using the ``read`` key-word argument. :type read: dict, default None :param arrow_to_pandas: Dictionary of options to use when converting from ``pyarrow.Table`` to a pandas ``DataFrame`` object. Only used by the "arrow" engine. :type arrow_to_pandas: dict, default None :param \*\*kwargs: 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. :type \*\*kwargs: dict (of dicts) .. rubric:: Examples >>> df = dd.read_parquet('s3://bucket/my-parquet-data') # doctest: +SKIP