dask.dataframe.to_parquet

dask.dataframe.to_parquet

dask.dataframe.to_parquet(df, path, engine='auto', compression='snappy', write_index=True, append=False, overwrite=False, ignore_divisions=False, partition_on=None, storage_options=None, custom_metadata=None, write_metadata_file=None, compute=True, compute_kwargs=None, schema='infer', name_function=None, filesystem=None, **kwargs)[source]

Store Dask.dataframe to Parquet files

Parameters
dfdask.dataframe.DataFrame
pathstring or pathlib.Path

Destination directory for data. Prepend with protocol like s3:// or hdfs:// for remote data.

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

Parquet library to use. Defaults to ‘auto’, which uses pyarrow if it is installed, and falls back to fastparquet otherwise.

compressionstring or dict, default ‘snappy’

Either a string like "snappy" or a dictionary mapping column names to compressors like {"name": "gzip", "values": "snappy"}. Defaults to "snappy".

write_indexboolean, default True

Whether or not to write the index. Defaults to True.

appendbool, default False

If False (default), construct data-set from scratch. If True, add new row-group(s) to an existing data-set. In the latter case, the data-set must exist, and the schema must match the input data.

overwritebool, default False

Whether or not to remove the contents of path before writing the dataset. The default is False. If True, the specified path must correspond to a directory (but not the current working directory). This option cannot be set to True if append=True. NOTE: overwrite=True will remove the original data even if the current write operation fails. Use at your own risk.

ignore_divisionsbool, default False

If False (default) raises error when previous divisions overlap with the new appended divisions. Ignored if append=False.

partition_onlist, default None

Construct directory-based partitioning by splitting on these fields’ values. Each dask partition will result in one or more datafiles, there will be no global groupby.

storage_optionsdict, default None

Key/value pairs to be passed on to the file-system backend, if any.

custom_metadatadict, default None

Custom key/value metadata to include in all footer metadata (and in the global “_metadata” file, if applicable). Note that the custom metadata may not contain the reserved b”pandas” key.

write_metadata_filebool or None, default None

Whether to write the special _metadata file. If None (the default), a _metadata file will only be written if append=True and the dataset already has a _metadata file.

computebool, default True

If True (default) then the result is computed immediately. If False then a dask.dataframe.Scalar object is returned for future computation.

compute_kwargsdict, default True

Options to be passed in to the compute method

schemapyarrow.Schema, dict, “infer”, or None, default “infer”

Global schema to use for the output dataset. Defaults to “infer”, which will infer the schema from the dask dataframe metadata. This is usually sufficient for common schemas, but notably will fail for object dtype columns that contain things other than strings. These columns will require an explicit schema be specified. The schema for a subset of columns can be overridden by passing in a dict of column names to pyarrow types (for example schema={"field": pa.string()}); columns not present in this dict will still be automatically inferred. Alternatively, a full pyarrow.Schema may be passed, in which case no schema inference will be done. Passing in schema=None will disable the use of a global file schema - each written file may use a different schema dependent on the dtypes of the corresponding partition. Note that this argument is ignored by the “fastparquet” engine.

name_functioncallable, default None

Function to generate the filename for each output partition. The function should accept an integer (partition index) as input and return a string which will be used as the filename for the corresponding partition. Should preserve the lexicographic order of partitions. If not specified, files will created using the convention part.0.parquet, part.1.parquet, part.2.parquet, … and so on for each partition in the DataFrame.

filesystem: “fsspec”, “arrow”, or fsspec.AbstractFileSystem backend to use.

Note that the “fastparquet” engine only supports “fsspec” or an explicit pyarrow.fs.AbstractFileSystem object. Default is “fsspec”.

**kwargs

Extra options to be passed on to the specific backend.

See also

read_parquet

Read parquet data to dask.dataframe

Notes

Each partition will be written to a separate file.

Examples

>>> df = dd.read_csv(...)  
>>> df.to_parquet('/path/to/output/', ...)

By default, files will be created in the specified output directory using the convention part.0.parquet, part.1.parquet, part.2.parquet, … and so on for each partition in the DataFrame. To customize the names of each file, you can use the name_function= keyword argument. The function passed to name_function will be used to generate the filename for each partition and should expect a partition’s index integer as input and return a string which will be used as the filename for the corresponding partition. Strings produced by name_function must preserve the order of their respective partition indices.

For example:

>>> name_function = lambda x: f"data-{x}.parquet"
>>> df.to_parquet('/path/to/output/', name_function=name_function)

will result in the following files being created:

/path/to/output/
    ├── data-0.parquet
    ├── data-1.parquet
    ├── data-2.parquet
    └── ...

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