Source code for

from dask_expr import from_legacy_dataframe

[docs]def read_hdf( pattern, key, start=0, stop=None, columns=None, chunksize=1000000, sorted_index=False, lock=True, mode="r", ): from import read_hdf as _read_hdf df = _read_hdf( pattern, key, start=start, stop=stop, columns=columns, chunksize=chunksize, sorted_index=sorted_index, lock=lock, mode=mode, ) return from_legacy_dataframe(df)
[docs]def to_hdf( df, path, key, mode="a", append=False, scheduler=None, name_function=None, compute=True, lock=None, dask_kwargs=None, **kwargs, ): """Store Dask Dataframe to Hierarchical Data Format (HDF) files This is a parallel version of the Pandas function of the same name. Please see the Pandas docstring for more detailed information about shared keyword arguments. This function differs from the Pandas version by saving the many partitions of a Dask DataFrame in parallel, either to many files, or to many datasets within the same file. You may specify this parallelism with an asterix ``*`` within the filename or datapath, and an optional ``name_function``. The asterix will be replaced with an increasing sequence of integers starting from ``0`` or with the result of calling ``name_function`` on each of those integers. This function only supports the Pandas ``'table'`` format, not the more specialized ``'fixed'`` format. Parameters ---------- path : string, pathlib.Path Path to a target filename. Supports strings, ``pathlib.Path``, or any object implementing the ``__fspath__`` protocol. May contain a ``*`` to denote many filenames. key : string Datapath within the files. May contain a ``*`` to denote many locations name_function : function A function to convert the ``*`` in the above options to a string. Should take in a number from 0 to the number of partitions and return a string. (see examples below) compute : bool Whether or not to execute immediately. If False then this returns a ``dask.Delayed`` value. lock : bool, Lock, optional Lock to use to prevent concurrency issues. By default a ``threading.Lock``, ``multiprocessing.Lock`` or ``SerializableLock`` will be used depending on your scheduler if a lock is required. See dask.utils.get_scheduler_lock for more information about lock selection. scheduler : string The scheduler to use, like "threads" or "processes" **other: See pandas.to_hdf for more information Examples -------- Save Data to a single file >>> df.to_hdf('output.hdf', '/data') # doctest: +SKIP Save data to multiple datapaths within the same file: >>> df.to_hdf('output.hdf', '/data-*') # doctest: +SKIP Save data to multiple files: >>> df.to_hdf('output-*.hdf', '/data') # doctest: +SKIP Save data to multiple files, using the multiprocessing scheduler: >>> df.to_hdf('output-*.hdf', '/data', scheduler='processes') # doctest: +SKIP Specify custom naming scheme. This writes files as '2000-01-01.hdf', '2000-01-02.hdf', '2000-01-03.hdf', etc.. >>> from datetime import date, timedelta >>> base = date(year=2000, month=1, day=1) >>> def name_function(i): ... ''' Convert integer 0 to n to a string ''' ... return base + timedelta(days=i) >>> df.to_hdf('*.hdf', '/data', name_function=name_function) # doctest: +SKIP Returns ------- filenames : list Returned if ``compute`` is True. List of file names that each partition is saved to. delayed : dask.Delayed Returned if ``compute`` is False. Delayed object to execute ``to_hdf`` when computed. See Also -------- read_hdf: to_parquet: """ from import to_hdf as _to_hdf return _to_hdf( df.to_legacy_dataframe(), path, key, mode=mode, append=append, scheduler=scheduler, name_function=name_function, compute=compute, lock=lock, dask_kwargs=dask_kwargs, **kwargs, )