# dask.dataframe.DataFrame.to_hdf

DataFrame.to_hdf(path_or_buf, key, mode='a', append=False, **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

  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`

  Examples

  Save Data to a single file

      >>> df.to_hdf('output.hdf', '/data')

  Save data to multiple datapaths within the same file:

      >>> df.to_hdf('output.hdf', '/data-*')

  Save data to multiple files:

      >>> df.to_hdf('output-*.hdf', '/data')

  Save data to multiple files, using the multiprocessing scheduler:

      >>> df.to_hdf('output-*.hdf', '/data', scheduler='processes')

  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)