# dask.dataframe.Index.apply

Index.apply(func, convert_dtype=<no_default>, meta=<no_default>, args=(), **kwds)

  Parallel version of pandas.Series.apply

  Parameters:
    func function

      Function to apply

    convert_dtype boolean, default True

      Try to find better dtype for elementwise function results. If False, leave as dtype=object.

    meta pd.DataFrame, pd.Series, dict, iterable, tuple, optional

      An empty `pd.DataFrame` or `pd.Series` that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a `DataFrame`, a `dict` of `{name: dtype}` or iterable of `(name, dtype)` can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of `(name, dtype)` can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing `meta` is recommended. For more information, see `dask.dataframe.utils.make_meta`.

    args tuple

      Positional arguments to pass to function in addition to the value.

    Additional keyword arguments will be passed as keywords to the function.
  Returns:
    applied Series or DataFrame if func returns a Series.

  See also

  `dask.Series.map_partitions`

  Examples

      >>> import dask.dataframe as dd
      >>> s = pd.Series(range(5), name='x')
      >>> ds = dd.from_pandas(s, npartitions=2)

  Apply a function elementwise across the Series, passing in extra arguments in `args` and `kwargs`:

      >>> def myadd(x, a, b=1):
      ...     return x + a + b
      >>> res = ds.apply(myadd, args=(2,), b=1.5)

  By default, dask tries to infer the output metadata by running your provided function on some fake data. This works well in many cases, but can sometimes be expensive, or even fail. To avoid this, you can manually specify the output metadata with the `meta` keyword. This can be specified in many forms, for more information see `dask.dataframe.utils.make_meta`.

  Here we specify the output is a Series with name `'x'`, and dtype `float64`:

      >>> res = ds.apply(myadd, args=(2,), b=1.5, meta=('x', 'f8'))

  In the case where the metadata doesn’t change, you can also pass in the object itself directly:

      >>> res = ds.apply(lambda x: x + 1, meta=ds)