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# pandas.api.typing.Expanding.median

Expanding.median(numeric_only=False, engine=None, engine_kwargs=None)

  Calculate the expanding median.

  At each point in time, returns the median of all values observed up to that point, respecting the `min_periods` threshold.

  Parameters:
    numeric_only bool, default False

      Include only float, int, boolean columns.

    engine str, default None

      - `'cython'` : Runs the operation through C-extensions from cython.

      - `'numba'` : Runs the operation through JIT compiled code from numba.

      - `None` : Defaults to `'cython'` or globally setting `compute.use_numba`

    engine_kwargs dict, default None

      - For `'cython'` engine, there are no accepted `engine_kwargs`

      - For `'numba'` engine, the engine can accept `nogil` and `parallel` dictionary keys. The values must either be `True` or `False`. The default `engine_kwargs` for the `'numba'` engine is `{'nogil': False, 'parallel': False}`

  Returns:
    Series or DataFrame

      Return type is the same as the original object with `np.float64` dtype.

  See also

  `Series.expanding`

    Calling expanding with Series data.

  `DataFrame.expanding`

    Calling expanding with DataFrames.

  `Series.median`

    Aggregating median for Series.

  `DataFrame.median`

    Aggregating median for DataFrame.

  Notes

  See Numba engine and Numba (JIT compilation) for extended documentation and performance considerations for the Numba engine.

  Examples

      >>> ser = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"])
      >>> ser.expanding().median()
      a    1.0
      b    1.5
      c    2.0
      d    2.5
      dtype: float64