# xarray.DataArray.to_series

#### DataArray.to_series()[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/dataarray.py#L4150-L4182)

Convert this array into a pandas.Series.

The Series is indexed by the Cartesian product of index coordinates
(in the form of a [`pandas.MultiIndex`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html#pandas.MultiIndex)).

If the underlying data is a [`sparse.COO`](https://sparse.pydata.org/en/latest/api/COO/#sparse.COO) array, the result
instead only contains that array’s stored (non-fill-value) entries,
indexed by their coordinates - the full Cartesian product is never
materialized, which avoids a .todense() call that could raise
MemoryError for large, genuinely sparse data.

* **Returns:**
  **result** ([`Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series)) – DataArray as a pandas Series.

#### SEE ALSO
[`DataArray.to_pandas`](xarray.DataArray.to_pandas.html.md#xarray.DataArray.to_pandas), [`DataArray.to_dataframe`](xarray.DataArray.to_dataframe.html.md#xarray.DataArray.to_dataframe)