pandas.Series.to_xarray

Series.to_xarray() [source]

Return an xarray object from the pandas object.

Returns:	a DataArray for a Series a Dataset for a DataFrame a DataArray for higher dims

Notes

See the xarray docs

Examples

>>> df = pd.DataFrame({'A' : [1, 1, 2],
                       'B' : ['foo', 'bar', 'foo'],
                       'C' : np.arange(4.,7)})
>>> df
   A    B    C
0  1  foo  4.0
1  1  bar  5.0
2  2  foo  6.0

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:  (index: 3)
Coordinates:
  * index    (index) int64 0 1 2
Data variables:
    A        (index) int64 1 1 2
    B        (index) object 'foo' 'bar' 'foo'
    C        (index) float64 4.0 5.0 6.0

>>> df = pd.DataFrame({'A' : [1, 1, 2],
                       'B' : ['foo', 'bar', 'foo'],
                       'C' : np.arange(4.,7)}
                     ).set_index(['B','A'])
>>> df
         C
B   A
foo 1  4.0
bar 1  5.0
foo 2  6.0

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:  (A: 2, B: 2)
Coordinates:
  * B        (B) object 'bar' 'foo'
  * A        (A) int64 1 2
Data variables:
    C        (B, A) float64 5.0 nan 4.0 6.0

>>> p = pd.Panel(np.arange(24).reshape(4,3,2),
                 items=list('ABCD'),
                 major_axis=pd.date_range('20130101', periods=3),
                 minor_axis=['first', 'second'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second

>>> p.to_xarray()
<xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)>
array([[[ 0,  1],
        [ 2,  3],
        [ 4,  5]],
       [[ 6,  7],
        [ 8,  9],
        [10, 11]],
       [[12, 13],
        [14, 15],
        [16, 17]],
       [[18, 19],
        [20, 21],
        [22, 23]]])
Coordinates:
  * items       (items) object 'A' 'B' 'C' 'D'
  * major_axis  (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03  # noqa
  * minor_axis  (minor_axis) object 'first' 'second'