DataFrame.to_xarray() [source]
从pandas对象返回一个xarray对象。
返回值 ： xarray.DataArray 或 xarray.Dataset
如果对象是DataFrame，则将pandas结构中的数据转换为Dataset，如果对象是Series，则转换为DataArray。
Notes
请参阅xarray文档
例子
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 4)
Coordinates:
* index (index) int64 0 1 2 3
Data variables:
name (index) object 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 389.0 24.0 80.5 nan
num_legs (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray() <xarray.DataArray 'max_speed' (index: 4)> array([389. , 24. , 80.5, nan]) Coordinates: * index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot',
... 'falcon', 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions: (animal: 2, date: 2)
Coordinates:
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
speed (date, animal) int64 350 18 361 15