pandas.Series.to_xarray#

Series.to_xarray()[source]#

Return an xarray object from the pandas object.

Returns:

xarray.DataArray or xarray.Dataset

Data in the pandas structure converted to Dataset if the object isa DataFrame, or a DataArray if the object is a Series.

See also

DataFrame.to_hdf

Write DataFrame to an HDF5 file.

DataFrame.to_parquet

Write a DataFrame to the binary parquet format.

Notes

See thexarray docs

Examples

>>>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_legs0  falcon    bird      389.0         21  parrot    bird       24.0         22    lion  mammal       80.5         43  monkey  mammal        NaN         4

>>>df.to_xarray()<xarray.Dataset>Dimensions:    (index: 4)Coordinates:  * index      (index) int64 32B 0 1 2 3Data variables:    name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'    class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'    max_speed  (index) float64 32B 389.0 24.0 80.5 nan    num_legs   (index) int64 32B 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                   speeddate       animal2018-01-01 falcon    350           parrot     182018-01-02 falcon    361           parrot     15

>>>df_multiindex.to_xarray()<xarray.Dataset>Dimensions:  (date: 2, animal: 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