pandas.DataFrame.to_xarray # 数据框。to_xarray ( ) [来源] # 从 pandas 对象返回一个 xarray 对象。 返回: xarray.DataArray 或 xarray.Dataset如果对象是 DataFrame,则 pandas 结构中的数据转换为 Dataset;如果对象是 Series,则转换为 DataArray。 也可以看看 DataFrame.to_hdf将 DataFrame 写入 HDF5 文件。 DataFrame.to_parquet将 DataFrame 写入二进制 parquet 格式。 笔记 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 32B 0 1 2 3 Data 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 speed date animal 2018-01-01 falcon 350 parrot 18 2018-01-02 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