pandas.Series.to_xarray#
- Series.to_xarray()[源代码]#
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。
参见
DataFrame.to_hdf
将 DataFrame 写入 HDF5 文件。
DataFrame.to_parquet
将一个 DataFrame 写入二进制 parquet 格式。
备注
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[s] 2018-01-01 2018-01-02 * animal (animal) object 'falcon' 'parrot' Data variables: speed (date, animal) int64 350 18 361 15