用法:
Series.to_xarray()
從 pandas 對象返回一個 xarray 對象。
- xarray.DataArray 或 xarray.Dataset
如果對象是 DataFrame,則 pandas 結構中的數據轉換為 Dataset,如果對象是 Series,則轉換為 DataArray。
返回:
注意:
請參閱 xarray 文檔
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
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注:本文由純淨天空篩選整理自pandas.pydata.org大神的英文原創作品 pandas.Series.to_xarray。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。