Kubernetes 异常配置检测框架0 码力 | 31 页 | 9.57 MB | 1 年前3- pandas: powerful Python data analysis toolkit - 0.24.0'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 speeds of different birds. >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) (continues pandas: powerful Python data analysis toolkit, Release 0.24.0 (continued from previous page) >>> s2 falcon 345.0 eagle 200.0 duck 30.0 dtype: float64 Now, to combine the two datasets and view the highest0 码力 | 2973 页 | 9.90 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 1.0.0'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3015 页 | 10.78 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.25.0'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 2827 页 | 9.62 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.25.1'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 2833 页 | 9.65 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 1.1.1'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3231 页 | 10.87 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 1.1.0'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3229 页 | 10.87 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 1.0'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3091 页 | 10.16 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 1.0.4'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3081 页 | 10.24 MB | 1 年前3
- pandas: powerful Python data analysis toolkit -1.0.3'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) >>> s1 falcon 330.0 eagle 160.0 dtype: float64 >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) >>> s2 falcon 345.0 eagle 200.0 highest speeds of the birds across the two datasets >>> s1.combine(s2, max) duck NaN eagle 200.0 falcon 345.0 dtype: float64 In the previous example, the resulting value for duck is missing, because0 码力 | 3071 页 | 10.10 MB | 1 年前3
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