'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 highest

'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, because

'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, because

'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, because

'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, because

'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, because

'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, because

'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, because

'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, because

.: ], ...: index=["falcon", "parrot", "lion", "monkey", "leopard"], ...: columns=("class", "order", "max_speed"), ...: ) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389 StataWriter117 Low-level writer for version 117 files. Examples >>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon', ... 'parrot'], ... 'speed': [350, 18, 361, 15]}) >>> df.to_stata('animals.dta') StataReader >>> 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