Write a NumPy program to apply a cube function to each element of an array using np.vectorize. Create a function that computes the cube of array elements and compares the output with manual exponentiation. Use np.power to raise each element of an array to the third power and verify the re...
>>> from numpy import pi >>> np.linspace(0, 2, 9) # 9 numbers from 0 to 2 array([0\. , 0.25, 0.5 , 0.75, 1\. , 1.25, 1.5 , 1.75, 2\. ]) >>> x = np.linspace(0, 2 * pi, 100) # useful to evaluate function at lots of points >>> f = np.sin(x) 参见 array...
| Python's help() function finds this page whenever help() is called | on a ufunc. | | A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`. | | **Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)`` | | Apply `op` to the arguments `*x`...
更多函数all, alltrue, any, apply along axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, conjugate, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue...
函数function创建一个全是0的数组,函数ones创建一个全1的数组,函数empty创建一个内容随机并且依赖与内存状态的数组。默认创建的数组类型(dtype)都是float64。 >>>zeros( (3,4) )array([[0.,0.,0.,0.], [0.,0.,0.,0.], [0.,0.,0.,0.]]) ...
>>> from numpy import pi >>> np.linspace(0, 2, 9) # 9 numbers from 0 to 2 array([0\. , 0.25, 0.5 , 0.75, 1\. , 1.25, 1.5 , 1.75, 2\. ]) >>> x = np.linspace(0, 2 * pi, 100) # useful to evaluate function at lots of points >>> f = np.sin(x) 另请参阅...
函数function 创建一个全是0的数组,函数 ones 创建一个全1的数组,函数 empty 创建一个内容随机并且依赖与内存状态的数组。默认创建的数组类型(dtype)都是float64。 >>> zeros( (3,4) ) array([[0., 0., 0., 0.], ...
函数function 创建一个全是0的数组,函数 ones 创建一个全1的数组,函数 empty 创建一个内容随机并且依赖与内存状态的数组。默认创建的数组类型(dtype)都是float64。 >>> zeros( (3,4) ) array([[0., 0., 0., 0.], [0., 0., 0., 0.], ...
numpy.apply_along_axis(function, axis, A): 对于A中指定的轴应用函数function numpy.argmax(A): 返回最大值的索引 numpy.argmin(A): 返回最小值的索引 numpy.argsort(A):返回排序后的索引 numpy.average(A):平均值 其他还有很多: all, any, apply_along_axis, argmax, argmin, argsort, average, bin...
]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) 另见: array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.rand, numpy.random.randn, fromfunction, fromfile 3、打印数组 当你...