这篇文章给大家分享的是有关 numpy如何实现算术运算的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。
numpy算数运算函数
| name | descripe |
|---|---|
| add(x1, x2[, out]) | Add arguments element-wise. |
| reciprocal(x[, out]) | Return the reciprocal of the argument, element-wise. |
| negative(x[, out]) | Numerical negative, element-wise. |
| multiply(x1, x2[, out]) | Multiply arguments element-wise. |
| divide(x1, x2[, out]) | Divide arguments element-wise. |
| power(x1, x2[, out]) | First array elements raised to powers from second array, element-wise. |
| subtract(x1, x2[, out]) | Subtract arguments, element-wise. |
| true_divide(x1, x2[, out]) | Returns a true division of the inputs, element-wise. |
| floor_divide(x1, x2[, out]) | Return the largest integer smaller or equal to the division of the inputs. |
| fmod(x1, x2[, out]) | Return the element-wise remainder of division. |
| mod(x1, x2[, out]) | Return element-wise remainder of division. |
| modf(x[, out1, out2]) | Return the fractional and integral parts of an array, element-wise. |
| remainder(x1, x2[, out]) | Return element-wise remainder of division. |
1.numpy.add(x1, x2[, out ]) = ufunc‘add’
求和
>>> np.add(1.0, 4.0)5.0>>> x1 = np.arange(9.0).reshape((3, 3))[[ 0. 1. 2.] [ 3. 4. 5.] [ 6. 7. 8.]]>>> x2 = np.arange(3.0) [ 0. 1. 2.] >>> np.add(x1, x2) array([[ 0., 2., 4.], [ 3., 5., 7.], [ 6., 8., 10.]])
2.numpy.reciprocal(x[, out ]) = ufunc ‘reciprocal’
求倒数
>>> np.reciprocal(2.) 0.5>>> np.reciprocal([1, 2., 3.33])array([ 1. , 0.5 , 0.3003003])
3.numpy.negative(x[, out ]) = ufunc ‘negative’
求相反数
>>> np.negative([1.,-1.])array([-1., 1.])
4.numpy.multiply(x1, x2[, out ]) = ufunc ‘multiply’
求积
>>> np.multiply(2.0, 4.0)8.0>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]])
5.numpy.divide(x1, x2[, out ]) = ufunc ‘divide’
求商
>>> np.divide(2.0, 4.0)0.5>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.divide(x1, x2) array([[ NaN, 1. , 1. ], [ Inf, 4. , 2.5], [ Inf, 7. , 4. ]])
numpy.true_divide(x1, x2[, out ]) = ufunc ‘true_divide’
>>> x = np.arange(5) >>> np.true_divide(x, 4)array([ 0. , 0.25, 0.5 , 0.75, 1. ]) >>> x/4array([0, 0, 0, 0, 1]) >>> x//4array([0, 0, 0, 0, 1])
numpy.floor_divide(x1, x2[, out ]) = ufunc ‘floor_divide’
>>> np.floor_divide(7,3) 2 >>> np.floor_divide([1., 2., 3., 4.], 2.5)array([ 0., 0., 1., 1.])
6.numpy.power(x1, x2[, out ]) = ufunc ‘power’
求幂
>>> x1 = range(6)>>> x1 [0, 1, 2, 3, 4, 5]>>> np.power(x1, 3) array([ 0, 1, 8, 27, 64, 125])>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]>>> np.power(x1, x2) array([ 0., 1., 8., 27., 16., 5.])
7.numpy.subtract(x1, x2[, out ]) = ufunc ‘subtract’
求差
>>> np.subtract(1.0, 4.0) -3.0>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.subtract(x1, x2) array([[ 0., 0., 0.], [ 3., 3., 3.], [ 6., 6., 6.]])
8.numpy.fmod(x1, x2[, out ]) = ufunc ‘fmod’
求余
>>> np.fmod([-3, -2, -1, 1, 2, 3], 2) array([-1, 0, -1, 1, 0, 1]) >>> np.remainder([-3, -2, -1, 1, 2, 3], 2) array([1, 0, 1, 1, 0, 1]) >>> np.fmod([5, 3], [2, 2.]) array([ 1., 1.]) >>> a = np.arange(-3, 3).reshape(3, 2) >>> a array([[-3, -2], [-1, 0], [ 1, 2]]) >>> np.fmod(a, [2,2]) array([[-1, 0], [-1, 0], [ 1, 0]])
numpy.mod(x1, x2[, out ]) = ufunc ‘remainder’
>>> np.remainder([4, 7], [2, 3])array([0, 1]) >>> np.remainder(np.arange(7), 5)array([0, 1, 2, 3, 4, 0, 1])
numpy.remainder(x1, x2[, out ]) =
>>> np.remainder([4, 7], [2, 3])array([0, 1]) >>> np.remainder(np.arange(7), 5)array([0, 1, 2, 3, 4, 0, 1])
9.numpy.modf(x[, out1, out2 ]) = ufunc ‘modf’
求整，求小数
>>> np.modf([0, 3.5]) (array([ 0. , 0.5]), array([ 0., 3.])) >>> np.modf(-0.5) (-0.5, -0)
感谢各位的阅读！关于“ numpy如何实现算术运算”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！
免责声明：本站发布的内容（图片、视频和文字）以原创、转载和分享为主，文章观点不代表本网站立场，如果涉及侵权请联系站长邮箱：is@yisu.com进行举报，并提供相关证据，一经查实，将立刻删除涉嫌侵权内容。
