How to realize arithmetic Operation in numpy
This article is about how numpy implements arithmetic operations. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.
Numpy arithmetic function
Namedescripeadd (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'
Summation
> np.add (1.0,4.05.0) > > 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.], [3.5,5.7.], [6.8.10]])
2.numpy.reciprocal (x [, out]) = ufunc 'reciprocal'
Find the reciprocal
> np.reciprocal (2.) 0.5 > np.reciprocal ([1,2.3.33]) array ([1,0.5,0.3003003])
3.numpy.negative (x [, out]) = ufunc 'negative'
Find the opposite number
> np.negative ([1.]) array ([- 1, 1.])
4.numpy.multiply (x1, x2 [, out]) = ufunc 'multiply'
Quadrature
> 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.], [0.4,4.10.], [0.7,16.])
5.numpy.divide (x1, x2 [, out]) = ufunc 'divide'
Seek business
> > 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.], [Inf, 4. 5], [Inf, 7. 4.]])
Numpy.true_divide (x1, x2 [, out]) = ufunc 'true_divide'
> x = np.arange (5) > np.true_divide (x4) 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.,1.1,1.])
6.numpy.power (x1, x2 [, out]) = ufunc 'power'
Exponentiation
> > 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.,1.1,8.27.16.16.5.])
7.numpy.subtract (x1, x2 [, out]) = ufunc 'subtract'
Ask for a difference
> > np.subtract (1.0,4.0)-3.0,3.0 > > x1 = np.arange (9.0). Reshape ((3,3)) > > x2 = np.arange (3.0) > np.subtract (x1, x2) array ([[0.,0.,0.], [3.3,3.3.], [6.,6.6.6.])
8.numpy.fmod (x1, x2 [, out]) = ufunc 'fmod'
Beg for surplus
> > 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, 0, 1]) > > np.fmod ([5, 3], [2, 2.]) array ([1, 1.]) > > a = np.arange (- 3) 3) .reshape (3,2) > > aarray ([[- 3,-2], [- 1,0], [1,2]]) > > np.fmod (a, [2jue 2]) array ([[- 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'
To find a decimal.
> np.modf ([0,3.5]) (array ([0. , 0. 5]), array ([0, 3.]) > np.modf (- 0. 5) (- 0. 5) Thank you for reading! This is the end of the article on "how to implement arithmetic in numpy". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!
