Python - numpy 的使用方法

markdown [NumPy Reference](https://docs.scipy.org/doc/numpy-1.13.0/reference/index.html) 數列生成 ``` >>> np.zeros(5) #array([ 0., 0., 0., 0., 0.]) >>> np.ones(5) #array([ 1., 1., 1., 1., 1.]) >>> np.arange(5) #array([0, 1, 2, 3, 4]) ``` [更多的數列生成](https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.array-creation.html) 數列變形 ``` >>> np.arange(12) #array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) >>> np.arange(12).reshape(2,6) #array([[ 0, 1, 2, 3, 4, 5], # [ 6, 7, 8, 9, 10, 11]]) >>> np.arange(12).reshape(3,4) #array([[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]]) >>> np.arange(12).reshape(4,3) #array([[ 0, 1, 2], # [ 3, 4, 5], # [ 6, 7, 8], # [ 9, 10, 11]]) >>> np.arange(12).reshape(6,2) #array([[ 0, 1], # [ 2, 3], # [ 4, 5], # [ 6, 7], # [ 8, 9], # [10, 11]]) >>> np.arange(12).reshape(12,1).flatten() #array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) ``` 查看數列形狀 ``` >>> np.arange(12).reshape(1,2,3,2) #array([[[[ 0, 1], # [ 2, 3], # [ 4, 5]], # # [[ 6, 7], # [ 8, 9], # [10, 11]]]]) >>> np.arange(12).reshape(1,2,3,2).shape #(1, 2, 3, 2) ``` [更多的數列操作](https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.array-manipulation.html)