ScipyNumpy

Numpy cheat sheet

Matrix creation

Creation: vector cases

For the random part, one is expected to run a command like

rng = np.random.default_rng(12)

before anything, to call the generator rng.

Code	Result
x = np.zeros(9)
x = np.ones(9)
x = np.full(9, 0.5)
x = np.array([0, 0, 1, 0, 0, 0, 0, 0, 0])
x = np.arange(9)
x = rng.random(9)

Creation: matrix cases

Code	Result
M = np.ones((5, 9))
M = np.zeros((5, 9))
M = np.array(     [         [0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],         [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],         [0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]     ] )
M = arange(5 * 9).reshape(5, 9)
M = rng.random(9)
M = np.eye(5, 9)
M = np.diag(np.arange(5))
M = np.diag(np.arange(3), k=2)

Creation: tensor cases

Code	Result
T = np.zeros((3, 5, 9))
T = np.ones((3, 5, 9))
T = np.arange(3 * 5 * 9).reshape(3, 5, 9)
T = rng.random((3, rows, cols))

Matrix reshaping

We start here with

M = np.zeros((3, 4))
M[2, 2] = 1

Code	Result
M = M.reshape(4, 3)
M = M.reshape(12, 1)
M = M.reshape(1, 12)
M = M.reshape(6, 2)
M = M.reshape(2, 6)

Slicing

Start from a zero matrix:

M = np.zeros((5, 9))

Code	Result
M[...] = 1
M[:, ::2] = 1
M[::2, :] = 1
M[1, 1] = 1
M[:, 0] = 1
M[0, :] = 1
M[2:, 2:] = 1
M[:-2, :-2] = 1
M[2:4, 2:4] = 1
M[::2, ::2] = 1
M[3::2, 3::2] = 1

Resources

• This work is deeply inspired and adapted from the great work by Nicolas Rougier: https://github.com/rougier/numpy-tutorial