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identity The identity array is a square array with ones on the main diagonal. np.identity(1) >>> array([[1.]]) np.identity(2) >>> array([[1., 0.], [0., 1.]]) np.identity(3) >>> array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) np.identity(4) >>> array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])

linspace numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0) Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. endpoint : bool, optional If True, stop is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (samples, step), wh..

np.r_[] It concatenates arrays along first axis. np.c_[] It concatenates arrays along second axis. a = np.array([[1, 2, 3], [11,22,33]] ) b = np.array([[4, 5, 6], [44,55,66]] ) np.r_[a,b] >>> array([[ 1, 2, 3], [11, 22, 33], [ 4, 5, 6], [44, 55, 66]]) np.c_[a,b] >>> array([[ 1, 2, 3, 4, 5, 6], [11, 22, 33, 44, 55, 66]])

axis=0 It will act on all the ROWS in each column, means along "indexes". It's a row-wise operation. axis=1 It will act on all the COLUMNS in each row, means along "columns". It's a column-wise operation.a = np.array([[1, 2]])b = np.array([[5, 6]])row_wise=np.concatenate((a, b), axis=0)column_wise=np.concatenate((a, b), axis=1)print(row_wise)print(column_wise)>>>[[1 2] [5 6]] [[1 2 5 6]]- multi ..

sample_size = 10000 rand=np.random.rand(sample_size) randn=np.random.randn(sample_size) randint=np.random.randint(low=10, high=15, size=10) random_integers=np.random.random_integers(1, 6, 10) pdf, bins, patches = plt.hist(rand, bins=20, range=(0, 1), density=True) plt.title('rand') plt.show() pdf, bins, patches = plt.hist(randn, bins=20, range=(-4, 4), density=True) plt.title('randn') plt.show()..