numpy random

    from numpy import  random
    random.seed(1001)
    array=np.random.normal(0,1,(3,4))  # <class 'numpy.ndarray'>
    print(array)
    # [[-1.08644637 - 0.89606513 - 0.30629937 - 1.33993366]
    #  [-1.20658558 - 0.64172681  1.30794563  1.84546043]
    # [0.82911495 - 0.02329881 - 0.20856395 - 0.91661975]]

　Random seed each time the same data out random

 from numpy  import random  

np.random.choice():

    Random numpy Import from 
    # TODO random.seed (1001), whether a seed number per set are valid for np.random.xx hereinafter 
    unique = np.random.choice (range (0,3) , (3,4 )) 
    # TODO [0,10) -> five pump, sampling with replacement [1 7 0 7 1 2 3 ], replace = False sampling without replacement 
    unique2 = np.random.choice (10,7, replace = True) 
    Print ( "the Range (0,3) ---> sampling with replacement:", UNIQUE, sep = "\ the n-") 
    Print ( "============") 
    Print ( " [0,10) -> five pump, sampling with replacement ", the unique2) 
    # # of dataframe SET Columns: 1.df.columns = [" X1 "," X1 ".....] 
    Print (PD. DataFrame (unique, columns = [f "x {i}" for i in range (4)]))