- Syntax
numpy.random.random(size=None)
- numpy.random.randint()
The method has three parameters low, high, size three parameters. The default is high None, if only low, that is the range [0, low). If there is high, the range is [low, high).
- Code
1 # import module 2 Import numpy AS NP . 3 DEF randomTest (): . 4 # used to create a one-dimensional random arrays . 5 A = np.random.random (size =. 5 ) . 6 Print (A) . 7 Print (type (A)) . 8 Print ( " *********************************************** * " ) . 9 10 # create a two-dimensional array . 11 B = np.random.random (size = (3,4- )) 12 is Print (B) 13 is Print ( "**************************** " ) 14 15 # Create a three-dimensional array 16 C = np.random.random (size = (2,3,4 )) . 17 Print (C) 18 is Print ( " ********************************************************* ******************************** ' ) . 19 20 is # random integer 21 is DEF randomintTest (): 22 is # generated 0- random integer (one-dimensional) between. 5 23 is a = np.random.randint (. 6, size = 10 ) 24 Print (a) 25 Print (type (a)) 26 is Print (" **************************** " ) 27 28 # to generate a random integer between 5-10 29 B = np.random.randint (5,11, size = (4,3- )) 30 Print (B) 31 is Print ( " ******* ***************************************** " ) 32 33 is # generates 5-10 a random number (D) between 34 is C = np.random.randint (5,11, size = (2,4,3 )) 35 Print (C) 36 Print ( " ********** ************************************** " ) 37 [ 38 is # Call 39 randomTest () 40 randomintTest ()
1 [0.58481035 0.89981199 0.4430777 0.79111114 0.80530942] 2 <class 'numpy.ndarray'> 3 ************************************************ 4 [[0.62548615 0.30478776 0.80835854 0.64659382] 5 [0.01354597 0.55416311 0.28586102 0.13735663] 6 [0.51881331 0.21160145 0.72725684 0.99663842]] 7 ************************************************ 8 [[[0.08170694 0.9253817 0.65025442 0.04592365] 9 [0.36387681 0.65987097 0.10837108 0.15505963] 10 [0.77084731 0.00175051 0.44813178 0.82772988]] 11 12 [[0.73729773 0.65277105 0.68823734 0.0330837 ] 13 [0.32585801 0.72776357 0.90520657 0.69459254] 14 [0.30655539 0.72554569 0.73852885 0.80749657]]] 15 ************************************************ 16 [3 4 4 1 4 2 4 5 4 0] 17 <class 'numpy.ndarray'> 18 ************************************************ 19 [[ 5 6 9] 20 [10 10 5] 21 [ 9 6 6] 22 [ 9 8 6]] 23 ************************************************ 24 [[[ 7 6 7] 25 [ 9 6 10] 26 [ 8 7 10] 27 [10 9 7]] 28 29 [[ 7 6 5] 30 [ 9 10 8] 31 [ 6 7 7] 32 [ 9 6 8]]] 33 ************************************************