Many times we need some data for testing or we need some random numbers. NumPy can be very effective in generating random integers, floats or random values between 0 and 1. You can fetch truly random values as well as values in normal distribution as well.
Following program has multiple methods of creating random number for use in program
| import numpy as np | |
| ######################### Random values in a given shape. | |
| a=np.random.rand(2,3) | |
| print(a) | |
| # [[ 0.7524278 0.21176809 0.73990734] | |
| # [ 0.28341776 0.11559792 0.15859365]] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0][0])) | |
| # <type 'numpy.float64'> | |
| ####################### Return a sample from the standard normal distribution | |
| a=np.random.randn(5) | |
| print(a) | |
| # [ 1.0000366 0.0906066 -0.05027158 -0.14745128 1.35046138] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.float64'> | |
| #######################Return a sample from the standard normal distribution | |
| a=np.random.randn(5,4) | |
| print(a) | |
| # [[ 1.48864593 -0.75508993 1.57585151 -0.02507804] | |
| # [-1.11795072 0.16357727 0.76753395 0.02291213] | |
| # [-1.39439533 0.66704929 -0.01020978 0.12887067] | |
| # [-0.19386682 0.70650588 0.71049381 -0.40089744] | |
| # [-0.6845585 0.35872981 0.18581329 -0.51889034]] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.float64'> | |
| print(type(a[0][0])) | |
| # <type 'numpy.float64'> | |
| ###############Return random integers from low (inclusive) to high (exclusive). | |
| a=np.random.randint(2,14,size=5) | |
| print(a) | |
| # [12 9 7 3 9] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.int64'> | |
| ###############Return random floats in the half-open interval [0.0, 1.0). | |
| a=np.random.random_sample(5) | |
| print(a) | |
| # [-0.20534297 0.4333096 0.94111548 -0.61324519 0.8843922 ] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.float64'> | |
| ###############Draw samples from a binomial distribution. | |
| n,p=10,0.5 # number of trials, probability of each trial | |
| a=np.random.binomial(n,p,100) | |
| print(a) | |
| # [5 2 5 6 5 3 6 8 5 7 3 4 8 4 9 5 5 6 3 5 7 6 6 2 6 5 6 6 5 3 6 5 6 6 4 6 2 | |
| # 7 5 6 7 6 3 3 3 8 8 3 2 5 7 6 4 2 5 7 6 4 5 6 5 5 5 7 4 2 8 3 5 3 6 5 4 4 | |
| # 3 3 5 7 7 4 4 6 4 5 6 7 5 5 6 6 4 7 4 4 3 2 6 6 7 3] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.int64'> | |
| ############### Draw samples from a uniform distribution. | |
| a=np.random.uniform(5,15,(3,)) | |
| print(a) | |
| # [ 13.81416285 5.82087405 13.24553233] | |
| print(type(a)) | |
| # <type 'numpy.ndarray'> | |
| print(type(a[0])) | |
| # <type 'numpy.float64'> |