## Generate alphanumeric random numbers in numpy

I want random numbers 100000+, I found numpy is suitable for my project based on performance it is good. But I want 4 places random number based on below pattern,

'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'

In the above case 26 small letters,26 capital letters and 10 digits equal to 62 total letters and let us take permutation and combinations,

I want 4 digits from those letters so,

62 ^ 4 / 4! = 615680 (Combinations)

If I take 26 small letters + 10 digits and the output is,

(26+10) ^ 4 / 4! = 69984 (Combinations)

From those two cases first one is best, It provides better random numbers, I did some logic here,

from numpy.random.mtrand import RandomState import binascii lo = 1000000000000000 hi = 999999999999999999 In [65]: %timeit [ binascii.b2a_hex(rand.randint(lo, hi, 2).tostring())[:4] for _ in xrange(100000)] 1 loops, best of 3: 272 ms per loop

But the random number count is below 100000, Because it only takes small lettes + digits

In [66]: len(set([binascii.b2a_hex(rand.randint(lo, hi, 2).tostring())[:4] for _ in xrange(100000)])) Out[66]: 51210

Any one suggest me how to implement this in numpy ?

As pointed out by NPE, you can use numpy.random.choice. Does this code achieve what you want?

import numpy as np LENGTH = 4 NO_CODES = 100000 alphabet = list('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789') np_alphabet = np.array(alphabet, dtype="|S1") np_codes = np.random.choice(np_alphabet, [NO_CODES, LENGTH]) codes = ["".join(np_codes[i]) for i in range(len(np_codes))] print(codes)

It takes a couple of seconds to execute with `NO_CODES = 1000000`

on my 2-years-old but not bad computer.

**Random Generator,** Generator exposes a number of methods for generating random numbers drawn from a dirichlet (alpha[, size]), Draw samples from the Dirichlet distribution. Generating random numbers with NumPy. array([-1.03175853, 1.2867365 , -0.23560103, -1.05225393]) Generate Four Random Numbers From The Uniform Distribution

It can be done much faster by using numpy view.

A, Z = np.array(["A","Z"]).view("int32") NO_CODES = 100 LEN = 20 np.random.randint(low=A,high=Z,size=NO_CODES*LEN,dtype="int32").view(f"U{LEN}")

**Random sampling (numpy.random),** random_integers (low[, high, size]), Random integers of type np.int between low and choice (a[, size, replace, p]), Generates a random sample from a given 1-D array dirichlet (alpha[, size]), Draw samples from the Dirichlet distribution. Related to these two methods, there is another method called normal([loc, scale, size]), using which we can generate random numbers from the normal distribution specified by loc and scale parameters. 3. randint (low[, high, size, dtype])

The accepted answer can be made a bit faster with the following code:

import numpy as np length = 4 n_codes = 100000 alpha_num = list('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789') np_codes = np.random.choice(alpha_num, size=[n_codes, length]) codes = [code.tostring() for code in np_codes]

Original runtime on my machine: `10 loops, best of 3: 204 ms per loop`

.

New runtime: `10 loops, best of 3: 27.6 ms per loop`

.

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Neither of the other two answers worked for me, but I was able to use them to create the following solution:

import numpy as np length = 4 n_codes = 100000 alpha_num = list('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789') np_codes = np.random.choice(alpha_num, size=[n_codes, length]) codes = [''.join(code) for code in np_codes]

**A Cheat Sheet on Generating Random Numbers in NumPy,** Note that the numbers specified in the rand() function correspond to the number of dimensions of the array that is to be generated. A special case 38B453. Note: we can get a different number and alphabet as this Program generates any random number and alphabet. and numbers will range from 0 – 9 and alphabet will range from A – B, as all alphabet will be capital because we have specified that.

**Random Generator,** Generator exposes a number of methods for generating random numbers drawn from a variety of probability distributions. The Python stdlib module random contains pseudo-random number dirichlet (alpha[, size]). The secrets module is used for generating cryptographically strong random numbers suitable for managing data such as passwords, account authentication, security tokens, and related secrets. In particularly, secrets should be used in preference to the default pseudo-random number generator in the random module, which is designed for modelling and simulation, not security or cryptography.

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**Python Random Data Generation,** rand() vs np.random.random()?; What is the difference between various numpy random functions? how to generate random integer values using Random String Generator. This form allows you to generate random text strings. The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs.