Random Module is used to generate random things by applying random function code lines. Learn more about the Random module in python here.
Random module: Creating a random user password
In order to create a random user password we can use the symbols provided in the string module. Specifically punctuation for punctuation symbols, ascii_letters for letters and digits for digits:
from string import punctuation, ascii_letters, digits
We can then combine all these symbols in a name named symbols:
symbols = ascii_letters + digits + punctuation
Remove either of these to create a pool of symbols with fewer elements.
After this, we can use random.SystemRandom to generate a password. For a 10 length password:
secure_random = random.SystemRandom()
password = "".join(secure_random.choice(symbols) for i in range(10))
print(password) # '^@g;J?]M6e'
Note that other routines made immediately available by the random module — such as random.choice, random.randint, etc. — are unsuitable for cryptographic purposes.
Behind the curtains, these routines use the Mersenne Twister PRNG, which does not satisfy the requirements of a CSPRNG. Thus, in particular, you should not use any of them to generate passwords you plan to use. Always use an instance of SystemRandom as shown above.
Python 3.x Version ≥ 3.6
Starting from Python 3.6, the secrets module is available, which exposes cryptographically safe functionality.
Quoting the official documentation, to generate “a ten-character alphanumeric password with at least one lowercase character, at least one uppercase character, and at least three digits,” you could:
import string
alphabet = string.ascii_letters + string.digits
while True:
password = ''.join(choice(alphabet) for i in range(10))
if (any(c.islower() for c in password)
and any(c.isupper() for c in password)
and sum(c.isdigit() for c in password) >= 3):
break
Random module: Create cryptographically secure random numbers
By default the Python random module use the Mersenne Twister PRNG to generate random numbers, which, although suitable in domains like simulations, fails to meet security requirements in more demanding environments.
In order to create a cryptographically secure pseudorandom number, one can use SystemRandom which, by using os.urandom, is able to act as a Cryptographically secure pseudorandom number generator, CPRNG.
The easiest way to use it simply involves initializing the SystemRandom class. The methods provided are similar to the ones exported by the random module.
from random import SystemRandom
secure_rand_gen = SystemRandom()
In order to create a random sequence of 10 ints in range [0, 20], one can simply call randrange():
print([secure_rand_gen.randrange(10) for i in range(10)])
[9, 6, 9, 2, 2, 3, 8, 0, 9, 9]
To create a random integer in a given range, one can use randint:
print(secure_rand_gen.randint(0, 20))
5
and, accordingly for all other methods. The interface is exactly the same, the only change is the underlying number generator.
You can also use os.urandom directly to obtain cryptographically secure random bytes.
Random module: Random and sequences: shu e, choice and sample
import random
shuffle()
You can use random.shuffle() to mix up/randomize the items in a mutable and indexable sequence. For example a list:
laughs = ["Hi", "Ho", "He"]
random.shuffle(laughs) # Shuffles in-place! Don't do: laughs = random.shuffle(laughs)
print(laughs)
Out: ["He", "Hi", "Ho"] # Output may vary! choice()
Takes a random element from an arbitrary sequence:
print(random.choice(laughs))
Out: He # Output may vary!
sample()
Like choice it takes random elements from an arbitrary sequence but you can specify how many:
|–sequence–|–number–|
print(random.sample( laughs , 1 )) # Take one element
Out: [‘Ho’] # Output may vary!
it will not take the same element twice:
print(random.sample(laughs, 3)) # Take 3 random element from the sequence.
Out: [‘Ho’, ‘He’, ‘Hi’] # Output may vary!
print(random.sample(laughs, 4)) # Take 4 random element from the 3-item sequence.
ValueError: Sample larger than population
Random module: Creating random integers and floats: randint, randrange, random, and uniform
import random
randint()
Returns a random integer between x and y (inclusive):
random.randint(x, y)
For example getting a random number between 1 and 8:
random.randint(1, 8) # Out: 8
randrange()
random.randrange has the same syntax as range and unlike random.randint, the last value is not inclusive:
random.randrange(100) # Random integer between 0 and 99
random.randrange(20, 50) # Random integer between 20 and 49
random.rangrange(10, 20, 3) # Random integer between 10 and 19 with step 3 (10, 13, 16 and 19)
random
Returns a random floating point number between 0 and 1:
random.random() # Out: 0.66486093215306317
uniform
Returns a random floating point number between x and y (inclusive):
random.uniform(1, 8) # Out: 3.726062641730108
Reproducible random numbers: Seed and State
Setting a specific Seed will create a fixed random-number series:
random.seed(5) # Create a fixed state
print(random.randrange(0, 10)) # Get a random integer between 0 and 9 # Out: 9
print(random.randrange(0, 10))
Out: 4
Resetting the seed will create the same “random” sequence again:
random.seed(5) # Reset the random module to the same fixed state.
print(random.randrange(0, 10)) Out: 9
print(random.randrange(0, 10))
Out: 4
Since the seed is fixed these results are always 9 and 4. If having specific numbers is not required only that the values will be the same one can also just use getstate and setstate to recover to a previous state:
save_state = random.getstate() # Get the current state
print(random.randrange(0, 10))
Out: 5
print(random.randrange(0, 10))
Out: 8
random.setstate(save_state) # Reset to saved state
print(random.randrange(0, 10))
Out: 5
print(random.randrange(0, 10))
Out: 8
To pseudo-randomize the sequence again you seed with None:
random.seed(None)
Or call the seed method with no arguments:
random.seed()
Random module: Random Binary Decision
import random
probability = 0.3
if random.random() < probability:
print("Decision with probability 0.3")
else:
print("Decision with probability 0.7")
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