# Random numbers and sequences in Python

Everybody knows about `random.randint(a, b)` in Python, which returns a ≤ n ≤ b:

``````random.randint(10, 99)
# 59
``````

But the `random` module has so much more to offer.

Like selecting a number from a range with a step:

``````random.randrange(10, 99, 3)
# 91
``````

Or a random sequence element:

``````numbers = [7, 9, 13, 42, 64, 99]
random.choice(numbers)
# 42
``````

Or multiple elements:

``````numbers = range(99, 10, -1)
random.choices(numbers, k=3)
# [32, 62, 76]
``````

How about choosing some elements more often than others? Sure:

``````numbers = [7, 9, 13, 42, 64, 99]
weights = [10, 1, 1, 1, 1, 1]

random.choices(numbers, weights, k=3)
# [42, 13, 7]

random.choices(numbers, weights, k=3)
# [7, 7, 7]

random.choices(numbers, weights, k=3)
# [13, 7, 7]
``````

Wanna see a sample without repetitions? No problem:

``````numbers = [7, 9, 13, 42, 64, 99]
random.sample(numbers, k=3)
# [42, 99, 7]
``````

Or even shuffle the whole sequence:

``````numbers = [1, 2, 3, 4, 5]
random.shuffle(numbers)
# [3, 2, 1, 5, 4]
``````

There are also countless real-valued distributions like `uniform()`, `gauss()`, `expovariate()`, `paretovariate()` and many more. Not gonna get into the specifics now — see for yourself if your are a statistics fan.

Last but not least. When testing, seed the generator with a constant so that it gives reproducible results:

``````random.seed(42)
``````

On the contrary, use `seed()` without arguments in production. Python will then use the sources of randomness provided by the operating system.

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