How to Use NumPy randint with Step Values in Python

Using NumPy randint with Step

In NumPy, the randint function does not directly support a step parameter like arange. However, you can simulate this behaviour by generating a random integer and scaling it using the step value.

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Concept Overview

• randint generates random integers within a range.
• It does not support step size directly.
• You can divide the range by the step and multiply the result back.

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Interactive Demo

Try changing the values below to simulate how NumPy logic works.

Start: Stop: Step:

Result

Click Generate

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Basic NumPy Example

Step 1 — Import NumPy

import numpy as np

Step 2 — Define Start, Stop and Step

start = 0
stop = 20
step = 5

Step 3 — Generate Random Number

random_number = np.random.randint(start // step, stop // step) * step
print(random_number)

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Python Console Example

>>> import numpy as np
>>> start = 0
>>> stop = 20
>>> step = 5

>>> np.random.randint(start // step, stop // step) * step
10

Possible outputs:

• 0
• 5
• 10
• 15

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Full Example

Generate Multiple Random Numbers

import numpy as np

start = 10
stop = 100
step = 10

random_numbers = [
 np.random.randint(start // step, stop // step) * step
 for _ in range(5)
]

print(random_numbers)

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Example Output

[70, 90, 10, 60, 40]

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Explanation

Why divide by step?

Dividing by step shrinks the range so that randint generates an index instead of the actual stepped number.

Why multiply by step?

Multiplying restores the stepped values.

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💡 Key Takeaways

• np.random.randint() does not support step values.
• You can simulate steps by scaling numbers.
• Divide the range using integer division.
• Multiply the result by the step value.
• This method works for any numeric interval.

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Related Topics

• When to Use np.random.rand vs np.random.randn in Python
• Comparison of np.zeros and np.empty in NumPy