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| 1 | +--- |
| 2 | +Title: 'randint()' |
| 3 | +Description: 'Generates random integers within a specified range using NumPy.' |
| 4 | +Subjects: |
| 5 | + - 'Computer Science' |
| 6 | + - 'Data Science' |
| 7 | +Tags: |
| 8 | + - 'Numpy' |
| 9 | + - 'Python' |
| 10 | + - 'Random' |
| 11 | +CatalogContent: |
| 12 | + - 'learn-python-3' |
| 13 | + - 'paths/computer-science' |
| 14 | +--- |
| 15 | + |
| 16 | +**`randint()`** is a function from NumPy's [`random`](https://www.codecademy.com/resources/docs/numpy/random-module) module that generates random integers. It can generate a single integer or an array of integers within a specified range, making it useful for simulations, testing, and randomized operations. |
| 17 | + |
| 18 | +## Syntax |
| 19 | + |
| 20 | +```pseudo |
| 21 | +numpy.random.randint(low, high=None, size=None, dtype=int) |
| 22 | +``` |
| 23 | + |
| 24 | +**Parameters:** |
| 25 | + |
| 26 | +- `low` (int): Lowest (inclusive) integer to be drawn. |
| 27 | +- `high` (int, optional): One above the highest integer to be drawn. If not provided, integers are drawn from the range `[0, low)`. |
| 28 | +- `size` (int or tuple of ints, optional): Output shape. If `None`, a single integer is returned. |
| 29 | +- `dtype` (data-type, optional): Desired data type of the output. Default is `int`. |
| 30 | + |
| 31 | +**Return value:** |
| 32 | + |
| 33 | +- `out` (int or ndarray): Random integer(s) from the specified range. |
| 34 | + - If `size` is `None`, returns a single integer. |
| 35 | + - If `size` is specified, returns a NumPy array of the given shape. |
| 36 | + |
| 37 | +## Example |
| 38 | + |
| 39 | +This example generates a random integer (0-9) and an array of 5 random integers (1-5): |
| 40 | + |
| 41 | +```py |
| 42 | +import numpy as np |
| 43 | +np.random.seed(15) |
| 44 | + |
| 45 | +# Single random integer from 0 to 9 |
| 46 | +single_int = np.random.randint(10) |
| 47 | +print(single_int) |
| 48 | + |
| 49 | +# Array of 5 random integers from 1 to 5 |
| 50 | +arr = np.random.randint(1, 6, size=5) |
| 51 | +print(arr) |
| 52 | +``` |
| 53 | + |
| 54 | +The output for this code will be: |
| 55 | + |
| 56 | +```shell |
| 57 | +8 |
| 58 | +[5 1 5 4 4] |
| 59 | +``` |
| 60 | + |
| 61 | +> **Note:** Exact output values may vary depending on NumPy version, but the format will be as shown. |
| 62 | +
|
| 63 | +Here: |
| 64 | + |
| 65 | +- `np.random.seed(15)` ensures reproducible results. |
| 66 | +- `np.random.randint(10)` generates a single integer between 0 and 9. |
| 67 | +- `np.random.randint(1, 6, size=5)` generates an array of 5 integers between 1 and 5. |
| 68 | + |
| 69 | +## Codebyte Example |
| 70 | + |
| 71 | +This codebyte sample generates a single random integer and a 2×3 array of random integers from specified ranges, ensuring reproducible results using a fixed random seed: |
| 72 | + |
| 73 | +```codebyte/python |
| 74 | +import numpy as np |
| 75 | +np.random.seed(42) |
| 76 | +
|
| 77 | +# Single random integer between 0 and 9 |
| 78 | +num = np.random.randint(10) |
| 79 | +print(f"Random integer: {num}") |
| 80 | +
|
| 81 | +# 2x3 array of random integers between 1 and 10 |
| 82 | +arr = np.random.randint(1, 11, size=(2, 3)) |
| 83 | +print(f"Random integers array:\n{arr}") |
| 84 | +``` |
| 85 | + |
| 86 | +- `np.random.seed(42)` ensures the code produces the same results every time. |
| 87 | +- `size=(2, 3)` generates a 2D array with 2 rows and 3 columns of random integers. |
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