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                created the required randint.md file
              
              
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  content/numpy/concepts/random-module/terms/randint/randint.md
  
  
      
      
   
        
      
      
    
  
    
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              | Original file line number | Diff line number | Diff line change | 
|---|---|---|
| @@ -0,0 +1,87 @@ | ||
| --- | ||
| Title: 'randint()' | ||
| Description: 'Generates random integers within a specified range using NumPy.' | ||
| Subjects: | ||
| - 'Computer Science' | ||
| - 'Data Science' | ||
| Tags: | ||
| - 'Numpy' | ||
| - 'Python' | ||
| - 'Random' | ||
| CatalogContent: | ||
| - 'learn-python-3' | ||
| - 'paths/computer-science' | ||
| --- | ||
|  | ||
| **`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. | ||
|  | ||
| ## Syntax | ||
|  | ||
| ```pseudo | ||
| numpy.random.randint(low, high=None, size=None, dtype=int) | ||
| ``` | ||
|  | ||
| **Parameters:** | ||
|  | ||
| - `low` (int): Lowest (inclusive) integer to be drawn. | ||
| - `high` (int, optional): One above the highest integer to be drawn. If not provided, integers are drawn from the range `[0, low)`. | ||
| - `size` (int or tuple of ints, optional): Output shape. If `None`, a single integer is returned. | ||
| - `dtype` (data-type, optional): Desired data type of the output. Default is `int`. | ||
|  | ||
| **Return value:** | ||
|  | ||
| - `out` (int or ndarray): Random integer(s) from the specified range. | ||
| - If `size` is `None`, returns a single integer. | ||
| - If `size` is specified, returns a NumPy array of the given shape. | ||
|  | ||
| ## Example | ||
|  | ||
| This example generates a random integer (0-9) and an array of 5 random integers (1-5): | ||
|  | ||
| ```py | ||
| import numpy as np | ||
| np.random.seed(15) | ||
|  | ||
| # Single random integer from 0 to 9 | ||
| single_int = np.random.randint(10) | ||
| print(single_int) | ||
|  | ||
| # Array of 5 random integers from 1 to 5 | ||
| arr = np.random.randint(1, 6, size=5) | ||
| print(arr) | ||
| ``` | ||
|  | ||
| The output for this code will be: | ||
|  | ||
| ```shell | ||
| 8 | ||
| [5 1 5 4 4] | ||
| ``` | ||
|  | ||
| > **Note:** Exact output values may vary depending on NumPy version, but the format will be as shown. | ||
|  | ||
| Here: | ||
|  | ||
| - `np.random.seed(15)` ensures reproducible results. | ||
| - `np.random.randint(10)` generates a single integer between 0 and 9. | ||
| - `np.random.randint(1, 6, size=5)` generates an array of 5 integers between 1 and 5. | ||
|  | ||
| ## Codebyte Example | ||
|  | ||
| 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: | ||
|  | ||
| ```codebyte/python | ||
| import numpy as np | ||
| np.random.seed(42) | ||
|  | ||
| # Single random integer between 0 and 9 | ||
| num = np.random.randint(10) | ||
| print(f"Random integer: {num}") | ||
|  | ||
| # 2x3 array of random integers between 1 and 10 | ||
| arr = np.random.randint(1, 11, size=(2, 3)) | ||
| print(f"Random integers array:\n{arr}") | ||
| ``` | ||
|  | ||
| - `np.random.seed(42)` ensures the code produces the same results every time. | ||
| - `size=(2, 3)` generates a 2D array with 2 rows and 3 columns of random integers. | ||
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