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+---
+Title: '.binomial()'
+Description: 'Draw random samples from a binomial distribution.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Data'
+  - 'Functions'
+  - 'Numpy'
+  - 'Probability'
+  - 'Statistics'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In NumPy's `random` module, the **`.binomial()`** method generates random samples from a binomial distribution, representing the number of successful outcomes in `n` independent trials, each with a fixed probability `p` of success.
+
+## Syntax
+
+```pseudo
+numpy.random.binomial(n, p, size=None)
+```
+
+**Parameters:**
+
+- `n` (int or array-like of ints): Number of trials (must be non-negative).
+- `p` (float or array-like of floats): Probability of success on each trial (must be between 0 and 1 inclusive).
+- `size` (Optional): The shape of the output array. If `None`, a single value is returned. If given as a tuple, it specifies the shape of the output array.
+
+**Return value:**
+
+The `.binomial()` function returns one or more random integers representing the number of successes across `n` trials with success probability `p`.
+
+- If `size` is `None`, a single integer is returned.
+- If `size` is specified, an array of integers is returned with the given shape.
+
+## Example: Generating Random Binomial Samples in NumPy
+
+The following example simulates 10 independent trials, repeated 5 times. Each value in the result represents the number of successes in one set of 10 trials:
+
+```py
+import numpy as np
+
+# Generate 5 random samples from a binomial distribution
+result = np.random.binomial(n=10, p=0.5, size=5)
+
+print(result)
+```
+
+A possible output of this code could be:
+
+```shell
+[3 5 4 5 4]
+```
+
+## Codebyte Example
+
+In this Codebyte example, we generate a single random value from a binomial distribution with 15 trials and a 60% chance of success:
+
+```codebyte/python
+import numpy as np
+
+# Define number of trials and probability
+n = 15
+p = 0.6
+
+# Generate one random sample
+sample = np.random.binomial(n, p)
+
+print(sample)
+```
+
+> **Note:** The output will vary with each execution since it is randomly sampled.