From e6d66c60fee4fb772003969c892e014f8869a6db Mon Sep 17 00:00:00 2001
From: diusazzad <sazzad35-283@diu.edu.bd>
Date: Sat, 23 May 2026 22:14:21 +0600
Subject: [PATCH] feat: add weighted average algorithm in maths

---
 maths/weighted_average.py | 68 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 maths/weighted_average.py

diff --git a/maths/weighted_average.py b/maths/weighted_average.py
new file mode 100644
index 000000000000..62bbc5ab308b
--- /dev/null
+++ b/maths/weighted_average.py
@@ -0,0 +1,68 @@
+"""
+Weighted Average (Weighted Mean)
+
+A weighted average is an average in which each value has a specific weight
+assigned to it. Values with higher weights contribute more to the final result.
+
+Formula: weighted_avg = sum(value * weight) / sum(weights)
+
+Reference: https://en.wikipedia.org/wiki/Weighted_arithmetic_mean
+"""
+
+from __future__ import annotations
+
+
+def weighted_average(values: list[float], weights: list[float]) -> float:
+    """
+    Calculate the weighted average of a list of values with corresponding weights.
+
+    :param values: List of numeric values.
+    :param weights: List of weights corresponding to each value.
+    :return: The weighted average as a float.
+
+    >>> weighted_average([10, 20, 30], [1, 2, 3])
+    23.333333333333332
+    >>> weighted_average([100, 200], [0.5, 0.5])
+    150.0
+    >>> weighted_average([5], [2])
+    5.0
+    >>> weighted_average([], [])
+    Traceback (most recent call last):
+        ...
+    ValueError: Values and weights lists must not be empty
+    >>> weighted_average([1, 2], [1])
+    Traceback (most recent call last):
+        ...
+    ValueError: Values and weights must have the same length
+    >>> weighted_average([1, 2, 3], [-1, 2, 3])
+    Traceback (most recent call last):
+        ...
+    ValueError: All weights must be non-negative
+    >>> weighted_average([1, 2, 3], [0, 0, 0])
+    Traceback (most recent call last):
+        ...
+    ZeroDivisionError: Sum of weights must not be zero
+    """
+
+    # ১. Error Handling
+    if not values or not weights:
+        raise ValueError("Values and weights lists must not be empty")
+
+    if len(values) != len(weights):
+        raise ValueError("Values and weights must have the same length")
+
+    if any(w < 0 for w in weights):
+        raise ValueError("All weights must be non-negative")
+
+    total_weight = sum(weights)
+    if total_weight == 0:
+        raise ZeroDivisionError("Sum of weights must not be zero")
+
+    # ২. Main Calculation
+    return sum(value * weight for value, weight in zip(values, weights)) / total_weight
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()