Add Pearson correlation coefficient algorithm for data analysis

Author: saurav856

data_structures/pearson_correlation.py

@@ -0,0 +1,77 @@
+"""
+Pearson Correlation Coefficient: Measures the linear relationship between two
+variables. The result is a value between -1 and 1, where:
+    1  = perfect positive correlation
+    0  = no correlation
+   -1  = perfect negative correlation
+
+It is widely used in data analysis, statistics, and machine learning to
+understand relationships between features in a dataset.
+
+Reference: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+"""
+
+
+def pearson_correlation(x: list[float], y: list[float]) -> float:
+    """
+    Calculate the Pearson Correlation Coefficient between two lists.
+
+    Parameters
+    ----------
+    x: list[float], first list of numbers
+    y: list[float], second list of numbers
+
+    Returns
+    -------
+    float: Pearson correlation coefficient between -1 and 1
+
+    >>> pearson_correlation([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])
+    1.0
+    >>> pearson_correlation([1, 2, 3, 4, 5], [5, 4, 3, 2, 1])
+    -1.0
+    >>> pearson_correlation([1, 2, 3], [4, 5, 6])
+    1.0
+    >>> round(pearson_correlation([1, 2, 3, 4], [1, 2, 1, 2]), 4)
+    0.4472
+    >>> pearson_correlation([], [1, 2, 3])
+    Traceback (most recent call last):
+        ...
+    ValueError: lists must not be empty
+    >>> pearson_correlation([1, 2, 3], [1, 2])
+    Traceback (most recent call last):
+        ...
+    ValueError: lists must have the same length
+    >>> pearson_correlation([1, 1, 1], [2, 2, 2])
+    Traceback (most recent call last):
+        ...
+    ValueError: standard deviation of x or y is zero
+    """
+    if not x or not y:
+        raise ValueError("lists must not be empty")
+    if len(x) != len(y):
+        raise ValueError("lists must have the same length")
+
+    n = len(x)
+    mean_x = sum(x) / n
+    mean_y = sum(y) / n
+
+    numerator = sum((x[i] - mean_x) * (y[i] - mean_y) for i in range(n))
+    std_x = sum((xi - mean_x) ** 2 for xi in x) ** 0.5
+    std_y = sum((yi - mean_y) ** 2 for yi in y) ** 0.5
+
+    if std_x == 0 or std_y == 0:
+        raise ValueError("standard deviation of x or y is zero")
+
+    return round(numerator / (std_x * std_y), 10)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    x = [1, 2, 3, 4, 5]
+    y = [2, 4, 5, 4, 5]
+    print(f"x: {x}")
+    print(f"y: {y}")
+    print(f"Pearson correlation: {pearson_correlation(x, y)}")