Percentile Scaling Data Transformation

pandas/io/percentile_scaling.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+def percentile_scaling(data):
+    data = np.array(data)
+    min_val = np.min(data)
+    max_val = np.max(data)
+    if max_val == min_val:
+        raise ValueError("Cannot scale data with identical values.")
+
+    scaled = 100 * (data - min_val) / (max_val - min_val)
+    return scaled.tolist()

pandas/tests/io/test_percentile_scaling.py

@@ -0,0 +1,21 @@
+import unittest
+from pandas.io.percentile_scaling import percentile_scaling
+
+class TestPercentileScaling(unittest.TestCase):
+    def test_scaling(self):
+        data = [10, 20, 30, 40, 50]
+        expected = [0.0, 25.0, 50.0, 75.0, 100.0]
+        result = percentile_scaling(data)
+        for r, e in zip(result, expected):
+            self.assertAlmostEqual(r, e)
+
+    def test_identical_values(self):
+        with self.assertRaises(ValueError):
+            percentile_scaling([5, 5, 5])
+
+    def test_empty(self):
+        with self.assertRaises(ValueError):
+            percentile_scaling([])
+
+if __name__ == "__main__":
+    unittest.main()