viewing the O(log n) and the O(n^2) in real time

bigO nation/O(log n).py

@@ -0,0 +1,60 @@
+import time
+
+
+class Efficiency:
+    def __init__(self):
+        pass
+
+    def generate_array(self, n):
+        return [i for i in range(1, n)]
+
+    def binary_search(self, arr, value):
+        start_time = time.time()
+        lowest_index = 0
+        highest_index = len(arr) - 1
+
+        while (lowest_index <= highest_index):
+            middle_index = (lowest_index + highest_index) // 2
+            if (arr[middle_index] < value):
+                lowest_index = middle_index + 1
+            elif (arr[middle_index] > value):
+                highest_index = middle_index - 1
+            else:
+
+                print("the" + str(value) + " " +
+                      "is found at index" + str(middle_index))
+                lowest_index = middle_index + 1
+
+    def buble_sort(self, arr):
+        length = len(arr)
+        for i in range(length - 1):
+            for j in range(length - i - 1):
+                if arr[i] > arr[j + 1]:
+                    arr[j], arr[j + 1] = arr[j + 1], arr[j]
+        print(arr)
+
+
+obj = Efficiency()
+arr = obj.generate_array(100)
+start_time = time.time()
+obj.binary_search(arr, 100)
+end_time = time.time()
+
+execution_time = end_time - start_time
+
+
+start_time = time.time()
+
+
+execution_time = end_time - start_time
+
+print(f"Execution time: {execution_time} seconds")
+
+start_time = time.time()
+obj.buble_sort(arr)
+
+print(arr)
+
+execution_time = end_time - start_time
+end_time = time.time()
+print(end_time)