bench: Add test for map() vs inplace operations in array-likes.

map() is 5 times slower. That's mostly because of inefficiency of creating
containers from iterables of unknown length (like map()).

tests/bench/arrayop-1-list_inplace.py

@@ -0,0 +1,12 @@
+# Array operation
+# Type: list, inplace operation using for. What's good about this
+# method is that it doesn't require any extra memory allocation.
+import bench
+
+def test(num):
+    for i in iter(range(num//10000)):
+        arr = [0] * 1000
+        for i in range(len(arr)):
+            arr[i] += 1
+
+bench.run(test)

tests/bench/arrayop-2-list_map.py

@@ -0,0 +1,12 @@
+# Array operation
+# Type: list, map() call. This method requires allocation of
+# the same amount of memory as original array (to hold result
+# array). On the other hand, input array stays intact.
+import bench
+
+def test(num):
+    for i in iter(range(num//10000)):
+        arr = [0] * 1000
+        arr2 = list(map(lambda x: x + 1, arr))
+
+bench.run(test)

tests/bench/arrayop-3-bytearray_inplace.py

@@ -0,0 +1,12 @@
+# Array operation
+# Type: bytearray, inplace operation using for. What's good about this
+# method is that it doesn't require any extra memory allocation.
+import bench
+
+def test(num):
+    for i in iter(range(num//10000)):
+        arr = bytearray(b"\0" * 1000)
+        for i in range(len(arr)):
+            arr[i] += 1
+
+bench.run(test)

tests/bench/arrayop-4-bytearray_map.py

@@ -0,0 +1,12 @@
+# Array operation
+# Type: list, map() call. This method requires allocation of
+# the same amount of memory as original array (to hold result
+# array). On the other hand, input array stays intact.
+import bench
+
+def test(num):
+    for i in iter(range(num//10000)):
+        arr = bytearray(b"\0" * 1000)
+        arr2 = bytearray(map(lambda x: x + 1, arr))
+
+bench.run(test)