diff --git a/src/modules/exercises/essentials.py b/src/modules/exercises/essentials.py
index 2cff5ea..4959c65 100644
--- a/src/modules/exercises/essentials.py
+++ b/src/modules/exercises/essentials.py
@@ -20,3 +20,32 @@ def find_missing(input_aray: list[int]):
 	return sum(get_array_range(1, len(input_aray) + 1)) - sum(input_aray)
 
 def trap_rain_water(input_aray: list[int]):
+	ceiling = max(input_aray)
+	# Step 1: create a matrix with the graph
+	container = []
+	for wall_index in range(0, len(input_aray)):
+		wall = []
+		for coord_index in range(0, ceiling):
+			if (coord_index < input_aray[wall_index]):
+				wall.insert(coord_index, "w")
+			else:
+				wall.insert(coord_index, "a")
+		container.insert(wall_index, wall)
+	
+	total_water = 0
+	# Step 2: fill the air with water
+	for wall_x in range(0, len(input_aray)):
+		for wall_y in range(0, input_aray[wall_x]):
+			if (container[wall_x][wall_y] == "a"):
+				continue
+			# Step 2.1 count distance with next wall
+			temp_water = 0
+			for water_x in range(wall_x + 1, len(input_aray)):
+				if (container[water_x][wall_y] == "a"):
+					temp_water += 1
+				else:
+					total_water += temp_water
+					break
+
+	return total_water
+
diff --git a/src/modules/statistics.py b/src/modules/statistics.py
index 322af79..43d5b2c 100644
--- a/src/modules/statistics.py
+++ b/src/modules/statistics.py
@@ -1,5 +1,34 @@
 from math import sqrt, pi, e
 
+def mean(list):
+	return sum(list) / len(list)
+
+def weighted_mean(items, weights):
+	if (len(items) != len(weights)):
+		return
+	total = 0
+	for i in range(len(items)):
+		total += items[i] * weights[i]
+	return total / sum(weights)
+
+def weighted_mean_inline(items, weights):
+	return sum(s * w for s, w in zip(items, weights)) / sum(weights)
+
+# also called 50% quantile
+def median(items):
+	ordered = sorted(items)
+	length = len(ordered)
+	pair = length % 2 == 0
+	mid = int(length / 2) - 1 if pair else int(n/2)
+
+	if pair:
+		return (ordered[mid] + ordered[mid+1]) / 2
+	else:
+		return ordered[mid]
+
+def mode(items):
+	sums = []
+
 def population_variance(difference_list, mean):
 	summatory = 0.0
 	for diff in difference_list:
@@ -29,6 +58,12 @@ def probability_density_function(x: float, mean: float, standard_deviation: floa
 
 def test_statistics_module():
 	print("=== Statistics module ===")
+	list = [ 1, 2, 3, 4, 5, 6]
+	print(">> The mean of {0} is {1}".format(list, mean(list)))
+	weights = [0.2, 0.5, 0.7, 1, 0, 0.9]
+	print(">> The weighted_mean of {0} is {1} and it is equivalent to {2}".format(list, weighted_mean(list, weights), weighted_mean_inline(list, weights)))
+	print(">> The mean is {0}".format(median(list)))
+
 	differences = [ -6.571, -5.571, -1.571, 0.429, 2.429, 3.429, 7.429 ]
 	print("The population variance is", population_variance(differences, sum(differences) / len(differences)), population_variance_inline(differences))
 	print("The standard deviation is", standard_deviation(differences, False))
diff --git a/tests/test_essentials.py b/tests/test_essentials.py
index 0b80fb8..2c82a7f 100644
--- a/tests/test_essentials.py
+++ b/tests/test_essentials.py
@@ -25,3 +25,17 @@ def test_find_missing():
 	result = find_missing(input)
 	expected = 4
 	assert result == expected
+
+def test_rain_watter():
+	input = [3,0,1,0,4,0,2]
+	expected = 10
+	result = trap_rain_water(input)
+	assert result == expected
+	input = [3,0,2,0,4]
+	expected = 7
+	result = trap_rain_water(input)
+	assert result == expected
+	input = [1,2,3,4]
+	expected = 0
+	result = trap_rain_water(input)
+	assert result == expected