diff --git a/python/clt.py b/python/clt.py
new file mode 100644
index 0000000..94bfd54
--- /dev/null
+++ b/python/clt.py
@@ -0,0 +1,61 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from numpy.random import random
+
+
+def get_group_averages(num_groups, group_size):
+    averages = []
+
+    rand_values = random(num_groups * group_size)
+    curr_num = 0
+    for i, num in enumerate(rand_values):
+        curr_num += num
+
+        if i % group_size == 0:
+            averages.append(curr_num / group_size)
+            curr_num = 0
+
+    return averages
+
+
+def get_bins(values, num_bins, min_val = 0, max_val = 1):
+    bins = [0 for _ in range(num_bins)]
+    
+    value_range = abs(min_val - max_val)
+    bin_size = value_range / num_bins
+
+    for value in values:
+        for curr_bin in range(num_bins):
+            bin_start = curr_bin * bin_size
+            bin_end = bin_start + bin_size
+
+            if value > bin_start and value < bin_end:
+                bins[curr_bin] += 1
+                break
+
+    return bins
+
+
+def main(argv):
+    from argparse import ArgumentParser
+    parser = ArgumentParser()
+    parser.add_argument('num_values')
+    parser.add_argument('group_size')
+    parser.add_argument('num_bins')
+    args = parser.parse_args(argv[1:])
+
+    num_values = int(args.num_values)
+    group_size = int(args.group_size)
+    num_bins = int(args.num_bins)
+
+    values = get_group_averages(num_values, group_size)
+    bins = get_bins(values, num_bins)
+
+    plt.plot(bins)
+    plt.savefig('out.png')
+
+
+if __name__ == '__main__':
+    from sys import argv
+    main(argv)