diff --git a/hands_on/pyanno_voting/pyanno/tests/test_voting.py b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
index f21a10c..7e89da1 100644
--- a/hands_on/pyanno_voting/pyanno/tests/test_voting.py
+++ b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
@@ -3,8 +3,11 @@
 from pyanno import voting
 from pyanno.voting import MISSING_VALUE as MV
 
+from math import isclose
+
 
 def test_labels_count():
+    #given
     annotations = [
         [1,  2, MV, MV],
         [MV, MV,  3,  3],
@@ -13,10 +16,49 @@ def test_labels_count():
     ]
     nclasses = 5
     expected = [0, 3, 1, 3, 0]
+
+    #when
     result = voting.labels_count(annotations, nclasses)
+
+    #then
     assert result == expected
 
 
+def test_labels_frequency():
+    #given
+    matrix = [
+        [1, 2, 2, -1],
+        [2, 2, 2, 2],
+        [1, 1, 3, 3],
+        [1, 3, 3, 2],
+        [-1, 2, 3, 1],
+        [-1, -1, -1, 3],
+    ]
+
+    matrix2 = [
+        [-1, -1, -1, -1],
+        [-1, -1, -1, -1]
+    ]
+
+    lowerlimit = 0
+    upperlimit = 1
+    nclasses = 4
+    
+    expected2 = np.zeros(nclasses)
+
+
+    #when
+    result = voting.labels_frequency(matrix, nclasses)
+    result2 = voting.labels_frequency(matrix2, nclasses)
+
+    #then
+    assert np.all([res != None for res in result])
+    assert len(result) == nclasses
+    assert np.all(result2 == expected2)
+    assert np.all([i >= lowerlimit and i <= upperlimit for i in result])
+    assert isclose(np.sum(result),upperlimit) or isclose(np.sum(result), lowerlimit,abs_tol=1e-12)
+
+
 def test_majority_vote():
     annotations = [
         [1, 2, 2, MV],
diff --git a/hands_on/pyanno_voting/pyanno/voting.py b/hands_on/pyanno_voting/pyanno/voting.py
index d5b5747..3694cc7 100644
--- a/hands_on/pyanno_voting/pyanno/voting.py
+++ b/hands_on/pyanno_voting/pyanno/voting.py
@@ -100,3 +100,20 @@ def labels_frequency(annotations, nclasses):
         freq[k] is the frequency of elements of class k in `annotations`, i.e.
         their count over the number of total of observed (non-missing) elements
     """
+    annotations_array = np.ravel(annotations)
+    result = np.zeros(nclasses)
+
+    dim = 0
+    for number in annotations_array:
+        if number != -1:
+            dim = dim + 1
+    if dim !=0:
+        for cl in np.arange(nclasses):
+            aux = 0
+            for anot in annotations_array:
+                if cl == anot:
+                    aux = aux + 1
+            result[cl] = aux / dim
+        return(result)
+    else:
+        return(0)