diff --git a/your-code/main.py b/your-code/main.py
index 78c792b..3be3ea0 100644
--- a/your-code/main.py
+++ b/your-code/main.py
@@ -1,68 +1,81 @@
 #1. Import the NUMPY package under the name np.
-
+import numpy as np
+import random as rd
 
 
 #2. Print the NUMPY version and the configuration.
-
+np.__version__
 
 
 #3. Generate a 2x3x5 3-dimensional array with random values. Assign the array to variable "a"
 # Challenge: there are at least three easy ways that use numpy to generate random arrays. How many ways can you find?
-
+a = np.random.randint(0, 9, size = (2,3,5))
+a_1 = np.random.random((2,3,5))
+a_2 = np.array([rd.uniform(10, 60) for i in range(20)])
 
 
 #4. Print a.
+print(a)
+print(a_1)
+print(a_2)
 
 
+#5. Create a 5x2x3 3-dimensional array with all values equaling 1.y
 
-#5. Create a 5x2x3 3-dimensional array with all values equaling 1.
 #Assign the array to variable "b"
 
-
+b = np.ones((5,2,3))
 
 #6. Print b.
-
+print(b)
 
 
 #7. Do a and b have the same size? How do you prove that in Python code?
 
-
-
+a.size == b.size
 
 #8. Are you able to add a and b? Why or why not?
 
+"Yes, I am able to do it if I transpose one of the array"
 
 
 #9. Transpose b so that it has the same structure of a (i.e. become a 2x3x5 array). Assign the transposed array to varialbe "c".
 
-
+a.shape
+c = b.transpose(1, 2, 0)
 
 #10. Try to add a and c. Now it should work. Assign the sum to varialbe "d". But why does it work now?
 
+d= a + c 
+"I can do the sum now because both arrays have the same shape"
 
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
 
-
+print(a)
+print(d)
 
 
 #12. Multiply a and c. Assign the result to e.
 
-
+e = a * c
 
 #13. Does e equal to a? Why or why not?
 
-
+e.shape == a.shape
 
 
 #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
 
-
+d_max = np.max(d)
+d_min = np.min(d)
+d_mean = np.mean(d)
 
 
 #15. Now we want to label the values in d. First create an empty array "f" with the same shape (i.e. 2x3x5) as d using `np.empty`.
 
-
+f = np.empty([2,3,5])
+print(f)
 
 
 """
@@ -75,6 +88,19 @@
 Note: you don't have to use Numpy in this question.
 """
 
+for i in range(2):
+    for j in range(3):
+        for k in range(5):
+            if d[i][j][k] > d_min and d[i][j][k] < d_mean:
+                f[i][j][k] = 25
+            elif d[i][j][k] > d_mean and d[i][j][k] < d_max:
+                f[i][j][k] = 75
+            elif d[i][j][k] == d_mean:
+                f[i][j][k] = 50
+            elif d[i][j][k] == d_min:
+                f[i][j][k] = 0
+            elif d[i][j][k] == d_max:
+                f[i][j][k] = 100
 
 
 
@@ -88,7 +114,10 @@
        [[1.44747908, 1.31673383, 1.02000951, 1.52218947, 1.97066381],
         [1.79129243, 1.74983003, 1.96028037, 1.85166831, 1.65450881],
         [1.18068344, 1.9587381 , 1.00656599, 1.93402165, 1.73514584]]])
+"""
+print(d)
 
+"""
 Your f should be:
 array([[[ 75.,  75.,  75.,  25.,  75.],
         [ 75.,  75.,  25.,  25.,  25.],
@@ -98,7 +127,7 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print(f)
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -111,4 +140,24 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
\ No newline at end of file
+"""
+
+f_str = np.empty((2, 3, 5), dtype=str)
+
+
+for i in range(2):
+    for j in range(3):
+        for k in range(5):
+            if d[i][j][k] > d_min and d[i][j][k] < d_mean:
+                f_str[i][j][k] = 'B'
+            elif d[i][j][k] > d_mean and d[i][j][k] < d_max:
+                f_str[i][j][k] = 'D'
+            elif d[i][j][k] == d_mean:
+                f_str[i][j][k] = 'C'
+            elif d[i][j][k] == d_min:
+                f_str[i][j][k] = 'A'
+            elif d[i][j][k] == d_max:
+                f_str[i][j][k] = 'E'
+
+
+print(f_str)
\ No newline at end of file