diff --git a/your-code/main.py b/your-code/main.py
index 78c792b..bdcb363 100644
--- a/your-code/main.py
+++ b/your-code/main.py
@@ -1,69 +1,77 @@
 #1. Import the NUMPY package under the name np.
 
-
+import numpy as np
 
 #2. Print the NUMPY version and the configuration.
 
-
+print(np.version.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.random(2,3,5)
+a = np.random.randint(200, size =(2, 3, 5))
 
 
-#4. Print a.
 
+#4. Print a.
+print(a)
 
 
 #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)
 
-#6. Print b.
-
-
+print(b)
 
 #7. Do a and b have the same size? How do you prove that in Python code?
 
-
+print(np.shape (a) == np.shape(b))
 
 
 #8. Are you able to add a and b? Why or why not?
 
-
+#no, they should have the same shape
 
 #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".
-
-
+print(np.shape(b))
+c = b.transpose(1,2,0)
+print(np.shape(c))
 
 #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
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
 
+print (f" matrix a = {a}")
+print (f" matrix d = {d}")
 
-
+# both matrix have a difference of "1" between each of its elements => element_matrix_d _position_1 = element_matrix_d _position_1 + 1
 
 #12. Multiply a and c. Assign the result to e.
 
+e = a * c
 
 
 #13. Does e equal to a? Why or why not?
 
-
+print(a == e) # yes, as any number*1 = number
 
 
 #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)
 
-
-
+print(d_max,d_min,d_mean)
 #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))
 
 """
 #16. Populate the values in f. For each value in d, if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f.
@@ -74,7 +82,21 @@
 In the end, f should have only the following values: 0, 25, 50, 75, and 100.
 Note: you don't have to use Numpy in this question.
 """
+for a in range(2):
+    for b in range(3):
+        for i in range(5):
+            if  d_min < d[a][b][i] <d_mean:
+                f[a][b][i] = 25
+            if  d_mean < d[a][b][i] <d_max:
+                f[a][b][i] = 75
+            if  d[a][b][i] == d_mean:
+                f[a][b][i]= 50
+            if d[a][b][i] == d_min:
+                f[a][b][i] = 0
+            if d[a][b][i] == d_max:
+                f[a][b][i] = 100
 
+print(f" matrix f =:   {f}")
 
 
 
@@ -111,4 +133,22 @@
         [ '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
+"""
+
+p = np.empty((2,3,5), str)
+
+for a in range(2):
+    for b in range(3):
+        for i in range(5):
+            if  d_min < d[a][b][i] <d_mean:
+                p[a][b][i] = 'B'
+            if  d_mean < d[a][b][i] <d_max:
+                p[a][b][i] = 'D'
+            if  d_mean < d[a][b][i] == d_mean:
+                p[a][b][i]= 'C'
+            if d_mean < d[a][b][i] == d_min:
+                p[a][b][i] = 'A'
+            if d_mean < d[a][b][i] == d_max:
+                p[a][b][i] = 'E'
+
+print(p)
\ No newline at end of file