diff --git a/your-code/main.py b/your-code/main.py
index 78c792b..61f6a09 100755
--- a/your-code/main.py
+++ b/your-code/main.py
@@ -1,71 +1,82 @@
 #1. Import the NUMPY package under the name np.
-
-
+import numpy as np
+from numpy.core.fromnumeric import size
+from numpy.random.mtrand import randn
 
 #2. Print the NUMPY version and the configuration.
-
-
-
+print(np.__version__)
+print(np.show_config())
 #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(1,high =10,size=(2,3,5))
+a_1 = np.random.random_sample((2,3,5))
+a_2 = np.random.random((2,3,5))
 #4. Print a.
-
-
-
-#5. Create a 5x2x3 3-dimensional array with all values equaling 1.
+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.
-
-
+print(b)
 
 #7. Do a and b have the same size? How do you prove that in Python code?
-
-
+print(np.size(a))
+print(np.size(b))
 
 
 #8. Are you able to add a and b? Why or why not?
-
-
-
+try:
+        addition = a + b
+        print (addition)
+except:
+        pass
+# you can't because they aren't the same size
 #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".
-
-
+c = b.reshape((5,3,2))
+c = c.transpose()
+print ('size c',size(c))
+print('c reshape',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
+# they work because they have the same structure
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
-
+print  ('a',a)
+print  ('d',d)
 
 
 
 #12. Multiply a and c. Assign the result to e.
-
-
+e = a * c
+print(e)
 
 #13. Does e equal to a? Why or why not?
-
+# yes because b array is full of ones
 
 
 
 #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
+d_max = d.max()
+d_min = d.min()
+d_mean = d.mean()
 
+print(d_max)
+print(d_min)
+print(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`.
 
 
+#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',f)
+print (f[0][1][0])
 
 
-"""
+'''
 #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.
 If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.
 If a value equals to d_mean, assign 50 to the corresponding value in f.
@@ -73,9 +84,20 @@
 Assign 100 to the corresponding value(s) in f for d_max in d.
 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 vals_1 in range(len(d)):
+        for vals_2 in range(len(d[vals_1])):
+                for vals_3 in range(len(d[vals_1][vals_2])):
+                        search = d[vals_1][vals_2][vals_3]
+                        if search < d_mean and search > d_min:
+                                f[vals_1][vals_2][vals_3]= 25
+                        elif search > d_mean and search < d_max:
+                                f[vals_1][vals_2][vals_3]= 75
+                        elif search == d_min:
+                                f[vals_1][vals_2][vals_3]= 0
+                        elif search == d_max:
+                                f[vals_1][vals_2][vals_3]= 100
 
 
 """
@@ -98,7 +120,8 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print ('d',d)
+print ('f',f)
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -111,4 +134,21 @@
         [ '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 = f.astype(np.string_)
+
+for vals_1 in range(len(d)):
+        for vals_2 in range(len(d[vals_1])):
+                for vals_3 in range(len(d[vals_1][vals_2])):
+                        search = d[vals_1][vals_2][vals_3]
+                        if search < d_mean and search > d_min:
+                                f[vals_1][vals_2][vals_3]= "B"
+                        elif search > d_mean and search < d_max:
+                                f[vals_1][vals_2][vals_3]= "D"
+                        elif search == d_min:
+                                f[vals_1][vals_2][vals_3]= "A"
+                        elif search == d_max:
+                                f[vals_1][vals_2][vals_3]= "E"
+
+print("F con strings ",f)
+print("F con strings ",f[-1][-1][-1])
\ No newline at end of file