task completed with bonus

your-code/main.py

@@ -1,68 +1,80 @@
 #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.randint(101, size=(2,3,5))
 
 #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.random.randint(1,2, size = (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
+"No they don't have the same size"
 
 #8. Are you able to add a and b? Why or why not?
 
+"No, because they don't have the same shape. One of them needs to be transposed."
 
 
 #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 = np.transpose(b, (1,2,0))
+print(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(a)
+print(d)
 
-
+"Yes. The in array 'd' every element is the result of every element in array 'a' plus 1, because all elements in array 'b' were 1."
 
 #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?
 
+a == e
 
-
+"Yes, because all elements are being multiplied by one so it gives the same result"
 
 #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
 
-
-
+import numpy as np
+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)
 
 
 """
@@ -74,7 +86,21 @@
 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 i in range(d.shape[0]):
+        for x in range(d.shape[1]):
+                for z in range(d.shape[2]):
+                        val = d[i][x][z]
+                        if  val > d_min and val < d_mean:
+                                f[i][x][z] = 25
+                        elif val > d_mean and val < d_max:
+                                f[i][x][z] = 75
+                        elif val == d_mean:
+                                f[i][x][z] = 50
+                        elif val == d_min:
+                                f[i][x][z] = 0
+                        elif val == d_max:
+                                f[i][x][z] = 100
+print(f)
 
 
 
@@ -98,7 +124,8 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print(d)
+print(f)
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -111,4 +138,24 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+
+g = np.empty((2,3,5), dtype=str)
+print(g)
+
+labels = {0:"A", 25: "B", 50: "C", 75: "D", 100: "E"}
+for i in range(g.shape[0]):
+        for x in range(g.shape[1]):
+                for z in range(g.shape[2]):
+                        val = d[i][x][z]
+                        if  val > d_min and val < d_mean:
+                                g[i][x][z] = labels[25]
+                        elif val > d_mean and val < d_max:
+                                g[i][x][z] = labels[75]
+                        elif val == d_mean:
+                                g[i][x][z] = labels[50]
+                        elif val == d_min:
+                                g[i][x][z] = labels[0]
+                        elif val == d_max:
+                                g[i][x][z] = labels[100]
+print(g)