:)

your-code/main.py

@@ -1,69 +1,89 @@
 #1. Import the NUMPY package under the name np.
-
-
+import numpy as np
 
 #2. Print the NUMPY version and the configuration.
 
-
+print("NumPy Version:", np.__version__)
+print("NumPy Configuration:")
+print(np.show_config())
+print('FUNCIONA')
 
 #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.rand(2, 3, 5)
 
+# Can also use numpy.random.randint() and numpy.random.randn()
 
 #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.
 
-
+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?
 
+print(a+b)
 
+#cannot add them together because they have different shapes
 
 #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.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
+print(d)
 
+#it works now because the 2 arrays are now 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)
 
+# the values in d are the values in a+1
+# because b is just an array full of ones, so even after it is transposed as c the valuse are all 1, so d just adds 1 to every value in a.
 
 
 #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?
 
-
+e == a
+# e = a. Because all we've done in e is multiply a x1, so the values of e are the same as a.
 
 
 #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)
+print(d_max)
+d_min = np.min(d)
+print(d_min)
+d_mean = np.mean(d)
+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`.
 
-
-
+f = np.empty((2, 3, 5))
+print(f)
+print(d)
 
 """
 #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.
@@ -74,9 +94,19 @@
 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(2): #because there are two blocks
+        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
 
 """
 #17. Print d and f. Do you have your expected f?
@@ -98,7 +128,10 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
+print(d)
+print(f)
 
+#I did not really understand this at all, the indexing looks so complex
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -111,4 +144,20 @@
         [ '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)
+for i in range(2): #because there are two blocks
+        for j in range(3):
+                for k in range(5):
+                        if d[i][j][k] > d_min and d[i][j][k] < d_mean:
+                                g[i][j][k] = "B"
+                        elif d[i][j][k] > d_mean and d[i][j][k] < d_max:
+                                g[i][j][k] = "D"
+                        elif d[i][j][k] == d_mean:
+                                g[i][j][k] = "C"
+                        elif d[i][j][k] == d_min:
+                                g[i][j][k] = "A"
+                        elif d[i][j][k] == d_max:
+                                g[i][j][k] = "E"
+
+print(g)