PP - FT - MX Daniel Mota, Ulises Aquino, Eduardo Castro

your-code/main.py

@@ -1,68 +1,73 @@
 #1. Import the NUMPY package under the name np.
 
-
+import numpy as np
 
 #2. Print the NUMPY version and the configuration.
 
-
+print(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.random((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.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(b.size == a.size) #True if same shape a(x,y) = b(x,y), False else
 
 #8. Are you able to add a and b? Why or why not?
 
-
+#We can't add arrays with different 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".
 
-
+c = b.reshape(2,3,5)
 
 #10. Try to add a and c. Now it should work. Assign the sum to varialbe "d". But why does it work now?
 
-
+d = np.add(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)
 
-
+#Each individual value of 'c' was added to the same positional value of 'a'
 
 #12. Multiply a and c. Assign the result to e.
 
-
+e = np.multiply(a,c)
 
 #13. Does e equal to a? Why or why not?
 
-
+#Yes, because we're multypliying each value for 1 which does not change values
 
 
 #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), dtype=float)
 
 
 """
@@ -75,7 +80,19 @@
 Note: you don't have to use Numpy in this question.
 """
 
-
+for i in range(len(f)):
+   for j in range(len(f[i])):
+       for k in range(len(f[i,j])):
+            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:
+                f[i,j,k] = 50
+            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_max: 
+                f[i,j,k] = 100
+            elif d[i,j,k] == d_min:
+                f[i,j,k] = 0
 
 
 """
@@ -98,7 +115,7 @@
         [ 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 +128,20 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+z = np.empty((2,3,5), dtype=str)
+
+for i in range(len(f)):
+   for j in range(len(f[i])):
+       for k in range(len(f[i,j])):
+            if d[i,j,k] > d_min and d[i,j,k]<d_mean:
+                z[i,j,k] = 'B'
+            elif d[i,j,k] == d_mean:
+                z[i,j,k] = 'C'
+            elif d[i,j,k] > d_mean and d[i,j,k]<d_max:
+                z[i,j,k] = 'D'
+            elif d[i,j,k] == d_max: 
+                z[i,j,k] = 'E'
+            elif d[i,j,k] == d_min:
+                z[i,j,k] = 'A'
+print(z)