Leon

your-code/main.py

@@ -1,67 +1,76 @@
 #1. Import the NUMPY package under the name np.
-
+import numpy as np
 
 
 #2. Print the NUMPY version and the configuration.
-
-
+print(np.__version__)
+print(np.__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?
-
-
+import random as rd
+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?
-
-
+a.size == b.size
+ # Yes
 
 
 #8. Are you able to add a and b? Why or why not?
-
-
+try:
+    a + b
+except:
+    print("Error")
+# Can't add a and b while 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 = np.transpose(b, (1,2,0))
+c.shape
 
 
 #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
+# Works now because a and c has equal shape
 
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
+print(a) 
+print("\n")
+print(d)
 
-
-
+# The difference for each position between both arrays is 1
 
 #12. Multiply a and c. Assign the result to e.
-
+e = a * c
 
 
 #13. Does e equal to a? Why or why not?
-
+# Yes, because each element of a is multiplied by 1, so the result is the same
 
 
 
 #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()
 
 
 
 #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((d.shape), dtype="int")
 
 
 
@@ -75,7 +84,19 @@
 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]): 
+            if d[i][x][z] == d_min:
+                f[i][x][z] = 0
+            elif (d_min < d[i][x][z] < d_mean):
+                f[i][x][z] = 25
+            elif d[i][x][z] == d_mean:
+                f[i][x][z] = 50
+            elif (d_max > d[i][x][z] > d_mean):
+                f[i][x][z] = 75
+            elif d[i][x][z] == d_max:
+                f[i][x][z] = 100
 
 
 """
@@ -98,7 +119,9 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print(d)
+print("\n")
+print(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.
-"""
+"""
+g = np.empty(d.shape, dtype="str") 
+
+for i in range(d.shape[0]):
+    for x in range(d.shape[1]):
+        for z in range(d.shape[2]): 
+            if d[i][x][z] == d_min:
+                g[i][x][z] = "A"
+            elif (d_min < d[i][x][z] < d_mean):
+                g[i][x][z] = "B"
+            elif d[i][x][z] == d_mean:
+                g[i][x][z] = "C"
+            elif (d_max > d[i][x][z] > d_mean):
+                g[i][x][z] = "D"
+            elif d[i][x][z] == d_max:
+                g[i][x][z] = "E"
+
+print(g)