numpy vb

your-code/main.py

@@ -1,68 +1,72 @@
 #1. Import the NUMPY package under the name np.
 
-
+import numpy as np
+import random  
 
 #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(100, 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.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 (a.size == b.size)
 
 
 #8. Are you able to add a and b? Why or why not?
 
-
+# No 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 = np.transpose(b, (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 
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
 
+print (a)
+print (d)
 
-
+# c dds 1 to every value in a, so the values of d are larger by magnitude of 1
 
 #12. Multiply a and c. Assign the result to e.
 
-
+e = a * c
 
 #13. Does e equal to a? Why or why not?
 
-
+print (e == a) # Yes, since c multiplies a by 1
 
 
 #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
 
-
+d_max = max(d.flatten())
+d_min = min(d.flatten())
+d_mean = np.mean(d.flatten())
 
 
 #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)) 
 
 
 """
@@ -76,6 +80,21 @@
 """
 
 
+for i in range(2): 
+        for j in range(3): 
+                for k in range(5):
+                          element = d[i, j, k]
+                          if d_min < element < d_mean:
+                                f[i, j, k] = 25
+                          elif  d_mean < element < d_max:
+                                f[i, j, k] = 75
+                          elif element == d_mean:
+                                f[i, j, k] = 50
+                          elif element == d_min:
+                                f[i, j, k] = 0
+                          elif element == d_max:
+                                f[i, j, k] = 100
+
 
 
 """
@@ -99,6 +118,9 @@
         [ 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 +133,25 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+
+labels = {0: "A", 25: "B", 50: "C", 75: "D", 100: "E"}
+
+f_l = np.empty((2, 3, 5), dtype=str)
+
+for i in range(2): 
+        for j in range(3): 
+                for k in range(5):
+                          element_l = d[i,j,k]
+                          if d_min < element_l < d_mean:
+                                f_l[i,j,k] = labels[25]
+                          elif  d_mean < element_l < d_max:
+                                f_l[i,j,k] = labels[75]
+                          elif element_l == d_mean:
+                                f_l[i,j,k] = labels[50]
+                          elif element_l == d_min:
+                                f_l[i,j,k] = labels[0]
+                          elif element_l == d_max:
+                                f_l[i,j,k] = labels[100]
+
+print (f_l)