PP-MX-FT (Brisa Araujo, Gustavo Rangel)

your-code/main.py

@@ -1,69 +1,76 @@
+# coding=utf-8
 #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?
-
-
+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.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?
-
-
-
+print(a.shape)
+print(b.shape)
 
 #8. Are you able to add a and b? Why or why not?
-
-
+'''
+Answer: No, because they do not have the same 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 = np.reshape(b, (2,3,5))
+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 = 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("Array a:\n", a)
+print("Array d:\n", d)
+'''
+Explain: Array "a" values have been added with correspondent index in array "d".
+'''
 
 #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?
-
-
-
+print("Array e:\n", e)
+'''
+Explain: Yes, they are equal because array c only has the number 1 as elements; Any number multiplied by 1 == the same number
+'''
 
 #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)
 
+print("\nd_max:", d_max)
+print("\nd_min:", d_min)
+print("\nd_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((2,3,5))
 
 """
 #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.
@@ -75,8 +82,19 @@
 Note: you don't have to use Numpy in this question.
 """
 
-
-
+for k, x in enumerate(d):
+    for m, y in enumerate(x):
+        for n, z in enumerate(y):
+            if z > d_min and z < d_mean:
+                f[k,m,n] = 25
+            if z > d_mean and z < d_max:
+                f[k, m, n] = 75
+            if z == d_min:
+                f[k, m, n] = 0
+            if z == d_max:
+                f[k, m, n] = 100
+            if z == d_mean:
+                f[k, m, n] = 50
 
 """
 #17. Print d and f. Do you have your expected f?
@@ -99,6 +117,8 @@
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
 
+print("Array D:\n", d)
+print("Array F:\n", f)
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -111,4 +131,20 @@
         [ '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=object)
+for k, x in enumerate(d):
+    for m, y in enumerate(x):
+        for n, z in enumerate(y):
+            if z > d_min and z < d_mean:
+                g[k,m,n] = "B"
+            if z > d_mean and z < d_max:
+                g[k, m, n] = "D"
+            if z == d_min:
+                g[k, m, n] = "A"
+            if z == d_max:
+                g[k, m, n] = "E"
+            if z == d_mean:
+                g[k, m, n] = "C"
+
+print("Array G:\n", g)