Lab-numpy[Jesus Escamilla]

your-code/main.py

@@ -1,68 +1,83 @@
 #1. Import the NUMPY package under the name np.
-
+import numpy as np
 
 
 #2. Print the NUMPY version and the configuration.
-
+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.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.full((5,2,3),1)
 
 
 
 #6. Print b.
-
+print(b)
 
 
 #7. Do a and b have the same size? How do you prove that in Python code?
-
+if a.size == b.size:
+    print("True")
+else:
+    print("False")
 
 
 
 #8. Are you able to add a and b? Why or why not?
-
+a+b
 
 
 #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))
 
 
 #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) #las dimensiones son las mismas para poder ejecutar la suma
 
 
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
-
-
+print(a)
+print(d)
+#se sumo "1" a la matriz 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?
-
+print("Si por que se multiplico a por 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 = np.amax(d)
+print(d_max)
+d_min = np.amin(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)
 
 
 """
@@ -74,7 +89,33 @@
 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.
 """
-
+temp = []
+
+for i in d:
+    #print(i)
+    for j in i:
+        #print(j)
+        for x in j:
+            ##print(x)
+            if x > d_min and x < d_mean:
+                temp.append(25)
+                #print("25")
+            elif x > d_mean and x <d_max:
+                 temp.append(75)
+                #print("75")
+            elif x == d_mean:
+                 temp.append(50)
+                #print("50")
+            elif x == d_min:
+                 temp.append(0)
+                #print("0")
+            elif x == d_max:
+                 temp.append(100)
+               # print("100")
+               # 
+
+f= np.reshape(temp,(2,3,5))
+print(f)
 
 
 
@@ -98,7 +139,7 @@
         [ 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 +152,32 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+temp = []
+
+for i in d:
+    #print(i)
+    for j in i:
+        #print(j)
+        for x in j:
+            ##print(x)
+            if x > d_min and x < d_mean:
+                temp.append('B')
+                #print("25")
+            elif x > d_mean and x <d_max:
+                 temp.append('D')
+                #print("75")
+            elif x == d_mean:
+                 temp.append('C')
+                #print("50")
+            elif x == d_min:
+                 temp.append('A')
+                #print("0")
+            elif x == d_max:
+                 temp.append('E')
+               # print("100")
+               # 
+
+f = np.reshape(temp,(2,3,5))
+print(f)
+