[Uri] lab-numpy

your-code/main.py

@@ -1,68 +1,81 @@
 #1. Import the NUMPY package under the name np.
 
-
+import numpy as np
 
 #2. Print the NUMPY version and the configuration.
-
-
+print("2.-----------------")
+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?
-
-
+print("3.-----------------")
+a = np.random.rand(2, 3, 5)
+a = np.random.randn(2, 3, 5)
+a = np.random.randint(0, 10, size=(2, 3, 5))
 
 #4. Print a.
-
-
+print("4.-----------------")
+print(a)
 
 #5. Create a 5x2x3 3-dimensional array with all values equaling 1.
 #Assign the array to variable "b"
-
-
+print("5.-----------------")
+b = np.ones((5, 2, 3))
 
 #6. Print b.
-
-
+print("6.-----------------")
+print(b)
 
 #7. Do a and b have the same size? How do you prove that in Python code?
-
-
-
+print("7.-----------------")
+print(a.size, "=", b.size)
 
 #8. Are you able to add a and b? Why or why not?
-
-
+print("8.-----------------")
+""" print(a + b)"""
+# Not possible because it's not the same shape. But they have the same size and dimension so we could transpose a to the same shape of b to add them toghether
 
 #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".
-
-
+print("9.-----------------")
+c = b.transpose(1, 2, 0)
+print(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?
-
-
+print("10.-----------------")
+d = a + c
+print(d)
+#Because now they have the same shape
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
-
-
-
+print("11.-----------------")
+print(a)
+print(d.astype(int))
+# 'd' array has all its values from 'a' array plus 1. Because all values in 'b' array are 1 then they are transposed into 'c' so they can be added to 'a' and make 'd'
 
 #12. Multiply a and c. Assign the result to e.
-
-
+print("12.-----------------")
+e = a * c
 
 #13. Does e equal to a? Why or why not?
-
-
+print("13.-----------------")
+print(e == a)
+# yes because all values from 'a' are being multiplied by 1 from array 'c'
 
 
 #14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
-
-
+print("14.-----------------")
+d_max = np.max(d)
+d_min = np.min(d)
+d_mean = np.mean(d)
+print(f"d_max is: {d_max}")
+print(f"d_min is: {d_min}")
+print(f"d_mean is: {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`.
-
-
+print("15.-----------------")
+f = np.empty((2, 3, 5))
+print(f)
 
 
 """
@@ -74,9 +87,12 @@
 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.
 """
-
-
-
+print("16.-----------------")
+f[(d_min < d) & (d < d_mean)] = 25
+f[d == d_mean] = 50
+f[(d_mean < d) & (d < d_max)] = 75
+f[d == d_min] = 0
+f[d == d_max] = 100
 
 """
 #17. Print d and f. Do you have your expected f?
@@ -85,7 +101,7 @@
         [1.75354326, 1.69403643, 1.36729252, 1.61415071, 1.12104981],
         [1.72201435, 1.1862918 , 1.87078449, 1.7726778 , 1.88180042]],
 
-       [[1.44747908, 1.31673383, 1.02000951, 1.52218947, 1.97066381],
+        [[1.44747908, 1.31673383, 1.02000951, 1.52218947, 1.97066381],
         [1.79129243, 1.74983003, 1.96028037, 1.85166831, 1.65450881],
         [1.18068344, 1.9587381 , 1.00656599, 1.93402165, 1.73514584]]])
 
@@ -94,11 +110,12 @@
         [ 75.,  75.,  25.,  25.,  25.],
         [ 75.,  25.,  75.,  75.,  75.]],
 
-       [[ 25.,  25.,  25.,  25., 100.],
+        [[ 25.,  25.,  25.,  25., 100.],
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print("17.-----------------")
+print(f)
 
 """
 #18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
@@ -107,8 +124,17 @@
         [ 'D',  'D',  'B',  'B',  'B'],
         [ 'D',  'B',  'D',  'D',  'D']],
 
-       [[ 'B',  'B',  'B',  'B',  'E'],
+        [[ 'B',  'B',  'B',  'B',  'E'],
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+print("18.-----------------")
+h = np.empty((2, 3, 5)).astype(str)
+
+h[(d_min < d) & (d < d_mean)] = "B"
+h[d == d_mean] = "C"
+h[(d_mean < d) & (d < d_max)] = "D"
+h[d == d_min] = "A"
+h[d == d_max] = "E"
+print(h)