Junior - lab numpy done

your-code/main.py

@@ -1,68 +1,92 @@
 #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.show_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?
 
-#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
 
+a = np.random.random((2, 3, 5))
+# a2 = np.random.randint(2, 3, 5)
+# a3 = np.random.choice(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?
 
-
+if a.size == b.size:
+       print("a and b have the same size")
+else:
+      print("a and b do not have the same size")
 
 
 #8. Are you able to add a and b? Why or why not?
 
-
+        # Answer: 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 = 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?
 
+a = (2, 3, 5)
+c = np.array(a)
 
-
-#11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
+d = np.array([[2, 3, 5], [2, 3, 5]])
+print(d) # a and c 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(a)
+print(d)
 
 #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?
 
-
+        # Answer: No, beacuse e it's a result of a multiplication (a * c)
+        # and c is a transposed version of b
 
 
 #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("Max value:", d_max)
+print("Min value:", d_min)
+print("Mean value:", 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))
+print(f)
 
 
 """
@@ -75,7 +99,12 @@
 Note: you don't have to use Numpy in this question.
 """
 
-
+f = np.where((d > d_min) & (d < d_mean), 25,
+             np.where((d > d_mean) & (d < d_max), 75,
+                      np.where(d == d_mean, 50,
+                               np.where(d <= d_min, 0,
+                                        np.where(d >= d_max, 100, d)))))
+print(f)
 
 
 """