Martina Rivero

your-code/main.py

@@ -1,69 +1,88 @@
 #1. Import the NUMPY package under the name np.
-
+import numpy as np
 
 
 #2. Print the NUMPY version and the configuration.
 
+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?
 
-
+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)
 
+#You can prove it by using the method .size
 
 #8. Are you able to add a and b? Why or why not?
 
+print(a + b)
+
+print(a.size)
+print(b.size)
+
+#It cannot be done because the sizes of the two arrays do not coincide.
 
 
 #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)
 
 
 #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)
+print(d)
 
+#It works because the size of "c" now coincides with the size of "a". 
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
 
-
-
-
+print(a)
+print(d)
+# They are different because d includes the sum of a value number "1" in variable b.
 #12. Multiply a and c. Assign the result to e.
 
-
+e = np.multiply (a, c) 
+print(e)
 
 #13. Does e equal to a? Why or why not?
 
+print(e == a)
 
-
+#They are all equal because array b only contains zeros. Array c is transposing. Variable d and e are only adding and multiplying.
 
 #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)
 
 
 #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], list)
+print(f)
 
 """
 #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.