Noemí - lab-numpy

your-code/main.py

@@ -1,68 +1,93 @@
 #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.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.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(f"The size of a is {a.size}")
+print(f"The size of b is {b.size}")
 
-
+# -> We can prove it by itering all the elements in the array
+def count_elements(array):
+        counter = 0
+        for i in array:
+                for j in i:
+                        for x in j:
+                                counter += 1
+        return counter
+print(f"a has {count_elements(a)} elements")
+print(f"b has {count_elements(a)} elements")
 
 
 #8. Are you able to add a and b? Why or why not?
-
+try:
+        addition = np.add(a, b)
+        print(addition)
+except ValueError:
+        print("The arrays cannot be added because they have different 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.transpose(b, (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)
+# -> Now it works because both 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)
+# As the transposed b had ones, the result of d is that all the elements f a have changed to the previous value + 1.
 
 
 #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?
-
-
+# Yes, it equals because what multiply does is that multiplies each element of the array a for 1 (array c).
 
 
 #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 the results
+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((2, 3, 5))
+print(f)
 
 
 """
@@ -74,8 +99,12 @@
 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.
 """
-
-
+f[(d > d_min) & (d < d_mean)] = 25
+f[d == d_mean] = 50
+f[(d > d_mean) & (d < d_max)] = 75
+f[d == d_min] = 0
+f[d == d_max] = 100
+print(f)
 
 
 """
@@ -98,6 +127,8 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
+print(d)
+print(f)
 
 
 """
@@ -111,4 +142,11 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+x = np.empty((2, 3, 5)).astype(str)
+x[(d > d_min) & (d < d_mean)] = 'B'
+x[d == d_mean] = 'C'
+x[(d > d_mean) & (d < d_max)] = 'D'
+x[d == d_min] = 'A'
+x[d == d_max] = 'E'
+print(x)