Miki_Puig

your-code/main.py

@@ -1,85 +1,143 @@
 #1. Import the NUMPY package under the name np.
-
-
+import numpy as np
 
 #2. Print the NUMPY version and the configuration.
-
-
+print(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(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)
+print(b.size)
+
+'''a and b have the same size, 30'''
+
+'''
+In order to see if the sizes of the 2 arrays are the same we are going to compare them, 
+after running it it shows us True, so we can say that both have the same size 
+'''
+
+a.size == b.size
 
 
 
 #8. Are you able to add a and b? Why or why not?
 
+a + b
 
+a.shape
+b.shape
+
+'''We can't add a and b because they don't have the same shape/structure'''
 
 #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)
+
+c.shape
+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 = a + c
+print(d)
+
+''' It works because now the matrix a and the new matrix created 
+from transposing b have the same shape/structure'''
 
 #11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
 
+print(a)
+print(d)
 
 
+''' Altought they have the same size ,30, the values innit are 
+not the same. Array (a)  hasn't changed from the begining, 
+and the new array (d) is the same as (b) but transposed plus array (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?
 
+e == a 
+''' True '''
 
+''' As (b) was a array made by values = 1, multipling it by the array (a) it does not change the value from (a),
+making the result of the array (e), equal to (a)'''
 
 
 #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)
+print(d_max)
+d_min = np.min(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)
+print(d)
+
+
 
 
 
-"""
 #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.
+
+'''
 If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.
 If a value equals to d_mean, assign 50 to the corresponding value in f.
 Assign 0 to the corresponding value(s) in f for d_min in d.
 Assign 100 to the corresponding value(s) in f for d_max in d.
 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_min < d) & (d_mean > d)] = 25
+f[(d > d_mean) & (d < d_max)] = 75
+f[d_mean == d] = 50
+f[(d_min == d)] = 0
+f[(d_max == d)] = 100
+
+print(f)
+
 
 
-"""
 #17. Print d and f. Do you have your expected f?
+
+"""
 For instance, if your d is:
 array([[[1.85836099, 1.67064465, 1.62576044, 1.40243961, 1.88454931],
         [1.75354326, 1.69403643, 1.36729252, 1.61415071, 1.12104981],
@@ -98,11 +156,14 @@
         [ 75.,  75.,  75.,  75.,  75.],
         [ 25.,  75.,   0.,  75.,  75.]]])
 """
-
+print(d)
+print(f)
 
 """
-#18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values 
-("A", "B", "C", "D", and "E") to label the array elements? You are expecting the result to be:
+#18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), 
+how to use string values 
+("A", "B", "C", "D", and "E") to label the array elements? 
+You are expecting the result to be:
 array([[[ 'D',  'D',  'D',  'B',  'D'],
         [ 'D',  'D',  'B',  'B',  'B'],
         [ 'D',  'B',  'D',  'D',  'D']],
@@ -111,4 +172,15 @@
         [ 'D',  'D',  'D',  'D',  'D'],
         [ 'B',  'D',   'A',  'D', 'D']]])
 Again, you don't need Numpy in this question.
-"""
+"""
+
+
+new_array = np.empty((2,3,5), dtype=str)
+
+new_array[(d_min < d) & (d < d_mean)] = 'A'
+new_array[(d > d_mean) & (d < d_max)] = 'B'
+new_array[d_mean == d] = 'C'
+new_array[(d_min == d)] = 'D'
+new_array[(d_max == d)] = 'E'
+
+print(new_array)