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[Pere] NumPy lab delivered #24

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143 changes: 126 additions & 17 deletions your-code/main.py
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Original file line number Diff line number Diff line change
@@ -1,68 +1,111 @@
#1. Import the NUMPY package under the name np.
print("\n1. Import the NUMPY package under the name np.\n" + "-"*50+"\n")
print("See coding..")


import numpy as np

#2. Print the NUMPY version and the configuration.
print("\n2. Print the NUMPY version and the configuration\n" + "-"*50+"\n")


print("Version: " + 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?
print("\n3. 3. Generate a 2x3x5 3-dimensional array with random values. Assign the array to variable a\n" + "-"*50+"\n")
print("See coding..")


a = np.random.rand(2,3,5)

#4. Print a.


print("\n4. Print a.\n" + "-"*50+"\n")
print(a)

#5. Create a 5x2x3 3-dimensional array with all values equaling 1.
#Assign the array to variable "b"


print("\n5.Create a 5x2x3 3-dimensional array with all values equaling 1...\n" + "-"*50+"\n")
print("See code...")
b = np.ones((5,2,3), dtype=int)

#6. Print b.
print("\n6. Print b.\n" + "-"*50+"\n")


print(b)

#7. Do a and b have the same size? How do you prove that in Python code?
print("\n7. Do a and b have the same size? How do you prove that in Python code?.\n" + "-"*50+"\n")



print("Yes, they have same size. We compare them True is returned\n")
print(a.size == b.size)

#8. Are you able to add a and b? Why or why not?
print("\n8. Are you able to add a and b? Why or why not?.\n" + "-"*50+"\n")

'''Arrays can be add only if they have same shape. Otherwise you'll have an error.'''


#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("Arrays can be add only if they have same shape. Otherwise you'll have an error.")

#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("\n9. Transpose b so that it has the same structure of a (i.e. become a 2x3x5 array). Assign the transposed array to variable.\n" + "-"*50+"\n")

print(a.shape)
print(b.shape)
c = b.transpose(1,2,0)
print(a.shape == 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("\n10. Try to add a and c. Now it should work. Assign the sum to variable ''d''. But why does it work now?\n" + "-"*50+"\n")

'''
Now will work because after transpose() action , both array shapes are equal.
'''

d = a+c
print(d)
print('Now will work because after transpose() action , both array shapes are equal.')

#11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.
print("\n11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.\n" + "-"*50+"\n")

print(a)
print(d)


print("All values of arrays have been increased in 1. That's because we add an array generated with ones()\n")

#12. Multiply a and c. Assign the result to e.
print("\n12. Multiply a and c. Assign the result to e.\n" + "-"*50+"\n")


e = a * c
print(e)

#13. Does e equal to a? Why or why not?
print("\n13. Does e equal to a? Why or why not?\n" + "-"*50+"\n")


print("e is equal to a?: YES")
print("because we multiplied a by 1 (c array are ones)")


#14. Identify the max, min, and mean values in d. Assign those values to variables "d_max", "d_min", and "d_mean"
print("\n14. Identify the max, min, and mean values in d. Assign those values to variables d_max, d_min, and d_mean\n" + "-"*50+"\n")

d_max = d.max()
d_min = d.min()
d_mean = d.mean()

print(f"Max: {d_max}")
print(f"Min: {d_min}")
print(f"Mean: {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`.

#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("\n15. 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`.\n" + "-"*50+"\n")

f = np.empty((2,3,5),dtype=int)
print(f.size)
print(f.shape)
print(f)


"""
Expand All @@ -75,7 +118,34 @@
Note: you don't have to use Numpy in this question.
"""


print("\n16. Populate the values in f. For each value in d, if it's larger than d_min but smaller than.....\n" + "-"*50+"\n")


l1=0
l2=0
l3=0

for i in d:
for y in i:
for z in y:
if z > d_min and z < d_mean:
f[l1][l2][l3] = 25
if z > d_mean and z < d_max:
f[l1][l2][l3] = 75
if z == d.mean:
f[l1][l2][l3] = 50
if z == d_min:
f[l1][l2][l3] = 0
if z == d_max:
f[l1][l2][l3] = 100
l3+=1
l2+=1
l3=0
l1+=1
l2=0


print(f)


"""
Expand All @@ -99,6 +169,13 @@
[ 25., 75., 0., 75., 75.]]])
"""

print("\n17.Print d and f. Do you have your expected f?.....\n" + "-"*50+"\n")

print(d)
print("."*50)
print(f)

print("\nYES: f HAS EXPECTED VALUES COMING FROM COMPARISON OF d WITH d_ min,d_max,d_mean\n")

"""
#18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values
Expand All @@ -111,4 +188,36 @@
[ 'D', 'D', 'D', 'D', 'D'],
[ 'B', 'D', 'A', 'D', 'D']]])
Again, you don't need Numpy in this question.
"""
"""


print("\n18. Bonus question: instead of using numbers (i.e. 0, 25, 50, 75, and 100), how to use string values .....\n" + "-"*50+"\n")

f_string = np.empty((2,3,5),dtype=str)

l1=0
l2=0
l3=0

for i in d:
for y in i:
for z in y:
if z > d_min and z < d_mean:
f_string[l1][l2][l3] = "B"
if z > d_mean and z < d_max:
f_string[l1][l2][l3] = "D"
if z == d.mean:
f_string[l1][l2][l3] = "C"
if z == d_min:
f_string[l1][l2][l3] = "A"
if z == d_max:
f_string[l1][l2][l3] = "E"
l3+=1
l2+=1
l3=0
l1+=1
l2=0


print(f_string)