Skip to content

PP-MX-FT(Luis G - Rodolfo) #9

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
rodolfopardo wants to merge 1 commit into
base: master
Choose a base branch
from
Open

PP-MX-FT(Luis G - Rodolfo) #9

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
150 changes: 92 additions & 58 deletions your-code/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,114 +1,148 @@
#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.randint(0,9, size=(2,3,5))

#4. Print a.

print(a)


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



b = np.ones((5,2,3), dtype = int)



#6. Print b.

print(b)
print(b.shape)


#7. Do a and b have the same size? How do you prove that in Python code?

#7. Do a and b have the same size? How do you prove that in Python code?

if a.size == b.size:
print("Son iguales")
else:
print("No son iguales")


#8. Are you able to add a and b? Why or why not?


print("No soy capaz porque ambos tienen diferentes dimensiones")
print(a.shape)
print(b.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".

print(c)
print(c.shape)

c = np.reshape(b, (2,3,5))

#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)
print("Lo puedo lograr el metodo add debido a que tengo dos arrays con iguales dimensiones")

#11. Print a and d. Notice the difference and relation of the two array in terms of the values? Explain.

print(a)
print(d)

print("A los valores de A fueron sumados por los valores de b original para obtener una sumatoria que es d")


#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?


if e.all() == a.all():
print("Son iguales porque todo numero multiplicado por 1 da el mismo numero")
else:
print("Son distintos")


#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(d_max)
print(d_min)
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), int)
print(f)


#16



for x,ele in enumerate(d):
for y, ele1 in enumerate(ele):
for z, ele2 in enumerate(ele1):
print(ele2)
if ele2 > d_min and ele2 < d_mean:
f[x,y,z] = 25
if ele2 > d_mean and ele2 < d_max:
f[x,y,z] = 75
if ele2 == d_mean:
f[x,y,z] = 50
if ele2 == d_min:
f[x,y,z] = 0
if ele2 == d_max:
f[x,y,z] = 100



#17

print(d)
print(f)

#18

nueva_lista = np.empty((2,3,5), dtype=object)


for x,ele in enumerate(d):
for y, ele1 in enumerate(ele):
for z, ele2 in enumerate(ele1):
print(ele2)
if ele2 > d_min and ele2 < d_mean:
nueva_lista[x,y,z] = "B"
if ele2 > d_mean and ele2 < d_max:
nueva_lista[x,y,z] = "D"
if ele2 == d_mean:
nueva_lista[x,y,z] = "A"
if ele2 == d_min:
nueva_lista[x,y,z] = "C"
if ele2 == d_max:
nueva_lista[x,y,z] = "E"
print(nueva_lista)



"""
#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.
"""




"""
#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],
[1.72201435, 1.1862918 , 1.87078449, 1.7726778 , 1.88180042]],

[[1.44747908, 1.31673383, 1.02000951, 1.52218947, 1.97066381],
[1.79129243, 1.74983003, 1.96028037, 1.85166831, 1.65450881],
[1.18068344, 1.9587381 , 1.00656599, 1.93402165, 1.73514584]]])

Your f should be:
array([[[ 75., 75., 75., 25., 75.],
[ 75., 75., 25., 25., 25.],
[ 75., 25., 75., 75., 75.]],

[[ 25., 25., 25., 25., 100.],
[ 75., 75., 75., 75., 75.],
[ 25., 75., 0., 75., 75.]]])
"""


"""
#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']],

[[ 'B', 'B', 'B', 'B', 'E'],
[ 'D', 'D', 'D', 'D', 'D'],
[ 'B', 'D', 'A', 'D', 'D']]])
Again, you don't need Numpy in this question.
"""