Parcial1_Ejercicio5

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Awesome! Everything worked as it should have: your code passed all the test cases. Congratulations!

Sorry, but your code failed to solve the problem. It does not solve correctly the problem you were asked for.
The output after running your code (what it prints to the screen), is different from the output the system expected.
If the administrator allowed it, you may be able to see the difference between your output and the expected output.
Tips: Read carefully what you are asked to do and check again your code.
Test your code with different inputs. You can do it in your machine or you can use the "custom input" in the tools section.

PRESENTATION ERROR

The data in your output is correct, but it is not formatted in the proper way. Check the problem statement to understand the correct format of the output.
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COMPILATION ERROR

Your code does not compile :/. Something is faulty in your code:
It may be a misplaced colon, bracket, quotation mark, or symbol; you may be using a reserved keyword as a variable; you may have chosen a different programming language; among other options.
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Tips: Check for infinite loops. Try to find another solution, one that takes less time to compute.

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OUTPUT LIMIT EXCEEDED

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Tips: Check for print expressions in a loop.

GRADING RUNTIME ERROR

Error related to notebook file submissions. This points out a runtime error while running the grading testing code.
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If you get this message, do not worry, There's Nothing Wrong With Your Code!
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We apologise in advance. Please reload the page and try submitting again! If you still get the same result, please wait a while and try again or contact your professor.

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If you get this, it means that we had a problem running and assessing your code. Wait for a while and try to send a submission again.
This might be also caused by a too small time limit in the task configuration (talk to your professor about it).

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El ejercicio debe ser resuelto adjuntando un archivo .ipynb sólo con la función que resuelva el problema. Tenga en cuenta que no puede usar funciones pre-establecidas de python

Enunciado del problema

Imagine que usted es el/la ingeniero/a que va a programar una máquina ubicada en un parqueadero que recibe el pago de los usuarios y da el cambio ("Devuelta","vueltas") en billetes y monedas

Escriba una función llamada "Maquina" que reciba las Horas de parquero H, la tarifa por hora T que el parqueadero aplica ese día y el valor V del billete que ingresa el usuario a la máquina (asuma que los usuarios sólo pueden pagar con billetes de 50.000, 20.000 o 10.000). Cada vez que un usuario realice un pago, el algoritmo de la máquina debe devolver la cantidad mínima de billetes y monedas posible. Debe devolver primero con los billetes que sea posible y el resto con monedas. (Asuma que la máquina tiene billetes de 20.000, 10.000, 5.000, 2.000, y que tiene monedas de 1.000, 500, 200, 100, 50 pesos). La función debe retornar la cantidad de billetes+monedas que la máquina le devuelve al usuario. Ejemplo: Si H es 3, T es 4500 y V es 20.000. La máquina debe devolver 6500 de la siguiente manera: 1 billete de 5.000, 1 moneda de 1000 y 1 moneda de 500. En este caso, la función "Maquina" debe retornar el número 3 (Dado que es 1 billete + 2 monedas).

SUPUESTOS: - Asuma que el usuario siempre va a ingresar un valor V mayor al monto total que debe pagar por sus horas de parqueo. - Asuma que el valor a pagar siempre será menor a 50 Mil pesos.

Ejemplos

Entrada Ejemplo 1

3,4500,20000

Salida Ejemplo 1

Entrada Ejemplo 2

1,3850,50000

Salida Ejemplo 2

Entrada Ejemplo 3

5,2550,20000

Salida Ejemplo 3

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In general, when submitting a notebook, all Python 3.6 code is supported on UNCode. However, Ipython notebooks and python code differ in some functionalities, then UNCode might support them or not. Also, there are some things you should bear in mind when creating your notebook.

Python modules:
You are able to use the next python modules in addition to the pre installed python modules:
import pandas, numpy, matplotlib, scipy, scikit-learn, seaborn, datascience, plotly, cufflinks, bokeh, tabulate, nltk, tensorflow, Keras

Installing modules:
All the modules and data you require are already installed and supported by UNCode.
Then, when you have lines installing a module, for instance: !pip install pandas or using conda, these lines are automatically removed from your notebook to avoid runtime execution errors.

In case you want to use one module and it is not in the previous list, do not hesitate on reaching out for support.
Print expressions:
Usually while executing your notebook on your runtime you might add some print expressions, though, these expressions are deleted and these outputs are not shown in the submission feedback.
Internet access in grading environment:
The internet access when your code is being graded is dependant on the task. Some tasks might allow it or not.
In case you are allowed to access internet, this mainly will work to download files or data, for instance datasets. As stated above, you cannot install modules.
In case there is not access, be careful when using code that requires internet access as it will fail while grading.
Magic functionality execution:
Magic functions are not allowed when your notebook is graded. For example the next line:
%matplotlib inline
In case your notebook contains some lines like this, they will be automatically removed to avoid runtime execution errors.
Commands execution:
Python Notebooks provide a functionality to execute commands like these:
!ls
!pwd
However, this kind of lines are transformed as follows:
!ls is turned into the following code:
import subprocess
call = subprocess.run(["ls"], stdout=subprocess.PIPE)
print(call.stdout.decode('utf-8')
That code will do the same for you as in the notebook. However, you are not allowed to assign the result of command execution to variables, for instance the next line will return an error:
result = !ls.
Google Colab modules
When you use Google Colaboratory (Colab) and you want to load and read data from Google Drive, you are required to use some python modules unique in Colab. These modules are not supported by UNCode. Then, make sure when you submit your notebook, you do not include it.

Max file size: 1.0MB
Extensions autorisées: .ipynb

Date limite	25/04/2022 09:00:55
Limite de soumission	No limitation