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.
• Acceso a internet en el ambiente de evaluación:
  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.
• Ejecución de la funcionalidad 'Magic':
  Magic son funciones no permitidas al momento de la evaluación. Por ejemplo la siguiente línea:
  %matplotlib inline
  In case your notebook contains some lines like this, they will be automatically removed to avoid runtime execution errors.
• Ejecutar comandos:
  Python Notebooks provide a functionality to execute commands like these:
  !ls
!pwd
  However, this kind of lines are transformed as follows:
  !ls es transformado en el siguiente código:
  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
  Cuando usa Google Colaboratory (Colab) y usa unos módulos específicos de Google Colab, estos no son soportados por UNCode. Asegúrese de que esos módulos no se incluyen al momento de realizar el submission.