Provide the console output of the routines that you have implemented in the text input box in the format as specified in the problems (whenever required).
Provide a pdf file of the iPython notebook of your code. Check the following link for a convenient way to convert an iPython notebook to a pdf (Links to an external site.) document:
Create an iPython notebook to work on the following problems. [How To link: https://www.dataquest.io/blog/jupyter-notebook-tutorial/ (Links to an external site.)]
Clearly put the problem numbers in appropriate headers and sub-headers on the notebook.
Do not display information that is not being sought.
Images or data files, if any, should be kept in folders such as “./images/im_name.jpg” or “./data/data_file.cvs.”
You should build upon the code already provided in the updated notebook (the link to Google Drive is on the Module now) in this module wherever appropriate .
If you have any questions etc., bring it on the discussion board for this Module.
Please note that peer-review is only learning from other’s work. You can comment and critique. Grading will be performed by the grader.
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cIn the text input box, provide the console output of the procedures you’ve built in the format indicated in the problems (whenever required).
Provide a pdf file of your code’s iPython notebook. For a quick approach to convert an iPython notebook to a pdf (Links to an external site.) document, see the following link:
Make an iPython notebook to work on the problems below. (Links to an external site.)] [How to link: https://www.dataquest.io/blog/jupyter-notebook-tutorial/
Put the problem numbers in the notebook’s proper headers and subheaders.
Do not display information that is not being sought.
Images or data files, if any, should be kept in folders such as “./images/im_name.jpg” or “./data/data_file.cvs.”
You should build upon the code already provided in
