Table of Contents

1. Challenge

   Start Jupyter Notebook Server and Access on Your Local Machine

   Connecting to the Jupyter Notebook Server

   Make sure that you have activated the virtual environment!

   1. Use the following to activate the virtual environment:

   conda activate base
   

   2. To start the server, run the following:

   python get_notebook_token.py
   

   This is a simple script that starts the Jupyter notebook server and sets it to continue to run outside of the terminal.

   On the terminal is a token, please copy this and save it to a text file on your local machine.

   On Your Local Machine

   1. In a terminal window, enter the following:

   ssh -N -L localhost:8087:localhost:8086 cloud_user@<the public IP address of the Playground server>
   

   It will ask you for your password; this is the password you use to login to the Playground remote server.

   Leave this terminal open, it will appear nothing has happened, but it must remain open while you use the Jupyter Notebook server in this session.

   2. In the browser of your choice, enter the following address:

   http://localhost:8087

   This will open a Jupyter Notebook site that asks for the token you copied from the remote server.

2. Challenge

   Read the File Into a DataFrame

   # open file for reading
   f = open('dow_jones_index.data')
   
   # print the first two lines
   print(f.readline())
   print(f.readline())
   
   f.close()
   

   It appears the file is CSV. Read the file into a dataframe.

   import pandas as pd
   
   stock_df = pd.read_csv('dow_jones_index.data')
                          
   stock_df.head()
   

3. Challenge

   Create the Excel Workbook

   Create a dataframe for each of the requested stocks

   ge_df = stock_df[stock_df.stock=='GE']
   ibm_df = stock_df[stock_df.stock=='IBM']
   krft_df = stock_df[stock_df.stock=='KRFT']
   

   Write the Excel file

   with pd.ExcelWriter('stocks.xlsx') as writer:  
       ge_df.to_excel(writer, sheet_name='GE')
       ibm_df.to_excel(writer, sheet_name='IBM')
       krft_df.to_excel(writer, sheet_name='KRFT')
   
   

4. Challenge

   Check the Excel Workbook Contains the Requested Data

   Load the file into an ordered dict dataframe and then check that each worksheet is populated.

   my_stock_df = pd.read_excel('stocks.xlsx', sheet_name=None)  
   
   my_stock_df.keys()
   

   my_stock_df['GE']
   

   my_stock_df['IBM']
   

   my_stock_df['KRFT']