Table of Contents

1. Challenge

   Start Jupyter Notebook Server and Access on the Local Machine

   Connecting to the Jupyter Notebook Server

   Make sure the virtual environment it activated!

   To activate the virtual environment:

   conda activate base
   

   To start the server:

   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 the local machine.

   On the Local Machine

   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 for a password. This is the password used to log in to the Playground remote server.

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

   In the browser, enter http://localhost:8087 in the address bar. This will open a Jupyter Notebook site that asks for the token copied from the remote server.

2. Challenge

   Examine the Data

   Open the notebook hol_2_2_l.

   In the cell below, take the data and make it a pandas DataFrame.

   import pandas as pd
   restaurant_sales_data = pd.read_csv("./tips_csv.txt", header=0)
   

   Examine the data using head and describe.

   restaurant_sales_data.describe()
   

   restaurant_sales_data.head()
   

3. Challenge

   Tip Percent

   Create a series with the tip percent.

   percent_tip = pd.Series(restaurant_sales_data['tip']/restaurant_sales_data['meal_total'],name='tip_percent')
   

   Create a new DataFrame containing the original data and the new tip percents.

   rsd_per_tips = pd.concat([restaurant_sales_data, percent_tip], axis=1)
   

   Use head to make sure the DataFrame looks correct.

   rsd_per_tips.head()
   

   Determine how many are above 25 percent.

   rsd_per_tips[rsd_per_tips.tip_percent>=0.25].count()
   

4. Challenge

   Waitstaff

   Use unique to determine the names of the waitstaff.

   restaurant_sales_data.wait_staff.unique()
   

5. Challenge

   Tips Received

   For each waitstaff, determine the total tips and the average tips.

   Create a dataframe for each waitstaff that holds only their personal data.

   marcia = restaurant_sales_data[restaurant_sales_data.wait_staff=='Marcia']
   jan = restaurant_sales_data[restaurant_sales_data.wait_staff=='Jan']
   greg = restaurant_sales_data[restaurant_sales_data.wait_staff=='Greg']
   bobby = restaurant_sales_data[restaurant_sales_data.wait_staff=='Bobby']
   peter = restaurant_sales_data[restaurant_sales_data.wait_staff=='Peter']
   cindy = restaurant_sales_data[restaurant_sales_data.wait_staff=='Cindy']
   

   Calculate and print the total tips and average tips for each staff member.

   print(f"Marcia:\t ${marcia.tip.sum():.2f}\t Average: ${marcia.tip.mean():.2f}")
   print(f"Jan:\t ${jan.tip.sum():.2f}\t Average: ${jan.tip.mean():.2f}")
   print(f"Greg:\t ${greg.tip.sum():.2f}\t Average: ${greg.tip.mean():.2f}")
   print(f"Bobby:\t ${bobby.tip.sum():.2f}\t Average: ${bobby.tip.mean():.2f}")
   print(f"Peter:\t ${peter.tip.sum():.2f}\t Average: ${peter.tip.mean():.2f}")
   print(f"Cindy:\t ${cindy.tip.sum():.2f}\t Average: ${cindy.tip.mean():.2f}")
   

6. Challenge

   Extra Credit

   Determine the average tip percent based on weekday and meal type.

   rsd_per_tips.groupby(['weekday', 'meal_type']).tip_percent.mean()