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Simple pandas Operations In Jupyter Notebook

Explore using pandas to explore a set of data and the information that can be gleaned from it. In this lab, we use `.describe` and `.head` to determine what the data looks like. We will also use `.concat` to add a calculated column and `boolean slicing` to further look for insights into what the data shows us. The PDF of the notebook for this lab is [here.](

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Path Info

Clock icon Beginner
Clock icon 45m
Clock icon Mar 13, 2020

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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:


    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.

  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.


    Determine how many are above 25 percent.

  4. Challenge


    Use unique to determine the names of the waitstaff.

  5. Challenge

    Tips Recieved

    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()

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