Exploring Mathematics and Data Science with Technology

(a)

Copy this list into a Code cell, and add data for at least three more students to the list.

(b)

Data is often stored in a spreadsheet or database as a table. An illustration of this is given in 3.1.4.

Tables can be created programmatically by using the technique from 3.1.1 or by modifying 3.1.5.

Table(labels=["Name", "Age", "Favorite Teacher"]).with_rows([
    ["Jessica", 19, "Dr. Clontz"],
    ["Lynn", 18, "Ms. Gilbreath"],
    ["Julia", 17, "Mr. Fullinwider"],
])

Run from datascience import Table in a Code cell, and then copy 3.1.5 to another Code cell. Finally, append the student data you created in the previous task.

(c)

Since data usually isn't hard-coded into a script, but instead entered by folks who may not be programmers into a spreadsheet or similar file, we will need a way to read external files containing data.

Open a spreadsheet application such as Microsoft Excel or Google Sheets. Copy the table of data displayed in the previous task and paste it into your spreadsheet.

Save the spreadsheet to a Comma-Separated Values file named student_data.csv and use the (+) New menu to upload it to CoCalc in the same folder as this Jupyter notebook.

Finally, create a Code cell with the line Table.read_table('student_data.csv') to check that your CSV data can be imported into a datascience table.

(a)

Visit the website https://vincentarelbundock.github.io/Rdatasets/datasets.html to get a CSV with the transactions for a (fictional) pizza parlor for the year 2015. (Hint: Use Ctrl+F to search for “pizza”.) If you like, you can download the CSV and view it in a spreadsheet program such as Microsoft Excel.

For this activity however, you should right-click and copy the URL to the CSV file. In a Code cell, save this URL to the CS variable named pizza_url, and then import this data directly by running pizza_table = Table.read_table(pizza_url) and pizza_table.show(20).

(b)

There are almost fifty thousand transactions stored in this dataset. You could write pizza_table.show() without specifying how many records to to try and show them all, but CoCalc will complain that 50,000 rows are too many.

Insert an integer into the .show() tool to display exactly one hundred rows from the dataset.

(c)

The code pizza_table.where("date","2015-02-01").show(10) allows you to view the first ten records of a pizza purchased on Feburary 1, 2015.

Display the first 20 pizza pies purchased on \(\pi\)-Day (that is, March 14, 2015).

(d)

You can filter by any column of the table by modifying the where() arguments. Display the first 20 medium pizzas purchased in this parlor.

(e)

Display the first 20 “Mexicana” pizzas purchased in this parlor. (Hint: make sure you filter the data based on how its string appears in the table.)

(f)

Chain two where()s and a show() together to display the first 20 large-sized veggie pizzas purchased in this parlor.

(g)

Running pizza_table.where("date","2015-07-22").sort("size").show() displays the large pizzas sold on July 22nd first, followed by medium, small, and extra-large pizzas (in that order). Running pizza_table.where("date","2015-07-22").sort("size",descending=True).show() displays them in the order extra-large, small, medium, then large.

Write a sentence or two explaining why you suspect the datascience library orders the pizzas in this way.

(h)

Display all the Hawaiian pizzas ordered on April 12, once in increasing order, and once in descreasing order.

(a)

Save a plain-text copy of Peter Pan by J. M. Barrie as the file peterpan.txt in the same folder as this notebook by using the link https://www.gutenberg.org/ebooks/16. This task may be confirmed by running f=open("peterpan.txt"); print(f.readline()); f.close() in a Code cell.

(b)

Open the file and manually delete the header and footer information, so the text file begins at the start of “Chapter 1” and ends with the words “THE END”. Re-run print(open("peterpan.txt").readline()), and you should see the line Chapter 1 PETER BREAKS THROUGH.

(c)

Fix the code in 3.1.7 to save the chapters of the book to a datascience table.

with open("peterpan.txt") as book_file:
    # Fix the order of the following lines
    book_table = Table().with_column("Chapter", book_chapters) # creates table of chapters
    book_chapters = book_string.split("Chapter ")[1:] # makes list of strings for each chapter
    book_table.show() # displays table
    book_string = book_file.read() # saves contents of the text file to a string

(d)

The code in 3.1.8 counts how many times Peter Pan is mentioned in each chapter of the book. Extend this code to also count how many times the Darling siblings Wendy, Michael, and John are mentioned in each chapter.

book_table = book_table.with_columns([
    "Peter", [c.count("Peter") for c in book_chapters],
])
book_table.show()

(e)

Sort this table to find the chapter where Wendy is mentioned the most, and the chapter where she is mentioned the least.