Programming, problem solving, and algorithms

CPSC 203, 2026 W1

October 6, 2026

Announcements

• Practice examlet 2 is available.
  
• Examlet 2 next week (Thursday Oct 15 to Monday October 19).
• Lab 4 and POTW 4 due Sunday October 11, 8pm.
• Project 1 due Monday October 26, 8pm.

Part 1: Song History

The Question

Given a year of charts,

• What do the paths of the number ones look like?

• Do most songs debut at the top? How fast do they fall?

We can quantify these things, and analyze them, but a picture will help us see if there’s anything interesting in the data.

We are asking harder questions about our data:

• Working with multiple weeks of data.
• Visualizing song trajectories.

In the process, we will practice the stuff we learned previously, and learn some new stuff.

First: Sketch Where We Are Going

Draw a loose sketch of how you might visualize the paths through the Hot 100 chart for all songs which hit #1.

• What are some reasonable axes?
• Imagine what the path of one song looks like.
• How many paths are there for all songs?

• x-axis is time, y-axis is rank. But #1 should be at the top on the y-axis.
• One line per song.
• What happens when a song does not have a rank?

Key Steps

1. Assemble the data we want to analyze.
2. Find the list of Number 1 songs.
3. For each of those songs, make a list of their ranks over time.
4. Rearrange the data into a form suitable for plotting.
5. Generate the plot.

• Launch Class Activity: Billboard (Visualization) in PrairieLearn.
• Open challenge1_nb.py.

Step 3: Filter for Multiple Possible Values

We have a list of song titles that hit #1.

Now we want all rows for those songs — their complete chart history.

Useful Pandas: .isin()

.isin(list) method returns True for rows where the value appears in the list.

We’ve already loaded some data behind the scenes…

Step 4: Reshaping for Plotting

Our data looks like this:

• One row per song per week.
• Multiple rows for each week.
• Multiple rows for each song.

date	title	rank
2025-01-04	Song A	1
2025-01-04	Song B	15
2025-01-11	Song A	3
2025-01-11	Song B	1

But for plotting we want

• One row per week (x-axis value).
• One column per song (one line).
• Rank is no longer a column.

date	Song A	Song B
2025-01-04	1	15
2025-01-11	3	1

Pivoting is a very common operation on tabular data.

• The unique entries in one input column (or perhaps a combination of input columns) become the rows of the output.
• The unique entries in another input column (or perhaps a combination of input columns) become the columns of the output.
• Some aggregation of the entries in yet another column (or perhaps a combination of input columns) for the matching rows become the entries of the output.
• This case is a particularly simple pivot because each entry in the output table corresponds to a single row of the input table.

This part of the code is magic Pandas for me.

Useful Pandas

• groupby() method organizes rows into groups. In this case, one group per (date, title) pair.
  • Result is a Series with a multi-level index (date, title).
  • Each group has exactly one row, so sum() method just extracts that row’s value.
• unstack() method takes the inner index level (title) and makes it into columns in a DataFrame.
  • Now each column is a song, each row is a date!

Try It Yourself

• Launch Class Activity: Billboard (Visualization) in PrairieLearn.
• Open STUDENT_viz_nb.py and work through “Part 1: Visualizing Chart Trajectories”.

Basically the same steps as above, but gives a better idea of what kind of code we would expect you to be able to read vs write from scratch.

Visualization Discussion

• What patterns do you see in the #1 songs?
• Do most songs debut at the top?
• How quickly do they typically fall?
• Any songs with unusual trajectories?

Part 2: Staying Power

The Question

Does a strong debut mean a song will stick around longer?

Or do slow climbers have more staying power?

Let’s investigate with a scatter plot!

Key Steps

1. Assemble the data we want to analyze.
2. Find the debut position of each song.
3. Find the total number of weeks each song stayed on the chart.
4. Rearrange the data into a form suitable for plotting.
5. Generate the plot.

• Launch Class Activity: Billboard (Visualization) in PrairieLearn.
• Open challenge2_nb.py.

Useful Pandas

• rename() method allows you to rename columns.
• toframe() method can convert a Series into a DataFrame.
• pd.merge() function can combine the data in two tables.

• Often we don’t need to worry about Series vs DataFrame. Pandas is surprisingly forgiving.

Visualization Discussion

• Is there a correlation between debut position and staying power?
• Any outliers — songs that debuted low but stayed forever?
• Or songs that debuted at #1 but disappeared quickly?

Practice for yourself:

• Launch Class Activity: Billboard (Visualization) in PrairieLearn.
• Open STUDENT_viz_nb.py and work through “Part 2: Staying Power”.

Basically the same steps as above, but gives a better idea of what kind of code we would expect you to be able to read vs write from scratch.

Summary

Concept	Code
Filter by condition	df[df['rank'] == 1]
Get unique values	df['title'].unique()
Filter by list	df[df['title'].isin(list)]
Reshape for plotting	.groupby([...]).unstack()
Combining tables	pd.merge()

Resources

https://pymotw.com/2/datetime/

https://www.dataschool.io/best-python-pandas-resources/

https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

https://queirozf.com/entries/pandas-dataframe-plot-examples-with-matplotlib-pyplot