Programming, problem solving, and algorithms

CPSC 203, 2025 W1

October 7, 2025

Announcements

Today

Warm Up

Exploring pandas:

• Filtering Data
• Making charts
• Asking harder questions

Warm Up

datetime: a Python library that simplifies date computation.

Objects we need

• date — a calendar day (year-month-day)
• timedelta — a number of days you can add/subtract

Core calls

• today=date.today(): today’s date
• today.weekday(): an integer 0..6 with Mon=0, … , Sun=6
• today - timedelta(days=k): the date k days earlier

Billboard constraint - The library expects Saturday chart dates. We need the most recent Saturday at or before today.

Last Saturday

Let \(w =\) today.weekday() \(\in \{0,\dots,6\}\) and note Saturday \(= 5\).

offset: How many days to step back to reach Saturday.

• If today is Saturday (\(w=5\)): offset \(=0\)
  
• If today is Sunday (\(w=6\)): offset \(=1\)
  
• If today is Monday (\(w=0\)): offset \(=2\)
  
• …

\[ \texttt{offset} = (w - 5)\bmod 7 \]

\[ \texttt{lastSaturday} = \texttt{today} - \texttt{timedelta(days=offset)}. \]

Code

Data Frames

pandas and data frames

import pandas

Instead of saying pandas.read_csv('file.csv')

we can say

 

This function returns a dataframe containing the data from file.csv.

To give a name to that dataframe, we say:

CSV files

To implement: df = pd.read_csv('bill_week.csv')

bill_week.csv must have field names in row 1, and data beginning in row 2.

bill_week.csv

,week,title,artist,rank,last_week,peak_pos,weeks_on_chart
0,2019-09-21,Truth Hurts,Lizzo,1,1,1,19
1,2019-09-21,Senorita,Shawn Mendes & Camila Cabello,2,2,1,12
2,2019-09-21,Goodbyes,Post Malone Featuring Young Thug,3,10,3,10
3,2019-09-21,Circles,Post Malone,4,7,4,2
4,2019-09-21,Bad Guy,Billie Eilish,5,3,1,24
5,2019-09-21,Ran$om,Lil Tecca,6,4,4,15
6,2019-09-21,No Guidance,Chris Brown Featuring Drake,7,6,6,14

Selecting rows

• df.nlargest(10,'last_week')

  Returns top 10 hits from last week.

• df[ df['weeks_on_chart'] > 10 ]

  Returns all songs that have been on the charts for more than 10 weeks.

Adding a column

• df['gradient'] = df['last_week'] - df['rank']

  Adds a column to the DataFrame containing the difference for every row.

• df[ df['gradient'] > 10 ]

  Returns all songs that have moved more than 10 spaces in the last week.

Some challenges…

Given last week’s chart:

1. How many new songs were there?

2. What’s the average peak?

3. Among those who were on the list for more than 10wk, what’s the average peak? (Is it very different than the previous answer?)

4. Which song changed the most? Was it rising or falling?

5. Write and answer your own question:

   _____________________________________________________

df2 = pd.read_csv('bill_week.csv')
df3 = df2[ df2['weeks_on_chart'] > 10 ]
print("all:  ")
print(df2)
print("endurance:  ")
print(df3)

df4 = df2[ df2['last_week'] == 101].count()['title']
df5 = df3['peak_pos'].mean()
print("new count")
print(df4)
print("peak avg")
print(df5)

Let’s Write Code

Open CA5.2 Billboard, and load class_nb.py.

PrairieLearn Activity

Some challenges…

How many new songs are there?

C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C

Filter, apply function, think about output (sometimes means choosing columns, too)

df[df['last_week'] == 101][['title', 'artist', 'weeks_on_chart']] .count()[‘title']

Some challenges…

What’s the average peak?

C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C

df5 = df3['peak_pos'].mean()

Some challenges…

Among those who were on the list for more than 10wk, what’s the average peak? (Is it very different than the previous answer?)

C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C

enduring_peak = df[df['weeks_on_chart']>=10]['peak_pos'].mean()
print("enduring avg peak: " + str(enduring_peak))

Some challenges…

Which song moved the most? Did it rise or fall?

C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C
C	C	C	C	C	C

df['gradient'] = df['rank'] - df['last_week']
print(df.loc[df['gradient'].abs().idxmax()])

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