Updating week 5 after lecture 5A.

UBC-CS · Feb 5, 2025 · 4722847 · 4722847
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diff --git a/files/Lec09_WebAsData.pdf b/files/Lec09_WebAsData.pdf
diff --git a/notes/week05/billboard_demo.py b/notes/week05/billboard_demo.py
@@ -0,0 +1,62 @@
+# This is a copy of the solution for A_demo.py from the Class 5A workspace.
+
+import pandas as pd
+import billboard
+
+# We will write some code to get the billboard 100 for last week using the billboard.py package
+
+chart = billboard.ChartData('hot-100','2024-02-06')
+# print(chart) # messy but complete!
+
+song = chart[0]
+print(song.title)
+
+# didn't work! Why?
+# Hint: look at what song is/was
+for i in range(len(chart)):
+    print(song.artist)
+
+# Print out only the chart artists!
+for song in chart:
+    print(song.artist)
+
+# Task: Create a list of dictionaries
+#       with each dictionary being one song
+#       The attributes we want are:
+#       title, artist, peakPos, lastPost, weeks, rank, isNew
+
+chart_songs = []
+
+for song in chart:
+    testdict = {}
+
+    testdict['title'] = song.title
+    testdict['artist'] = song.artist
+    testdict['peakPos'] = song.peakPos
+    testdict['lastPos'] = song.lastPos
+    testdict['weeks'] = song.weeks
+    testdict['rank'] = song.rank
+    testdict['isNew'] = song.isNew
+
+    chart_songs.append(testdict)
+
+print(type(chart_songs),len(chart_songs)) # it works!
+
+# Task: Convert list of dictionaries to a dataframe
+
+df = pd.DataFrame(chart_songs)
+
+print(type(df),len(df)) # Good! It's a dataframe!
+
+print(df)
+
+# filtering examples
+# print only rows where weeks < 10
+
+print(df[df['weeks']<10])
+
+# Task: Export to CSV
+df.to_csv('top100.csv')
+
+# Remove the index column
+df.to_csv('top100.csv', index=None)
diff --git a/notes/week05/class5A.md b/notes/week05/class5A.md
@@ -1,20 +1,40 @@
 # Class Meeting 5A
 
+Due to a weather disruption, today's class was presented remotely via zoom.  [Link to zoom recording](https://ubc.zoom.us/rec/share/xfO_r8AAmjtVhAdAuCTZJrJwpozieJMpxeFOeiKZ2XTqPAkWQexp3qypifZOxtYc.w0hkvsDLdlRNdkW7) and you will need passcode `n4vvf$WB` to access it.  *Note:* There is no Panopto recording for today's lecture.
+
+
 Below are the slides from today's class embedded.
-Feel free to download them to keep them locally, or leave them archived here and just bookmark them.
-We will leave the website open even after the course is over for a reasonable number of years.
 
 <div>
 <iframe src="../../Lec09_WebAsData.pdf" width="100%" height="600px" frameBorder="0"> </iframe>
 </div>
 
 [Download the Slides from today](https://github.com/ubc-cs/cpsc203/raw/main/files/Lec09_WebAsData.pdf)
 
+During the lecture we played around with the `ChartData` and `ChartEntry` classes from the `billboard` module.  I wrote and deleted a lot of code as we explored the VSCode environment and these classes, but here is the final version of the code.  It should print one line for each entry in the Hot-100 list showing the current position and the position for the previous week.
+
+```python
+import pandas as pd
+import billboard
+
+# We will write some code to get the billboard 100 for last week using the billboard.py package
+chart = billboard.ChartData('hot-100','2025-01-04')
+
+for i, s in enumerate(chart.entries):
+    print("position " + str(i) + ", last week: "+ str(s.lastPos))
+```
+
+Your task to complete *before* Thursday's lecture is:
+* Start with the data in `chart.entries: List[ChartEntry]`, which has one `ChartEntry` object for each song in the Hot-100 list.
+* Create a variable `chart_songs: List[Dict]` with one dictionary for each song in the Hot-100 list.
+* Each dictionary should have one key for each attribute of the corresponding `ChartEntry` object: `title`, `artist`, `peakPos`, `lastPos`, `weeks`, `rank`, `isNew`.  The value for each key should be the value for the corresponding attribute for that song.
+
+In other words, you are coverting from `ChartEntry` objects to dictionary objects.  On Thursday we will load those dictionaries into a Pandas dataframe and then play around with the data.
+
 ## Links for today
 
-- [beautifulsoup library](https://pypi.org/project/beautifulsoup4/) and [Beautiful Soup documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/).
-- [billboard library](https://github.com/guoguo12/billboard-charts).
-- [LOTS More on Pandas](https://firas.moosvi.com/courses/data301/2022_WT2/notes/week05/Class5A/Class5A.html) (courtesy of Dr. Moosvi's former DATA 301 course at UBC-O).
+- [beautifulsoup library](https://pypi.org/project/beautifulsoup4/) (and [Beautiful Soup documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)).
+- [billboard library](https://github.com/guoguo12/billboard-charts) (and [The Python Package Index version](https://pypi.org/project/billboard.py/))
 
 <!--
 ## Important links for today:

diff --git a/notes/week05/class5B.md b/notes/week05/class5B.md
@@ -10,7 +10,8 @@ We will leave the website open even after the course is over for a reasonable nu
 
 [Download the Slides from today](https://github.com/ubc-cs/cpsc203/raw/main/files/Lec10_Plotting_DataFrames.pdf)
 
-## Optional links for today
+## Links for today
+- [LOTS More on Pandas](https://firas.moosvi.com/courses/data301/2022_WT2/notes/week05/Class5A/Class5A.html) (courtesy of Dr. Moosvi's former DATA 301 course at UBC-O).
 
 <!--
 ## Important links for today: