Warmest 100

Justin Warren gave a talk at Pycon on the Warmest 100, which is an attempt at predicting the Triple J Hottest 100 by analysing social media posts.

I’ve often wondered if the listeners only vote for what’s played on the radio the most. So I set out to see if that was true. (It’ll give me a chance to try Python’s pandas library.)

Gathering the data

I needed two lists:

  1. The Hottest 100 for 2015.
  2. The Triple J playlist for 2015.

Getting the Hottest 100 results

The list was scraped from the Hottest 100 2015 web page, using BeautifulSoup.

curl 'http://www.abc.net.au/triplej/hottest100/15/countdown/' > results.html
from bs4 import BeautifulSoup
import re
import pandas

def get():
    results = []
    with open('results.html') as f:
        for t in BeautifulSoup(f, 'html.parser').find_all('div', class_='hottest-countdown-track'):
            # Extract position, artist, title
            pos = t.find(class_='hottest-countdown-position').string
            title = t.find(class_='title').string
            artists = [t.find(class_='artist').string]
            # Sanitize (pull featured artists from title)
            matches = re.search(r' {Ft. ([^}]+)}', title)
            if (matches):
                artists += matches.group(1).split('/')
            title = re.sub(r' {Ft. ([^}]+)}', '', title)
            # Append to list of dicts
                {'rank': pos, 'name': title, 'artists': tuple(sorted(artists))})
    # Return as a DataFrame
    return pandas.DataFrame(results)

I cleaned it up a bit, such as removing {Radio edit} from one song title.

Getting the playlist for all of 2015

Justin pointed me to @triplejplays which is a feed of songs being played, but it turns out Triple J has an api which lets me enter a date range, and gives json back with plenty more data if needed.

Because the api is used for lazy-loading, it only returns 10 songs at a time. So to get the full year’s playlist took 9833 requests.


# Loop from start to end date
while [[ $(date -d $start +"%s") -lt $(date -d $end +"%s") ]]; do
    echo $start >&2
    # The URL and output filename
    # Download the list
    curl $url > $output_fn
    # Get the time of the last song played
    start=$(jq -r '.items[].played_time' $output_fn |tail -n1)
    # Add one second so it doesn't include the last song next time
    start=$(date -u -d "$start + 1 second" +"%Y-%m-%dT%H:%M:%S")

It pulled in 1.3G of json data. I used jq to extract just the parts we needed, (title and artist):

for i in raw/*; do echo $i; jq -r '[.items[].recording | {name: .title, artists: [.artists[].name]}]' $i > clean/$(basename $i); done

Did a bit of manual data sanitizing at this point, like replacing “DOWNTOWN” with “Downtown”. The data wasn’t great, and I didn’t spend much time cleaning it up.

Eventually we have the playlist - 16261 songs played 98330 times.


I’ll load the hottest and warmest lists and merge them.

>>> import hottest, warmest
>>> h = hottest.get()
>>> w = warmest.get()
>>> m = h.merge(w, on='name', how='left', suffixes=('', '_plays'))[['rank_votes', 'name', 'plays', 'rank_plays']]

Now we can look at the top 10 voted songs:

>>> m.head(10)
    rank_votes                        name  plays  rank_plays
  0          1                       Hoops    145        50.5
  1          2                  King Kunta    138        76.0
  2          3                     Lean On    171        13.5
  3          4  The Less I Know The Better    153        32.0
  4          5               Let It Happen    109       227.5
  5          6         The Trouble With Us    115       190.5
  6          7             Do You Remember    181         6.0
  7          8                    The Buzz    122       148.0
  8          9          Can't Feel My Face    119       166.5
  9         10                     Magnets    139        70.0

From this it looks like there is not a strong relationship between plays and votes. A song like Let It Happen was voted 5th even though it was the 227th most played song. Maybe it was played during peak times?

On the other hand, Lean On and Do You Remember are near the top in both plays and votes.

Let’s look at the bottom 10:

>>> m.tail(10)
      rank_votes                    name  plays  rank_plays
   90         91          Be Your Shadow    120       159.0
   91         92                  No One    232         3.0
   92         93           Indian Summer    110       220.0
   93         94                   Ghost    236         2.0
   94         95  Nobody Really Cares...    124       137.5
   95         96             Rumour Mill    124       137.5
   96         97        Twilight Driving    121       153.0
   97         98             Be Together    120       159.0
   98         99            True Friends     90       335.5
   99        100                Hell Boy    115       190.5

Here we see Golden Features No One and Halsey’s Ghost were second and third most played songs, but rated lowly in the vote.

“People only vote for what gets played on the radio” - this is somewhat true. There are no songs in the Hottest 100 that were not played on Triple J. But Courtney Barnett’s Depreston made the list after being played only 23 times. Big Jet Plane made it after being played only 12 times. So it’s not fair to say that people only vote for songs that were on high rotation throughout the year.