Music Taste Analysis

Ever been asked what sort of music you like and felt unable to describe it convincingly? This notebook represents my effort to once and for all answer the question, because, yes, I regard it to be this complicated.

How to Use

My first pass at this depended upon Watsonbox's Exportify, but I decided I didn't like his version because of bugs and inadequate output detail. So I went and forked it, cleaned up the code, and hosted it myself.

As such, the code here depends on .csv inputs in the format output by my version.

1. To get started, hop on over there, sign in to Spotify to give the app access to your playlists, and export whatever you like.
2. Next, either download this .ipynb file and run the notebook yourself or launch it in Binder.
3. Either put the downloaded .csv in the same directory as the notebook, or upload it in Binder.
4. Open the .ipynb through your browser, update the filename variable in the first code cell to point to your playlist instead, and shift+enter in each following code cell to generate the corresponding plot. (Or select Cell -> Run All from the menu to make all graphs at once.)

Read the Data

For years I've been accumulating my favorite songs in a single master playlist called music that tickles my fancy. It's thousands of songs. This is what I'll be analyzing. Let's take a look at the first few rows to get a sense of what we're dealing with.

filename = 'music_that_tickles_my_fancy.csv'

from matplotlib import pyplot
import seaborn
import pandas
from collections import defaultdict
from scipy.stats import pareto, gamma
from datetime import date

# read the data
data = pandas.read_csv(filename)
print("total songs:", data.shape[0])
print(data[:3])

total songs: 3088
               Spotify ID                                     Artist IDs  \
0  3T9HSgS5jBFdXIBPav51gj  0nJvyjVTb8sAULPYyA1bqU,5yxyJsFanEAuwSM5kOuZKc   
1  2bdZDXDoFLzazaomjzoER8                         1P6U1dCeHxPui5pIrGmndZ   
2  1fE3ddAlmjJ99IIfLgZjTy                         0id62QV2SZZfvBn9xpmuCl   

                   Track Name  \
0  Fanfare for the Common Man   
1            Highschool Lover   
2             I Need a Dollar   

                                          Album Name  \
0  Copland Conducts Copland - Expanded Edition (F...   
1                                    Virgin Suicides   
2                                    I Need A Dollar   

                            Artist Name(s) Release Date  Duration (ms)  \
0  Aaron Copland,London Symphony Orchestra         1963         196466   
1                                      Air         2000         162093   
2                               Aloe Blacc   2010-03-16         244373   

   Popularity              Added By              Added At  ... Key  Loudness  \
0          41  spotify:user:pvlkmrv  2014-12-28T00:57:17Z  ...  10   -15.727   
1           0  spotify:user:pvlkmrv  2014-12-28T00:59:35Z  ...   1   -15.025   
2           3  spotify:user:pvlkmrv  2014-12-28T01:03:38Z  ...   8   -11.825   

   Mode  Speechiness  Acousticness  Instrumentalness  Liveness  Valence  \
0     1       0.0382         0.986             0.954    0.0575   0.0378   
1     0       0.0302         0.952             0.959    0.2520   0.0558   
2     0       0.0384         0.178             0.000    0.0863   0.9620   

     Tempo  Time Signature  
0  104.304               4  
1  130.052               4  
2   95.516               4  

[3 rows x 23 columns]

Artist Bar Chart

Number of songs binned by artist.

# count songs per artist
artists = defaultdict(int)
for i,song in data.iterrows():
	for musician in song['Artist Name(s)'].split(','):
		artists[musician] += 1

# sort for chart
artists = pandas.DataFrame(artists.items(), columns=['Artist', 'Num Songs']
                          ).sort_values('Num Songs', ascending=False).reset_index(drop=True)
print("number of unique artists:", artists.shape[0])

pyplot.figure(figsize=(18, 6))
pyplot.bar(artists['Artist'], artists['Num Songs'])
pyplot.xticks(visible=False)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('everybody')
pyplot.show()

number of unique artists: 1387

Note I've attributed songs with multiple artists to multiple bars, so the integral here is the number of unique song-artist pairs, not just the number of songs.

It seems to follow a Pareto distribution. Let's try to fit one.

# Let's find the best parameters. Need x, y data 'sampled' from the distribution for
# parameter fit.
y = []
for i in range(artists.shape[0]):
	for j in range(artists['Num Songs'][i]):
		y.append(i) # just let y have index[artist] repeated for each song 

# sanity check. If the dataframe isn't sorted properly, y isn't either.
#pyplot.figure()
#pyplot.hist(y, bins=30)
        
# The documentation is pretty bad, but this is okay:
# https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-
# ones-with-scipy-python
param = pareto.fit(y, 100)
pareto_fitted = len(y)*pareto.pdf(range(artists.shape[0]), *param)
# param = gamma.fit(y) # gamma fits abysmally; see for yourself by uncommenting
# gamma_fitted = len(y)*gamma.pdf(range(artists.shape[0]), *param)

pyplot.figure(figsize=(18, 6))
pyplot.bar(artists['Artist'], artists['Num Songs'])
pyplot.plot(pareto_fitted, color='r')
#pyplot.plot(gamma_fitted, color='g')
pyplot.xticks(visible=False)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('everybody');

/home/pavel/.local/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:2381: RuntimeWarning: invalid value encountered in double_scalars
  Lhat = muhat - Shat*mu
/home/pavel/.local/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1682: RuntimeWarning: divide by zero encountered in log
  return log(self._pdf(x, *args))

Best fit is still too sharp for the data, and I tried for a good long while to get it to fit better, so I conclude this doesn't quite fit a power law.

Let's plot the top 50 artists so we can actually read who they are.

pyplot.figure(figsize=(18, 10))
pyplot.bar(artists['Artist'][:50], artists['Num Songs'][:50])
pyplot.xticks(rotation=80)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('top 50');

Volume Added Over Time

My proclivity to add songs to this playlist is a proxy for my interest in listening to music generally. How has it waxed and waned over time?

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters() # to suppress warning

# Plot of added volume over time
parse_date = lambda d:(int(d[:4]), int(d[5:7]), int(d[8:10]))
pyplot.figure(figsize=(10, 6))
pyplot.hist([date(*parse_date(d)) for d in data['Added At']], bins=30)
pyplot.title('volume added over time');

The initial spike is from when I first stared using Spotify as the home for this collection and manually added hundreds from my previous list.

Eclecticness Measure (Frequency Transform)

This one is a personal favorite. I want to know how many of my songs are one-offs from that artist for me--just individual pieces I found fantastic and ended up adding after a few listens--, how many are two-offs, et cetera. I know it must be heavily skewed toward the low numbers.

# bar chart of first bar chart == hipster diversity factor
frequency = defaultdict(int)
for n in artists['Num Songs']:
	frequency[n] += n
frequency = pandas.DataFrame(frequency.items(), columns=['Unique Count', 'Volume']
                           ).sort_values('Volume', ascending=False)
print("number of song-artist pairs represented in the eclecticness chart:",
      sum(frequency['Volume']))

pyplot.figure(figsize=(10, 6))
pyplot.bar(frequency['Unique Count'].values, frequency['Volume'].values)
pyplot.title('volume of songs binned by |songs from that artist|')
pyplot.xlabel('quasi-frequency domain')
pyplot.ylabel(frequency.columns[1]);

number of song-artist pairs represented in the eclecticness chart: 3430

So, yes, it's much more common for an artist to make it in my list a few times than many times. In fact, the plurality of my top songs come from unique artists.

Conversely, this view also makes stark those few musicians from whom I've collected dozens.

Note that here, as in the artist bar charts, some songs are doubly-counted, because in cases artists collaborated I listed the song in both bins.

Genres Bar Chart

Alright, enough messing around. All the above were possible with the output from Watsonbox's Exportify. Let's get to the novel stuff you came here for.

People describe music by genre. As we'll see, genre names are flippin' hilarious and extremely varied, but in theory if I cluster around a few, that should give you a flavor of my tastes.

# count songs per genre
genres = defaultdict(int)
for i,song in data.iterrows():
    if type(song['Genres']) is str: # some times there aren't any, and this is NaN
        for genre in song['Genres'].split(','):
            if len(genre) > 0: # empty string seems to be a legit genre
                genres[genre] += 1

# sort for chart
genres = pandas.DataFrame(genres.items(), columns=['Genre', 'Num Songs']
                          ).sort_values('Num Songs', ascending=False).reset_index(drop=True)
print("number of unique genres:", genres.shape[0])

pyplot.figure(figsize=(18, 6))
pyplot.bar(genres['Genre'], genres['Num Songs'])
pyplot.xticks(visible=False)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('All the genera');

number of unique genres: 755

So many! Let's do the same thing as with the artists and for giggles see if it fits a power law.

y = []
for i in range(genres.shape[0]):
	for j in range(genres['Num Songs'][i]):
		y.append(i) 

# sanity check
#pyplot.figure()
#pyplot.hist(y, bins=30)

param = pareto.fit(y, 100)
pareto_fitted = len(y)*pareto.pdf(range(genres.shape[0]), *param)

pyplot.figure(figsize=(18, 6))
pyplot.bar(genres['Genre'], genres['Num Songs'])
pyplot.plot(pareto_fitted, color='r')
pyplot.xticks(visible=False)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('All the genera');

/home/pavel/.local/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:2381: RuntimeWarning: invalid value encountered in double_scalars
  Lhat = muhat - Shat*mu

Still too sharp, but fits better than with the artists.

Let's look at the top 50 so we can read the names.

pyplot.figure(figsize=(18, 10))
pyplot.bar(genres['Genre'][:50], genres['Num Songs'][:50])
pyplot.xticks(rotation=80)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('top 50');

"Indie poptimism" lol. wtf? "Dreamo", "Vapor soul", "Freak folk", "Tropical house", "Post-grunge", "Hopebeat", "Noise pop", "Mellow gold"

These are too good. Next time someone asks me my music taste, I'm definitely using these.

If these are the most popular names, what are the really unique ones at the bottom of the chart?

pyplot.figure(figsize=(18, 1))
pyplot.bar(genres['Genre'][-50:], genres['Num Songs'][-50:])
pyplot.xticks(rotation=80)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('bottom 50');

"hauntology", "psychadelic folk", "stomp and whittle", "dark trap", "filthstep", "shamanic", "deep underground hip hop", "future garage"

That was fun.

Release Dates

Which era of music do I prefer?

years = defaultdict(int)
for i,song in data.iterrows():
    years[song['Release Date'][:4]] += 1

years = pandas.DataFrame(years.items(), columns=['Year', 'Num Songs']
                          ).sort_values('Year')

pyplot.figure(figsize=(10, 6))
pyplot.bar(years['Year'], years['Num Songs'])
pyplot.xticks(rotation=80)
pyplot.xlabel(years.columns[0])
pyplot.ylabel(years.columns[1])
pyplot.title('Songs per year');

It seems to follow a Gamma distribution! This makes sense because I'm more likely to have heard things that are nearer me in time, and it takes a while for them to get through my process and become favorites.

Let's fit that gamma to the time-reversed data.

# Some years are missing, so transform to a dataframe that covers full time period.
eldest = int(years['Year'].values[0])
youngest = int(years['Year'].values[-1])
missing_years = [str(x) for x in range(eldest+1, youngest) if
                 str(x) not in years['Year'].values]
ago = years.append(pandas.DataFrame.from_dict(
    {'Year': missing_years, 'Num Songs': [0 for x in range(len(missing_years))]})
                  ).sort_values('Year', ascending=False).reset_index(drop=True)

y = []
for i in range(ago.shape[0]):
	for j in range(int(ago['Num Songs'][i])):
		y.append(i)

# sanity check histogram to make sure I'm constructing y properly
#pyplot.figure()
#pyplot.hist(y, bins=30)
        
param = gamma.fit(y, 10000)
gamma_fitted = len(y)*gamma.pdf(range(ago.shape[0]), *param)

pyplot.figure(figsize=(10, 6))
pyplot.bar(range(len(ago['Year'])), ago['Num Songs'])
pyplot.plot(gamma_fitted, color='g')
pyplot.xlabel('Years Ago')
pyplot.ylabel(ago.columns[1])
pyplot.title('Songs per year (in absolute time)');

print('Oldest Hall of Fame')
print(data[['Track Name', 'Artist Name(s)', 'Release Date']].sort_values(
    'Release Date')[:10])

Oldest Hall of Fame
                                            Track Name  \
3063                                      That's Amore   
2509                     (Where Do I Begin) Love Story   
3021                                   Autumn Nocturne   
2484                                         Take Five   
2697                                       Stand by Me   
0                           Fanfare for the Common Man   
554                                 Get Ready For This   
1422                                          New Math   
1905                       Yesterday - Remastered 2009   
2123  Il Buono, Il Brutto, Il Cattivo: Titoli Di Testa   

                                  Artist Name(s) Release Date  
3063  Dean Martin,Dick Stabile And His Orchestra         1954  
2509                               Andy Williams         1957  
3021                               Lou Donaldson         1958  
2484                    The Dave Brubeck Quartet   1959-12-14  
2697                                 Ben E. King   1962-08-20  
0        Aaron Copland,London Symphony Orchestra         1963  
554                                  2 Unlimited         1965  
1422                                  Tom Lehrer   1965-01-01  
1905                                 The Beatles   1965-08-06  
2123                             Ennio Morricone         1966  

Pretty good fit! I seem to be extra partial to music from about 5 years ago. We'll see whether the present or maybe the further past catches up.

Popularity Contest

I was happy to find popularity listed as a field in Spotify's track JSON. It's a percentile between 0 and 100, rather than an absolute number of plays. Still, it can be used to give a notion of how hipster I am.

popularity = defaultdict(int)
for i,song in data.iterrows():
    popularity[song['Popularity']] += 1

popularity = pandas.DataFrame(popularity.items(), columns=['Popularity', 'Num Songs']
                          ).sort_values('Popularity')

pyplot.figure(figsize=(10, 6))
pyplot.bar(popularity['Popularity'].values, popularity['Num Songs'].values)
pyplot.xlabel(popularity.columns[0])
pyplot.ylabel(popularity.columns[1])
pyplot.title('popularity distribution');

print("Average song popularity: ", popularity['Popularity'].mean())
print("Median song popularity: ", popularity['Popularity'].median())
print("Max song popularity: ", popularity['Popularity'].max())

Average song popularity:  43.59090909090909
Median song popularity:  43.5
Max song popularity:  95

Damn, I'm a hipster.

Track Duration

Do I prefer long songs or short ones?

pyplot.figure(figsize=(10,6))
pyplot.hist(data['Duration (ms)']/1000, bins=50);
pyplot.xlabel('Duration (s)')
pyplot.ylabel('Num Songs')
pyplot.title('Histogram of song lengths')

mean = data['Duration (ms)'].mean()/1000
median = data['Duration (ms)'].median()/1000
print("Average song length: " + str(int(mean//60)) + (":" if mean%60 >=10 else ":0")
      + str(mean%60))
print("Median song length: " + str(int(median//60)) + (":" if median%60 >=10 else ":0")
      + str(median%60))

Average song length: 4:06.183588730569966
Median song length: 3:56.16

Median is lower than the mean, so I'm skewed right. That is, I like a few really long songs. What are they?

print("Longest Hall of Fame:")
print(data[['Track Name', 'Artist Name(s)', 'Release Date', 'Duration (ms)']].sort_values(
    'Duration (ms)', ascending=False)[:10])

Longest Hall of Fame:
                                             Track Name  \
705                                               Irene   
1954  The Return of the King (From The Lord of the R...   
464                                   The Cure For Pain   
2406              Shine On You Crazy Diamond (Pts. 1-5)   
142   Two Step - Live At Piedmont Park, Atlanta, GA ...   
1474                                          Cage-Nerd   
2407              Shine On You Crazy Diamond (Pts. 6-9)   
144   Warehouse - Live At Piedmont Park, Atlanta, GA...   
143   Don't Drink the Water - Live At Piedmont Park,...   
2717                                          The Alien   

                                 Artist Name(s) Release Date  Duration (ms)  
705                                 Beach House   2012-05-15        1017013  
1954  The City of Prague Philharmonic Orchestra   2004-01-01         976893  
464                                mewithoutYou   2002-01-01         908840  
2406                                 Pink Floyd   1975-09-12         811077  
142                          Dave Matthews Band   2007-12-11         808226  
1474                                Tim Minchin   2011-04-04         778250  
2407                                 Pink Floyd   1975-09-12         747325  
144                          Dave Matthews Band   2007-12-11         743906  
143                          Dave Matthews Band   2007-12-11         743493  
2717                 Ben Salisbury,Geoff Barrow   2018-02-23         723579  

Musical Features

In the interest of understanding user tastes and providing the best possible music recommendations, Spotify has done some really sophisticated analysis of actual track content. Music is a time series, but most similarity metrics (and most ML methods generally) require inputs to be vectors, that is: points in some feature-space. So they've transformed the tracks to numerical metrics like Energy and Valence (continuous) and Key (discrete).

For the continuous metrics, they provide distributions across all music. Here they are next to similar plots of my own songs.

pyplot.figure(figsize=(20,40))

for i,category in enumerate(['Tempo', 'Acousticness', 'Instrumentalness', 'Liveness',
                            'Valence', 'Speechiness', 'Loudness', 'Energy', 'Danceability']):
    pyplot.subplot(9, 2, i*2+1)
    # It would be nice to show the KDE on these plots, but there isn't a way
    # to show it on unnormalized https://github.com/mwaskom/seaborn/issues/479
    pyplot.hist(data[category], bins=30)
    pyplot.text(min(data[category]), 0, r'$\mu=$'+str(data[category].mean())[:7], fontsize=12)
    pyplot.xlabel('Value')
    pyplot.ylabel('Num Songs')
    pyplot.title(category)

    pyplot.subplot(9, 2, i*2+2)
    pyplot.imshow(pyplot.imread('https://developer.spotify.com/assets/audio/'+
                                category.lower()+'.png'))
    pyplot.axis('off')  

pyplot.tight_layout(h_pad=2)