Spotify Analytics – Ryan Horn’s Website

Below is analytics of multiple different parameters related to the following Spotify dataframe.

import pandas as pd
from itables import init_notebook_mode,show
init_notebook_mode(all_interactive=False)

spotify = pd.read_csv('https://bcdanl.github.io/data/spotify_all.csv')

show(spotify)

init_notebook_modetrusted

pid	playlist_name	pos	artist_name	track_name	duration_ms	album_name
Loading ITables v2.2.4 from the `init_notebook_mode` cell... (need help?)

This code displays all the tracks of one of my favorite artists (Oasis) in this data frame.

oasis_df = (
    spotify
    [spotify['artist_name'] == 'Oasis']
    .drop_duplicates(subset='track_name')
)
show(oasis_df)

	pid	playlist_name	pos	artist_name	track_name	duration_ms	album_name
Loading ITables v2.2.4 from the `init_notebook_mode` cell... (need help?)

This code displays the total number of tracks by Oasis

(
    oasis_df
    .value_counts(['track_name']).shape[0]
)

This code changes the duration from ms to minutes to make it more understandable then prints the top 5 longest songs by Oasis

oasis_df['duration_sec'] = oasis_df['duration_ms'] / 1000
oasis_df['duration_min'] = oasis_df['duration_sec'] / 60

sorted_oasis = (
    oasis_df
    .sort_values(by='duration_min', ascending=False)
    [['track_name', 'duration_min']]
    .head(5)
)

show(sorted_oasis)

	track_name	duration_min
Loading ITables v2.2.4 from the `init_notebook_mode` cell... (need help?)

Top 10 Artists by Track Count & POS Distributions

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the full Spotify dataset
spotify = pd.read_csv('https://bcdanl.github.io/data/spotify_all.csv')

# 1. Find the top 10 most‐prolific artists
top10 = spotify['artist_name'].value_counts().nlargest(10).index.tolist()

# 2. Subset and plot the POS distributions with violins
df_top10 = spotify[spotify['artist_name'].isin(top10)]
plt.figure(figsize=(12, 6))
sns.violinplot(
    x='artist_name',
    y='pos',
    data=df_top10,
    cut=0,
    inner='quartile',
    palette='tab10'
)
plt.xticks(rotation=45, ha='right')
plt.title('POS Distribution for Top 10 Artists')
plt.xlabel('Artist')
plt.ylabel('Track Position (pos)')
plt.tight_layout()
plt.show()

/var/folders/gp/qrzfglvs0plg_zk9wtgwkzyr0000gn/T/ipykernel_75043/340212433.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.violinplot(

Interpretation:
Among the ten busiest artists, some—like the #1 artist—show very tight POS violins centered in the top‐20 (“early” positions), indicating consistent front‐loaded playlist placement. Others (e.g. the #4 and #7 artists) have much wider violins stretching into the bottom half, meaning their tracks appear across both early and late positions. Most artists fall between these extremes, with moderate variation in where their songs land.