Spotify Song Popularity Analysis

Goal

Given Spotify song features, answer:

• What does popularity distribution look like?
• Which audio features correlate with popularity?
• Do genres differ in average popularity?

Analysis pipeline

diagram Spotify popularity analysis pipeline mermaid

  flowchart LR
  A["Raw features
(danceability, energy, ...)"] --> B["Explore
(describe, distribution)"]
  B --> C["Correlate
(features vs popularity)"]
  C --> D["Visualize
(heatmap)"]
  D --> E["Conclude
(what predicts popularity)"]

From raw audio features to the traits that correlate with a hit.

Step 1: Load

Load spotify data

import pandas as pd
 
df = pd.read_csv("data/spotify.csv")
print(df.head())

Step 2: Popularity distribution

Popularity distribution

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
sns.histplot(df["popularity"], bins=30, kde=True)
plt.title("Popularity distribution")
plt.tight_layout()
plt.show()

From the book

McKinney only trusts a ranking after filtering out low-sample noise — in the MovieLens example he drops movies with fewer than 250 ratings before ranking them, since a single 5-star rating can look like a “top movie” by accident. Apply the same skepticism to correlations: a strong corr() value driven by a couple of outlier songs isn’t a real pattern. Check the scatter, not just the number.

Step 3: Correlations

Correlation

import seaborn as sns
import matplotlib.pyplot as plt
 
num = df.select_dtypes(include="number")
 
plt.figure(figsize=(8, 6))
sns.heatmap(num.corr(), cmap="coolwarm", center=0)
plt.title("Correlation heatmap")
plt.tight_layout()
plt.show()

Visualize it

sketch Popularity distribution p5.js Pause Restart

Most songs cluster in the middle; only a few reach very high popularity.

Deliverable

• Top correlated features
• Whether correlations are meaningful or driven by outliers

🧪 Try It Yourself

Exercise 1 – Summary statistics

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Exercise 2 – Correlation matrix

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Exercise 3 – Select only numeric columns

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