Kendrick Lamar Discography
Whether you’re a fan of Kendrick’s poetic lyricism or analyzing trends.
@kaggle.willianoliveiragibin_kendrick_lamar_discography
Whether you’re a fan of Kendrick’s poetic lyricism or analyzing trends.
@kaggle.willianoliveiragibin_kendrick_lamar_discography
This dataset contains comprehensive streaming data on Kendrick Lamar, meticulously extracted from Spotify. It offers in-depth insights into his innovative discography, detailing song attributes, popularity metrics, and collaborations. Whether you’re a fan captivated by Kendrick’s poetic lyricism or a researcher aiming to analyze trends in modern hip-hop and rap, this dataset is a powerful tool for exploring music trends, sentiment, and artistic influence.
Applications of the Dataset:
Music Trend Analysis:
Examine the evolution of Kendrick Lamar’s music and lyrical themes over time, identifying key turning points in his career.
Collaboration Insights:
Delve into his collaborative efforts, analyzing the artists Kendrick frequently works with and how those partnerships influence track features and success.
Featured Tracks Analysis:
Investigate songs where Kendrick is a featured artist, assessing how his contributions elevate their musical and commercial appeal.
Audio Feature Exploration:
Explore acoustic and musical characteristics such as tempo, energy, and valence, which define Kendrick’s distinctive sound and appeal.
Sentiment & Mood Analysis:
Assess the emotional depth and tonal shifts across his discography to understand the underlying narratives and themes.
Data Visualization Projects:
Create engaging visualizations to showcase Kendrick’s artistic journey, including timelines, heatmaps, or genre correlations.
CREATE TABLE kendrick_tracks_new (
"artist_name" VARCHAR,
"track_name" VARCHAR,
"album_name" VARCHAR,
"release_date" TIMESTAMP,
"release_year" BIGINT,
"is_album_track" BOOLEAN,
"explicit" BOOLEAN,
"is_feature" BOOLEAN,
"popularity" BIGINT,
"track_id" VARCHAR,
"duration_ms" BIGINT,
"danceability" DOUBLE,
"energy" DOUBLE,
"valence" DOUBLE,
"tempo" DOUBLE,
"loudness" DOUBLE,
"speechiness" DOUBLE,
"acousticness" DOUBLE,
"instrumentalness" DOUBLE,
"liveness" DOUBLE,
"duration_min" VARCHAR
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