Songs Db

By shrijeetverma13 · April 16, 2026

This dataset is a collection of songs with structured metadata, including attributes such as song title, artist, genre, movie (if applicable), and…

The analysis successfully identified the top 10 artists with the most songs in the database. A bar chart and data table have been generated showing the ranking of artists by their song counts, with colors indicating relative volume.

The analysis successfully produced a donut pie chart and data table showing how songs are distributed across different languages in the dataset. The visualization breaks down the market composition, revealing which languages dominate the music catalog and their respective percentage shares.

The analysis produced two interactive bar charts and supporting data tables showing the most common emotion in each language's song catalog. The first chart highlights the dominant emotion per language, while the second shows the full emotion distribution across all languages grouped together.

The analysis identified 1 artist in the dataset who demonstrates cross-market versatility by singing in multiple languages. A bar chart has been generated showing the breakdown of songs per language for this artist, making it easy to see how their catalog spans different linguistic markets.

The analysis identifies which artists appear across the most movies, signaling high industry demand. A bar chart and supporting data tables have been generated showing the top 15 artists ranked by the number of unique movies they've contributed to. The visualization uses a color gradient to make it easy to spot the most prolific artists at a glance.

The analysis of emotions across all 101 songs has been visualized in both a bar chart and a pie chart, giving you a clear picture of how different emotions are represented in the dataset. The bar chart shows the count of songs per emotion, while the pie chart illustrates the proportional breakdown.

Across all three languages in the dataset, Love is the most common emotion. Two bar charts were generated — one showing raw counts and another showing percentage breakdowns — making it easy to compare emotion patterns across English, Hindi, and Telugu songs.

The analysis identified artists who specialize in specific emotions, meaning at least 60% of their songs fall into a single mood category. A bar chart and supporting data tables were generated showing these emotional specialists, ranked by how concentrated their music is in one emotion.

The analysis reveals which artists express the greatest emotional diversity across their songs. Two visualizations were generated: a bar chart showing the top 15 artists ranked by number of distinct emotions (color-coded by song count), and a stacked bar chart breaking down the specific emotions for the top 5 most emotionally diverse artists. Data tables with the full rankings are also available.

The analysis reveals which movies have the highest number of songs, identifying the most music-heavy productions. A bar chart and data tables were generated showing the top 15 movies ranked by song count. The leading movies — Geetha Govindam, Ala Vaikunthapurramuloo, Sita Ramam, and Dear Comrade — each feature 3 songs, making them the most music-rich films in the dataset.

The horizontal bar chart shows all emotions ranked from least to most represented in the dataset, making it easy to spot where content gaps exist. Emotions with the shortest bars on the left side of the chart are the most underrepresented — these are your biggest opportunities for creating new content. The color scale (red to green) further highlights which emotions have the fewest songs versus the most.

Out of 82 unique movies analyzed, the vast majority — 67 movies (81.7%) — feature only one song, while just 15 movies (18.3%) have multiple songs. A pie chart has been generated to visually illustrate this breakdown.

The analysis identified the artist-movie combinations that produced the most songs together. A horizontal bar chart and supporting data tables have been generated showing the top 15 pairings ranked by song count, with the most prolific collaborations highlighted using a color gradient.

The bar chart reveals which movies feature the most diverse mix of song emotions, ranking the top 15 films by the number of unique emotional categories found across their soundtracks. Movies with taller bars have songs spanning more emotional tones, while the color gradient indicates how many total songs each movie contains.

The analysis compared sad and happy songs across different languages in the dataset. Three data tables were generated showing the breakdown of sad versus happy songs by language, along with their ratios.

The analysis reveals that the vast majority of artists in the dataset stick to a single language. Out of all artists analyzed, 47 are exclusive to one language, while only 1 artist crosses language boundaries by performing in multiple languages. Two charts were generated: a pie chart showing the overall split between single-language and multi-language artists, and a bar chart highlighting the multi-language artist(s) with their language combinations.

Yes! There are emotion categories completely missing within specific languages. English is missing the 'Trust' emotion, while Hindi is missing both 'Anticipation' and 'Anger'. A heatmap visualization was generated showing the full breakdown of song counts across all language-emotion combinations, making it easy to spot the gaps (shown as 0).

The analysis examined how concentrated artist representation is across different language segments using the Herfindahl-Hirschman Index (HHI) — a standard measure of market concentration. Two visualizations were generated: a dual-axis chart showing HHI scores and top artist share per language, and a stacked bar chart displaying the top 5 artists per language segment. Higher HHI values (above 2,500) indicate highly concentrated segments dominated by a few artists, while lower values suggest a more diverse and competitive artist pool.