Dataset research report
Spotify Tracks Dataset research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Content This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly. Usage The dataset can be used for: Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss! Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.
Research Context
Spotify Tracks Dataset: 500 rows by 20 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
Total popularity by explicit
Top explicit values ranked by summed popularity.
Open and export this chartpopularity vs duration_ms
popularity vs duration_ms, coloured by explicit.
Open and export this chartCorrelation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartpopularity by explicit
Spread of popularity across explicit groups.
Open and export this chartFollow-Up Queries
Preview Rows
| # | track_idtext | artiststext | album_nametext | track_nametext | popularityinteger | duration_msinteger | explicitboolean | danceabilityfloat |
|---|---|---|---|---|---|---|---|---|
| 1 | 5SuOikwiRyPMVoIQDJUgSV | Gen Hoshino | Comedy | Comedy | 73 | 230666 | False | 0.676 |
| 2 | 4qPNDBW1i3p13qLCt0Ki3A | Ben Woodward | Ghost (Acoustic) | Ghost - Acoustic | 55 | 149610 | False | 0.42 |
| 3 | 1iJBSr7s7jYXzM8EGcbK5b | Ingrid Michaelson;ZAYN | To Begin Again | To Begin Again | 57 | 210826 | False | 0.438 |
| 4 | 6lfxq3CG4xtTiEg7opyCyx | Kina Grannis | Crazy Rich Asians (Original Motion Picture Soundtrack) | Can't Help Falling In Love | 71 | 201933 | False | 0.266 |
| 5 | 5vjLSffimiIP26QG5WcN2K | Chord Overstreet | Hold On | Hold On | 82 | 198853 | False | 0.618 |
| 6 | 01MVOl9KtVTNfFiBU9I7dc | Tyrone Wells | Days I Will Remember | Days I Will Remember | 58 | 214240 | False | 0.688 |
Data Dictionary
- Unnamed: 0 numeric
- track_id text
- artists text
- album_name text
- track_name text
- popularity numeric
- duration_ms numeric
- explicit bool
- danceability numeric
- energy numeric
- key numeric
- loudness numeric
- mode numeric
- speechiness numeric
- acousticness numeric
- instrumentalness numeric
- liveness numeric
- valence numeric
- tempo numeric
- time_signature datetime
- track_genre categorical
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.