spotify dataset analysis

Let’s talk about countries, I took a sample of some top musical countries. Make learning your daily ritual. CONCLUSION. Want to Be a Data Scientist? Due to the relatively small size of our data set, we adopt a string parsing approach for our model (which could be easily scaled with Python’s NLTK package for larger data sets or more advanced modeling). Spotify Tracks DB Music database (232k tracks) key, mode and time signature are cleaned. If you want to keep up to date with my articles then follow me. Using the dataframe of playlists - and specifically the playlist id column - we iterate over all tracks in every playlist and pull relevant audio features which could potentially be helpful in predicting the success of a playlist. Podcasts are exploding in popularity. Sampled from the over 2 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest dataset of music playlists in the world. Finally - the number of followers is concatenated to the playlist dataframe. Analysis of the Spotify Top 200 Daily Including Clustering Listening Behavior by Country - andrewpaster/spotify_top200_dataset Interaction terms are considered because genre may have an effect on the relationships between audio features and the number of playlist followers. The master dataframe is saved for both EDA and modeling purposes next and final dataframe size is presented. Acoustic music primarily uses instruments that produce sound through acoustic means, as opposed to electronic means. Let’s see how it looks like. The data set provides the 50 most listened to songs on Spotify in 2019. It’s surprising to see significantly lesser number of songs in India probably because when we get obsessed with a song we do not allow other songs to get featured in the list for a long time. Dataset for podcast research. Size of the bubble is defined their popularity score. analysis_url: string: An HTTP URL to access the full audio analysis of this track. Audio features refers to acousticness, energy, key, valence and etc. List of songs featured in top 30 in maximum countries: If we go a bit deeper and analyze these 34.7k songs featured exclusively in a country, we can get the sense of which countries’ regional songs dominate the top 200 list in that country. Featured number on the y-axis is in hundreds e.g. As we can see in acousticness, Arijit Singh, Armaan Malik and AR Rahman are in the top 15. rock ranks highest in instrumentalness. In Italy, top 6 genres include the regional ones like Italian hip hop and Italian pop. The logistic regression model worked slightly better with an accuracy of 70.1%. DEMO. Second, we obtain the list of 30 artists who appear most often in playlists with 35,000+ followers. Greener the shade more is the average feature value. The playlists were created by Spotify users between January 2010 and November 2017. This inner merge leads to a loss of 126 playlists in total (i.e., there was no overlap between the two dataframes across these playlists). Similarly for France, french hip hop and pop urbaine are the 2 most prominent genres and both rank high in danceability, hence France ranks highest in danceability. I projected all the songs that belong to the genres: french hip hop, dance-pop and reggaeton. Five columns represent the number of times top 50 artists (in terms of artist followers) appear in the playlists (bucketed in 10 artist intervals each), Two columns represent the mean and standard deviation of artist followers per playlist, Two columns represent the mean and standard deviation of artist popularity per playlist, After reading in the full dataset and the playlist dataset, we perform a left join based on playlist ID and add the playlist name to the full dataset, We search for 12 categories of specific strings that cover ‘Best’, ‘Workout’, ‘Party’, ‘Chill’, ‘Acoustic’, ‘2000s’, ‘1990s’, ‘1980s’, ‘1970s’, ‘1960s’, and ‘1950s’ using the str.contain function, After creating these 12 boolean variables, we transform them to binary ones (0 or 1) by multiplying 1, Lastly, we include those binary variables in the dataframe as predictor variables. I did hyperparameter tuning on alpha and applied linear SVM as well. Dataset contains more than 160.000 songs collected from Spotify Web API. Stronger is the association deeper is the shade. 2. Once all the genres are one-hot encoded, the dataframes are grouped by playlist to enable feature engineering. After working at Spotify for only a few months, I was talking about term weighting and signing up for internal courses on the R programming language. Over 40,000+ Tracks labeled hit or flop, with their features. On the left side, we have the distribution with the country selected as ‘global’ and on the right we have Japan. First, a function is defined to retrieve artist information given an artist name. For this, both a so-called “client id” and “client secret id” are required. If we look at the concentric circle labeled as valence, the top 7 genres highest in average valence score are dance-pop, dutch hip hop, reggaeton, dutch urban, latin pop, tropical and latin. But do they prefer energetic songs in other genres. We decide to focus on Spotify’s own “featured” playlists - i.e., those produced by Spotify itself given specific genres / moods / artists etc.. The first step is to bucket the genres (with a total of more than 100 specific genres) into broader categories. Ed Sheeran has been featured 48,000 times. These are the word cloud of most featured artists in the mentioned genres. String parsing follows the below example methods: The following section describes the process of creating interaction terms between genres and audio features. For example, different levels of energy may be more popular for rap music than for acoustic music. This summer, we’re celebrating Data + Music—music trends, artists, genres, and towns—in a series of visualizations from the Tableau community. If data discovery is time-consuming, it significantly increases the time it takes to produce insights, which means either it might take longer to make a decision informed by those insights, or worse, we won’t have enough data and insights to inform a decision. The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The data in the chart begin from Dec’16 onwards. Tags. EDM songs have a very well defined tempo range. ABOUT. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. I selected 100 songs from the most recent of my Discover Weekly playlist and passed them through my model. Spotify api provides some track-analysis. spotifyr is an R wrapper for pulling track audio features and other information from Spotify’s Web API in bulk. The dataset by Farooq Ansari has features for tracks fetched using Spotify's Web API, base on the tracks labeled hit or flop by the author, which can be used to make a classification model to predict whether any given track would be a 'Hit' or not. The features include song, artist, release date as well as some characteristics of song such as acousticness, danceability, loudness, tempo and so on. [big room, brostep, deep big room, edm, electr... [east coast hip hop, gangster rap, hip hop, po... ['boy band', 'dance pop', 'europop', 'pop', 'p... ['alternative metal', 'nu metal', 'post-grunge... ['dwn trap', 'pop rap', 'rap', 'southern hip h... ['dwn trap', 'trap music', 'underground hip hop']. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Country-wise list of top 10 most featured artists: Let’s check if there is a similarity in the songs of a particular genre. In this project, I investigated country-specific music preferences. Spotify is the largest music st r eaming service available. Download (15 MB) New Notebook. I am going to cover the details of this topic in the next blog. Next, the track feature dataframe is loaded. I took all the songs from 3 genres (dance-pop, french hip hop and reggaeton). The data set contains the following fields: Track.Name — Name of Track; Artist.Name — Name of the Artist; Genre — Genre of Track The obtained baseline playlist features are converted into a large dataframe next. I fetched details like track popularity (score between 0–100), artist URL, artist popularity (0–100), artist followers, and artist genre from the track URL. Acousticness and energy have a stronger negative association in comparison with other pairs. ... Now our dataset is ready for some cool analysis which will be covered in next part. WSDM Cup: The Music Streaming Sessions Dataset Nov 15, 2018. . Here is another view to look at the data. The dataset is available on Kaggle. By automatically batching API requests, it allows you to enter an artist’s name and retrieve their entire discography in seconds, along with Spotify’s audio features and track/album popularity metrics. My fav genre dance-pop is not in the top 7 genres in danceability as its name suggests. Regardless, things might get a bit geeky. E.g. Écouter, ça change tout - Spotify Beginning with the playlist dataframe. Artist features are extracted using the code below - note running this code on all playlists takes a significant amount of time (measured in hours). The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. Audio features are extracted using the below code - note running this code on all playlists takes a significant amount of time (measured in hours). I tried to make the text as simple and as clear as possible while still providing technical details. Python: 6 coding hygiene tips that helped me get promoted. Popularity of top rap artists is more than that of others. BPM— Beats per minute. We explored some existing datasets including Million Song Dataset, related complementary datasets and Yahoo music dataset, as well as several music APIs including Spotify API, YouTube API and Genius API. To begin pulling playlist data from the Spotify API, first a connection with the API needs to be made. Date range is from … Energy— The energy of a song — the higher the value, the more energetic the song 3. business_center. Following a similar procedure as the audio feature extraction, artist information for every track in every playlist is extracted next. In the sheet below, I have taken 200 most followed artists on Spotify and ranked them in each audio feature on the basis of their average score of audio features across their songs. In a process, I can explore the most popular tracks from the top artists in my fav genres and expand my playlist. By the way, I used Google Colab. Valence has a slight positive correlation with danceability, energy, and loudness. arts and entertainment x 8796. subject > arts and entertainment, music. 5. Kubernetes is deprecating Docker in the upcoming release, Ridgeline Plots: The Perfect Way to Visualize Data Distributions with Python. Specifically, for each audio feature (such as acousticness, duration, energy) mined from Spotify, the mean and standard deviation across all playlist tracks is computed. Then, we count the amount of times these artists show up in a given playlist and record the counts as predictors in the final dataframe. The Spotify ID for the track. P!nk Floyd, Coldplay and Khalid are top artists in instrumentalness. Once these “id’s” are obtained, we follow the below outlined steps to set up the API connection: The main idea of this project is twofold: (i) to infer about key predictors (whether track features or artist features) which are statistically significant in determining a playlist’s success in terms of number of followers; and (ii) to create a custom playlist that is deemed to be succesful (i.e., would obtain many followers). 4. Check this link to analyze yours. track_href: string: A link to the Web API endpoint providing full details of the track. I compare 2 of my playlists from Spotify: 1. Credit goes to Spotify for calculating the audio feature values. uri: string: The Spotify URI for the track. Spotify Audio Analysis. This site only works if JavaScript is enabled in your Browser Contains 100,000 episodes from thousands of different shows on Spotify, including audio files and speech transcriptions. We find that the interaction terms listed below are significant. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. (I’m so proud ). This dataset provides a song’s tags and most similar … The first step is to load all the dataframes separately. To this extent, the first step in doing any further analysis is to obtain the playlists we want to run our predictions on. Since 2015, we’ve added hundreds of thousands of shows, and users are listening more and more [...] Data Science; Developer Tools; Machine Learning; April 15, 2020 Reach for the Top: How Spotify Built Shortcuts in Just Six Months. An access token is required to access this … Most of the songs that I listen to are from the dance-pop genre. This post talks about the development of the app. Running the feature extraction from Spotify could take a significant amount of time and also tend to raise errors in the process. I trained the model on randomly selected 70% data (training data) and tested its accuracy on the remaining 30% test data with 5-fold cross-validation (cv=5). Pop artists start with a good popularity score as compared with other genres. Please refer to Data Mining & Wrangling for the source code (Jupyter Notebook). Liked playlist (630 songs) 2. We can further analyze the pairs, say energy and loudness with the Joint kernel density estimate plot as shown below. I ran the model for 10000 iterations with a perplexity value of 75. Now, let’s talk about the audio features that I extracted. Pearson correlation coefficient (r) is a measure of the strength of a linear association between two variables. 5.9. I have labeled the most followed artists in each genre. It shows the number of songs and artists associated with a particular Towards Data Sciencegenre. more_vert. I scrapped the data from Spotify's weekly regional chart’. Note, however, that sample audio can be fetched from services like 7digital, using code we provide. India ranks high in acousticness. As you can notice, t-SNE has tried to separate the different points and form clustered groups of similar points. Below are the interaction terms that are created. Following the above outlined steps, we are able to produce a dataframe consisting of, in excess 1400, playlists with relevant information such as playlist id, number of playlist tracks, and number of playlist followers. You can see each section, bar, beat, segment, and tatum on a timeline, skip to each timestamp, and see the pitch and timbre vectors for the current segment. I recently started using Spotify and was amazed by the sophisticated technology that drives Spotify’s recommendation system based on collaborative filtering and NLP. Trap artist’s popularity ranges from 52–93 and most of them lie in the higher side of this range while in other genres distribution is more balanced. Genre-wise no. The digital music company with more than 100 million users, have been busy this year enhancing their service through several acquisitions. Spotipy is a lightweight Python library for the Spotify Web API and it allows full access to all of the music data provided by the Spotify platform. I did that by labeling the values above 66 percentile in each feature as 1 and others as 0 and then created the heatmap. Disliked playlist (537 songs) After using Python and some data wranglingtechniques, the data frame below is what I use to do some exploratory data analysis (EDA). Distribution of both these features is left-skewed, which means the majority of the songs have a higher value of these features. In the above ‘split violin plot’ their taste in energetic music is pretty much evident in genres like latin, reggaeton and tropical as compared to global. Once all the audio features are extracted, they are converted into the main audio feature dataframe and saved as a large csv file. If we look at the below chart (whole chart contains 64 bars for a total of 64 countries) out of 48.2k songs, 34.7k songs have been featured in a single country and 4.3k songs in 2 countries. Latin artists have widest range of popularity scores. It was extracted from the Organize Your Music site. Explore and run machine learning code with Kaggle Notebooks | Using data from Top Spotify Tracks of 2017 Each song is associated with an artist and each artist may have multiple genres. Hip hop, rap and trap rank high in speechiness. Dataset for researching how to model user listening and interaction behavior in music streaming. I hope you enjoyed the article. For each song, I also extracted audio features using Spotipy. Boxen plot (an enhanced version of classic box-plot) of the distribution of artist popularity score in each genre is shown below: Let me share some interesting insights into some of the most popular genres. Analysis of the distribution of a few audio features in various genres. In the below bubble plot, I have plotted the artists from the 3 mentioned genres with their average tempo on the x-axis and average speechiness on the y-axis. Dance — The higher the value, the easier it is to dance to this song. Since each artists usually falls under multiple genres, we can use this column to build a co-occurrence matrix. Analyzing Spotify Dataset Python is beautifully complemented by Pandas when it comes to data analysis. The audio features for each song were extracted using the Spotify Web API and the spotipy Python library. To make a better sense of the data, I have highlighted the top 7 countries (out of 21 countries) in each concentric circle i.e. The plot shows the distribution of audio feature ‘energy’ in each mentioned genre. If you are looking for songs with positive vibes, you can check the list of artists in the valence column. Both have a positive association with the Pearson r-value of 0.73. Once all data is extracted from Spotify, the next step is to combine the separate dataframes (i.e., for playlists, audio features and artists) and to perform some initial feature engineering in the hope of creating useful data for inference and prediction of playlist success. A positive correlation is indicated by blue and negative with red shade. I also participated in a hackathon where I developed a Spotify App code-named Genderify that tapped into our massive data-set to determine exactly how “manly” a playlist is. Take a look, Python Alone Won’t Get You a Data Science Job. 1. These are categorical variables indicating whether a playlist has a specific artist. So if you want to make a playlist to dance on, you can check out the above list of top artists in danceability. Explore and run machine learning code with Kaggle Notebooks | Using data from Spotify Song Attributes I will explain my analysis in Exploratory Data Analysis approach of the Spotify Dataset using Python. Introducing the Spotify Podcast Dataset and TREC Challenge 2020. Thank you for reading! Data Tasks Notebooks (10) Discussion (5) Activity Metadata. of times a song is featured in top 30: The pie chart of the top 12 genres of my saved songs in Spotify is shown below. french hip hop has the highest speechiness value among them. An interactive visualisation of the musical structure of a song on Spotify Learn more about the audio properties of your favourite tracks, including detailed rhythmic information. There are 48261 unique songs, 9805 artists, and 1579 genres in the dataset. Median artist's popularity (indicated by dark line in the middle) in the rap genre is the highest. Spotify est un service de musique qui vous donne accès à des millions de titres. Top 50 songs listened in 2019 on spotify. in each feature. spotify:user:spotify:playlist:37i9dQZF1DXcBWIG... https://api.spotify.com/v1/users/spotify/playl... spotify:user:spotify:playlist:37i9dQZF1DX0XUsu... spotify:user:spotify:playlist:37i9dQZF1DX4dyzv... spotify:user:spotify:playlist:37i9dQZF1DX4SBhb... spotify:user:spotify:playlist:37i9dQZF1DXcF6B6... [alternative metal, nu metal, post-grunge, rap... [dwn trap, trap music, underground hip hop]. Therefore, genres are one-hot encoded to convert these genre lists into predictors we can run models on.

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