Spotify Playlist importer, queries, and dashboard

1-playlist-importer.py

import spotipy
from neo4j import GraphDatabase
from spotipy.oauth2 import SpotifyClientCredentials, SpotifyOAuth

# ------------------------------------ Configuration parameters ------------------------------------ #
user_id = "<<YOUR SPOTIFY USER ID>>"                      # Spotify user ID.
client = "<<YOUR SPOTIFY CLIENT ID>>"                     # Spotify client ID.
secret = "<<YOUR SPOTIFY CLIENT SECRET>>"                 # Spotify client secret.
playlist_uri = "spotify:playlist:1eCqsRrwBAFc2lf5ZLGa5m"  # public playlist with songs to be sorted.
neo4j_url = "neo4j://localhost:7687"                      # bolt url of the neo4j database.
neo4j_username = "<<YOUR NEO4J USERNAME>>"                # neo4j username. defaults to 'neo4j'.
neo4j_password = "<<YOUR NEO4J DB PASSWORD>>"             # neo4j password.
scope = 'playlist-modify-private'                         # Spotify scope required to manage playlists.
redirect_uri = 'http://localhost:8888/callback'           # Spotify callback url. Set to localhost for development.
cache_path = "spotify_cache.tmp"                          # Where spotify caches the session variables.
create_constraints = True                                 # Whether to create constraints.
spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id=client, client_secret=secret))
# ------------------------------------ Configuration parameters end --------------------------------- #

def load_graph_using_spotify_api():
    neo4j = create_neo4j_session(url=neo4j_url, user=neo4j_username, password=neo4j_password)
    print("dropping and creating constraints...")
    recreate_contraints(neo4j)

    print("creating tracks...")
    tracks = get_tracks()
    tracks = get_track_audio_features(tracks)
    neo4j.run("UNWIND $tracks as track CREATE (t:Track{id: track.id}) SET t = track",
              parameters={'tracks': list(tracks.values())})

    print("creating albums...")
    albums = get_album_info(tracks)
    neo4j.run("UNWIND $albums as album CREATE (a:Album{id: a.id}) SET a = album",
              parameters={'albums': list(albums.values())})

    print("creating artists...")
    artists = get_artist_info(tracks)
    neo4j.run("UNWIND $artists as artist CREATE (a:Artist{id: a.id}) SET a = artist",
              parameters={'artists': list(artists.values())})

    print("finding related artists..")
    related_artists = get_related_artists(artists)
    neo4j.run("""UNWIND $relatedartists as artist  MERGE (a:Artist {id: artist.id}) SET a = artist """,
              parameters={'relatedartists': list(related_artists.values())})
    neo4j.run("""MATCH (a:Artist) WHERE EXISTS (a.original_artist) WITH a
                   MATCH (a2:Artist{id: a.original_artist})
                   MERGE (a)-[:SPOTIFY_RELATES_TO]->(a2)""")
            
    print("creating genres..")
    genres = get_genres(albums, artists)
    neo4j.run("UNWIND $genres as genre MERGE (g:Genre{name: genre})",
              parameters={'genres': list(genres)})

    print("Linking tracks to albums, genres, and artists...")
    neo4j.run("MATCH (t:Track), (a:Album{id: t.album}) CREATE (t)-[:TRACK_IN_ALBUM]->(a);")
    neo4j.run("MATCH (t:Track) UNWIND t.artists as artist MATCH (a:Artist{id: artist}) CREATE (t)-[:TRACK_HAS_ARTIST]->(a)")
    neo4j.run("MATCH (a:Artist) UNWIND a.genres as genre MATCH (g:Genre{name: genre}) CREATE (a)-[:ARTIST_HAS_GENRE]->(g)")
    neo4j.run("MATCH (a1:Artist)<--(t:Track)-->(a2:Artist) WHERE id(a1)<id(a2) MERGE (a1)-[:WORKED_WITH {track:t.uri}]->(a2)")
    neo4j.run("MATCH (ar:Artist)<--(t:Track)-->(al:Album) MERGE (al)-[:ALBUM_HAS_ARTIST]->(ar)")

    print("Calculate artist similarity using GDS..")
    neo4j.run("""
        MATCH (item:`Artist`)-[:`ARTIST_HAS_GENRE`]->(category:`Genre`)
        WITH {item:id(item), categories: collect(distinct id(category))} as userData
        WITH collect(userData) as dataset
        CALL gds.alpha.similarity.overlap.write({
            data: dataset,
            weightproperty: null,
            nodeProjection: '*',
            writeProperty: 'score',
            writeRelationshipType: 'GDS_ARTIST_SIMILAR_OVERLAP',
            similarityCutoff: 0.05,
            degreeCutoff: 0 })
        YIELD nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, stdDev, p25, p50, p75, p90, p95, p99, p999, p100
        RETURN nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, p95""")

    print("Calculate artist pagerank-spotify using GDS..")
    neo4j.run("""
        CALL gds.pageRank.write({
            nodeProjection: 'Artist',
            relationshipProjection: {
                relType: {
                type: 'SPOTIFY_RELATES_TO',
                orientation: 'NATURAL',
                properties: {}
                }
            },
            relationshipWeightProperty: null,
            dampingFactor: 0.85,
            maxIterations: 20,
            writeProperty: 'pagerank-spotify'})
        """)

    print("Calculate artist pagerank-workedwith using GDS..")
    neo4j.run("""
        CALL gds.pageRank.write({
            nodeProjection: 'Artist',
            relationshipProjection: {
                relType: {
                type: 'WORKED_WITH',
                orientation: 'UNDIRECTED',
                properties: {}
                }
            },
            relationshipWeightProperty: null,
            dampingFactor: 0.85,
            maxIterations: 20,
            writeProperty: 'pagerank-workedwith'
            })
        """)

    print("Calculate artist pagerank-similarity using GDS..")
    neo4j.run("""
        CALL gds.pageRank.write({
        nodeProjection: 'Artist',
        relationshipProjection: {
            relType: {
            type: 'GDS_ARTIST_SIMILAR_OVERLAP',
            orientation: 'UNDIRECTED',
            properties: {}
            }
        },
        relationshipWeightProperty: null,
        dampingFactor: 0.85,
        maxIterations: 20,
        writeProperty: 'pagerank-similarity'
            })
        """)

    print("Calculate artist Louvain community using GDS..")
    neo4j.run("""
        CALL gds.louvain.write({
        nodeProjection: 'Artist',
        relationshipProjection: {
            relType: {
            type: 'SPOTIFY_RELATES_TO',
            orientation: 'UNDIRECTED',
            properties: {}
            }
        },
        relationshipWeightProperty: null,
        includeIntermediateCommunities: false,
        seedProperty: 'valence',
        nodeProperties: [
            'valence'
            ],
        writeProperty: 'louvain-community'})
        """)    

print("Starting the loading!")


def recreate_contraints(neo4j):
    # recreate constraints / indices and clear existing database.
    results = neo4j.run("CALL db.constraints")
    for constraint in results:
        result = neo4j.run("DROP " + constraint['description'])
    neo4j.run("CREATE CONSTRAINT ON (g:Genre) ASSERT g.name IS UNIQUE")
    neo4j.run("CREATE CONSTRAINT ON (a:Album) ASSERT a.id IS UNIQUE")
    neo4j.run("CREATE CONSTRAINT ON (a:Artist) ASSERT a.id IS UNIQUE")
    neo4j.run("CREATE CONSTRAINT ON (t:Track) ASSERT t.id IS UNIQUE")
    neo4j.run("MATCH (n) DETACH DELETE n;")

def get_tracks():
    results = spotify.playlist(playlist_uri)['tracks']
    items = {}
    while results['next'] or results['previous'] is None:
        for track in results["items"]:
            if track['track']['id']:
                track['track']['artists'] = [artist if type(artist) == str else artist['id'] for artist in
                                             track['track']['artists']]
                track['track']['album'] = track['track']['album'] if type(track['track']['album']) == str else \
                    track['track']['album']['id']
                items[track['track']['id']] = track['track']
            for field in track['track']:
                if track is not None and type(track['track'][field]) == dict:
                    track['track'][field] = None
        if not results['next']:
            break
        results = spotify.next(results)
    return items


def get_track_audio_features(tracks, page_size=100):
    page_count = len(tracks) / page_size
    for i in range(int(page_count) + 1):
        ids = list(tracks.keys())[i * page_size:(i + 1) * page_size]
        if len(ids) == 0:
            break
        audio_features = spotify.audio_features(tracks=ids)
        for track_features in audio_features:
            if track_features is None:
                continue
            track_id = track_features['id']
            for feature, value in track_features.items():
                if feature != 'type':
                    tracks[track_id][feature] = value
    return tracks


def get_album_info(tracks, page_size=20):
    album_ids = set()
    for track_id in tracks.keys():
        album_ids.add(tracks[track_id]['album'])

    all_albums = {}
    page_count = len(album_ids) / page_size
    for i in range(int(page_count) + 1):
        ids = list(album_ids)[i * page_size:(i + 1) * page_size]
        results = spotify.albums(ids)

        for album in results['albums']:
            album['artists'] = [artist['id'] for artist in album['artists']]
            album['images'] = album['images'][1]['url']
            album['external_ids'] = None
            album['external_urls'] = None
            album['tracks'] = len(album['tracks'])
            album['copyrights'] = len(album['copyrights'])
            all_albums[album['id']] = album
    return all_albums


def get_artist_info(items, page_size=50):
    all_artists = {}
    artist_ids = set()
    for track_id in items.keys():
        for artist_nr in items[track_id]['artists']:
            artist_id = artist_nr
            artist_ids.add(artist_id)

    # after we have a list of all artists, get the details from the API
    page_count = len(artist_ids) / page_size
    for i in range(int(page_count) + 1):
        ids = list(artist_ids)[i * page_size:(i + 1) * page_size]
        results = spotify.artists(ids)
        for artist in results['artists']:
            if artist["images"]:
                artist['images'] = artist['images'][1]['url']
            artist['followers'] = artist['followers']['total']
            artist['external_urls'] = None
            all_artists[artist['id']] = artist
    return all_artists

def get_related_artists(items, page_size=50):
    page_count = len(items) / page_size
    new_artists = {}
    for i in range(int(page_count) + 1):
        ids = list(items)[i * page_size:(i + 1) * page_size]
        for id in ids:
            related_artists = spotify.artist_related_artists(id)
            for related_artist in related_artists['artists']:
                related_artist['original_artist'] = id
                if related_artist["images"]:
                    related_artist['images'] = related_artist['images'][1]['url']
                related_artist['followers'] = related_artist['followers']['total']
                related_artist['external_urls'] = None
                new_artists[related_artist['id']] = related_artist
    return new_artists

def get_genres(albums, artists):
    genres = set()
    for item in albums:
        for genre in albums[item]['genres']:
            genres.add(genre)
    for item in artists:
        for genre in artists[item]['genres']:
            genres.add(genre)
    return genres

def create_neo4j_session(url, user, password):
    driver = GraphDatabase.driver(url, auth=(user, password))
    return driver.session()

if __name__ == '__main__':
    load_graph_using_spotify_api()

print("Done!")

2-cypher queries.cql

// structure of the graph
match (n)
return "Node" as Type,labels(n) as Name,count(n) as Count
union
match ()-[r]->()
return "Relationship" as Type,type(r) as Name, count(r) as Count

//links between two artists
match (a1:Artist), (a2:Artist),
path = allshortestpaths ((a1)-[*]-(a2))
where toUpper(a1.name) contains "BRUCE"
and toUpper(a2.name) contains "TOM"
return path
limit 10;

//table of interesting artists
match (a:Artist)
return a.name as ArtistName, a.`pagerank-spotify` as SpotifyPagerank, a.`pagerank-workedwith` as WorkedWithPageRank, a.`pagerank-similarity` as PageRankSimilarity
order by a.`pagerank-spotify` desc
limit 10

//neighborhood of most important artists
match path = ((a:Artist)-[*..2]-(conn))
return path
order by a.`pagerank-spotify` desc
limit 10

//some stats about number of tracks per artist
match (a:Artist)<--(t:Track)
return a.name as Artist, count(t) as NumberOfTracks
order by NumberOfTracks desc
limit 10;

//some stats about number of tracks per album
match (ar:Artist)<--(t:Track)-->(al:Album)-->(a)
return ar.name, al.name, count(t)
order by count(t) desc
limit 10

3-neodash-spotify-dashboard.json

{
    "title": "Welcome to my Spotify Dashboard!",
    "version": "1.0",
    "editable": true,
    "reports": [
      {
        "title": "Based on the following projects",
        "width": 12,
        "height": 4,
        "type": "text",
        "query": "* [Niels' Spofify Playlist Builder](https://nielsdejong.nl/neo4j%20projects/2020/09/23/spotify-playlist-builder.html)\n* [Niels' NeoDash project](https://nielsdejong.nl/neo4j%20projects/2020/11/16/neodash.html) - which enables this page!\n\n\n![Spotify logo](https://developer.spotify.com/assets/branding-guidelines/logo@2x.png)\n\nIn this Dashboard, we will show you how you can take a look at the small Spotify Graph that we created.!\nYou will find all the code on [github of course](https://gist.github.com/rvanbruggen/23a0eccbffa663a9203b8fea5be58468)..\n",
        "page": 1,
        "properties": [],
        "parameters": "",
        "refresh": 1
      },
      {
        "title": "Structure of the graph - queries:",
        "width": 12,
        "height": 4,
        "type": "text",
        "query": "Graphically:\n\n```\ncall db.schema.visualizations()\n```\n\nOr as a table:\n```\nmatch (n)\nreturn \"Node\" as Type,labels(n) as Name,count(n) as Count\nunion\nmatch ()-[r]->()\nreturn \"Relationship\" as Type,type(r) as Name, count(r) as Count\n```",
        "page": 1,
        "properties": [],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Structure of the graph",
        "width": 6,
        "height": 4,
        "type": "graph",
        "query": "call db.schema.visualization()",
        "page": 24,
        "properties": [
          "name",
          "name",
          "name",
          "name"
        ],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Structure of the graph",
        "width": 6,
        "height": 4,
        "type": "table",
        "query": "// structure of the graph\nmatch (n)\nreturn \"Node\" as Type,labels(n) as Name,count(n) as Count\nunion\nmatch ()-[r]->()\nreturn \"Relationship\" as Type,type(r) as Name, count(r) as Count\n",
        "page": 1,
        "properties": [],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Cypher queries:",
        "width": 12,
        "height": 8,
        "type": "text",
        "query": "#### Links between \"BRUCE\" and \"TOM\":\n```\nmatch (a1:Artist), (a2:Artist),\npath = allshortestpaths ((a1)-[*]-(a2))\nwhere toUpper(a1.name) contains \"BRUCE\"\nand toUpper(a2.name) contains \"TOM\"\nreturn path\nlimit 10;\n```\n\n#### Table of interesting artists\n```\nmatch (a:Artist)\nreturn a.name as ArtistName, a.`pagerank-spotify` as SpotifyPagerank, a.`pagerank-workedwith` as WorkedWithPageRank, a.`pagerank-similarity` as PageRankSimilarity\norder by a.`pagerank-spotify` desc\nlimit 10\n```\n\n#### Neighborhood of most important artists\n```\nmatch path = ((a:Artist)-[*..2]-(conn))\nreturn path\norder by a.`pagerank-spotify` desc\nlimit 10\n```\n\nSee below for all the results!\n",
        "page": 1,
        "properties": [],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Links between \"BRUCE\" and \"TOM\"",
        "width": 12,
        "height": 4,
        "type": "graph",
        "query": "match (a1:Artist), (a2:Artist), path = allshortestpaths((a1)-[*]-(a2))\nwhere toUpper(a1.name) contains \"BRUCE\"\nand toUpper(a2.name) contains \"TOM\"\nreturn path\nlimit 10",
        "page": 38,
        "properties": [
          "name",
          "name"
        ],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Table of Interesting Artists",
        "width": 12,
        "height": 4,
        "type": "table",
        "query": "match (a:Artist)\nreturn a.name as ArtistName, a.`pagerank-spotify` as SpotifyPagerank, a.`pagerank-workedwith` as WorkedWithPageRank, a.`pagerank-similarity` as PageRankSimilarity\norder by a.`pagerank-spotify` desc\nlimit 10",
        "page": 1,
        "properties": [],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "Neighborhood of important artists",
        "width": 12,
        "height": 4,
        "type": "graph",
        "query": "match path = ((a:Artist)-[*..2]-(conn))\nreturn path\norder by a.`pagerank-spotify` desc\nlimit 10",
        "page": 11,
        "properties": [
          "name"
        ],
        "parameters": "",
        "refresh": 0
      },
      {
        "title": "How many songs per Artist?",
        "width": 12,
        "height": 8,
        "type": "bar",
        "query": "match (a:Artist)<--(t:Track)\nreturn a.name as Artist, count(t) as NumberOfTracks\norder by NumberOfTracks desc\nlimit 10;\n",
        "page": 18,
        "properties": [],
        "parameters": "{\"x\":\"Artist\",\"y\":10}",
        "refresh": 0
      },
      {
        "title": "Number of Songs in an Album",
        "width": 12,
        "height": 8,
        "type": "bar",
        "query": "match (ar:Artist)<--(t:Track)-->(al:Album)-->(a)\nreturn ar.name as Artist, al.name as Album, count(t) as NumberOfSongs\norder by count(t) desc\nlimit 10\n",
        "page": 7,
        "properties": [
          "Album",
          "NumberOfSongs"
        ],
        "parameters": "{\"x\":\"Album\",\"y\":10}",
        "refresh": 0
      },
      {}
    ]
  }

MusConv Tool is a powerful music transfer tool that simplifies the process of moving music playlists and tracks between different streaming services.