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#sae2.04
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import pandas as pd
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import getpass
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import matplotlib.pyplot as plt
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import numpy as np
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from sqlalchemy import create_engine, exc, text
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df = pd.read_csv('spotify_songs.csv', sep=',', encoding="latin-1")
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print(df)
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print(df.columns)
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'''
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renvoie ['track_id', 'track_name', 'track_artist', 'track_popularity',
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'track_album_id', 'track_album_name', 'track_album_release_date',
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'playlist_name', 'playlist_id', 'playlist_genre', 'playlist_subgenre',
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'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
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'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
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'duration_ms']
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'''
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df = df.drop(columns=['key','mode','instrumentalness'])
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# Ces colonnes sont inutiles ici
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df = df.dropna()
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print(df['duration_ms'])
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df['duration_ms'] = pd.to_timedelta(df['duration_ms'], unit='ms')
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df = df.rename(columns={"duration_ms": "duration_m"})
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print(df['duration_m'])
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# Nous avons ici modifier la durée des chansons afin qu'elles soyent en minutes dans la base
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df = df.drop(151)
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# Cette ligne ne fonctionnant pas, nous la supprimons
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dfT = df[['track_id', 'track_name', 'track_popularity', 'duration_m', 'danceability', 'energy', 'loudness','speechiness', 'acousticness', 'liveness', 'valence', 'tempo', 'track_artist', 'track_album_id','playlist_id']]
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dfA = df[['track_album_id', 'track_album_name', 'track_album_release_date', 'track_artist']]
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dfP = df[['playlist_id', 'playlist_name', 'playlist_genre', 'playlist_subgenre']]
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dfArtist = df[['track_artist']]
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# Nous créons des dataframes qui seront plus tard les tables de notre base
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dfArtist = dfArtist.drop_duplicates()
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dfA = dfA.drop_duplicates()
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dfA['track_album_id'] = dfA['track_album_id'].drop_duplicates()
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dfA = dfA.dropna()
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dfP = dfP.drop_duplicates()
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dfP['playlist_id'] = dfP['playlist_id'].drop_duplicates()
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dfP = dfP.dropna()
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dfT = dfT.drop_duplicates()
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dfT['track_id'] = dfT['track_id'].drop_duplicates()
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dfT = dfT.dropna()
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# Nous traitons les données pour enlever les doublons et les lignes NaN
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co = None
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engine = create_engine("postgresql://reneveu:achanger@londres/dbreneveu")
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try :
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co = engine.connect()
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# Création de la base de données
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"""
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co.execute(text('''DROP TABLE IF EXISTS Artist CASCADE;'''))
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co.execute(text('''CREATE TABLE Artist(
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track_artist varchar(150),
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PRIMARY KEY (track_artist)
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);'''))
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co.execute(text('''DROP TABLE IF EXISTS Album CASCADE;'''))
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co.execute(text('''CREATE TABLE Album(
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track_album_id varchar(150),
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track_album_name varchar(500),
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track_album_release_date varchar(15),
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track_artist varchar(150) REFERENCES Artist,
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PRIMARY KEY (track_album_id)
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);'''))
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co.execute(text('''DROP TABLE IF EXISTS Playlist CASCADE;'''))
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co.execute(text('''CREATE TABLE Playlist(
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playlist_id varchar(150) PRIMARY KEY,
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playlist_name varchar(150),
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playlist_genre varchar(50),
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playlist_subgenre varchar(150)
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);'''))
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co.execute(text('''DROP TABLE IF EXISTS Track CASCADE;'''))
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co.execute(text('''CREATE TABLE Track(
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track_id varchar(150),
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track_name varchar(150),
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track_popularity numeric,
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duration_m time,
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danceability numeric,
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energy numeric,
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loudness numeric,
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speechiness numeric,
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acousticness numeric,
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liveness numeric,
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valence numeric,
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tempo numeric,
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track_artist varchar(150) REFERENCES Artist,
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track_album_id varchar(150) REFERENCES Album,
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playlist_id varchar(150) REFERENCES Playlist,
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PRIMARY KEY (track_id)
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);'''))
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for row in dfArtist.itertuples():
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co.execute(text('''INSERT INTO Artist VALUES(:1);'''),
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{'1': row.track_artist})
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co.execute(text('''SELECT * FROM Artist;'''))
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co.commit()
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for row in dfA.itertuples():
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co.execute(text('''INSERT INTO Album VALUES(:1, :2, :3, :4);'''),
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{'1': row.track_album_id, '2': row.track_album_name, '3': row.track_album_release_date, '4': row.track_artist})
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co.execute(text('''SELECT * FROM Album;'''))
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co.commit()
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for row in dfP.itertuples():
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co.execute(text('''INSERT INTO Playlist VALUES(:1, :2, :3, :4);'''),
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{'1': row.playlist_id, '2': row.playlist_name, '3': row.playlist_genre, '4': row.playlist_subgenre})
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co.execute(text('''SELECT * FROM Playlist;'''))
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co.commit()
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for row in dfT.itertuples():
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co.execute(text('''INSERT INTO Track VALUES(:1, :2, :3, :4, :5, :6, :7, :8, :9, :10, :11, :12, :13, :14, :15);'''),
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{'1': row.track_id, '2': row.track_name, '3': row.track_popularity, '4': row.duration_m, '5': row.danceability, '6': row.energy, '7': row.loudness,
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'8': row.speechiness, '9': row.acousticness, '10': row.liveness, '11': row.valence, '12': row.tempo, '13': row.track_artist, '14': row.track_album_id,
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'15': row.playlist_id})
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co.execute(text('''SELECT * FROM Track;'''))
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co.commit()
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"""
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# Tentative de graphique
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pop = pd.read_sql(text('''SELECT substr(a.track_album_release_date, 1,4) date,
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t.track_name nom,
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t.track_popularity pop
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FROM Track t
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JOIN Album a ON a.track_album_id = t.track_album_id
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WHERE t.track_popularity > 90
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GROUP BY date,nom,pop ORDER BY date,nom,pop;'''), con=co)
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#(SELECT track_name FROM Track NATURAL JOIN Album a2 WHERE substr(a2.track_album_release_date, 1,4) = ) and track_popularity = max(t.track_popularity)
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# pop["track_album_release_date"]=pop["track_album_release_date"].astype('str')
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fig = pop.plot(x='date', y=['nom', 'pop'], style=['o-', 'x--'])
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fig.set_title("Popularité de titre par année")
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fig.legend(['Titre', 'Popularité'])
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fig.set_xlabel("Année")
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fig.set_ylabel("Popularité")
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fig.set_ylim(0)
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plt.show()
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test = pd.read_sql(text('''SELECT substr(a.track_album_release_date, 1,4) date, t.track_name nom FROM Track t
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NATURAL JOIN Album a
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GROUP BY date, nom ORDER BY date,nom LIMIT 1;'''), con=co)
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test["date"]=test["date"].astype('int')
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fig = test.plot(x='nom', y='date', legend=False , kind='bar')
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plt.xticks(rotation=0)
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# fig = test.plot(x='annee', y='consototale')
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fig.set_title("date des titres")
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fig.set_xlabel("Année")
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fig.set_ylabel("Titre")
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fig.set_ylim(0)
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plt.show()
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except exc.SQLAlchemyError as e:
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print(e)
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finally :
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if co is not None:
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co.close()
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# Création d'un graphique montrant les pourcentage de chaque genre par playlist
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df['playlist_genre'].value_counts().plot.pie(ylabel='', autopct='%1.1f%%')
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plt.show()
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# Travail d'Adryen
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# Afficher la version de pandas
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print(pd.__version__)
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# Lecture du fichier csv
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df_pop = pd.read_csv("spotify_songs.csv")
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print(df_pop.head(5))
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# Supprimer les lignes avec des valeurs manquantes
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df_pop = df_pop.dropna()
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# Trier le DataFrame par artiste
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sorted_df = df_pop.sort_values(by='track_artist')
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artistes = sorted_df["track_artist"].unique()
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# Créer une liste des artistes uniques
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print(artistes)
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# Créer une figure pour le premier graphique
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plt.figure(figsize=(10, 6))
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# Extraire les noms des chansons et leur popularité
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names = sorted_df["track_name"]
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popularity = sorted_df["track_popularity"]
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# Filtrer les chansons avec une popularité >= 90
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filtered_names = [name for name, pop in zip(names, popularity) if pop >= 90]
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filtered_popularity = [pop for pop in popularity if pop >= 90]
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# Tracer le premier graphique en barres horizontales
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plt.barh(filtered_names, filtered_popularity, color='skyblue')
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plt.xlabel('Indice de popularité')
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plt.ylabel('Chansons')
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plt.title('Analyse des indices de popularité des chansons')
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plt.grid(True)
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plt.show()
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# Créer un dictionnaire pour stocker la popularité par artiste
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popularity_by_artist = {}
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# Remplir le dictionnaire avec les indices de popularité correspondant à chaque artiste
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for name, pop in zip(names, popularity):
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artiste = sorted_df.loc[sorted_df['track_name'] == name, 'track_artist'].iloc[0]
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if artiste not in popularity_by_artist:
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popularity_by_artist[artiste] = []
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popularity_by_artist[artiste].append(pop)
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# Créer un dictionnaire pour stocker la popularité moyenne par artiste
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average_popularity_by_artist = {}
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# Calculer la popularité moyenne pour chaque artiste
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for artiste, popularity_list in popularity_by_artist.items():
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average_popularity = np.mean(popularity_list)
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average_popularity_by_artist[artiste] = average_popularity
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# Filtrer les artistes avec une popularité moyenne supérieure à 85
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selected_artists = {artiste: popularity for artiste, popularity in average_popularity_by_artist.items() if popularity > 80}
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# Créer un graphique pour la deuxième analyse
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plt.figure(figsize=(10, 6))
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# Tracer un graphique des indices de popularité moyens des artistes sélectionnés
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plt.bar(selected_artists.keys(), selected_artists.values(), color='skyblue')
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plt.xlabel('Artistes')
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plt.ylabel('Popularité moyenne')
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plt.title('Analyse de la popularité moyenne des artistes (Popularité moyenne > 85)')
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plt.grid(True)
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plt.xticks(rotation=90) # Pour faire pivoter les étiquettes des axes x
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plt.tight_layout() # Ajuster automatiquement les sous-graphiques pour éviter les chevauchements
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plt.show()
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