pushing - 1

functions/visualizations.py

@@ -5,20 +5,23 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 import networkx as nx
 import plotly.graph_objects as go
-from itertools import  chain,combinations
+from itertools import chain, combinations
 import numpy as np
 from collections import Counter
 
 
 def generate_popularity_trends(df):
     st.header("Popularity Trends Over Time")
-    tab1, tab2, tab3 = st.tabs(["Average Popularity", "Individual Songs", "Top 10 Songs"])
-    
+    tab1, tab2, tab3 = st.tabs(
+        ["Average Popularity", "Individual Songs", "Top 10 Songs"])
+
     with tab1:
-        st.markdown("**Average Popularity by Decade:** This chart shows how the average popularity of songs has changed over different decades.")
+        st.markdown(
+            "**Average Popularity by Decade:** This chart shows how the average popularity of songs has changed over different decades.")
         if 'Decade' in df.columns:
-            top_decades = df.groupby('Decade')['Popularity'].mean().reset_index().nlargest(10, 'Popularity')
-            
+            top_decades = df.groupby('Decade')['Popularity'].mean(
+            ).reset_index().nlargest(10, 'Popularity')
+
             fig1 = go.Figure()
             fig1.add_trace(go.Scatter(
                 x=top_decades['Decade'],
@@ -26,7 +29,8 @@ def generate_popularity_trends(df):
                 mode='lines+markers',
                 fill='tonexty',
                 line=dict(color='royalblue', width=3),
-                marker=dict(size=8, color='darkblue', line=dict(width=2, color='white')),
+                marker=dict(size=8, color='darkblue',
+                            line=dict(width=2, color='white')),
                 name='Popularity',
                 hovertext=top_decades['Decade']
             ))
@@ -41,9 +45,10 @@ def generate_popularity_trends(df):
             st.plotly_chart(fig1)
         else:
             st.error("Cannot plot: 'Decade' column missing.")
-    
+
     with tab2:
-        st.markdown("**Top 10 Individual Songs:** This scatter plot highlights the popularity of the top 10 most popular songs over time.")
+        st.markdown(
+            "**Top 10 Individual Songs:** This scatter plot highlights the popularity of the top 10 most popular songs over time.")
         if 'Year' in df.columns:
             top_songs = df.nlargest(10, 'Popularity')
             fig2 = px.scatter(
@@ -64,17 +69,20 @@ def generate_popularity_trends(df):
             st.plotly_chart(fig2)
         else:
             st.error("Cannot plot: 'Year' column missing.")
-    
+
     with tab3:
-        st.markdown("**Top 10 Most Popular Songs:** This bar chart displays the top 10 songs based on their popularity scores.")
+        st.markdown(
+            "**Top 10 Most Popular Songs:** This bar chart displays the top 10 songs based on their popularity scores.")
         if 'Track Name' in df.columns and 'Popularity' in df.columns:
-            top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Artist Name(s)', 'Popularity']]
+            top_songs = df.nlargest(10, 'Popularity')[
+                ['Track Name', 'Artist Name(s)', 'Popularity']]
             fig3 = px.bar(
                 top_songs, y='Track Name', x='Popularity',
                 orientation='h', color='Popularity',
                 color_continuous_scale='deep',
                 title='Top 10 Most Popular Songs',
-                labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score'},
+                labels={'Track Name': 'Song Title',
+                        'Popularity': 'Popularity Score'},
                 hover_data=['Track Name', 'Artist Name(s)']
             )
             fig3.update_layout(
@@ -89,21 +97,22 @@ def generate_popularity_trends(df):
             st.error("Cannot plot: 'Track Name' or 'Popularity' column missing.")
 
 
-def generate_audio_features(df): 
+def generate_audio_features(df):
     st.header("Audio Features Analysis")
 
     feature = st.selectbox(
         "Select Feature", ['Danceability', 'Energy', 'Tempo', 'Loudness']
     )
-    
+
     tab1, tab2 = st.tabs(["Distribution", "By Decade"])
-    
+
     with tab1:
-        st.markdown(f"**Top 20 {feature} Values:** This bar chart displays the distribution of the top 20 songs based on {feature}.")
+        st.markdown(
+            f"**Top 20 {feature} Values:** This bar chart displays the distribution of the top 20 songs based on {feature}.")
         top_features = df.nlargest(20, feature)
 
         fig = px.bar(
-            top_features, x='Track Name', y=feature, 
+            top_features, x='Track Name', y=feature,
             color='Decade' if 'Decade' in df.columns else None,
             title=f'Top 20 Songs by {feature}',
             color_discrete_sequence=px.colors.qualitative.Set2,
@@ -111,12 +120,14 @@ def generate_audio_features(df):
         )
         fig.update_layout(xaxis_tickangle=-45, template='plotly_white')
         st.plotly_chart(fig)
-    
+
     with tab2:
-        st.markdown(f"**{feature} by Decade:** This line chart compares the top {feature} trends over different decades.")
+        st.markdown(
+            f"**{feature} by Decade:** This line chart compares the top {feature} trends over different decades.")
 
         if 'Decade' in df.columns:
-            avg_feature_by_decade = df.groupby('Decade')[feature].mean().reset_index()
+            avg_feature_by_decade = df.groupby(
+                'Decade')[feature].mean().reset_index()
 
             fig2 = px.line(
                 avg_feature_by_decade, x='Decade', y=feature,
@@ -130,14 +141,18 @@ def generate_audio_features(df):
         else:
             st.error("Cannot plot: 'Decade' column missing.")
 
+
 def generate_genre_analysis(df):
     st.header("Genre & Artist Analysis")
-    tab1, tab2, tab3 = st.tabs(["Top Genres", "Genre Distribution", "Artist Popularity"])
-    
+    tab1, tab2, tab3 = st.tabs(
+        ["Top Genres", "Genre Distribution", "Artist Popularity"])
+
     with tab1:
-        st.markdown("**Top Genres in Top 10 Songs:** Displays the most common genres among the top 10 most popular songs.")
+        st.markdown(
+            "**Top Genres in Top 10 Songs:** Displays the most common genres among the top 10 most popular songs.")
         top_songs = df.nlargest(10, 'Popularity')
-        top_genres = top_songs.explode('Genres')['Genres'].value_counts().reset_index()
+        top_genres = top_songs.explode(
+            'Genres')['Genres'].value_counts().reset_index()
         fig1 = px.bar(
             top_genres, x='count', y='Genres',
             orientation='h', color='count',
@@ -148,45 +163,55 @@ def generate_genre_analysis(df):
         )
         fig1.update_layout(template='plotly_white', width=900, height=500)
         st.plotly_chart(fig1)
-    
+
     with tab2:
-        st.markdown("**Genre Distribution in Top 10 Songs:** Shows how different genres contribute to the top 10 songs.")
+        st.markdown(
+            "**Genre Distribution in Top 10 Songs:** Shows how different genres contribute to the top 10 songs.")
         genre_song_data = top_songs.explode('Genres')
         fig2 = px.bar(
             genre_song_data, x='Track Name', y='Popularity', color='Genres',
             title='Genre Distribution in Top 10 Songs',
-            labels={'Track Name': 'Song Title', 'Popularity': 'Popularity Score', 'Genres': 'Genre'},
+            labels={'Track Name': 'Song Title',
+                    'Popularity': 'Popularity Score', 'Genres': 'Genre'},
             barmode='stack',
             hover_data=['Track Name', 'Genres']
         )
         fig2.update_layout(template='plotly_white', width=900, height=500)
         st.plotly_chart(fig2)
-    
+
     with tab3:
-        st.markdown("**Artist Popularity in Top 10 Songs:** Visualizes the most popular artists in the top 10 songs with their song count and names.")
-        artist_popularity = top_songs.groupby('Artist Name(s)').agg({'Popularity': 'sum', 'Track Name': lambda x: list(x)}).reset_index().sort_values(by='Popularity', ascending=False)
-        artist_popularity['Song Count'] = artist_popularity['Track Name'].apply(len)
+        st.markdown(
+            "**Artist Popularity in Top 10 Songs:** Visualizes the most popular artists in the top 10 songs with their song count and names.")
+        artist_popularity = top_songs.groupby('Artist Name(s)').agg(
+            {'Popularity': 'sum', 'Track Name': lambda x: list(x)}).reset_index().sort_values(by='Popularity', ascending=False)
+        artist_popularity['Song Count'] = artist_popularity['Track Name'].apply(
+            len)
         fig3 = px.bar(
             artist_popularity, x='Popularity', y='Artist Name(s)',
             orientation='h', color='Popularity',
             color_continuous_scale='blues',
             title='Artist Popularity in Top 10 Songs',
-            labels={'Artist Name(s)': 'Artist Name', 'Popularity': 'Total Popularity Score', 'Song Count': 'Number of Songs'},
-            hover_data={'Artist Name(s)': True, 'Popularity': True, 'Song Count': True, 'Track Name': True}
+            labels={'Artist Name(s)': 'Artist Name',
+                                 'Popularity': 'Total Popularity Score', 'Song Count': 'Number of Songs'},
+            hover_data={'Artist Name(s)': True, 'Popularity': True,
+                        'Song Count': True, 'Track Name': True}
         )
         fig3.update_layout(template='plotly_white', width=900, height=500)
         st.plotly_chart(fig3)
 
+
 def generate_explicit_trends(df):
     st.header("Explicit Content Trends")
     st.markdown("**Explicit vs Non-Explicit Songs Over Time:** This line chart shows how the number of explicit and non-explicit songs has changed over different decades.")
     if 'Decade' in df.columns and 'Explicit' in df.columns:
-        explicit_trends = df.groupby(['Decade', 'Explicit']).size().reset_index(name='Count')
+        explicit_trends = df.groupby(
+            ['Decade', 'Explicit']).size().reset_index(name='Count')
         fig = px.line(
             explicit_trends, x='Decade', y='Count', color='Explicit',
             markers=True, line_shape='linear',
             title='Explicit vs Non-Explicit Songs Over Time',
-            labels={'Decade': 'Decade', 'Count': 'Number of Songs', 'Explicit': 'Song Type'},
+            labels={'Decade': 'Decade', 'Count': 'Number of Songs',
+                    'Explicit': 'Song Type'},
             color_discrete_map={True: 'purple', False: 'green'}
         )
         fig.update_layout(template='plotly_white', width=900, height=500)
@@ -194,12 +219,14 @@ def generate_explicit_trends(df):
     else:
         st.error("Cannot plot: 'Decade' or 'Explicit' column missing.")
 
+
 def generate_album_insights(df):
     st.header("Album & Label Insights")
     tab1, tab2 = st.tabs(["Top Labels", "Album Popularity"])
-    
+
     with tab1:
-        st.markdown("**Top Record Labels:** Displays the most dominant record labels based on the number of songs they have released.")
+        st.markdown(
+            "**Top Record Labels:** Displays the most dominant record labels based on the number of songs they have released.")
         if 'Label' in df.columns:
             top_labels = df['Label'].value_counts().nlargest(10).reset_index()
             fig9 = px.sunburst(
@@ -212,17 +239,21 @@ def generate_album_insights(df):
             st.plotly_chart(fig9)
         else:
             st.error("Cannot plot: 'Label' column missing.")
-    
+
     with tab2:
-        st.markdown("**Album Popularity:** Compares the popularity of albums based on the number of songs and their average popularity score.")
+        st.markdown(
+            "**Album Popularity:** Compares the popularity of albums based on the number of songs and their average popularity score.")
         if 'Album Name' in df.columns and 'Popularity' in df.columns:
-            album_pop = df.groupby('Album Name')['Popularity'].agg(['mean', 'count']).reset_index()
-            album_pop = album_pop.sort_values(by=['mean', 'count'], ascending=[False, False]).nlargest(10, 'mean')
+            album_pop = df.groupby('Album Name')['Popularity'].agg(
+                ['mean', 'count']).reset_index()
+            album_pop = album_pop.sort_values(by=['mean', 'count'], ascending=[
+                                              False, False]).nlargest(10, 'mean')
             fig10 = px.strip(
                 album_pop, x='mean', y='Album Name',
                 color='count',
                 title='Top 10 Albums by Popularity',
-                labels={'Album Name': 'Album', 'mean': 'Average Popularity Score', 'count': 'Number of Songs'},
+                labels={'Album Name': 'Album',
+                        'mean': 'Average Popularity Score', 'count': 'Number of Songs'},
                 hover_data={'Album Name': True, 'count': True, 'mean': True},
                 color_discrete_sequence=px.colors.qualitative.Pastel
             )
@@ -239,16 +270,19 @@ def generate_tempo_mood(df):
         st.markdown("**Tempo Trends:** Tracks tempo changes.")
         if 'Year' in df.columns and 'Tempo' in df.columns:
             tempo_by_year = df.groupby('Year')['Tempo'].mean().reset_index()
-            fig11 = px.line(tempo_by_year, x='Year', y='Tempo', title='Average Tempo Over Time', color_discrete_sequence=['orange'])
+            fig11 = px.line(tempo_by_year, x='Year', y='Tempo',
+                            title='Average Tempo Over Time', color_discrete_sequence=['orange'])
             fig11.update_layout(template='plotly_white', width=800, height=400)
             st.plotly_chart(fig11)
         else:
             st.error("Cannot plot: 'Year' or 'Tempo' column missing.")
     with tab2:
-        st.markdown("**Mood Analysis (Valence & Energy):** Categorizes songs based on mood and energy.")
+        st.markdown(
+            "**Mood Analysis (Valence & Energy):** Categorizes songs based on mood and energy.")
         if 'Valence' in df.columns and 'Energy' in df.columns:
             top_songs = df.nlargest(10, 'Popularity')
-            mood_by_valence = top_songs.groupby('Valence')['Energy'].mean().reset_index()
+            mood_by_valence = top_songs.groupby(
+                'Valence')['Energy'].mean().reset_index()
             fig12 = px.bar(
                 mood_by_valence, x='Valence', y='Energy',
                 title='Average Energy Levels by Valence (Mood Analysis)',
@@ -258,14 +292,17 @@ def generate_tempo_mood(df):
             st.plotly_chart(fig12)
         else:
             st.error("Cannot plot: 'Valence' or 'Energy' column missing.")
+
+
 def generate_top_artists_songs(df):
     st.header("Top Artists and Songs")
     tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
-    
+
     with tab1:
         st.markdown("**Most Featured Artists:** Shows top artists.")
         if 'Artist Name(s)' in df.columns:
-            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(
+                10).reset_index()
             fig13 = px.bar(
                 top_artists, x='count', y='Artist Name(s)',
                 orientation='h',
@@ -276,11 +313,12 @@ def generate_top_artists_songs(df):
             st.plotly_chart(fig13)
         else:
             st.error("Cannot plot: 'Artist Name(s)' column missing.")
-    
+
     with tab2:
         st.markdown("**Top 10 Songs:** Lists top songs.")
         if 'Track Name' in df.columns and 'Popularity' in df.columns:
-            top_songs = df.nlargest(10, 'Popularity')[['Track Name', 'Popularity']]
+            top_songs = df.nlargest(10, 'Popularity')[
+                ['Track Name', 'Popularity']]
             fig14 = px.pie(
                 top_songs, values='Popularity', names='Track Name',
                 title='Top 10 Songs by Popularity', color_discrete_sequence=px.colors.qualitative.Set3
@@ -297,8 +335,10 @@ def generate_album_release_trends(df):
     with tab1:
         st.markdown("**Albums per Year:** Tracks release patterns.")
         if 'Year' in df.columns:
-            albums_per_year = df['Year'].value_counts().sort_index().reset_index()
-            fig15 = px.line(albums_per_year, x='Year', y='count', title='Number of Albums Released per Year', color_discrete_sequence=['purple'])
+            albums_per_year = df['Year'].value_counts(
+            ).sort_index().reset_index()
+            fig15 = px.line(albums_per_year, x='Year', y='count',
+                            title='Number of Albums Released per Year', color_discrete_sequence=['purple'])
             fig15.update_layout(template='plotly_white', width=800, height=400)
             st.plotly_chart(fig15)
         else:
@@ -307,11 +347,13 @@ def generate_album_release_trends(df):
         st.markdown("**Songs by Artists and Years:** Visualizes trends.")
         if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
             # Filter to only show the top 10 most featured artists
-            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).index
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(
+                10).index
             filtered_df = df[df['Artist Name(s)'].isin(top_artists)]
-            
+
             # Grouping data
-            artist_year = filtered_df.groupby(['Year', 'Artist Name(s)']).size().reset_index(name='Count')
+            artist_year = filtered_df.groupby(
+                ['Year', 'Artist Name(s)']).size().reset_index(name='Count')
 
             # Create a grouped bar chart
             fig16 = px.bar(
@@ -325,6 +367,8 @@ def generate_album_release_trends(df):
             st.plotly_chart(fig16)
         else:
             st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
+
+
 def generate_duration_analysis(df):
     st.header("Track Duration Analysis")
     tab1, tab2 = st.tabs(["Distribution", "By Decade"])
@@ -333,7 +377,8 @@ def generate_duration_analysis(df):
     df = df[df['Track Duration (ms)'] <= 900000]
 
     with tab1:
-        st.markdown("**Track Duration Distribution:** Illustrates how track durations vary, helping identify common song lengths.")
+        st.markdown(
+            "**Track Duration Distribution:** Illustrates how track durations vary, helping identify common song lengths.")
         if 'Track Duration (ms)' in df.columns:
             fig17 = px.histogram(
                 df, x='Track Duration (ms)',
@@ -345,12 +390,14 @@ def generate_duration_analysis(df):
             st.plotly_chart(fig17)
         else:
             st.error("Cannot plot: 'Track Duration (ms)' column missing.")
-    
+
     with tab2:
-        st.markdown("**Duration by Decade:** Compares the evolution of average track durations across decades, showing historical trends.")
+        st.markdown(
+            "**Duration by Decade:** Compares the evolution of average track durations across decades, showing historical trends.")
         if 'Decade' in df.columns and 'Track Duration (ms)' in df.columns:
             fig18 = px.pie(
-                df.groupby('Decade')['Track Duration (ms)'].mean().reset_index(),
+                df.groupby('Decade')[
+                    'Track Duration (ms)'].mean().reset_index(),
                 names='Decade', values='Track Duration (ms)',
                 title='Average Track Duration by Decade',
                 color_discrete_sequence=px.colors.qualitative.Set2
@@ -358,53 +405,63 @@ def generate_duration_analysis(df):
             fig18.update_layout(template='plotly_white', width=800, height=400)
             st.plotly_chart(fig18)
         else:
-            st.error("Cannot plot: 'Decade' or 'Track Duration (ms)' column missing.")
-
+            st.error(
+                "Cannot plot: 'Decade' or 'Track Duration (ms)' column missing.")
 
 
 def generate_streaming_insights(df):
     st.header("Streaming and Engagement Insights")
     tab1, tab2 = st.tabs(["Popularity vs Duration", "Time Signature"])
-    
+
     with tab1:
-        st.markdown("**Popularity vs Track Duration:** This line chart shows the trend of song popularity based on their duration.")
-        
+        st.markdown(
+            "**Popularity vs Track Duration:** This line chart shows the trend of song popularity based on their duration.")
+
         if 'Track Duration (ms)' in df.columns and 'Popularity' in df.columns:
             df['Duration (minutes)'] = df['Track Duration (ms)'] / 60000
-            duration_bins = pd.cut(df['Duration (minutes)'], bins=[0, 2, 4, 6, 8, 10, 15], labels=['0-2', '2-4', '4-6', '6-8', '8-10', '10+'])
-            avg_popularity = df.groupby(duration_bins)['Popularity'].mean().reset_index()
+            duration_bins = pd.cut(df['Duration (minutes)'], bins=[
+                                   0, 2, 4, 6, 8, 10, 15], labels=['0-2', '2-4', '4-6', '6-8', '8-10', '10+'])
+            avg_popularity = df.groupby(duration_bins)[
+                'Popularity'].mean().reset_index()
 
             fig1 = px.line(
-                avg_popularity, 
-                x='Duration (minutes)', 
+                avg_popularity,
+                x='Duration (minutes)',
                 y='Popularity',
                 title='Popularity vs. Track Duration',
                 markers=True,  # Adds points to the line
                 line_shape='spline',  # Smoothens the line
                 color_discrete_sequence=['blue']
             )
-            fig1.update_layout(template='plotly_white', xaxis_title='Track Duration (Minutes)', yaxis_title='Average Popularity')
+            fig1.update_layout(
+                template='plotly_white', xaxis_title='Track Duration (Minutes)', yaxis_title='Average Popularity')
             st.plotly_chart(fig1)
         else:
-            st.error("Cannot plot: 'Track Duration (ms)' or 'Popularity' column missing.")
+            st.error(
+                "Cannot plot: 'Track Duration (ms)' or 'Popularity' column missing.")
 
     with tab2:
-        st.markdown("**Popularity by Time Signature:** This bar chart compares the average popularity of songs based on their time signatures.")
+        st.markdown(
+            "**Popularity by Time Signature:** This bar chart compares the average popularity of songs based on their time signatures.")
 
         if 'Time Signature' in df.columns and 'Popularity' in df.columns:
-            pop_by_time = df.groupby('Time Signature')['Popularity'].mean().reset_index()
+            pop_by_time = df.groupby('Time Signature')[
+                'Popularity'].mean().reset_index()
             fig2 = px.bar(
-                pop_by_time, 
-                x='Time Signature', 
+                pop_by_time,
+                x='Time Signature',
                 y='Popularity',
                 title='Average Popularity by Time Signature',
                 color='Popularity',
                 color_continuous_scale='purples'
             )
-            fig2.update_layout(template='plotly_white', xaxis_title='Time Signature', yaxis_title='Average Popularity')
+            fig2.update_layout(
+                template='plotly_white', xaxis_title='Time Signature', yaxis_title='Average Popularity')
             st.plotly_chart(fig2)
         else:
-            st.error("Cannot plot: 'Time Signature' or 'Popularity' column missing.")
+            st.error(
+                "Cannot plot: 'Time Signature' or 'Popularity' column missing.")
+
 
 def generate_feature_comparisons(df):
     st.header("Feature Comparisons Across Decades")
@@ -412,7 +469,8 @@ def generate_feature_comparisons(df):
     with tab1:
         st.markdown("**Feature Comparison:** Compares features across decades.")
         if 'Decade' in df.columns:
-            features_by_decade = df.groupby('Decade')[['Danceability', 'Energy', 'Valence']].mean().reset_index()
+            features_by_decade = df.groupby(
+                'Decade')[['Danceability', 'Energy', 'Valence']].mean().reset_index()
             fig21 = px.bar(features_by_decade.melt(id_vars='Decade'), x='Decade', y='value', color='variable',
                            barmode='group', title='Feature Comparison by Decade', color_discrete_sequence=px.colors.qualitative.Pastel)
             fig21.update_layout(template='plotly_white', width=800, height=400)
@@ -422,21 +480,26 @@ def generate_feature_comparisons(df):
     with tab2:
         st.markdown("**Loudness Over Time:** Tracks loudness trends.")
         if 'Year' in df.columns and 'Loudness' in df.columns:
-            loudness_by_year = df.groupby('Year')['Loudness'].mean().reset_index()
-            fig22 = px.line(loudness_by_year, x='Year', y='Loudness', title='Average Loudness Over Time', color_discrete_sequence=['green'])
+            loudness_by_year = df.groupby(
+                'Year')['Loudness'].mean().reset_index()
+            fig22 = px.line(loudness_by_year, x='Year', y='Loudness',
+                            title='Average Loudness Over Time', color_discrete_sequence=['green'])
             fig22.update_layout(template='plotly_white', width=800, height=400)
             st.plotly_chart(fig22)
         else:
             st.error("Cannot plot: 'Year' or 'Loudness' column missing.")
 
+
 def generate_top_artists_songs(df):
     st.header("Top Artists and Songs")
     tab1, tab2 = st.tabs(["Top Artists", "Top Songs"])
-    
+
     with tab1:
-        st.markdown("**Most Featured Artists:** Displays the top 10 artists with the highest song counts, highlighting their dominance in the dataset.")
+        st.markdown(
+            "**Most Featured Artists:** Displays the top 10 artists with the highest song counts, highlighting their dominance in the dataset.")
         if 'Artist Name(s)' in df.columns:
-            top_artists = df['Artist Name(s)'].value_counts().nlargest(10).reset_index()
+            top_artists = df['Artist Name(s)'].value_counts().nlargest(
+                10).reset_index()
             top_artists.columns = ['Artist Name(s)', 'Count']
             fig13 = px.sunburst(
                 top_artists, path=['Artist Name(s)'], values='Count',
@@ -448,11 +511,13 @@ def generate_top_artists_songs(df):
             st.plotly_chart(fig13)
         else:
             st.error("Cannot plot: 'Artist Name(s)' column missing.")
-    
+
     with tab2:
-        st.markdown("**Songs by Artists and Years:** Analyzes song release trends across different years, focusing on the top artists.")
+        st.markdown(
+            "**Songs by Artists and Years:** Analyzes song release trends across different years, focusing on the top artists.")
         if 'Artist Name(s)' in df.columns and 'Year' in df.columns:
-            artist_year = df.groupby(['Artist Name(s)', 'Year']).size().reset_index(name='Count')
+            artist_year = df.groupby(
+                ['Artist Name(s)', 'Year']).size().reset_index(name='Count')
             fig16 = px.sunburst(
                 artist_year, path=['Year', 'Artist Name(s)'], values='Count',
                 title='Songs Released by Artists Over the Years',
@@ -464,60 +529,74 @@ def generate_top_artists_songs(df):
         else:
             st.error("Cannot plot: 'Artist Name(s)' or 'Year' column missing.")
 
+
 def generate_network_analysis(df):
     st.header("Network Analysis")
     tab1, tab2 = st.tabs(["Artist Collaborations", "Genre Crossover"])
-    
+
     # Ensure column names are stripped of spaces
     df.columns = df.columns.str.strip()
-    
+
     with tab1:
-        st.markdown("**Top Collaborating Artists:** This chart highlights artists who frequently collaborate with each other.")
+        st.markdown(
+            "**Top Collaborating Artists:** This chart highlights artists who frequently collaborate with each other.")
         if 'Artist Name(s)' in df.columns:
-            df['Artist Name(s)'] = df['Artist Name(s)'].astype(str).str.split(', ')
+            df['Artist Name(s)'] = df['Artist Name(s)'].astype(
+                str).str.split(', ')
             collaborations = []
             for artists in df['Artist Name(s)']:
                 collaborations.extend(combinations(sorted(artists), 2))
-            
+
             collab_counts = Counter(collaborations)
-            top_collabs = sorted(collab_counts.items(), key=lambda x: x[1], reverse=True)[:20]
-            
+            top_collabs = sorted(collab_counts.items(),
+                                 key=lambda x: x[1], reverse=True)[:20]
+
             G = nx.Graph()
             for (artist1, artist2), weight in top_collabs:
                 G.add_edge(artist1, artist2, weight=weight)
-            
+
             pos = nx.spring_layout(G, seed=42)
             plt.figure(figsize=(12, 8))
-            edges = nx.draw_networkx_edges(G, pos, alpha=0.5, width=[G[u][v]['weight'] for u,v in G.edges()])
-            nodes = nx.draw_networkx_nodes(G, pos, node_size=700, node_color='orange')
-            labels = nx.draw_networkx_labels(G, pos, font_size=10, font_weight='bold')
+            edges = nx.draw_networkx_edges(G, pos, alpha=0.5, width=[
+                                           G[u][v]['weight'] for u, v in G.edges()])
+            nodes = nx.draw_networkx_nodes(
+                G, pos, node_size=700, node_color='orange')
+            labels = nx.draw_networkx_labels(
+                G, pos, font_size=10, font_weight='bold')
             plt.title("Top 20 Artist Collaborations")
             st.pyplot(plt)
         else:
-            st.error("Cannot plot: 'Artist Name(s)' column missing. Available columns: " + ", ".join(df.columns))
-    
+            st.error(
+                "Cannot plot: 'Artist Name(s)' column missing. Available columns: " + ", ".join(df.columns))
+
     with tab2:
-        st.markdown("**Genre Crossover:** This chart shows how different music genres are connected and often blend together.")
+        st.markdown(
+            "**Genre Crossover:** This chart shows how different music genres are connected and often blend together.")
         if 'Genres' in df.columns:
             df['Genres'] = df['Genres'].astype(str).str.split(', ')
             genre_pairs = []
             for genres in df['Genres']:
                 genre_pairs.extend(combinations(sorted(set(genres)), 2))
-            
+
             genre_counts = Counter(genre_pairs)
-            top_genre_pairs = sorted(genre_counts.items(), key=lambda x: x[1], reverse=True)[:20]
-            
-            labels = list(set(chain.from_iterable([pair[0] for pair in top_genre_pairs])))
+            top_genre_pairs = sorted(
+                genre_counts.items(), key=lambda x: x[1], reverse=True)[:20]
+
+            labels = list(set(chain.from_iterable(
+                [pair[0] for pair in top_genre_pairs])))
             matrix = [[0] * len(labels) for _ in range(len(labels))]
-            
+
             label_index = {label: i for i, label in enumerate(labels)}
             for (genre1, genre2), count in top_genre_pairs:
                 i, j = label_index[genre1], label_index[genre2]
                 matrix[i][j] = count
                 matrix[j][i] = count
-            
-            fig = go.Figure(data=[go.Heatmap(z=matrix, x=labels, y=labels, colorscale='OrRd', text=matrix, hoverinfo='text')])
-            fig.update_layout(title="Genre Crossover Chord Diagram", xaxis_title="Genres", yaxis_title="Genres")
+
+            fig = go.Figure(data=[go.Heatmap(
+                z=matrix, x=labels, y=labels, colorscale='OrRd', text=matrix, hoverinfo='text')])
+            fig.update_layout(title="Genre Crossover Chord Diagram",
+                              xaxis_title="Genres", yaxis_title="Genres")
             st.plotly_chart(fig)
         else:
-            st.error("Cannot plot: 'Genres' column missing. Available columns: " + ", ".join(df.columns))
+            st.error(
+                "Cannot plot: 'Genres' column missing. Available columns: " + ", ".join(df.columns))