Upload app.py

app.py

@@ -30,7 +30,7 @@ if uploaded_file is not None:
 
     # Seleccion de categorias
     st.write(df_050.shape)
-    MAX_CAT = st.slider('Maximo numero de categorias: ', 10, 30, 20)
+    MAX_CAT = st.slider('Maximo numero de categorias: ', 10, 30, 30)
     
     # Depuración de columnas sólo para aquellas que contribuyen al clustering
     col_selec = []
@@ -47,9 +47,11 @@ if uploaded_file is not None:
     df_110 = pd.get_dummies(df_100)
     
     # Calcular línea base
-    df_linBase = df_110.mean() / df_110.max() * 100
-    df_linBase2 = pd.DataFrame(df_linBase)
-    csv_10 = df_linBase2.to_csv(encoding='iso-8859-1')
+    df_linbase = df_110.mean() / df_110.max() * 100
+    df_linbase2 = pd.DataFrame(df_linbase)
+    df_linbase2['col_cats'] = df_linbase2.index
+    df_linbase2.columns=['valor', 'col_cats']
+    csv_10 = df_linbase2.to_csv(encoding='iso-8859-1')
 
     st.download_button(
         label="Descargar CSV",
@@ -198,15 +200,25 @@ if uploaded_file is not None:
     mean_values = dfx.mean()/dfx.max()*100
     result = pd.concat([percentage_presence, mean_values])
     df2 = pd.DataFrame()
-    df2['a'] = result
-    df2['b'] = result.index
-    df2 = df2.sort_values(by='a', ascending=False)
-    st.write(df2.columns)
-    df3 = df2.head(20)
-    fig3 = px.line_polar(df3, r='a', theta='b')
+    df2['valor_hex'] = result
+    df2['col_cats'] = result.index
+    df2 = df2.sort_values(by='valor_hex', ascending=False)
+    df3 = df2.head(25)
+
+    st.subheader('Radar Porcentajes Categorias')
+    
+    fig3 = px.line_polar(df3, r='valor_hex', theta='col_cats')
     st.plotly_chart(fig3)
+
+    df_result = pd.merge(df3, df_linbase2, on='col_cats', how='left')
+    df_result = df_result[['col_cats', 'valor_hex', 'valor']]
+    df_result['diff_linbase'] = df_result.valor_hex - df_result.valor
+
+    st.subheader('Radar Diferencia con Linea Base')
+    fig4 = px.line_polar(df_result, r='diff_linbase', theta='col_cats')
+    st.plotly_chart(fig4)
     
-    csv2 = df2.to_csv(encoding='iso-8859-1')
+    csv2 = df_result.to_csv(encoding='iso-8859-1')
 
     st.download_button(
         label="Descargar CSV",
@@ -214,3 +226,5 @@ if uploaded_file is not None:
         file_name='frecuencias_experimento.csv',
         mime='text/csv'
     )
+
+