aesthetic changes 2

pages/2_Scenario_Planner.py

@@ -875,7 +875,7 @@ def scenario_planner_plots():
         # Add actual vs optimized spend bars
 
             
-        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Actual_spend'], name='Actual',
+        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'].apply(channel_name_formating), y=summary_df_sorted['Actual_spend'], name='Actual',
                                 text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '
                                 #  + 
                                 #  ' '+
@@ -884,7 +884,7 @@ def scenario_planner_plots():
                                 marker_color=light_blue))
             
             
-        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Optimized_spend'], name='Optimized',
+        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'].apply(channel_name_formating), y=summary_df_sorted['Optimized_spend'], name='Optimized',
                                 text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' '
                                 #    + 
                                 #  '</br> (' + optimized_spend_percentage.astype(int).astype(str) + '%)'
@@ -902,11 +902,11 @@ def scenario_planner_plots():
 
             # Add actual vs optimized Contribution
         fig = go.Figure()
-        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Old_sales'], 
+        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'].apply(channel_name_formating), y=summary_df_sorted['Old_sales'], 
                                 name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number),textposition='outside', 
                                 marker_color=light_blue,showlegend=True))
             
-        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['New_sales'],
+        fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'].apply(channel_name_formating), y=summary_df_sorted['New_sales'],
                                 name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number),textposition='outside', 
                                 marker_color=light_orange, showlegend=True))
             
@@ -924,10 +924,10 @@ def scenario_planner_plots():
         # Add actual vs optimized Efficiency bars
         fig = go.Figure()
         summary_df_sorted_p =  summary_df_sorted[summary_df_sorted['Channel_name']!="Panel"]
-        fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['old_efficiency'], 
+        fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'].apply(channel_name_formating), y=summary_df_sorted_p['old_efficiency'], 
                                 name='Actual Efficiency', text=summary_df_sorted_p['old_efficiency'].apply(format_number) ,textposition='outside',
                                 marker_color=light_blue,showlegend=True))
-        fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['new_efficiency'], 
+        fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'].apply(channel_name_formating), y=summary_df_sorted_p['new_efficiency'], 
                                 name='Optimized Efficiency',text=summary_df_sorted_p['new_efficiency'].apply(format_number),textposition='outside' , 
                                 marker_color=light_orange,showlegend=True))
             

response_curves_model_quality.py

@@ -6,6 +6,7 @@ from sklearn.preprocessing import MinMaxScaler
 import warnings
 warnings.filterwarnings("ignore")
 import plotly.graph_objects as go
+from utilities import (channel_name_formating)
 
 ## reading input data
 df= pd.read_csv('response_curves_input_file.csv')
@@ -479,7 +480,7 @@ def response_curves(channel,x_modified,y_modified):
 
     # Update layout with titles
     fig.update_layout(
-        title=channel+' Response Curve',
+        title=channel_name_formating(channel)+' Response Curve',
         xaxis_title='Weekly Spends',
         yaxis_title='Prospects'
     )

response_curves_model_quality_base.py

@@ -6,7 +6,7 @@ from sklearn.preprocessing import MinMaxScaler
 import warnings
 warnings.filterwarnings("ignore")
 import plotly.graph_objects as go
-
+from utilities_with_panel import (channel_name_formating)
 ## reading input data
 df= pd.read_csv('response_curves_input_file.csv')
 df.dropna(inplace=True)
@@ -221,7 +221,7 @@ def response_curves(channel,chart_typ):
     # Update layout with titles
     fig.update_layout(
         width=700, height=500,
-        title=channel+' Response Curve',
+        title=channel_name_formating(channel)+' Response Curve',
         xaxis_title='Weekly Spends',
         yaxis_title='Prospects'
     )

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dad0d11118c0472a05a46ab895793cc2cdb21bea157bdc8a39e31d7d42ffebfa
+oid sha256:bb50f23e164ddf0cae9b81a28e47f97561d83e444b951bcf2e8192d70eadc7ce
 size 1822