version changes

Streamlit_functions.py

@@ -37,6 +37,19 @@ spend_cols = ['tv_broadcast_spend',
                 'cm_spend', 
                 'audio_spend', 
                 'email_spend']
+spend_cols2 = ['tv_broadcast_spend', 
+                'tv_cable_spend', 
+                'stream_video_spend', 
+                'olv_spend', 
+                'disp_prospect_spend', 
+                'disp_retarget_spend', 
+                'social_prospect_spend', 
+                'social_retarget_spend', 
+                'search_brand_spend', 
+                'search_nonbrand_spend', 
+                'cm_spend', 
+                'audio_spend', 
+                'email_spend', 'Date']
 metric_cols = ['tv_broadcast_grp', 
                  'tv_cable_grp', 
                  'stream_video_imp', 
@@ -64,6 +77,20 @@ channels = [
  'DIGITAL PARTNERS',
  'AUDIO',
  'EMAIL']
+channels2 = [
+ 'BROADCAST TV',
+ 'CABLE TV',
+ 'CONNECTED & OTT TV',
+ 'VIDEO',
+ 'DISPLAY PROSPECTING',
+ 'DISPLAY RETARGETING',
+ 'SOCIAL PROSPECTING',
+ 'SOCIAL RETARGETING',
+ 'SEARCH BRAND',
+ 'SEARCH NON-BRAND',
+ 'DIGITAL PARTNERS',
+ 'AUDIO',
+ 'EMAIL','Date']
 contribution_cols = [
  'Broadcast TV_Prospects',
  'Cable TV_Prospects',
@@ -877,14 +904,162 @@ def scenario_spend_forecasting(delta_df,start_date,end_date):
 
     df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
     df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
-    df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).apply(numerize)
+    # df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).astype(int)
+    df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).astype(int)
+
     df_modified2.index = df_modified2["Channels"]
-    df_modified2["Spend Change"] = df_modified2["Delta_percent"]
-    df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].apply(numerize)
+    df_modified2["Spend Change"] = df_modified2["Delta_percent"].astype(int)
+    # df_modified2["Forecasted Spends"] = df_modified2["Forecasted Spends"].astype(int)
+    df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].astype(int)
     df_modified3 = df_modified2[["Last Year Spends","Forecasted Spends","Spend Change"]].transpose()
     # df_modified2["forecasted_spends"] = 
     # # df_modified = delta_percent
     # # df_modified["Optimised Spends"] = df_modified["Current Spends"]*
+
     return df_modified3
 
+def scenario_spend_forecasting2(delta_df,start_date,end_date):
+    
+    key_df = pd.DataFrame()
+    key_df["Channel_name"] = ["Email",
+                              "DisplayRetargeting",
+                          "\xa0Video",
+                          "BroadcastTV",
+                          "SocialRetargeting",
+                          "Connected&OTTTV",
+                          "SearchBrand",
+                          "Audio",
+                          "SocialProspecting",
+                          "CableTV",
+                          "DisplayProspecting",
+                          "SearchNon-brand",
+                          "DigitalPartners"]
+    key_df["Channels"] = [
+    "EMAIL",
+    "DISPLAY RETARGETING",
+    "VIDEO",
+    "BROADCAST TV",
+    "SOCIAL RETARGETING",
+    "CONNECTED & OTT TV",
+    "SEARCH BRAND",
+    "AUDIO",
+    "SOCIAL PROSPECTING",
+    "CABLE TV",
+    "DISPLAY PROSPECTING",
+    "SEARCH NON-BRAND",
+    "DIGITAL PARTNERS"
+]
+
+    import math    
+    start_date = pd.to_datetime(start_date)
+    end_date = pd.to_datetime(end_date)
+
+    cur_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+    cur_data = cur_data[spend_cols2]
+    cur_data.columns = channels2
+    
+    cur_data["Date2"] = cur_data["Date"]+ pd.Timedelta(days=5)
+    # cur_data["Date"] = delta_df["Date"]
+    cur_data["Date_diff"] = (cur_data["Date"]-start_date).dt.days
+    cur_data["Date_diff_months"] =(np.ceil(cur_data["Date_diff"] / 30))
+
+    data2 = cur_data.groupby("Date_diff_months").agg({
+        'Date':"min",
+        "Date2":"max"
+    }).reset_index()
+
+
+    data1 = cur_data.groupby("Date_diff_months").agg({
+        'BROADCAST TV':"sum",
+        'CABLE TV':"sum",
+        'CONNECTED & OTT TV':"sum",
+        'VIDEO':"sum",
+        'DISPLAY PROSPECTING':"sum",
+        'DISPLAY RETARGETING':"sum",
+        'SOCIAL PROSPECTING':"sum",
+        'SOCIAL RETARGETING':"sum",
+        'SEARCH BRAND':"sum",
+        'SEARCH NON-BRAND':"sum",
+        'DIGITAL PARTNERS':"sum",
+        'AUDIO':"sum",
+        'EMAIL':"sum"
+    }).transpose()
+    
+    months_list = cur_data["Date_diff_months"].unique()
+    data1["Channels"]=data1.index
+    df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
+    df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
+    df_modified2.index = df_modified2["Channels"]
+
+    data3 = pd.DataFrame(index = data1.index)
+    for c in months_list:
+        data3[c] = df_modified2[c]*(1+df_modified2["Delta_percent"]/100)
+    
+    df1 = df_modified2[months_list].transpose()
+    df1["Metrics"] = "Last Year Spends"
+    
+    data3 = data3.transpose()
+    data3 = data3.astype(int)
+    data2.index = data2["Date_diff_months"]
+    data2.columns = ["Date_diff_months","start date","end date"]
+    data3["start date"] = data2["start date"].dt.date
+    data3["end date"] = data2["end date"].dt.date
+    data3["Month"] = data3.index
+    cols = ["Month","start date","end date",'BROADCAST TV',
+            'CABLE TV',
+            'CONNECTED & OTT TV',
+            'VIDEO',
+            'DISPLAY PROSPECTING',
+            'DISPLAY RETARGETING',
+            'SOCIAL PROSPECTING',
+            'SOCIAL RETARGETING',
+            'SEARCH BRAND',
+            'SEARCH NON-BRAND',
+            'DIGITAL PARTNERS',
+            'AUDIO',
+            'EMAIL']
+    # data3["Metrics"] = "Forecasted Year Spends"
+
+    # df2 = df_modified2["Delta_percent"].transpose()
+    # df2["Metrics"] = "Percent Change"
+    # df_modified2["last_year_spends"] = 
+
+    # data3 = pd.DataFrame(index = data1.index)
+    # for c in months_list:
+    #     for idx in data3.index:
+    #         data3[c][idx] = df_modified2[c][idx]*df_modified2["Delta_percent"]
+
+    
+    # data1 = data1[['Date',"Date2",'BROADCAST TV',
+    #     'CABLE TV',
+    #     'CONNECTED & OTT TV',
+    #     'VIDEO','DISPLAY PROSPECTING',
+    #     'DISPLAY RETARGETING',
+    #     'SOCIAL PROSPECTING',
+    #     'SOCIAL RETARGETING',
+    #     'SEARCH BRAND',
+    #     'SEARCH NON-BRAND',
+    #     'DIGITAL PARTNERS',
+    #     'AUDIO',
+    #     'EMAIL',
+    #     ]]
+    # data1[channels] = data1[channels].astype(int)
+    # data1["Date"] = data1["Date"].dt.date
+    # data1["Date2"] = data1["Date2"].dt.date
+    # # pd.DataFrame(cur_data[channels].groupby("Date_diff_months").sum().transpose()).reset_index()
+    # # # data1.columns = ["Channels","last_year_spends"]
+
+    # # df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
+    # # df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
+    # # df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).apply(numerize)
+    # # df_modified2.index = df_modified2["Channels"]
+    # # df_modified2["Spend Change"] = df_modified2["Delta_percent"]
+    # # df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].apply(numerize)
+    # # df_modified3 = df_modified2[["Last Year Spends","Forecasted Spends","Spend Change"]].transpose()
+    # # # df_modified2["forecasted_spends"] = 
+    # # # # df_modified = delta_percent
+    # # # # df_modified["Optimised Spends"] = df_modified["Current Spends"]*
+    # # spend_cols1 = pd.DataFrame(spend_cols)[0].to_list()
+    
+    return data3[cols]
 

pages/2_Scenario_Planner.py

@@ -164,18 +164,18 @@ def update_sales():
     )
 
 
-def update_all_spends_abs_slider():
-    actual_spends = _scenario.actual_total_spends
-    if validate_input(st.session_state["total_spends_change_abs_slider"]):
-        modified_spends = extract_number_for_string(
-            st.session_state["total_spends_change_abs_slider"]
-        )
-        st.session_state["total_spends_change"] = round(
-            ((modified_spends / actual_spends) - 1) * 100
-        )
-        st.session_state["total_spends_change_abs"] = numerize(modified_spends, 1)
+# def update_all_spends_abs_slider():
+#     actual_spends = _scenario.actual_total_spends
+#     if validate_input(st.session_state["total_spends_change_abs_slider"]):
+#         modified_spends = extract_number_for_string(
+#             st.session_state["total_spends_change_abs_slider"]
+#         )
+#         st.session_state["total_spends_change"] = round(
+#             ((modified_spends / actual_spends) - 1) * 100
+#         )
+#         st.session_state["total_spends_change_abs"] = numerize(modified_spends, 1)
 
-        update_all_spends()
+#         update_all_spends()
 
 
 # def update_all_spends_abs_slider():
@@ -1351,7 +1351,7 @@ if auth_status == True:
     #     )
 
     with st.expander("Channel Spends Simulator", expanded=True):
-        _columns1 = st.columns((2, 2, 1,1))
+        _columns1 = st.columns((1.5, 1.5, 1,1))
         with _columns1[0]:
             optimization_selection = st.selectbox(
                 "Optimize", options=["Media Spends", target], key="optimization_key"
@@ -1416,6 +1416,23 @@ if auth_status == True:
                 )
 
             with _columns2[2]:
+                overall_lower_bound = st.number_input(
+                        "Overall Lower Bound for Spends",
+                        value = 50
+                        # key = overall_lower_bound,
+                        # on_change=partial(update_data_bound_min_overall)
+                    )
+            with _columns2[3]:
+                overall_upper_bound = st.number_input(
+                        "Overall Upper Bound for Spends",
+                        value = 50
+                        # key = overall_upper_bound,
+                        # on_change=partial(update_data_bound_max_overall)
+                    )
+
+
+
+
                 min_value = round(_scenario.actual_total_spends * 0.5)
                 max_value = round(_scenario.actual_total_spends * 1.5)
                 st.session_state["total_spends_change_abs_slider_options"] = [
@@ -1449,16 +1466,19 @@ if auth_status == True:
                     on_change=update_sales,
                 )
             with _columns2[2]:
-                st.text_input(
-                    "Overall Lower Bound"
+                overall_lower_bound =  st.number_input(
+                    "Overall Lower Bound for Spends",
+                        value = 50
                 )
             with _columns2[3]:
-                st.text_input(
-                    "Overall Upper Bound"
+            
+                overall_upper_bound = st.number_input(
+                    "Overall Upper Bound for Spends",
+                        value = 50
                 )
 
-                min_value = round(_scenario.actual_total_sales * 0.5)
-                max_value = round(_scenario.actual_total_sales * 1.5)
+                min_value = round(_scenario.actual_total_sales * (1-overall_lower_bound/100))
+                max_value = round(_scenario.actual_total_sales * (1+overall_upper_bound/100))
                 # st.write(min_value)
                 # st.write(max_value)
                 # for value in range(min_value, max_value + 1, int(100)):
@@ -1503,7 +1523,7 @@ if auth_status == True:
         )
 
         st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
-        _columns = st.columns((2, 2.5, 1.5, 1.5, 0.5))
+        _columns = st.columns((1.5,2.5,2,2, 1))
         with _columns[0]:
             generate_spending_header("Channel")
         with _columns[1]:
@@ -1536,7 +1556,7 @@ if auth_status == True:
             # st.write(st.session_state["scenario"].channels[channel_name].modified_total_spends)
             # st.write(st.session_state["scenario"].channels[channel_name].bounds)
             # st.write(st.session_state["scenario"].channels[channel_name].channel_bounds_min)
-            _columns = st.columns((2, 2.5, 1.5, 1.5, 0.5))
+            _columns = st.columns((1.5,2.5,2,2, 1))
             with _columns[0]:
                 st.write(channel_name_formating(channel_name))
                 bin_placeholder = st.container()
@@ -1554,11 +1574,6 @@ if auth_status == True:
                 # st.write(max_value)
                 ##print(st.session_state[channel_name])
 
-                
-
-                
-            
-
                 _columns_min = st.columns(2)
                 with _columns_min[0]:
                     spend_input = st.text_input(
@@ -1622,14 +1637,37 @@ if auth_status == True:
                     current_channel_spends
                 )
                 st.session_state["acutual_predicted"]["Delta"].append(spends_delta)
-                ## REMOVE
-                st.metric(
-                    "Spends",
-                    format_numbers(current_channel_spends),
-                    delta=numerize(spends_delta, 1),
-                    label_visibility="collapsed",
-                )
+                _spend_cols = st.columns(2)
+                with _spend_cols[0]:
+                    st.metric(
+                        label="Actual Spends",
+                        value=format_numbers(actual_channel_spends),
+                        # delta=numerize(spends_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+
+                    st.metric(
+                        label="Change",
+                        value= format_numbers_f(spends_delta),
+                        delta=numerize(spends_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+                with _spend_cols[1]:
+                    st.metric(
+                        label="Simulated Spends",
+                        value=format_numbers(current_channel_spends),
+                        # delta=numerize(spends_delta, 1),
+                        # label_visibility="collapsed",
+                    )
 
+                    st.metric(
+                        label="Percent Change",
+                        value=  numerize(( spends_delta/actual_channel_spends)*100,0) +"%",
+                        delta=numerize(spends_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+
+                    
             with _columns[3]:
                 # sales
                 current_channel_sales = float(_channel_class.modified_total_sales)
@@ -1638,13 +1676,38 @@ if auth_status == True:
                 st.session_state["acutual_predicted"]["Old_sales"].append(actual_channel_sales)
                 st.session_state["acutual_predicted"]["New_sales"].append(current_channel_sales)
                 #st.write(actual_channel_sales)
+                
+                _prospect_cols = st.columns(2)
+                with _prospect_cols[0]:
+                    st.metric(
+                        # target,
+                        label="Actual Prospects",
+                        value= format_numbers_f(actual_channel_sales),
+                        # delta=numerize(sales_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+                    st.metric(
+                        label="Change",
+                        value= format_numbers_f(_channel_class.delta_sales),
+                        delta=numerize(sales_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+                with _prospect_cols[1]:
+                    st.metric(
+                    label="Simulated Prospects",
+                    value= format_numbers_f(current_channel_sales),
+                    # delta=numerize(sales_delta, 1),
+                    # label_visibility="collapsed",
+                    )
 
-                st.metric(
-                    target,
-                    format_numbers_f(current_channel_sales),
-                    delta=numerize(sales_delta, 1),
-                    label_visibility="collapsed",
-                )
+                    st.metric(
+                        label="Percent Change",
+                        value=  numerize((_channel_class.delta_sales/actual_channel_sales)*100,0) +"%",
+                        delta=numerize(sales_delta, 1),
+                        # label_visibility="collapsed",
+                    )
+
+                    
 
             with _columns[4]:
 
@@ -1866,7 +1929,10 @@ if auth_status == True:
                     end_date1 = current_date + relativedelta(months = 12)-  relativedelta(years=1)
 
         forecasted_table_df = sf.scenario_spend_forecasting(summary_df_sorted,start_date1,end_date1)
-        st.table(forecasted_table_df)
+        st.dataframe(forecasted_table_df)
+
+        forecasted_table_df2 = sf.scenario_spend_forecasting2(summary_df_sorted,start_date1,end_date1)
+        st.dataframe(forecasted_table_df2)
         
         st.markdown("""
             <style>
@@ -1880,24 +1946,16 @@ if auth_status == True:
             </style>
             """, unsafe_allow_html=True)
         
-        def save_report_forecast(forecasted_table_df):
+        def save_report_forecast(forecasted_table_df,forecasted_table_df2):
             from io import BytesIO
-            # excel_file_path = filename+ '.xlsx'
-            # forecasted_table_df.to_excel(excel_file_path, index=False)
-
             # Convert the DataFrame to an Excel file in memory
             excel_file = BytesIO()
             with pd.ExcelWriter(excel_file, engine='openpyxl') as writer:
-                forecasted_table_df.to_excel(writer, index=False, sheet_name='Sheet1')
-
+                forecasted_table_df.to_excel(writer, index=True, sheet_name='Forecasted Spends')
+                forecasted_table_df2.to_excel(writer, index=False, sheet_name='Monthly Breakdown')
             # Seek to the beginning of the BytesIO buffer
             excel_file.seek(0)
             return excel_file
-            # message_container = st.empty()
-            # with message_container:
-            #     st.write(f'<div class="yellow-container">{"Report Saved!"}</div>', unsafe_allow_html=True)
-            #     time.sleep(5)
-            #     st.empty()
 
         st.subheader("Download Report")
         report_name = st.text_input(
@@ -1910,7 +1968,7 @@ if auth_status == True:
 
         st.download_button(
             "Download Report",
-            data = save_report_forecast(forecasted_table_df),
+            data = save_report_forecast(forecasted_table_df,forecasted_table_df2),
             file_name = report_name+".xlsx",
             mime="application/vnd.ms-excel",
             # on_click=lambda: save_report_forecast(forecasted_table_df,report_name),

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b1c5355ea93c754c8f7eee9dca0242123d67190ebe0468dec482dc4931a4449b
+oid sha256:2cdad2e46bd5810d3a714d9ef98b7644d6f5cdd61a62b160d3a1dd48b8bf9aec
 size 1822