Add Portfolio Manager export functionality and improve data export options; streamline data preparation and filtering for optimal lineups in NBA and WNBA.

app.py

@@ -308,6 +308,11 @@ def convert_df(array):
     array = pd.DataFrame(array, columns=column_names)
     return array.to_csv().encode('utf-8')
 
+@st.cache_data
+def convert_pm_df(array):
+    array = pd.DataFrame(array)
+    return array.to_csv().encode('utf-8')
+
 dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
 salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
 id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
@@ -360,8 +365,6 @@ with view_col:
         view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
 with site_col:
     site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
-    if 'working_seed' in st.session_state:
-        del st.session_state['working_seed']
 with league_col:
     league_var = st.radio("What League to load:", ('WNBA', 'NBA'), key='league_var')
     dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
@@ -431,30 +434,38 @@ with tab1:
                                     'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
     elif view_var2 == 'Simple':
         display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
-    export_data = display_proj.copy()
+    export_data = raw_baselines.copy()
+    export_data_pm = raw_baselines[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own']]
+    export_data_pm = export_data_pm.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'CPT_Own': 'captain ownership'})
 
     # display_proj = display_proj.set_index('Player')
     st.session_state.display_proj = display_proj.set_index('Player', drop=True)
+
+    reg_dl_col, pm_dl_col, blank_col = st.columns([2, 2, 6])
+    with reg_dl_col:
+        st.download_button(
+                    label="Export ROO (Regular)",
+                    data=convert_df_to_csv(export_data),
+                    file_name='NBA_ROO_export.csv',
+                    mime='text/csv',
+        )
+    with pm_dl_col:
+        st.download_button(
+                    label="Export ROO (Portfolio Manager)",
+                    data=convert_df_to_csv(export_data_pm),
+                    file_name='NBA_ROO_export.csv',
+                    mime='text/csv',
+        )
         
-    with display_container_1:
-        display_container = st.empty()
-        if 'display_proj' in st.session_state:
-            if pos_var2 == 'All':
-                st.session_state.display_proj = st.session_state.display_proj
-            elif pos_var2 != 'All':
-                st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
-            st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2),
-            height=1000, use_container_width = True)
+    if 'display_proj' in st.session_state:
+        if pos_var2 == 'All':
+            st.session_state.display_proj = st.session_state.display_proj
+        elif pos_var2 != 'All':
+            st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
+        st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2),
+        height=1000, use_container_width = True)
+    
     
-    with display_dl_container_1:
-            display_dl_container = st.empty()
-            if 'display_proj' in st.session_state:
-                st.download_button(
-                            label="Export Tables",
-                            data=convert_df_to_csv(export_data),
-                            file_name='NBA_ROO_export.csv',
-                            mime='text/csv',
-                )
 
 with tab2:
     with st.expander("Info and Filters"):
@@ -479,7 +490,7 @@ with tab2:
             for key in st.session_state.keys():
                 del st.session_state[key]
     
-        col1, col2, col3, col4, col5, col6 = st.columns(6)
+        col1, col2, col3, col4, col5 = st.columns(5)
         with col1:
             slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
         with col2:
@@ -522,43 +533,201 @@ with tab2:
                         player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
                 elif player_var1 == 'Full Slate':
                         player_var2 = fd_raw.Player.values.tolist()
-        if st.button("Prepare data export", key='data_export'):
-    
+        with col5:
             if site_var2 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    data_export = init_DK_lineups(slate_var1, league_var)
-                    data_export_names = data_export.copy()
-                    for col_idx in range(8):
-                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
-                elif slate_type_var1 == 'Showdown':
-                    data_export = init_DK_SD_lineups(slate_var1, league_var)
-                    data_export_names = data_export.copy()
-                    for col_idx in range(6):
-                        data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
-                
+                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 50000, value = 49000, step = 100, key = 'salary_min_var')
+                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var')
             elif site_var2 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    data_export = init_FD_lineups(slate_var1, league_var)
-                    data_export_names = data_export.copy()
-                    for col_idx in range(9):
-                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
-                elif slate_type_var1 == 'Showdown':
-                    data_export = init_FD_SD_lineups(slate_var1, league_var)
-                    data_export_names = data_export.copy()
-                    for col_idx in range(6):
-                        data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
-            st.download_button(
-                label="Export optimals (Names)",
-                data=convert_df(data_export_names),
-                file_name='NBA_optimals_export.csv',
-                mime='text/csv',
-            )
-            st.download_button(
-                label="Export optimals (IDs)",
-                data=convert_df(data_export),
-                file_name='NBA_optimals_export.csv',
-                mime='text/csv',
-            )
+                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 40000, value = 39000, step = 100, key = 'salary_min_var')
+                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 40000, value = 40000, step = 100, key = 'salary_max_var')
+
+        reg_dl_col, filtered_dl_col, blank_dl_col = st.columns([2, 2, 6])
+        with reg_dl_col:
+            if st.button("Prepare full data export", key='data_export'):
+                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                if site_var2 == 'Draftkings':
+                    if slate_type_var1 == 'Regular':
+                        if league_var == 'NBA':
+                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+                        elif league_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
+                    elif slate_type_var1 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                    for col_idx in map_columns:
+                        if slate_type_var1 == 'Regular':
+                            data_export[col_idx] = data_export[col_idx].map(id_dict)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export[col_idx] = data_export[col_idx].map(dk_id_dict_sd)
+                elif site_var2 == 'Fanduel':
+                    if slate_type_var1 == 'Regular':
+                        if league_var == 'NBA':
+                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'C2', 'UTIL']
+                        elif league_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
+                    elif slate_type_var1 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
+                    for col_idx in map_columns:
+                        if slate_type_var1 == 'Regular':
+                            data_export[col_idx] = data_export[col_idx].map(id_dict)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export[col_idx] = data_export[col_idx].map(fd_id_dict_sd)
+                reg_opt_col, pm_opt_col = st.columns(2)
+                with reg_opt_col:
+                    st.download_button(
+                        label="Export optimals set (IDs)",
+                        data=convert_df(data_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    st.download_button(
+                        label="Export optimals set (Names)",
+                        data=convert_df(name_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                with pm_opt_col:
+                    if site_var2 == 'Draftkings':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                data_export = data_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var2 == 'Fanduel':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                data_export = data_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (IDs)",
+                        data=convert_pm_df(data_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    
+                    if site_var2 == 'Draftkings':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                name_export = name_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var2 == 'Fanduel':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                name_export = name_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (Names)",
+                        data=convert_pm_df(name_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+        with filtered_dl_col:
+            if st.button("Prepare full data export (Filtered)", key='data_export_filtered'):
+                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                if site_var2 == 'Draftkings':
+                    if slate_type_var1 == 'Regular':
+                        if league_var == 'NBA':
+                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+                        elif league_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
+                    elif slate_type_var1 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                    for col_idx in map_columns:
+                        if slate_type_var1 == 'Regular':
+                            data_export[col_idx] = data_export[col_idx].map(id_dict)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export[col_idx] = data_export[col_idx].map(dk_id_dict_sd)
+                elif site_var2 == 'Fanduel':
+                    if slate_type_var1 == 'Regular':
+                        if league_var == 'NBA':
+                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'C2', 'UTIL']
+                        elif league_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
+                    elif slate_type_var1 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
+                    for col_idx in map_columns:
+                        if slate_type_var1 == 'Regular':
+                            data_export[col_idx] = data_export[col_idx].map(id_dict)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export[col_idx] = data_export[col_idx].map(fd_id_dict_sd)
+                data_export = data_export[data_export['salary'] >= salary_min_var]
+                data_export = data_export[data_export['salary'] <= salary_max_var]
+
+                name_export = name_export[name_export['salary'] >= salary_min_var]
+                name_export = name_export[name_export['salary'] <= salary_max_var]
+                
+                reg_opt_col, pm_opt_col = st.columns(2)
+                with reg_opt_col:
+                    st.download_button(
+                        label="Export optimals set (IDs)",
+                        data=convert_df(data_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    st.download_button(
+                        label="Export optimals set (Names)",
+                        data=convert_df(name_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                with pm_opt_col:
+                    if site_var2 == 'Draftkings':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                data_export = data_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var2 == 'Fanduel':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                data_export = data_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (IDs)",
+                        data=convert_pm_df(data_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    
+                    if site_var2 == 'Draftkings':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                name_export = name_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var2 == 'Fanduel':
+                        if slate_type_var1 == 'Regular':
+                            if league_var == 'NBA':
+                                name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            elif league_var == 'WNBA':
+                                name_export = name_export.set_index('G1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var1 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (Names)",
+                        data=convert_pm_df(name_export),
+                        file_name='NBA_optimals_export.csv',
+                        mime='text/csv',
+                    )
             
 
     if site_var2 == 'Draftkings':
@@ -608,7 +777,7 @@ with tab2:
                 elif slate_type_var1 == 'Showdown':
                     st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
             st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-
+    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'].between(salary_min_var, salary_max_var)]
     export_file = st.session_state.data_export_display.copy()
     if site_var2 == 'Draftkings':
         if slate_type_var1 == 'Regular':