Aiming to add grid exclusion option for NASCAR.

app.py

@@ -1276,7 +1276,7 @@ with st.container():
             elif site_var == 'Fanduel':
                 min_stacks_var = st.number_input("Minimum stacks used", min_value = 0, max_value = 4, value = 1, step = 1, key = 'min_stacks_var_fd')
                 max_stacks_var = st.number_input("Maximum stacks used", min_value = 0, max_value = 4, value = 4, step = 1, key = 'max_stacks_var_fd')
-
+    ## FIX: There's also gotta be a better way to handle this
     try:
         nfl_reg_salaries = grab_nfl_reg_salaries(slate_var3)
     except:
@@ -1681,6 +1681,19 @@ if selected_tab == 'Data Load':
                                 projections_file = init_pga_baselines(type_var, site_var, slate_var3)[1]
                             elif type_var == 'Showdown':
                                 projections_file = init_pga_baselines(type_var, site_var, slate_var3)[3]
+                    elif sport_var == 'NASCAR':
+                        if site_var == 'Draftkings':
+                            if type_var == 'Classic':
+                                projections_file = init_nascar_baselines(type_var, site_var, slate_var3)[0]
+                                grid_map = init_nascar_baselines(type_var, site_var, slate_var3)[8]
+                            elif type_var == 'Showdown':
+                                projections_file = init_nascar_baselines(type_var, site_var, slate_var3)[2]
+                        elif site_var == 'Fanduel':
+                            if type_var == 'Classic':
+                                projections_file = init_nascar_baselines(type_var, site_var, slate_var3)[1]
+                                grid_map = init_nascar_baselines(type_var, site_var, slate_var3)[8]
+                            elif type_var == 'Showdown':
+                                projections_file = init_nascar_baselines(type_var, site_var, slate_var3)[3]
                     st.session_state['db_projections_file'] = projections_file
                     st.session_state['projections_loaded'] = True
             if 'projections_df' in st.session_state:
@@ -1803,6 +1816,8 @@ if selected_tab == 'Data Load':
 
                 if sport_var == 'MMA':
                     st.session_state['map_dict']['opp_map'] = opp_map
+                elif sport_var == 'NASCAR':
+                    st.session_state['map_dict']['grid_map'] = grid_map
                 else:
                     st.session_state['map_dict']['opp_map'] = None
             
@@ -2211,6 +2226,10 @@ if selected_tab == 'Manage Portfolio':
                         qb_force = st.selectbox("Force QB Stacks?", options=['No', 'Yes'], index=0)
                     else:
                         qb_force = 'No'
+                    of sport == 'NASCAR':
+                        grid_exclude = st.multiselect("Exclude grids?", options=sorted(list(set(st.session_state['working_frame']['Grid'].unique()))), default=[])
+                    else:
+                        grid_exclude = []
                     
                     submitted_col, export_col = st.columns(2)
                     st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
@@ -2343,6 +2362,15 @@ if selected_tab == 'Manage Portfolio':
                                 # Create mask where the first player's team matches the Stack column
                                 include_mask = team_frame == parsed_frame['Stack']
                             parsed_frame = parsed_frame[include_mask]
+
+                        if grid_exclude:
+                            grid_frame = parsed_frame[st.session_state['player_columns']].apply(
+                                lambda x: x.map(st.session_state['map_dict']['grid_map'])
+                            )
+                            exclude_mask = grid_frame.apply(
+                                lambda row: not any(grid in list(row) for grid in grid_exclude), axis=1
+                            )
+                            parsed_frame = parsed_frame[exclude_mask]
                         st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
@@ -2460,6 +2488,24 @@ if selected_tab == 'Manage Portfolio':
                         
                         if size_include:
                             parsed_frame = parsed_frame[parsed_frame['Size'].isin(size_include)]
+                        
+                        if qb_force == 'Yes':
+                            if type_var == 'Classic':
+                                # Get team for the first player column for each lineup
+                                team_frame = parsed_frame.iloc[:, 0].map(st.session_state['map_dict']['team_map'])
+                                
+                                # Create mask where the first player's team matches the Stack column
+                                include_mask = team_frame == parsed_frame['Stack']
+                            parsed_frame = parsed_frame[include_mask]
+
+                        if grid_exclude:
+                            grid_frame = parsed_frame[st.session_state['player_columns']].apply(
+                                lambda x: x.map(st.session_state['map_dict']['grid_map'])
+                            )
+                            exclude_mask = grid_frame.apply(
+                                lambda row: not any(grid in list(row) for grid in grid_exclude), axis=1
+                            )
+                            parsed_frame = parsed_frame[exclude_mask]
                         st.session_state['export_base'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
 

database_queries.py

@@ -505,10 +505,14 @@ def init_nba_baselines(type_var: str, site_var: str, slate_var: str):
 
         raw_display = pd.DataFrame(list(cursor))
         live_dict = dict(zip(raw_display['Player'], raw_display['Live_Proj']))
+        live_median_dict = dict(zip(raw_display['Player'], raw_display['Median']))
 
         dk_roo_raw['median'] = dk_roo_raw['player_names'].map(live_dict)
         fd_roo_raw['median'] = fd_roo_raw['player_names'].map(live_dict)
 
+        dk_roo_raw['median'] = np.where(dk_roo_raw['median'] == 0, dk_roo_raw['player_names'].map(live_median_dict), dk_roo_raw['median'])
+        fd_roo_raw['median'] = np.where(fd_roo_raw['median'] == 0, fd_roo_raw['player_names'].map(live_median_dict), fd_roo_raw['median'])
+
         dk_sd_roo_raw = None
         fd_sd_roo_raw = None
         dk_sd_id_map = None
@@ -2062,7 +2066,7 @@ def init_nascar_baselines(type_var: str, site_var: str, slate_var: str):
         cursor = collection.find()
 
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['Player', 'Position', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate', 'site', 'version']]
+        raw_display = raw_display[['Player', 'Position', 'Grid', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate', 'site', 'version']]
         raw_display['Team'] = 'NASCAR'
         raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float)
         raw_display = raw_display.rename(columns={"player_id": "player_ID"})
@@ -2076,8 +2080,10 @@ def init_nascar_baselines(type_var: str, site_var: str, slate_var: str):
         fd_sd_roo_raw['player_ID'] = fd_sd_roo_raw['player_ID'].astype(str)
         fd_sd_roo_raw['player_ID'] = fd_sd_roo_raw['player_ID'].str.rsplit('-', n=1).str[0].astype(str)
 
-        dk_sd_roo_raw = dk_sd_roo_raw.drop(columns=['player_ID', 'slate', 'version', 'site'])
-        fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_ID', 'slate', 'version', 'site'])
+        grid_map = dict(zip(sd_raw['Player'], sd_raw['Grid']))
+
+        dk_sd_roo_raw = dk_sd_roo_raw.drop(columns=['player_ID', 'slate', 'version', 'site', 'Grid'])
+        fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_ID', 'slate', 'version', 'site', 'Grid'])
 
         dk_sd_roo_raw = dk_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
         fd_sd_roo_raw = fd_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
@@ -2092,7 +2098,7 @@ def init_nascar_baselines(type_var: str, site_var: str, slate_var: str):
         cursor = collection.find()
 
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['Player', 'Position', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id']]
+        raw_display = raw_display[['Player', 'Position', 'Grid', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id']]
         raw_display['Team'] = 'NASCAR'
         raw_display['Site'] = site_var
         raw_display = raw_display.rename(columns={"player_id": "player_ID"})
@@ -2104,8 +2110,10 @@ def init_nascar_baselines(type_var: str, site_var: str, slate_var: str):
         fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_ID']))
         raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
 
-        dk_roo_raw = dk_roo_raw.drop(columns=['player_ID'])
-        fd_roo_raw = fd_roo_raw.drop(columns=['player_ID'])
+        grid_map = dict(zip(raw_display['Player'], raw_display['Grid']))
+
+        dk_roo_raw = dk_roo_raw.drop(columns=['player_ID', 'Grid'])
+        fd_roo_raw = fd_roo_raw.drop(columns=['player_ID', 'Grid'])
 
         dk_roo_raw = dk_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
         fd_roo_raw = fd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
@@ -2115,7 +2123,7 @@ def init_nascar_baselines(type_var: str, site_var: str, slate_var: str):
         dk_sd_id_map = None
         fd_sd_id_map = None
     
-    return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map
+    return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, grid_map
 
 def init_DK_NASCAR_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, salary_min, salary_max, player_var2):