Enhance projections file upload functionality by adding a selection option for sourcing projections from either 'User Upload' or 'Paydirt DB'. Update init_nfl_baselines function to accept additional parameters for site and slate, and filter projections based on these parameters, improving data handling and user experience.

app.py

@@ -653,13 +653,18 @@ if selected_tab == 'Data Load':
 
     with col3:
         st.subheader("Projections File")
+        proj_options = st.selectbox("Select a projections source", options=['Paydirt DB', 'User Upload'])
         st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.")
         
         # Create two columns for the uploader and template button
         upload_col, template_col = st.columns([3, 1])
         
         with upload_col:
-            projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
+            if proj_options == 'User Upload':
+                projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
+            elif proj_options == 'Paydirt DB':
+                projections_file = init_nfl_baselines(type_var, site_var, 'Main Slate')
+                st.session_state['db_projections_file'] = projections_file
             if 'projections_df' in st.session_state:
                 del st.session_state['projections_df']
         

database_queries.py

@@ -13,7 +13,7 @@ right_nfl_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams',
 'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
 'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']
 
-def init_nfl_baselines(type_var: str):
+def init_nfl_baselines(type_var: str, site_var: str, slate_var: str):
 
     if type_var == 'Regular':
         collection = nfl_db["DK_NFL_ROO"] 
@@ -21,11 +21,12 @@ def init_nfl_baselines(type_var: str):
 
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own','player_id', 'slate', 'version']]
         raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
         load_display = raw_display[raw_display['Position'] != 'K']
         dk_roo_raw = load_display.dropna(subset=['Median'])
+        dk_roo_raw = dk_roo_raw[dk_roo_raw['version'] == 'overall']
+        dk_roo_raw = dk_roo_raw[dk_roo_raw['slate'] == slate_var]
 
         dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
 
@@ -34,16 +35,17 @@ def init_nfl_baselines(type_var: str):
 
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own','player_id', 'slate', 'version']]
         raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
         load_display = raw_display[raw_display['Position'] != 'K']
         fd_roo_raw = load_display.dropna(subset=['Median'])
+        fd_roo_raw = fd_roo_raw[fd_roo_raw['version'] == 'overall']
+        fd_roo_raw = fd_roo_raw[fd_roo_raw['slate'] == slate_var]
 
         fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
 
-        dk_roo_raw = dk_roo_raw.drop(columns=['player_id'])
-        fd_roo_raw = fd_roo_raw.drop(columns=['player_id'])
+        dk_roo_raw = dk_roo_raw.drop(columns=['player_id', 'slate', 'version'])
+        fd_roo_raw = fd_roo_raw.drop(columns=['player_id', 'slate', 'version'])
 
         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'})
@@ -58,7 +60,7 @@ def init_nfl_baselines(type_var: str):
 
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id']]
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate']]
         raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
         # load_display = raw_display[raw_display['Position'] != 'K']
         dk_sd_roo_raw = raw_display.dropna(subset=['Median'])
@@ -70,15 +72,15 @@ def init_nfl_baselines(type_var: str):
 
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id']]
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate']]
         raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
         # load_display = raw_display[raw_display['Position'] != 'K']
         fd_sd_roo_raw = raw_display.dropna(subset=['Median'])
 
         fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_id']))
 
-        dk_sd_roo_raw = dk_sd_roo_raw.drop(columns=['player_id'])
-        fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_id'])
+        dk_sd_roo_raw = dk_sd_roo_raw.drop(columns=['player_id', 'slate'])
+        fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_id', 'slate'])
 
         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'})