Refactor UI layout and improve data filtering options

app.py

@@ -58,7 +58,7 @@ def init_stat_load():
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
                                'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
-
+    raw_display = raw_display.rename(columns={'Minutes Proj': 'Minutes'})
     raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own', 'site', 'slate', 'timestamp']]
     raw_display.replace("", 'Welp', inplace=True)
     raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
@@ -77,290 +77,252 @@ def convert_df_to_csv(df):
 proj_raw, timestamp = init_stat_load()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info'])
-
-with tab2:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own'.")
-    col1, col2 = st.columns([1, 5])
-
-    with col1:
-        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
-    
-        if proj_file is not None:
-                  try:
-                            proj_dataframe = pd.read_csv(proj_file)
-                            try:
-                                proj_dataframe = proj_dataframe.replace(',','', regex=True)
-                                proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int)
-                            except:
-                                pass
-                  except:
-                            proj_dataframe = pd.read_excel(proj_file)
-                            try:
-                                proj_dataframe = proj_dataframe.replace(',','', regex=True)
-                                proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int)
-                            except:
-                                pass
-    with col2:
-        if proj_file is not None:  
-                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+with st.expander("Info and Filters"):
+    st.info(t_stamp)
+    if st.button("Load/Reset Data", key='reset1'):
+            st.cache_data.clear()
+            proj_raw, timestamp = init_stat_load()
+            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+    data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
+    site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+    slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
+    if site_var1 == 'Draftkings':
+        raw_baselines = proj_raw[proj_raw['site'] == 'Draftkings']
+        if slate_var1 == 'Main Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
+        elif slate_var1 == 'Secondary Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate']
+        raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+    elif site_var1 == 'Fanduel':
+        raw_baselines = proj_raw[proj_raw['site'] == 'Fanduel']
+        if slate_var1 == 'Main Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
+        elif slate_var1 == 'Secondary Slate':
+            raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate']
+        raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
+    check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq')
+    if check_seq == 'Single Player':
+        player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player')
+    elif check_seq == 'Top X Owned':
+        top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1)
+    Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
+    Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
+    pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
+    if pos_var1 == 'Specific Positions':
+        pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
+    elif pos_var1 == 'All Positions':
+        pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C']
+    split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+    if split_var1 == 'Specific Games':
+        team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1')
+    elif split_var1 == 'Full Slate Run':
+        team_var1 = raw_baselines.Team.values.tolist()
         
+placeholder = st.empty()
+displayholder = st.empty()
 
-with tab1:
-    col1, col2 = st.columns([1, 9])
-    with col1:
-        st.info(t_stamp)
-        if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              proj_raw, timestamp = init_stat_load()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-        data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
-        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
-        slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
+if st.button('Simulate appropriate pivots'):
+    with placeholder:
         if site_var1 == 'Draftkings':
-              if data_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif data_var1 != 'User':
-                  raw_baselines = proj_raw[proj_raw['site'] == 'Draftkings']
-                  if slate_var1 == 'Main Slate':
-                      raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
-                  elif slate_var1 == 'Secondary Slate':
-                      raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate']
-                  raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
-        elif site_var1 == 'Fanduel':
-              if data_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif data_var1 != 'User':
-                  raw_baselines = proj_raw[proj_raw['site'] == 'Fanduel']
-                  if slate_var1 == 'Main Slate':
-                      raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate']
-                  elif slate_var1 == 'Secondary Slate':
-                      raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate']
-                  raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
-        check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq')
+                    working_roo = raw_baselines
+                    working_roo.replace('', 0, inplace=True)
+        if site_var1 == 'Fanduel':
+                    working_roo = raw_baselines
+                    working_roo.replace('', 0, inplace=True)
+                    
+        own_dict = dict(zip(working_roo.Player, working_roo.Own))
+        team_dict = dict(zip(working_roo.Player, working_roo.Team))
+        pos_dict = dict(zip(working_roo.Player, working_roo.Position))
+        min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
+        total_sims = 1000
+
         if check_seq == 'Single Player':
-            player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player')
+            player_var = working_roo.loc[working_roo['Player'] == player_check]
+            player_var = player_var.reset_index()
+            working_roo = working_roo[working_roo['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))]
+            working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+            working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
+            working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
+
+            flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
+            flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+            flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+            flex_file['STD'] = (flex_file['Median']/4)
+            flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+            hold_file = flex_file.copy()
+            overall_file = flex_file.copy()
+            salary_file = flex_file.copy()
+
+            overall_players = overall_file[['Player']]
+
+            for x in range(0,total_sims):    
+                salary_file[x] = salary_file['Salary']
+
+            salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+            salary_file = salary_file.div(1000)
+
+            for x in range(0,total_sims):    
+                overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+            overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+            players_only = hold_file[['Player']]
+            raw_lineups_file = players_only
+
+            for x in range(0,total_sims):
+                maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+                raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+            players_only=players_only.drop(['Player'], axis=1)
+
+            salary_4x_check = (overall_file - (salary_file*4))
+            salary_5x_check = (overall_file - (salary_file*5))
+            salary_6x_check = (overall_file - (salary_file*6))
+            gpp_check = (overall_file - ((salary_file*5)+10))
+
+            players_only['Average_Rank'] = players_only.mean(axis=1)
+            players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+            players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+            players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+            players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+            players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+            players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+            players_only['Player'] = hold_file[['Player']]
+
+            final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+            final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+            final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+            final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+            final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+            final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+            final_Proj['Own'] = final_Proj['Own'].astype('float')
+            final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own']]
+            final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+            final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+            final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+            final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+            final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+            final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+
+            final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+            final_Proj = final_Proj.set_index('Player')
+            
+            st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+
         elif check_seq == 'Top X Owned':
-            top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1)
-        Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
-        Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
-        pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
-        if pos_var1 == 'Specific Positions':
-            pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
-        elif pos_var1 == 'All Positions':
-            pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C']
-        split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
-        if split_var1 == 'Specific Games':
-            team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1')
-        elif split_var1 == 'Full Slate Run':
-            team_var1 = raw_baselines.Team.values.tolist()
-        
-    with col2:
-        placeholder = st.empty()
-        displayholder = st.empty()
-
-        if st.button('Simulate appropriate pivots'):
-            with placeholder:
-                if site_var1 == 'Draftkings':
-                          working_roo = raw_baselines
-                          working_roo.replace('', 0, inplace=True)
-                if site_var1 == 'Fanduel':
-                          working_roo = raw_baselines
-                          working_roo.replace('', 0, inplace=True)
-                          
-                own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                pos_dict = dict(zip(working_roo.Player, working_roo.Position))
-                min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
-                total_sims = 1000
-
-                if check_seq == 'Single Player':
-                    player_var = working_roo.loc[working_roo['Player'] == player_check]
-                    player_var = player_var.reset_index()
-                    working_roo = working_roo[working_roo['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))]
-                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
-                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
-                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
-    
-                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
-                    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                    flex_file['STD'] = (flex_file['Median']/4)
-                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                    hold_file = flex_file.copy()
-                    overall_file = flex_file.copy()
-                    salary_file = flex_file.copy()
-
-                    overall_players = overall_file[['Player']]
-    
-                    for x in range(0,total_sims):    
-                        salary_file[x] = salary_file['Salary']
-    
-                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-    
-                    salary_file = salary_file.div(1000)
-    
-                    for x in range(0,total_sims):    
-                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-    
-                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-    
-                    players_only = hold_file[['Player']]
-                    raw_lineups_file = players_only
-    
-                    for x in range(0,total_sims):
-                        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
-                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
-    
-                    players_only=players_only.drop(['Player'], axis=1)
-
-                    salary_4x_check = (overall_file - (salary_file*4))
-                    salary_5x_check = (overall_file - (salary_file*5))
-                    salary_6x_check = (overall_file - (salary_file*6))
-                    gpp_check = (overall_file - ((salary_file*5)+10))
-
-                    players_only['Average_Rank'] = players_only.mean(axis=1)
-                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
-
-                    players_only['Player'] = hold_file[['Player']]
-
-                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
-    
-                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
-
-                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                    final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
-                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                    final_Proj['Own'] = final_Proj['Own'].astype('float')
-                    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own']]
-                    final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
-                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                    final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
-                    final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
-                    final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
-                    final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
-
-                    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
-                    final_Proj = final_Proj.set_index('Player')
-                    
-                    st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
-        
-                elif check_seq == 'Top X Owned':
-                    if pos_var1 == 'Specific Positions':    
-                            raw_baselines = raw_baselines[raw_baselines['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))]
-                    player_check = raw_baselines['Player'].head(top_x_var).tolist()
-                    st.write(player_check)
-                    final_proj_list = []
-                    for players in player_check:
-                        players_pos = pos_dict[players]
-                        player_var = working_roo.loc[working_roo['Player'] == players]
-                        player_var = player_var.reset_index()
-                        working_roo_temp = working_roo[working_roo['Team'].isin(team_var1)]
-
-                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)]
-                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)]
-
-                        flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                        flex_file['STD'] = (flex_file['Median']/4)
-                        flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                        hold_file = flex_file.copy()
-                        overall_file = flex_file.copy()
-                        salary_file = flex_file.copy()
-                        
-                        overall_players = overall_file[['Player']]
-
-                        for x in range(0,total_sims):    
-                            salary_file[x] = salary_file['Salary']
-        
-                        salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-        
-                        salary_file = salary_file.div(1000)
-        
-                        for x in range(0,total_sims):    
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-        
-                        overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-        
-                        players_only = hold_file[['Player']]
-                        raw_lineups_file = players_only
-        
-                        for x in range(0,total_sims):
-                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
-                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
-        
-                        players_only=players_only.drop(['Player'], axis=1)
-        
-                        salary_4x_check = (overall_file - (salary_file*4))
-                        salary_5x_check = (overall_file - (salary_file*5))
-                        salary_6x_check = (overall_file - (salary_file*6))
-                        gpp_check = (overall_file - ((salary_file*5)+10))
-
-                        players_only['Average_Rank'] = players_only.mean(axis=1)
-                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                        players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                        players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
-
-                        players_only['Player'] = hold_file[['Player']]
-
-                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
-        
-                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
-
-                        final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                        final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
-                        final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                        final_Proj['Own'] = final_Proj['Own'].astype('float')
-                        final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
-                        final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                        final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
-                        final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
-                        final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
-                        final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
-                        final_Proj['Pivot_source'] = players
-        
-                        final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
-                        
-                        final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
-                        final_proj_list.append(final_Proj)
-                        st.write(f'finished run for {players}')
+            if pos_var1 == 'Specific Positions':    
+                    raw_baselines = raw_baselines[raw_baselines['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))]
+            player_check = raw_baselines['Player'].head(top_x_var).tolist()
+            st.write(player_check)
+            final_proj_list = []
+            for players in player_check:
+                players_pos = pos_dict[players]
+                player_var = working_roo.loc[working_roo['Player'] == players]
+                player_var = player_var.reset_index()
+                working_roo_temp = working_roo[working_roo['Team'].isin(team_var1)]
+
+                working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)]
+                working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)]
+
+                flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
+                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                flex_file['STD'] = (flex_file['Median']/4)
+                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                hold_file = flex_file.copy()
+                overall_file = flex_file.copy()
+                salary_file = flex_file.copy()
                 
-                    # Concatenate all the final_Proj dataframes
-                    final_Proj_combined = pd.concat(final_proj_list)
-                    final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False)
-                    final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']]
-                    st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True)  # Assign the combined dataframe back to final_Proj
-
-            placeholder.empty()
-  
-        with displayholder.container():
-            if 'final_Proj' in st.session_state:
-                st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
-
-                st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(st.session_state.final_Proj),
-                    file_name='NBA_pivot_export.csv',
-                    mime='text/csv',
-                )
-            else:
-                st.write("Run some pivots my dude/dudette")
+                overall_players = overall_file[['Player']]
+
+                for x in range(0,total_sims):    
+                    salary_file[x] = salary_file['Salary']
+
+                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                salary_file = salary_file.div(1000)
+
+                for x in range(0,total_sims):    
+                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                players_only = hold_file[['Player']]
+                raw_lineups_file = players_only
+
+                for x in range(0,total_sims):
+                    maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                players_only=players_only.drop(['Player'], axis=1)
+
+                salary_4x_check = (overall_file - (salary_file*4))
+                salary_5x_check = (overall_file - (salary_file*5))
+                salary_6x_check = (overall_file - (salary_file*6))
+                gpp_check = (overall_file - ((salary_file*5)+10))
+
+                players_only['Average_Rank'] = players_only.mean(axis=1)
+                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+                players_only['Player'] = hold_file[['Player']]
+
+                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                final_Proj['Own'] = final_Proj['Own'].astype('float')
+                final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+                final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+                final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+                final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+                final_Proj['Pivot_source'] = players
+
+                final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+                
+                final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                final_proj_list.append(final_Proj)
+                st.write(f'finished run for {players}')
+        
+            # Concatenate all the final_Proj dataframes
+            final_Proj_combined = pd.concat(final_proj_list)
+            final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False)
+            final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']]
+            st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True)  # Assign the combined dataframe back to final_Proj
+
+    placeholder.empty()
+
+with displayholder.container():
+    if 'final_Proj' in st.session_state:
+        st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+        st.download_button(
+            label="Export Tables",
+            data=convert_df_to_csv(st.session_state.final_Proj),
+            file_name='NBA_pivot_export.csv',
+            mime='text/csv',
+        )
+    else:
+        st.write("Run some pivots my dude/dudette")