Add new data export functionality in app.py: introduce a separate function for exporting data in Portfolio Manager format, enhance the full data export process with additional filtering options, and streamline the download buttons for both optimals and filtered exports, improving user experience and data accessibility.

app.py

@@ -340,6 +340,11 @@ 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))
@@ -569,43 +574,170 @@ with tab2:
             elif site_var2 == 'Fanduel':
                 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')
-        if st.button("Prepare data export", key='data_export'):
-    
-            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]])
+
+        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':
+                            data_export = data_export.set_index('SP1').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':
+                            data_export = data_export.set_index('P').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':
+                            name_export = name_export.set_index('SP1').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':
+                            name_export = name_export.set_index('P').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]
                 
-            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',
-            )
+                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':
+                            data_export = data_export.set_index('SP1').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':
+                            data_export = data_export.set_index('P').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':
+                            name_export = name_export.set_index('SP1').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':
+                            name_export = name_export.set_index('P').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':