James McCool
Refactor stack analysis logic in Streamlit app for improved readability and maintainability. Consolidate repeated code blocks for handling different stack sizes and ensure consistent data processing before displaying results. Add functionality to export generated tables as CSV.
0c0ffea
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from itertools import combinations | |
| import pymongo | |
| st.set_page_config(layout="wide") | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["MLB_Database"] | |
| return db | |
| db = init_conn() | |
| game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', | |
| 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} | |
| team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', | |
| '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} | |
| wrong_acro = ['WSH', 'AZ', 'CHW'] | |
| right_acro = ['WAS', 'ARI', 'CWS'] | |
| st.markdown(""" | |
| <style> | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| padding: 4px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| height: 50px; | |
| white-space: pre-wrap; | |
| background-color: #FFD700; | |
| color: white; | |
| border-radius: 10px; | |
| gap: 1px; | |
| padding: 10px 20px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background-color: #DAA520; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #DAA520; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def init_stat_load(): | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Own%', 'Site', 'Slate']] | |
| raw_display = raw_display.rename(columns={'Own%': 'Own'}) | |
| initial_concat = raw_display.sort_values(by='Own', ascending=False) | |
| return initial_concat | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| proj_raw = init_stat_load() | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| with st.expander("Info and Filters"): | |
| 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] | |
| 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) | |
| split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('Full Slate Run', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var2 = st.multiselect('Which teams would you like to include in the analysis?', options = raw_baselines['Team'].unique(), key='team_var2') | |
| elif split_var2 == 'Full Slate Run': | |
| team_var2 = raw_baselines.Team.unique().tolist() | |
| pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2') | |
| if pos_split2 == 'Specific Positions': | |
| pos_var2 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF']) | |
| elif pos_split2 == 'All Positions': | |
| pos_var2 = 'All' | |
| if site_var1 == 'Draftkings': | |
| max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 50000, value = 35000, step = 100, key='max_sal2') | |
| elif site_var1 == 'Fanduel': | |
| max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 35000, value = 25000, step = 100, key='max_sal2') | |
| size_var2 = st.selectbox('What size of stacks are you analyzing?', options = ['3-man', '4-man', '5-man']) | |
| if size_var2 == '3-man': | |
| stack_size = 3 | |
| if size_var2 == '4-man': | |
| stack_size = 4 | |
| if size_var2 == '5-man': | |
| stack_size = 5 | |
| team_dict = dict(zip(raw_baselines.Player, raw_baselines.Team)) | |
| proj_dict = dict(zip(raw_baselines.Player, raw_baselines.Median)) | |
| own_dict = dict(zip(raw_baselines.Player, raw_baselines.Own)) | |
| cost_dict = dict(zip(raw_baselines.Player, raw_baselines.Salary)) | |
| with col2: | |
| stack_hold_container = st.empty() | |
| comb_list = [] | |
| if pos_split2 == 'All Positions': | |
| raw_baselines = raw_baselines | |
| elif pos_split2 != 'All Positions': | |
| raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))] | |
| for cur_team in team_var2: | |
| working_baselines = raw_baselines | |
| working_baselines = working_baselines[working_baselines['Team'] == cur_team] | |
| working_baselines = working_baselines[working_baselines['Position'] != 'SP'] | |
| working_baselines = working_baselines[working_baselines['Position'] != 'P'] | |
| order_list = working_baselines['Player'] | |
| comb = combinations(order_list, stack_size) | |
| for i in list(comb): | |
| comb_list.append(i) | |
| comb_DF = pd.DataFrame(comb_list) | |
| if stack_size == 3: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict)]) | |
| comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict), | |
| comb_DF[1].map(cost_dict), | |
| comb_DF[2].map(cost_dict)]) | |
| comb_DF['Own%'] = sum([comb_DF[0].map(own_dict), | |
| comb_DF[1].map(own_dict), | |
| comb_DF[2].map(own_dict)]) | |
| elif stack_size == 4: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict), | |
| comb_DF[3].map(proj_dict)]) | |
| comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict), | |
| comb_DF[1].map(cost_dict), | |
| comb_DF[2].map(cost_dict), | |
| comb_DF[3].map(cost_dict)]) | |
| comb_DF['Own%'] = sum([comb_DF[0].map(own_dict), | |
| comb_DF[1].map(own_dict), | |
| comb_DF[2].map(own_dict), | |
| comb_DF[3].map(own_dict)]) | |
| elif stack_size == 5: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict), | |
| comb_DF[3].map(proj_dict), | |
| comb_DF[4].map(proj_dict)]) | |
| comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict), | |
| comb_DF[1].map(cost_dict), | |
| comb_DF[2].map(cost_dict), | |
| comb_DF[3].map(cost_dict), | |
| comb_DF[4].map(cost_dict)]) | |
| comb_DF['Own%'] = sum([comb_DF[0].map(own_dict), | |
| comb_DF[1].map(own_dict), | |
| comb_DF[2].map(own_dict), | |
| comb_DF[3].map(own_dict), | |
| comb_DF[4].map(own_dict)]) | |
| comb_DF = comb_DF.sort_values(by='Proj', ascending=False) | |
| comb_DF = comb_DF.loc[comb_DF['Salary'] <= max_sal2] | |
| cut_var = 0 | |
| if stack_size == 3: | |
| while cut_var <= int(len(comb_DF)): | |
| try: | |
| if int(cut_var) == 0: | |
| cur_proj = float(comb_DF.iat[cut_var,4]) | |
| cur_own = float(comb_DF.iat[cut_var,6]) | |
| elif int(cut_var) >= 1: | |
| check_own = float(comb_DF.iat[cut_var,6]) | |
| if check_own > cur_own: | |
| comb_DF = comb_DF.drop([cut_var]) | |
| cur_own = cur_own | |
| cut_var = cut_var - 1 | |
| comb_DF = comb_DF.reset_index() | |
| comb_DF = comb_DF.drop(['index'], axis=1) | |
| elif check_own <= cur_own: | |
| cur_own = float(comb_DF.iat[cut_var,6]) | |
| cut_var = cut_var | |
| cut_var += 1 | |
| except: | |
| cut_var += 1 | |
| elif stack_size == 4: | |
| while cut_var <= int(len(comb_DF)): | |
| try: | |
| if int(cut_var) == 0: | |
| cur_proj = float(comb_DF.iat[cut_var,5]) | |
| cur_own = float(comb_DF.iat[cut_var,7]) | |
| elif int(cut_var) >= 1: | |
| check_own = float(comb_DF.iat[cut_var,7]) | |
| if check_own > cur_own: | |
| comb_DF = comb_DF.drop([cut_var]) | |
| cur_own = cur_own | |
| cut_var = cut_var - 1 | |
| comb_DF = comb_DF.reset_index() | |
| comb_DF = comb_DF.drop(['index'], axis=1) | |
| elif check_own <= cur_own: | |
| cur_own = float(comb_DF.iat[cut_var,7]) | |
| cut_var = cut_var | |
| cut_var += 1 | |
| except: | |
| cut_var += 1 | |
| elif stack_size == 5: | |
| while cut_var <= int(len(comb_DF)): | |
| try: | |
| if int(cut_var) == 0: | |
| cur_proj = float(comb_DF.iat[cut_var,6]) | |
| cur_own = float(comb_DF.iat[cut_var,8]) | |
| elif int(cut_var) >= 1: | |
| check_own = float(comb_DF.iat[cut_var,8]) | |
| if check_own > cur_own: | |
| comb_DF = comb_DF.drop([cut_var]) | |
| cur_own = cur_own | |
| cut_var = cut_var - 1 | |
| comb_DF = comb_DF.reset_index() | |
| comb_DF = comb_DF.drop(['index'], axis=1) | |
| elif check_own <= cur_own: | |
| cur_own = float(comb_DF.iat[cut_var,8]) | |
| cut_var = cut_var | |
| cut_var += 1 | |
| except: | |
| cut_var += 1 | |
| with stack_hold_container: | |
| stack_hold_container = st.empty() | |
| st.dataframe(comb_DF.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(comb_DF), | |
| file_name='MLB_Stack_Options_export.csv', | |
| mime='text/csv', | |
| ) |