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| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import gspread | |
| import pymongo | |
| import time | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["League_of_Legends_Database"] | |
| return db | |
| db = init_conn() | |
| percentages_format = {'Exposure': '{:.2%}'} | |
| freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} | |
| dk_columns = ['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_columns = ['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| def init_DK_seed_frames(league): | |
| if league == 'LCK': | |
| collection = db['LOL_LCK_seed_frame'] | |
| elif league =='LEC': | |
| collection = db['LOL_LEC_seed_frame'] | |
| elif league =='LTA': | |
| collection = db['LOL_LTA_seed_frame'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_baselines(): | |
| collection = db['Player_Range_Of_Outcomes'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display['Player'] = raw_display['Player'].astype(str) | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| load_display = raw_display.drop_duplicates(subset=['Player'], keep='first') | |
| dk_raw = load_display.dropna(subset=['Median']) | |
| return dk_raw | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| def calculate_DK_value_frequencies(np_array): | |
| unique, counts = np.unique(np_array[:, :6], return_counts=True) | |
| frequencies = counts / len(np_array) # Normalize by the number of rows | |
| combined_array = np.column_stack((unique, frequencies)) | |
| return combined_array | |
| def calculate_FD_value_frequencies(np_array): | |
| unique, counts = np.unique(np_array[:, :6], return_counts=True) | |
| frequencies = counts / len(np_array) # Normalize by the number of rows | |
| combined_array = np.column_stack((unique, frequencies)) | |
| return combined_array | |
| def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size): | |
| SimVar = 1 | |
| Sim_Winners = [] | |
| fp_array = seed_frame[:sharp_split, :] | |
| # Pre-vectorize functions | |
| vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) | |
| vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) | |
| st.write('Simulating contest on frames') | |
| while SimVar <= Sim_size: | |
| fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] | |
| sample_arrays1 = np.c_[ | |
| fp_random, | |
| np.sum(np.random.normal( | |
| loc=vec_projection_map(fp_random[:, :-7]), | |
| scale=vec_stdev_map(fp_random[:, :-7])), | |
| axis=1) | |
| ] | |
| sample_arrays = sample_arrays1 | |
| final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]] | |
| best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] | |
| Sim_Winners.append(best_lineup) | |
| SimVar += 1 | |
| return Sim_Winners | |
| DK_seed = init_DK_seed_frames('LCK') | |
| dk_raw = init_baselines() | |
| tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| DK_seed = init_DK_seed_frames('LCK') | |
| dk_raw = init_baselines() | |
| slate_var1 = st.radio("Which data are you loading?", ('LCK', 'LEC', 'LTA')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings')) | |
| if site_var1 == 'Draftkings': | |
| raw_baselines = dk_raw.copy() | |
| column_names = dk_columns | |
| team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') | |
| if team_var1 == 'Specific Teams': | |
| team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique()) | |
| elif team_var1 == 'Full Slate': | |
| team_var2 = dk_raw.Team.values.tolist() | |
| stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') | |
| if stack_var1 == 'Specific Stack Sizes': | |
| stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0]) | |
| elif stack_var1 == 'Full Slate': | |
| stack_var2 = [4, 3, 2, 1, 0] | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='LOL_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| with col2: | |
| if st.button("Load Data", key='load_data'): | |
| if site_var1 == 'Draftkings': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| st.session_state.working_seed = init_DK_seed_frames(slate_var1) | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| with st.container(): | |
| if 'data_export_display' in st.session_state: | |
| st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True) | |
| with tab1: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| DK_seed = init_DK_seed_frames('LCK') | |
| dk_raw = init_baselines() | |
| sim_slate_var1 = st.radio("Which data are you loading?", ('LCK', 'LEC', 'LTA'), key='sim_slate_var1') | |
| sim_site_var1 = st.radio("What site are you working with?", ('Draftkings'), key='sim_site_var1') | |
| if sim_site_var1 == 'Draftkings': | |
| raw_baselines = dk_raw.copy() | |
| column_names = dk_columns | |
| contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom')) | |
| if contest_var1 == 'Small': | |
| Contest_Size = 1000 | |
| elif contest_var1 == 'Medium': | |
| Contest_Size = 5000 | |
| elif contest_var1 == 'Large': | |
| Contest_Size = 10000 | |
| elif contest_var1 == 'Custom': | |
| Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...") | |
| strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very')) | |
| if strength_var1 == 'Not Very': | |
| sharp_split = 500000 | |
| elif strength_var1 == 'Below Average': | |
| sharp_split = 400000 | |
| elif strength_var1 == 'Average': | |
| sharp_split = 300000 | |
| elif strength_var1 == 'Above Average': | |
| sharp_split = 200000 | |
| elif strength_var1 == 'Very': | |
| sharp_split = 100000 | |
| with col2: | |
| if st.button("Run Contest Sim"): | |
| if 'working_seed' in st.session_state: | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| else: | |
| if sim_site_var1 == 'Draftkings': | |
| st.session_state.working_seed = init_DK_seed_frames(sim_slate_var1) | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| freq_copy = st.session_state.Sim_Winner_Display | |
| if sim_site_var1 == 'Draftkings': | |
| freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:7].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| freq_working['Freq'] = freq_working['Freq'].astype(int) | |
| freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map']) | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) | |
| freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100 | |
| freq_working['Exposure'] = freq_working['Freq']/(1000) | |
| freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] | |
| freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.player_freq = freq_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_working['Freq'] = cpt_working['Freq'].astype(int) | |
| cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map']) | |
| cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) | |
| cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / 600 | |
| cpt_working['Exposure'] = cpt_working['Freq']/(1000) | |
| cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own'] | |
| cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.cpt_freq = cpt_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| top_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:2].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| top_working['Freq'] = top_working['Freq'].astype(int) | |
| top_working['Position'] = top_working['Player'].map(maps_dict['Pos_map']) | |
| top_working['Salary'] = top_working['Player'].map(maps_dict['Salary_map']) | |
| top_working['Proj Own'] = top_working['Player'].map(maps_dict['Own_map']) / 105 | |
| top_working['Exposure'] = top_working['Freq']/(1000) | |
| top_working['Edge'] = top_working['Exposure'] - top_working['Proj Own'] | |
| top_working['Team'] = top_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.top_freq = top_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| jng_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,2:3].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| jng_working['Freq'] = jng_working['Freq'].astype(int) | |
| jng_working['Position'] = jng_working['Player'].map(maps_dict['Pos_map']) | |
| jng_working['Salary'] = jng_working['Player'].map(maps_dict['Salary_map']) | |
| jng_working['Proj Own'] = jng_working['Player'].map(maps_dict['Own_map']) / 135 | |
| jng_working['Exposure'] = jng_working['Freq']/(1000) | |
| jng_working['Edge'] = jng_working['Exposure'] - jng_working['Proj Own'] | |
| jng_working['Team'] = jng_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.jng_freq = jng_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| mid_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,3:4].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| mid_working['Freq'] = mid_working['Freq'].astype(int) | |
| mid_working['Position'] = mid_working['Player'].map(maps_dict['Pos_map']) | |
| mid_working['Salary'] = mid_working['Player'].map(maps_dict['Salary_map']) | |
| mid_working['Proj Own'] = mid_working['Player'].map(maps_dict['Own_map']) / 120 | |
| mid_working['Exposure'] = mid_working['Freq']/(1000) | |
| mid_working['Edge'] = mid_working['Exposure'] - mid_working['Proj Own'] | |
| mid_working['Team'] = mid_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.mid_freq = mid_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| adc_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,4:5].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| adc_working['Freq'] = adc_working['Freq'].astype(int) | |
| adc_working['Position'] = adc_working['Player'].map(maps_dict['Pos_map']) | |
| adc_working['Salary'] = adc_working['Player'].map(maps_dict['Salary_map']) | |
| adc_working['Proj Own'] = adc_working['Player'].map(maps_dict['Own_map']) / 135 | |
| adc_working['Exposure'] = adc_working['Freq']/(1000) | |
| adc_working['Edge'] = adc_working['Exposure'] - adc_working['Proj Own'] | |
| adc_working['Team'] = adc_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.adc_freq = adc_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| sup_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,5:6].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| sup_working['Freq'] = sup_working['Freq'].astype(int) | |
| sup_working['Position'] = sup_working['Player'].map(maps_dict['Pos_map']) | |
| sup_working['Salary'] = sup_working['Player'].map(maps_dict['Salary_map']) | |
| sup_working['Proj Own'] = sup_working['Player'].map(maps_dict['Own_map']) / 105 | |
| sup_working['Exposure'] = sup_working['Freq']/(1000) | |
| sup_working['Edge'] = sup_working['Exposure'] - sup_working['Proj Own'] | |
| sup_working['Team'] = sup_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.sup_freq = sup_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,6:7].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| team_working['Freq'] = team_working['Freq'].astype(int) | |
| team_working['Position'] = team_working['Player'].map(maps_dict['Pos_map']) | |
| team_working['Salary'] = team_working['Player'].map(maps_dict['Salary_map']) | |
| team_working['Proj Own'] = team_working['Player'].map(maps_dict['Own_map']) / 100 | |
| team_working['Exposure'] = team_working['Freq']/(1000) | |
| team_working['Edge'] = team_working['Exposure'] - team_working['Proj Own'] | |
| team_working['Team'] = team_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.team_freq = team_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| stack_working['Freq'] = stack_working['Freq'].astype(int) | |
| stack_working['Exposure'] = stack_working['Freq']/(1000) | |
| st.session_state.stack_freq = stack_working.copy() | |
| with st.container(): | |
| if st.button("Reset Sim", key='reset_sim'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if 'player_freq' in st.session_state: | |
| player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2') | |
| if player_split_var2 == 'Specific Players': | |
| find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique()) | |
| elif player_split_var2 == 'Full Players': | |
| find_var2 = st.session_state.player_freq.Player.values.tolist() | |
| if player_split_var2 == 'Specific Players': | |
| st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)] | |
| if player_split_var2 == 'Full Players': | |
| st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame | |
| if 'Sim_Winner_Display' in st.session_state: | |
| st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| if 'Sim_Winner_Export' in st.session_state: | |
| st.download_button( | |
| label="Export Full Frame", | |
| data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'), | |
| file_name='LOL_consim_export.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Stack Exposures', 'Overall Exposures', 'CPT Exposures', 'TOP Exposures', 'JNG Exposures', 'MID Exposures', 'ADC Exposures', 'SUP Exposures', 'Team Exposures']) | |
| with tab1: | |
| if 'stack_freq' in st.session_state and st.session_state.stack_freq is not None: | |
| st.dataframe(st.session_state.stack_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.stack_freq.to_csv().encode('utf-8'), | |
| file_name='stack_freq.csv', | |
| mime='text/csv', | |
| key='stack' | |
| ) | |
| with tab2: | |
| if 'player_freq' in st.session_state and st.session_state.player_freq is not None: | |
| st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.player_freq.to_csv().encode('utf-8'), | |
| file_name='player_freq_export.csv', | |
| mime='text/csv', | |
| key='overall' | |
| ) | |
| with tab3: | |
| if 'cpt_freq' in st.session_state and st.session_state.cpt_freq is not None: | |
| st.dataframe(st.session_state.cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width=True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.cpt_freq.to_csv().encode('utf-8'), | |
| file_name='cpt_freq.csv', | |
| mime='text/csv', | |
| key='cpt' | |
| ) | |
| with tab4: | |
| if 'top_freq' in st.session_state and st.session_state.top_freq is not None: | |
| st.dataframe(st.session_state.top_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.top_freq.to_csv().encode('utf-8'), | |
| file_name='top_freq.csv', | |
| mime='text/csv', | |
| key='top' | |
| ) | |
| with tab5: | |
| if 'jng_freq' in st.session_state and st.session_state.jng_freq is not None: | |
| st.dataframe(st.session_state.jng_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.jng_freq.to_csv().encode('utf-8'), | |
| file_name='jng_freq.csv', | |
| mime='text/csv', | |
| key='jng' | |
| ) | |
| with tab6: | |
| if 'mid_freq' in st.session_state and st.session_state.mid_freq is not None: | |
| st.dataframe(st.session_state.mid_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.mid_freq.to_csv().encode('utf-8'), | |
| file_name='mid_freq.csv', | |
| mime='text/csv', | |
| key='mid' | |
| ) | |
| with tab7: | |
| if 'adc_freq' in st.session_state and st.session_state.adc_freq is not None: | |
| st.dataframe(st.session_state.adc_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.adc_freq.to_csv().encode('utf-8'), | |
| file_name='adc_freq.csv', | |
| mime='text/csv', | |
| key='adc' | |
| ) | |
| with tab8: | |
| if 'sup_freq' in st.session_state and st.session_state.sup_freq is not None: | |
| st.dataframe(st.session_state.sup_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.sup_freq.to_csv().encode('utf-8'), | |
| file_name='sup_freq.csv', | |
| mime='text/csv', | |
| key='sup' | |
| ) | |
| with tab9: | |
| if 'team_freq' in st.session_state and st.session_state.team_freq is not None: | |
| st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.team_freq.to_csv().encode('utf-8'), | |
| file_name='team_freq.csv', | |
| mime='text/csv', | |
| key='team' | |
| ) | |