import streamlit as st st.set_page_config(layout="wide") for name in dir(): if not name.startswith('_'): del globals()[name] import pulp import numpy as np import pandas as pd import polars as pl import streamlit as st import gspread import time import random import scipy.stats import os @st.cache_resource def init_conn(): scope = ['https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive"] credentials = { "type": "service_account", "project_id": "sheets-api-connect-378620", "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", "client_id": "106625872877651920064", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" } gc = gspread.service_account_from_dict(credentials) return gc gc = 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%}'} player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', '4x%': '{:.2%}','GPP%': '{:.2%}'} freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} @st.cache_resource(ttl = 60) def set_slate_teams(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') worksheet = sh.worksheet('Site_Info') raw_display = pd.DataFrame(worksheet.get_all_records()) return raw_display @st.cache_resource(ttl = 60) def player_stat_table(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') worksheet = sh.worksheet('Player_Projections') raw_display = pd.DataFrame(worksheet.get_all_records()) return raw_display @st.cache_resource(ttl = 60) def load_dk_player_projections(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') worksheet = sh.worksheet('DK_ROO') # Get all records from the Google Sheet records = worksheet.get_all_records() # Convert to Polars DataFrame load_display = pl.DataFrame(records) # Replace empty strings with np.nan load_display = load_display.apply(lambda df: df.replace("", np.nan)) # Drop rows where 'Median' is NaN raw_display = load_display.filter(pl.col("Median").is_not_null()) return raw_display @st.cache_resource(ttl = 60) def load_fd_player_projections(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') worksheet = sh.worksheet('FD_ROO') # Get all records from the Google Sheet records = worksheet.get_all_records() # Convert to Polars DataFrame load_display = pl.DataFrame(records) # Replace empty strings with np.nan load_display = load_display.apply(lambda df: df.replace("", np.nan)) # Drop rows where 'Median' is NaN raw_display = load_display.filter(pl.col("Median").is_not_null()) return raw_display @st.cache_resource(ttl = 60) def set_export_ids(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') # Fetch DK_ROO worksheet and prepare Polars DataFrame worksheet_dk = sh.worksheet('DK_ROO') records_dk = worksheet_dk.get_all_records() load_display_dk = pl.DataFrame(records_dk) load_display_dk = load_display_dk.apply(lambda df: df.replace("", np.nan)) raw_display_dk = load_display_dk.filter(pl.col("Median").is_not_null()) dk_ids = dict(zip(raw_display_dk["Player"].to_list(), raw_display_dk["player_id"].to_list())) # Fetch FD_ROO worksheet and prepare Polars DataFrame worksheet_fd = sh.worksheet('FD_ROO') records_fd = worksheet_fd.get_all_records() load_display_fd = pl.DataFrame(records_fd) load_display_fd = load_display_fd.apply(lambda df: df.replace("", np.nan)) raw_display_fd = load_display_fd.filter(pl.col("Median").is_not_null()) fd_ids = dict(zip(raw_display_fd["Player"].to_list(), raw_display_fd["player_id"].to_list())) return dk_ids, fd_ids @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs): RunsVar = 1 seed_depth_def = seed_depth1 Strength_var_def = Strength_var strength_grow_def = strength_grow Teams_used_def = Teams_used Total_Runs_def = Total_Runs while RunsVar <= seed_depth_def: if RunsVar <= 3: FieldStrength = Strength_var_def RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1) FinalPortfolio = RandomPortfolio FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1) # Replace pd.concat with pl.vstack FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio2]) maps_dict.update(maps_dict2) del FinalPortfolio2 del maps_dict2 elif 3 < RunsVar <= 4: FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * 0.001)) FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1) FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1) # Replace pd.concat with pl.vstack FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio3, FinalPortfolio4]) # Replace drop_duplicates and reset_index FinalPortfolio = FinalPortfolio.drop_duplicates(subset=["Projection", "Own"]).sort("index") maps_dict.update(maps_dict3) maps_dict.update(maps_dict4) del FinalPortfolio3 del maps_dict3 del FinalPortfolio4 del maps_dict4 elif RunsVar > 4: FieldStrength = 1 FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1) FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1) # Replace pd.concat with pl.vstack FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio3, FinalPortfolio4]) # Replace drop_duplicates and reset_index FinalPortfolio = FinalPortfolio.drop_duplicates(subset=["Projection", "Own"]).sort("index") maps_dict.update(maps_dict3) maps_dict.update(maps_dict4) del FinalPortfolio3 del maps_dict3 del FinalPortfolio4 del maps_dict4 RunsVar += 1 return FinalPortfolio, maps_dict def create_stack_options(player_data, wr_var): # Assuming player_data is already a Polars DataFrame # Sort player_data by 'Median' in descending order data_raw = player_data.sort("Median", reverse=True) merged_frame = pl.DataFrame( { "QB": [], "Player": [] } ) for team in data_raw.select("Team").unique().get("Team"): data_split = data_raw.filter(pl.col("Team") == team) qb_frame = data_split.filter(pl.col("Position") == "QB") wr_frame = data_split.filter(pl.col("Position") == "WR").slice(wr_var - 1, wr_var) qb_name = qb_frame.head(1).get("Player")[0] if qb_frame.shape[0] > 0 else None if qb_name is not None: wr_frame = wr_frame.with_column(pl.lit(qb_name).alias("QB")) merge_slice = wr_frame.select("QB", "Player") merged_frame = merged_frame.vstack(merge_slice) # Reset index (not necessary in Polars as index doesn't exist in the same way as Pandas) # Build a dictionary from the DataFrame correl_dict = dict(zip(merged_frame.get("QB"), merged_frame.get("Player"))) del merged_frame del data_raw return correl_dict @st.cache_data def apply_range(s: pl.Series) -> pl.Series: return pl.Series("Var", list(range(s.len()))) def create_overall_dfs(pos_players, table_name, dict_name, pos): if pos == "FLEX": pos_players = pos_players.sort("Value", reverse=True) overall_table_name = pos_players.slice(0, round(pos_players.shape[0])) overall_table_name = overall_table_name.with_column(pl.col("Var").apply_range()) overall_dict_name = {row[0]: row[1] for row in overall_table_name.select(["Var", "Player"]).collect()} del pos_players elif pos != "FLEX": table_name_raw = pos_players.filter(pl.col("Position").str_contains(pos)) overall_table_name = table_name_raw.slice(0, round(table_name_raw.shape[0])) overall_table_name = overall_table_name.with_column(pl.col("Var").apply_range()) overall_dict_name = {row[0]: row[1] for row in overall_table_name.select(["Var", "Player"]).collect()} del pos_players return overall_table_name, overall_dict_name def get_overall_merged_df(): ref_dict = { 'pos': ['RB', 'WR', 'TE', 'FLEX'], 'pos_dfs': ['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], 'pos_dicts': ['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] } for i in range(0, 4): ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] = \ create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i]) # Assuming ref_dict['pos_dfs'] is a list of polars.Dataframe df_out = pl.concat(ref_dict['pos_dfs'], rechunk=True) def calculate_range_var(count, min_val, FieldStrength, field_growth): var = round(len(count[0]) * FieldStrength) var = max(var, min_val) var += round(field_growth) return min(var, len(count[0])) def create_random_portfolio(Total_Sample_Size, raw_baselines): O_merge, full_pos_player_dict = get_overall_merged_df() max_var = len(raw_baselines.filter(pl.col("Position") == "QB")) field_growth_rounded = round(field_growth) ranges_dict = {} # Calculate ranges for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 20, 30, 10], ['RB', 'WR', 'TE', 'FLEX']): count = create_overall_dfs(pos_players, df, dict_val, key) ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) if max_var <= 10: ranges_dict['qb_range'] = round(max_var) ranges_dict['dst_range'] = round(max_var) elif max_var > 10 and max_var <= 16: ranges_dict['qb_range'] = round(max_var / 1.5) ranges_dict['dst_range'] = round(max_var) elif max_var > 16: ranges_dict['qb_range'] = round(max_var / 2) ranges_dict['dst_range'] = round(max_var) # Generate random portfolios rng = np.random.default_rng() total_elements = [1, 2, 3, 1, 1, 1] keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst'] all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] # Create a Polars DataFrame RandomPortfolio = pl.DataFrame( {name: np.hstack([choice[:, i] if choice.shape[1] > i else choice[:, 0] for choice in all_choices]) for i, name in enumerate(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])} ) RandomPortfolio = RandomPortfolio.with_column(pl.col("User/Field").fill_none(0)) del O_merge return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict def get_correlated_portfolio_for_sim(Total_Sample_Size): sizesplit = round(Total_Sample_Size * sharp_split) RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines) stack_num = random.randint(1, 3) stacking_dict = create_stack_options(raw_baselines, stack_num) # Mapping series RandomPortfolio = RandomPortfolio.with_column( pl.col("QB").apply(lambda x: qb_dict.get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("RB1").apply(lambda x: full_pos_player_dict['pos_dicts'][0].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("RB2").apply(lambda x: full_pos_player_dict['pos_dicts'][0].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR1").apply(lambda x: stacking_dict.get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR2").apply(lambda x: full_pos_player_dict['pos_dicts'][1].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR3").apply(lambda x: full_pos_player_dict['pos_dicts'][1].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("TE").apply(lambda x: full_pos_player_dict['pos_dicts'][2].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("FLEX").apply(lambda x: full_pos_player_dict['pos_dicts'][3].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("DST").apply(lambda x: def_dict.get(x, x), return_dtype=pl.Utf8) ) # Creating 'plyr_list' and 'plyr_count' plyr_list_exprs = [pl.col(name).alias(f"{name}_item") for name in RandomPortfolio.columns] plyr_list = pl.col(plyr_list_exprs).apply(lambda x: list(set(x)), return_dtype=pl.List(pl.Utf8)).alias("plyr_list") plyr_count = plyr_list.apply(lambda x: len(set(x)), return_dtype=pl.Int64).alias("plyr_count") # Add these to RandomPortfolio RandomPortfolio = RandomPortfolio.with_columns([plyr_list, plyr_count]) # Filter out rows where 'plyr_count' is not 10 RandomPortfolio = RandomPortfolio.filter(pl.col("plyr_count") == 10).select_except("plyr_list", "plyr_count") # Since polars DataFrame is lazy, you may want to call .collect() to materialize it RandomPortfolio = RandomPortfolio.collect() # Map and cast to specific data types positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] for pos in positions: RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Salary_map'].get(x, x), return_dtype=pl.Int32).alias(f"{pos}s") ) RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Projection_map'].get(x, x), return_dtype=pl.Float32).alias(f"{pos}p") ) RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Own_map'].get(x, x), return_dtype=pl.Float32).alias(f"{pos}o") ) # Equivalent of converting to numpy array and performing einsum RandomPortfolio = RandomPortfolio.with_columns([ pl.sum([pl.col(f"{pos}s") for pos in positions]).alias('Salary'), pl.sum([pl.col(f"{pos}p") for pos in positions]).alias('Projection'), pl.sum([pl.col(f"{pos}o") for pos in positions]).alias('Own') ]) # Select the columns you want in the final DataFrame RandomPortfolio = RandomPortfolio.select( positions + ['User/Field', 'Salary', 'Projection', 'Own'] ) # Since DataFrame is lazy, call collect() to materialize RandomPortfolio = RandomPortfolio.collect() # Sorting based on some function # Note: Replace 'Sim_function' with the actual column name or expression you want to sort by RandomPortfolio = RandomPortfolio.sort('Sim_function', reverse=True) positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] if insert_port == 1: CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: maps_dict['Salary_map'].get(x, x), return_dtype=pl.Int16) for pos in positions ]).alias('Salary') ) CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: up_dict['Projection_map'].get(x, x), return_dtype=pl.Float16) for pos in positions ]).alias('Projection') ) CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: maps_dict['Own_map'].get(x, x), return_dtype=pl.Float16) for pos in positions ]).alias('Own') ) if site_var1 == 'Draftkings': RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') <= 50000) RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') >= (49500 - (5000 * (1 - (len(Teams_used) / 32))) - (FieldStrength * 1000))) elif site_var1 == 'Fanduel': RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') <= 60000) RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') >= (59500 - (5000 * (1 - (len(Teams_used) / 32))) - (FieldStrength * 1000))) # Sorting the DataFrame # Note: Replace 'Sim_function' with the actual column name or expression you want to sort by RandomPortfolioDF = RandomPortfolioDF.sort('Sim_function', reverse=True) # Selecting the columns you want to keep RandomPortfolioDF = RandomPortfolioDF.select(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) return RandomPortfolio, maps_dict def get_uncorrelated_portfolio_for_sim(Total_Sample_Size): sizesplit = round(Total_Sample_Size * sharp_split) RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines) stack_num = random.randint(1, 3) stacking_dict = create_stack_options(raw_baselines, stack_num) # Mapping series RandomPortfolio = RandomPortfolio.with_column( pl.col("QB").apply(lambda x: qb_dict.get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("RB1").apply(lambda x: full_pos_player_dict['pos_dicts'][0].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("RB2").apply(lambda x: full_pos_player_dict['pos_dicts'][0].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR1").apply(lambda x: stacking_dict.get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR2").apply(lambda x: full_pos_player_dict['pos_dicts'][1].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("WR3").apply(lambda x: full_pos_player_dict['pos_dicts'][1].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("TE").apply(lambda x: full_pos_player_dict['pos_dicts'][2].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("FLEX").apply(lambda x: full_pos_player_dict['pos_dicts'][3].get(x, x), return_dtype=pl.Utf8) ) RandomPortfolio = RandomPortfolio.with_column( pl.col("DST").apply(lambda x: def_dict.get(x, x), return_dtype=pl.Utf8) ) # Creating 'plyr_list' and 'plyr_count' plyr_list_exprs = [pl.col(name).alias(f"{name}_item") for name in RandomPortfolio.columns] plyr_list = pl.col(plyr_list_exprs).apply(lambda x: list(set(x)), return_dtype=pl.List(pl.Utf8)).alias("plyr_list") plyr_count = plyr_list.apply(lambda x: len(set(x)), return_dtype=pl.Int64).alias("plyr_count") # Add these to RandomPortfolio RandomPortfolio = RandomPortfolio.with_columns([plyr_list, plyr_count]) # Filter out rows where 'plyr_count' is not 10 RandomPortfolio = RandomPortfolio.filter(pl.col("plyr_count") == 10).select_except("plyr_list", "plyr_count") # Since polars DataFrame is lazy, you may want to call .collect() to materialize it RandomPortfolio = RandomPortfolio.collect() # Map and cast to specific data types positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] for pos in positions: RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Salary_map'].get(x, x), return_dtype=pl.Int32).alias(f"{pos}s") ) RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Projection_map'].get(x, x), return_dtype=pl.Float32).alias(f"{pos}p") ) RandomPortfolio = RandomPortfolio.with_column( pl.col(pos).apply(lambda x: maps_dict['Own_map'].get(x, x), return_dtype=pl.Float32).alias(f"{pos}o") ) # Equivalent of converting to numpy array and performing einsum RandomPortfolio = RandomPortfolio.with_columns([ pl.sum([pl.col(f"{pos}s") for pos in positions]).alias('Salary'), pl.sum([pl.col(f"{pos}p") for pos in positions]).alias('Projection'), pl.sum([pl.col(f"{pos}o") for pos in positions]).alias('Own') ]) # Select the columns you want in the final DataFrame RandomPortfolio = RandomPortfolio.select( positions + ['User/Field', 'Salary', 'Projection', 'Own'] ) # Since DataFrame is lazy, call collect() to materialize RandomPortfolio = RandomPortfolio.collect() # Sorting based on some function # Note: Replace 'Sim_function' with the actual column name or expression you want to sort by RandomPortfolio = RandomPortfolio.sort('Sim_function', reverse=True) positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] if insert_port == 1: CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: maps_dict['Salary_map'].get(x, x), return_dtype=pl.Int16) for pos in positions ]).alias('Salary') ) CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: up_dict['Projection_map'].get(x, x), return_dtype=pl.Float16) for pos in positions ]).alias('Projection') ) CleanPortfolio = CleanPortfolio.with_column( pl.sum([ pl.col(pos).apply(lambda x: maps_dict['Own_map'].get(x, x), return_dtype=pl.Float16) for pos in positions ]).alias('Own') ) if site_var1 == 'Draftkings': RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') <= 50000) RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') >= (49500 - (5000 * (1 - (len(Teams_used) / 32))) - (FieldStrength * 1000))) elif site_var1 == 'Fanduel': RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') <= 60000) RandomPortfolioDF = RandomPortfolioDF.filter(pl.col('Salary') >= (59500 - (5000 * (1 - (len(Teams_used) / 32))) - (FieldStrength * 1000))) # Sorting the DataFrame # Note: Replace 'Sim_function' with the actual column name or expression you want to sort by RandomPortfolioDF = RandomPortfolioDF.sort('Sim_function', reverse=True) # Selecting the columns you want to keep RandomPortfolioDF = RandomPortfolioDF.select(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) return RandomPortfolio, maps_dict player_stats = player_stat_table() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() dkid_dict, fdid_dict = set_export_ids() static_exposure = pd.DataFrame(columns=['Player', 'count']) overall_exposure = pd.DataFrame(columns=['Player', 'count']) tab1, tab2 = st.tabs(['Uploads', 'Contest Sim']) with tab1: with st.container(): col1, col2 = st.columns([3, 3]) with col1: st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.") 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) proj_dataframe = proj_dataframe.dropna(subset='Median') proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() try: proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) except: pass except: proj_dataframe = pd.read_excel(proj_file) proj_dataframe = proj_dataframe.dropna(subset='Median') proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() try: proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) except: pass st.table(proj_dataframe.head(10)) player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary)) player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median)) player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own)) player_team_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Team)) with col2: st.info("The Portfolio file must contain only columns in order and explicitly named: 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', and 'DST'. Upload your projections first to avoid an error message.") portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader') if portfolio_file is not None: try: portfolio_dataframe = pd.read_csv(portfolio_file) except: portfolio_dataframe = pd.read_excel(portfolio_file) try: try: portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] split_portfolio = portfolio_dataframe split_portfolio[['QB', 'QB_ID']] = split_portfolio.QB.str.split("(", n=1, expand = True) split_portfolio[['RB1', 'RB1_ID']] = split_portfolio.RB1.str.split("(", n=1, expand = True) split_portfolio[['RB2', 'RB2_ID']] = split_portfolio.RB2.str.split("(", n=1, expand = True) split_portfolio[['WR1', 'WR1_ID']] = split_portfolio.WR1.str.split("(", n=1, expand = True) split_portfolio[['WR2', 'WR2_ID']] = split_portfolio.WR2.str.split("(", n=1, expand = True) split_portfolio[['WR3', 'WR3_ID']] = split_portfolio.WR3.str.split("(", n=1, expand = True) split_portfolio[['TE', 'TE_ID']] = split_portfolio.TE.str.split("(", n=1, expand = True) split_portfolio[['FLEX', 'FLEX_ID']] = split_portfolio.FLEX.str.split("(", n=1, expand = True) split_portfolio[['DST', 'DST_ID']] = split_portfolio.DST.str.split("(", n=1, expand = True) split_portfolio['QB'] = split_portfolio['QB'].str.strip() split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() split_portfolio['TE'] = split_portfolio['TE'].str.strip() split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() split_portfolio['DST'] = split_portfolio['DST'].str.strip() st.table(split_portfolio.head(10)) split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), split_portfolio['RB1'].map(player_salary_dict), split_portfolio['RB2'].map(player_salary_dict), split_portfolio['WR1'].map(player_salary_dict), split_portfolio['WR2'].map(player_salary_dict), split_portfolio['WR3'].map(player_salary_dict), split_portfolio['TE'].map(player_salary_dict), split_portfolio['FLEX'].map(player_salary_dict), split_portfolio['DST'].map(player_salary_dict)]) split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), split_portfolio['RB1'].map(player_proj_dict), split_portfolio['RB2'].map(player_proj_dict), split_portfolio['WR1'].map(player_proj_dict), split_portfolio['WR2'].map(player_proj_dict), split_portfolio['WR3'].map(player_proj_dict), split_portfolio['TE'].map(player_proj_dict), split_portfolio['FLEX'].map(player_proj_dict), split_portfolio['DST'].map(player_proj_dict)]) split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), split_portfolio['RB1'].map(player_own_dict), split_portfolio['RB2'].map(player_own_dict), split_portfolio['WR1'].map(player_own_dict), split_portfolio['WR2'].map(player_own_dict), split_portfolio['WR3'].map(player_own_dict), split_portfolio['TE'].map(player_own_dict), split_portfolio['FLEX'].map(player_own_dict), split_portfolio['DST'].map(player_own_dict)]) split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict) split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict) split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict) split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict) split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict) split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict) split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict) split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict) split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict) split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team', 'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']] split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1 except: portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] split_portfolio = portfolio_dataframe split_portfolio[['QB_ID', 'QB']] = split_portfolio.QB.str.split(":", n=1, expand = True) split_portfolio[['RB1_ID', 'RB1']] = split_portfolio.RB1.str.split(":", n=1, expand = True) split_portfolio[['RB2_ID', 'RB2']] = split_portfolio.RB2.str.split(":", n=1, expand = True) split_portfolio[['WR1_ID', 'WR1']] = split_portfolio.WR1.str.split(":", n=1, expand = True) split_portfolio[['WR2_ID', 'WR2']] = split_portfolio.WR2.str.split(":", n=1, expand = True) split_portfolio[['WR3_ID', 'WR3']] = split_portfolio.WR3.str.split(":", n=1, expand = True) split_portfolio[['TE_ID', 'TE']] = split_portfolio.TE.str.split(":", n=1, expand = True) split_portfolio[['FLEX_ID', 'FLEX']] = split_portfolio.FLEX.str.split(":", n=1, expand = True) split_portfolio[['DST_ID', 'DST']] = split_portfolio.DST.str.split(":", n=1, expand = True) split_portfolio['QB'] = split_portfolio['QB'].str.strip() split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() split_portfolio['TE'] = split_portfolio['TE'].str.strip() split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() split_portfolio['DST'] = split_portfolio['DST'].str.strip() split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), split_portfolio['RB1'].map(player_salary_dict), split_portfolio['RB2'].map(player_salary_dict), split_portfolio['WR1'].map(player_salary_dict), split_portfolio['WR2'].map(player_salary_dict), split_portfolio['WR3'].map(player_salary_dict), split_portfolio['TE'].map(player_salary_dict), split_portfolio['FLEX'].map(player_salary_dict), split_portfolio['DST'].map(player_salary_dict)]) split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), split_portfolio['RB1'].map(player_proj_dict), split_portfolio['RB2'].map(player_proj_dict), split_portfolio['WR1'].map(player_proj_dict), split_portfolio['WR2'].map(player_proj_dict), split_portfolio['WR3'].map(player_proj_dict), split_portfolio['TE'].map(player_proj_dict), split_portfolio['FLEX'].map(player_proj_dict), split_portfolio['DST'].map(player_proj_dict)]) st.table(split_portfolio.head(10)) split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), split_portfolio['RB1'].map(player_own_dict), split_portfolio['RB2'].map(player_own_dict), split_portfolio['WR1'].map(player_own_dict), split_portfolio['WR2'].map(player_own_dict), split_portfolio['WR3'].map(player_own_dict), split_portfolio['TE'].map(player_own_dict), split_portfolio['FLEX'].map(player_own_dict), split_portfolio['DST'].map(player_own_dict)]) split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict) split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict) split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict) split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict) split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict) split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict) split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict) split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict) split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict) split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team', 'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']] split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1 except: split_portfolio = portfolio_dataframe split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), split_portfolio['RB1'].map(player_salary_dict), split_portfolio['RB2'].map(player_salary_dict), split_portfolio['WR1'].map(player_salary_dict), split_portfolio['WR2'].map(player_salary_dict), split_portfolio['WR3'].map(player_salary_dict), split_portfolio['TE'].map(player_salary_dict), split_portfolio['FLEX'].map(player_salary_dict), split_portfolio['DST'].map(player_salary_dict)]) split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), split_portfolio['RB1'].map(player_proj_dict), split_portfolio['RB2'].map(player_proj_dict), split_portfolio['WR1'].map(player_proj_dict), split_portfolio['WR2'].map(player_proj_dict), split_portfolio['WR3'].map(player_proj_dict), split_portfolio['TE'].map(player_proj_dict), split_portfolio['FLEX'].map(player_proj_dict), split_portfolio['DST'].map(player_proj_dict)]) split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), split_portfolio['RB1'].map(player_own_dict), split_portfolio['RB2'].map(player_own_dict), split_portfolio['WR1'].map(player_own_dict), split_portfolio['WR2'].map(player_own_dict), split_portfolio['WR3'].map(player_own_dict), split_portfolio['TE'].map(player_own_dict), split_portfolio['FLEX'].map(player_own_dict), split_portfolio['DST'].map(player_own_dict)]) split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict) split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict) split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict) split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict) split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict) split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict) split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict) split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict) split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict) split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team', 'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']] split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1) split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1 for player_cols in split_portfolio.iloc[:, :9]: static_col_raw = split_portfolio[player_cols].value_counts() static_col = static_col_raw.to_frame() static_col.reset_index(inplace=True) static_col.columns = ['Player', 'count'] static_exposure = pd.concat([static_exposure, static_col], ignore_index=True) static_exposure['Exposure'] = static_exposure['count'] / len(split_portfolio) static_exposure = static_exposure[['Player', 'Exposure']] del player_salary_dict del player_proj_dict del player_own_dict del player_team_dict del static_col_raw del static_col with st.container(): col1, col2 = st.columns([3, 3]) if portfolio_file is not None: with col1: team_split_var1 = st.radio("Are you wanting to isolate any lineups with specific main stacks?", ('Full Portfolio', 'Specific Stacks')) if team_split_var1 == 'Specific Stacks': team_var1 = st.multiselect('Which main stacks would you like to include in the Portfolio?', options = split_portfolio['Main_Stack'].unique()) elif team_split_var1 == 'Full Portfolio': team_var1 = split_portfolio.Main_Stack.values.tolist() with col2: player_split_var1 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players')) if player_split_var1 == 'Specific Players': find_var1 = st.multiselect('Which players must be included in the lineups?', options = static_exposure['Player'].unique()) elif player_split_var1 == 'Full Players': find_var1 = static_exposure.Player.values.tolist() split_portfolio = split_portfolio[split_portfolio['Main_Stack'].isin(team_var1)] if player_split_var1 == 'Specific Players': split_portfolio = split_portfolio[np.equal.outer(split_portfolio.to_numpy(copy=False), find_var1).any(axis=1).all(axis=1)] elif player_split_var1 == 'Full Players': split_portfolio = split_portfolio for player_cols in split_portfolio.iloc[:, :9]: exposure_col_raw = split_portfolio[player_cols].value_counts() exposure_col = exposure_col_raw.to_frame() exposure_col.reset_index(inplace=True) exposure_col.columns = ['Player', 'count'] overall_exposure = pd.concat([overall_exposure, exposure_col], ignore_index=True) overall_exposure['Exposure'] = overall_exposure['count'] / len(split_portfolio) overall_exposure = overall_exposure.groupby('Player').sum() overall_exposure.reset_index(inplace=True) overall_exposure = overall_exposure[['Player', 'Exposure']] overall_exposure = overall_exposure.set_index('Player') overall_exposure = overall_exposure.sort_values(by='Exposure', ascending=False) overall_exposure['Exposure'] = overall_exposure['Exposure'].astype(float).map(lambda n: '{:.2%}'.format(n)) with st.container(): col1, col2 = st.columns([1, 6]) with col1: if portfolio_file is not None: st.header('Exposure View') st.dataframe(overall_exposure) with col2: if portfolio_file is not None: st.header('Portfolio View') split_portfolio = split_portfolio.reset_index() split_portfolio['Lineup'] = split_portfolio['index'] + 1 display_portfolio = split_portfolio[['Lineup', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Main_Stack', 'Main_Stack_Size', 'Projection', 'Ownership']] display_portfolio = display_portfolio.set_index('Lineup') st.dataframe(display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Ownership']).format(precision=2)) del split_portfolio del exposure_col_raw del exposure_col with tab2: col1, col2 = st.columns([1, 7]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() dkid_dict, fdid_dict = set_export_ids() slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User')) site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) if site_var1 == 'Draftkings': if slate_var1 == 'User': raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif slate_var1 != 'User': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] elif site_var1 == 'Fanduel': if slate_var1 == 'User': raw_baselines = proj_dataframe elif slate_var1 != 'User': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation") insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1') if insert_port1 == 'Yes': insert_port = 1 elif insert_port1 == 'No': insert_port = 0 contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large')) if contest_var1 == 'Small': Contest_Size = 1000 elif contest_var1 == 'Medium': Contest_Size = 5000 elif contest_var1 == 'Large': Contest_Size = 10000 linenum_var1 = 1000 strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very')) if strength_var1 == 'Not Very': sharp_split = .33 Strength_var = .50 scaling_var = 5 elif strength_var1 == 'Average': sharp_split = .50 Strength_var = .25 scaling_var = 10 elif strength_var1 == 'Very': sharp_split = .75 Strength_var = .01 scaling_var = 15 with col2: if st.button("Simulate Contest"): try: del dst_freq del flex_freq del te_freq del wr_freq del rb_freq del qb_freq del player_freq del Sim_Winner_Export del Sim_Winner_Frame except: pass with st.container(): st.write('Contest Simulation Starting') seed_depth1 = 10 Total_Runs = 1000000 if Contest_Size <= 1000: strength_grow = .01 elif Contest_Size > 1000 and Contest_Size <= 2500: strength_grow = .025 elif Contest_Size > 2500 and Contest_Size <= 5000: strength_grow = .05 elif Contest_Size > 5000 and Contest_Size <= 20000: strength_grow = .075 elif Contest_Size > 20000: strength_grow = .1 field_growth = 100 * strength_grow Sort_function = 'Median' if Sort_function == 'Median': Sim_function = 'Projection' elif Sort_function == 'Own': Sim_function = 'Own' if slate_var1 == 'User': OwnFrame = proj_dataframe if contest_var1 == 'Small': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) if contest_var1 == 'Medium': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) if contest_var1 == 'Large': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] del OwnFrame elif slate_var1 != 'User': initial_proj = raw_baselines drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first') OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']] if contest_var1 == 'Small': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) if contest_var1 == 'Medium': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) if contest_var1 == 'Large': OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own']) OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%']) OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%']) OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum()) Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] del initial_proj del drop_frame del OwnFrame if insert_port == 1: UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']] elif insert_port == 0: UserPortfolio = pd.DataFrame(columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) Overall_Proj.replace('', np.nan, inplace=True) Overall_Proj = Overall_Proj.dropna(subset=['Median']) Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000))) Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2 Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False) Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own']) Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0] Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25) Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor']) Overall_Proj['STDev'] = Overall_Proj['Median'] / 4 Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True) Teams_used = Teams_used.reset_index() Teams_used['team_item'] = Teams_used['index'] + 1 Teams_used = Teams_used.drop(columns=['index']) Teams_used_dictraw = Teams_used.drop(columns=['team_item']) Teams_used_dict = Teams_used_dictraw.to_dict() del Teams_used_dictraw team_list = Teams_used['Team'].to_list() item_list = Teams_used['team_item'].to_list() FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01) FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size)) del FieldStrength_raw if FieldStrength < 0: FieldStrength = Strength_var field_split = Strength_var for checkVar in range(len(team_list)): Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list) qbs_raw = Overall_Proj[Overall_Proj.Position == 'QB'] qbs_raw.dropna(subset=['Median']).reset_index(drop=True) qbs_raw = qbs_raw.reset_index(drop=True) qbs_raw = qbs_raw.sort_values(by=['Median'], ascending=False) qbs = qbs_raw.head(round(len(qbs_raw))) qbs = qbs.assign(Var = range(0,len(qbs))) qb_dict = pd.Series(qbs.Player.values, index=qbs.Var).to_dict() defs_raw = Overall_Proj[Overall_Proj.Position.str.contains("D")] defs_raw.dropna(subset=['Median']).reset_index(drop=True) defs_raw = defs_raw.reset_index(drop=True) defs_raw = defs_raw.sort_values(by=['Own', 'Value'], ascending=False) defs = defs_raw.head(round(len(defs_raw))) defs = defs.assign(Var = range(0,len(defs))) def_dict = pd.Series(defs.Player.values, index=defs.Var).to_dict() rbs_raw = Overall_Proj[Overall_Proj.Position == 'RB'] rbs_raw.dropna(subset=['Median']).reset_index(drop=True) rbs_raw = rbs_raw.reset_index(drop=True) rbs_raw = rbs_raw.sort_values(by=['Own', 'Value'], ascending=False) wrs_raw = Overall_Proj[Overall_Proj.Position == 'WR'] wrs_raw.dropna(subset=['Median']).reset_index(drop=True) wrs_raw = wrs_raw.reset_index(drop=True) wrs_raw = wrs_raw.sort_values(by=['Own', 'Median'], ascending=False) tes_raw = Overall_Proj[Overall_Proj.Position == 'TE'] tes_raw.dropna(subset=['Median']).reset_index(drop=True) tes_raw = tes_raw.reset_index(drop=True) tes_raw = tes_raw.sort_values(by=['Own', 'Value'], ascending=False) pos_players = pd.concat([rbs_raw, wrs_raw, tes_raw]) pos_players.dropna(subset=['Median']).reset_index(drop=True) pos_players = pos_players.reset_index(drop=True) del qbs_raw del defs_raw del rbs_raw del wrs_raw del tes_raw if insert_port == 1: try: # Initialize an empty DataFrame for Raw Portfolio Raw_Portfolio = pd.DataFrame() # Loop through each position and split the data accordingly positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] for pos in positions: temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True) temp_df.columns = [pos, 'Drop'] Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1) # Select only necessary columns and strip white spaces CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip()) CleanPortfolio.reset_index(inplace=True) CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 CleanPortfolio.drop(columns=['index'], inplace=True) CleanPortfolio.replace('', np.nan, inplace=True) CleanPortfolio.dropna(subset=['QB'], inplace=True) # Create frequency table for players cleaport_players = pd.DataFrame( np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)), columns=['Player', 'Freq'] ).sort_values('Freq', ascending=False).reset_index(drop=True) cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) # Merge and update nerf_frame nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') for col in ['Median', 'Floor', 'Ceiling', 'STDev']: nerf_frame[col] *= 0.90 del Raw_Portfolio except: CleanPortfolio = UserPortfolio.reset_index() CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 CleanPortfolio.drop(columns=['index'], inplace=True) # Replace empty strings and drop rows with NaN in 'QB' column CleanPortfolio.replace('', np.nan, inplace=True) CleanPortfolio.dropna(subset=['QB'], inplace=True) # Create frequency table for players cleaport_players = pd.DataFrame( np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)), columns=['Player', 'Freq'] ).sort_values('Freq', ascending=False).reset_index(drop=True) cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) # Merge and update nerf_frame nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') for col in ['Median', 'Floor', 'Ceiling', 'STDev']: nerf_frame[col] *= 0.90 elif insert_port == 0: CleanPortfolio = UserPortfolio cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) nerf_frame = Overall_Proj ref_dict = { 'pos':['RB', 'WR', 'TE', 'FLEX'], 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] } maps_dict = { 'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)), 'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)), 'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)), 'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)), 'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)), 'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)), 'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)), 'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)), 'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)) } up_dict = { 'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)), 'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)), 'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)), 'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)), 'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)), 'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)), 'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)), 'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)), 'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team)) } del cleaport_players del Overall_Proj del nerf_frame st.write('Seed frame creation') FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs) Sim_size = linenum_var1 SimVar = 1 Sim_Winners = [] fp_array = FinalPortfolio.values if insert_port == 1: up_array = CleanPortfolio.values # Pre-vectorize functions vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) if insert_port == 1: vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__) vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__) st.write('Simulating contest on frames') while SimVar <= Sim_size: if insert_port == 1: fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))] elif insert_port == 0: 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[:, :-5]), scale=vec_stdev_map(fp_random[:, :-5])), axis=1) ] if insert_port == 1: sample_arrays2 = np.c_[ up_array, np.sum(np.random.normal( loc=vec_up_projection_map(up_array[:, :-5]), scale=vec_up_stdev_map(up_array[:, :-5])), axis=1) ] sample_arrays = np.vstack((sample_arrays1, sample_arrays2)) else: sample_arrays = sample_arrays1 final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] Sim_Winners.append(best_lineup) SimVar += 1 # del smple_arrays # del smple_arrays1 # del smple_arrays2 # del final_array # del best_lineup st.write('Contest simulation complete') # Initial setup Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy']) Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2 # Type Casting type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16} Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) # Sorting Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False) # Data Copying Sim_Winner_Export = Sim_Winner_Frame.copy() # Conditional Replacement columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] if site_var1 == 'Draftkings': replace_dict = dkid_dict elif site_var1 == 'Fanduel': replace_dict = fdid_dict for col in columns_to_replace: Sim_Winner_Export[col].replace(replace_dict, inplace=True) player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) player_freq['Freq'] = player_freq['Freq'].astype(int) player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map']) player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map']) player_freq['Proj Own'] = player_freq['Player'].map(maps_dict['Own_map']) / 100 player_freq['Exposure'] = player_freq['Freq']/(Sim_size) player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own'] player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): player_freq['Team'] = player_freq['Team'].replace(item_list, team_list) player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']] qb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) qb_freq['Freq'] = qb_freq['Freq'].astype(int) qb_freq['Position'] = qb_freq['Player'].map(maps_dict['Pos_map']) qb_freq['Salary'] = qb_freq['Player'].map(maps_dict['Salary_map']) qb_freq['Proj Own'] = qb_freq['Player'].map(maps_dict['Own_map']) / 100 qb_freq['Exposure'] = qb_freq['Freq']/(Sim_size) qb_freq['Edge'] = qb_freq['Exposure'] - qb_freq['Proj Own'] qb_freq['Team'] = qb_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): qb_freq['Team'] = qb_freq['Team'].replace(item_list, team_list) qb_freq = qb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] rb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) rb_freq['Freq'] = rb_freq['Freq'].astype(int) rb_freq['Position'] = rb_freq['Player'].map(maps_dict['Pos_map']) rb_freq['Salary'] = rb_freq['Player'].map(maps_dict['Salary_map']) rb_freq['Proj Own'] = rb_freq['Player'].map(maps_dict['Own_map']) / 100 rb_freq['Exposure'] = rb_freq['Freq']/Sim_size rb_freq['Edge'] = rb_freq['Exposure'] - rb_freq['Proj Own'] rb_freq['Team'] = rb_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): rb_freq['Team'] = rb_freq['Team'].replace(item_list, team_list) rb_freq = rb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] wr_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) wr_freq['Freq'] = wr_freq['Freq'].astype(int) wr_freq['Position'] = wr_freq['Player'].map(maps_dict['Pos_map']) wr_freq['Salary'] = wr_freq['Player'].map(maps_dict['Salary_map']) wr_freq['Proj Own'] = wr_freq['Player'].map(maps_dict['Own_map']) / 100 wr_freq['Exposure'] = wr_freq['Freq']/Sim_size wr_freq['Edge'] = wr_freq['Exposure'] - wr_freq['Proj Own'] wr_freq['Team'] = wr_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): wr_freq['Team'] = wr_freq['Team'].replace(item_list, team_list) wr_freq = wr_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] te_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) te_freq['Freq'] = te_freq['Freq'].astype(int) te_freq['Position'] = te_freq['Player'].map(maps_dict['Pos_map']) te_freq['Salary'] = te_freq['Player'].map(maps_dict['Salary_map']) te_freq['Proj Own'] = te_freq['Player'].map(maps_dict['Own_map']) / 100 te_freq['Exposure'] = te_freq['Freq']/Sim_size te_freq['Edge'] = te_freq['Exposure'] - te_freq['Proj Own'] te_freq['Team'] = te_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): te_freq['Team'] = te_freq['Team'].replace(item_list, team_list) te_freq = te_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) flex_freq['Freq'] = flex_freq['Freq'].astype(int) flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map']) flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map']) flex_freq['Proj Own'] = flex_freq['Player'].map(maps_dict['Own_map']) / 100 flex_freq['Exposure'] = flex_freq['Freq']/Sim_size flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own'] flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list) flex_freq = flex_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] dst_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) dst_freq['Freq'] = dst_freq['Freq'].astype(int) dst_freq['Position'] = dst_freq['Player'].map(maps_dict['Pos_map']) dst_freq['Salary'] = dst_freq['Player'].map(maps_dict['Salary_map']) dst_freq['Proj Own'] = dst_freq['Player'].map(maps_dict['Own_map']) / 100 dst_freq['Exposure'] = dst_freq['Freq']/Sim_size dst_freq['Edge'] = dst_freq['Exposure'] - dst_freq['Proj Own'] dst_freq['Team'] = dst_freq['Player'].map(maps_dict['Team_map']) for checkVar in range(len(team_list)): dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list) dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']] with st.container(): simulate_container = st.empty() st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(Sim_Winner_Export), file_name='NFL_consim_export.csv', mime='text/csv', ) with st.container(): freq_container = st.empty() tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures']) with tab1: st.dataframe(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=convert_df_to_csv(player_freq), file_name='player_freq_export.csv', mime='text/csv', ) with tab2: st.dataframe(qb_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=convert_df_to_csv(qb_freq), file_name='qb_freq_export.csv', mime='text/csv', ) with tab3: st.dataframe(rb_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=convert_df_to_csv(rb_freq), file_name='rb_freq_export.csv', mime='text/csv', ) with tab4: st.dataframe(wr_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=convert_df_to_csv(wr_freq), file_name='wr_freq_export.csv', mime='text/csv', ) with tab5: st.dataframe(te_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=convert_df_to_csv(te_freq), file_name='te_freq_export.csv', mime='text/csv', ) with tab6: st.dataframe(flex_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=convert_df_to_csv(flex_freq), file_name='flex_freq_export.csv', mime='text/csv', ) with tab7: st.dataframe(dst_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=convert_df_to_csv(dst_freq), file_name='dst_freq_export.csv', mime='text/csv', )