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James McCool
Refactor app.py: Remove 'Timestamp' column from sd_roo_data in init_baselines function. This change simplifies the data structure returned, enhancing data management and processing efficiency.
59c5c99
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gc | |
| import pymongo | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["PGA_Database"] | |
| return db | |
| db = init_conn() | |
| dk_player_url = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624' | |
| CSV_URL = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624' | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '100+%': '{:.2%}', '10x%': '{:.2%}', '11x%': '{:.2%}', | |
| '12x%': '{:.2%}','LevX': '{:.2%}'} | |
| dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own'] | |
| fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own'] | |
| def init_baselines(): | |
| collection = db["PGA_Range_of_Outcomes"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| timestamp = player_frame['Timestamp'][0] | |
| roo_data = player_frame.drop(columns=['_id', 'index', 'Timestamp']) | |
| roo_data['Salary'] = roo_data['Salary'].astype(int) | |
| collection = db["PGA_SD_ROO"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| sd_roo_data = player_frame.drop(columns=['_id', 'index']) | |
| sd_roo_data['Salary'] = sd_roo_data['Salary'].astype(int) | |
| return roo_data, sd_roo_data, timestamp | |
| def init_DK_lineups(): | |
| collection = db['PGA_DK_Seed_Frame_Name_Map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| collection = db["PGA_DK_Seed_Frame"] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']] | |
| dict_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'] | |
| for col in dict_columns: | |
| raw_display[col] = raw_display[col].map(names_dict) | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_lineups(): | |
| collection = db['PGA_DK_Seed_Frame_Name_Map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| collection = db["PGA_DK_Seed_Frame"] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']] | |
| dict_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'] | |
| for col in dict_columns: | |
| raw_display[col] = raw_display[col].map(names_dict) | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| roo_data, sd_roo_data, timestamp = init_baselines() | |
| dk_lineups = init_DK_lineups() | |
| fd_lineups = init_FD_lineups() | |
| hold_display = roo_data | |
| lineup_display = [] | |
| check_list = [] | |
| rand_player = 0 | |
| boost_player = 0 | |
| salaryCut = 0 | |
| tab1, tab2 = st.tabs(["Player Overall Projections", "Optimals and Exposures"]) | |
| with tab1: | |
| if st.button("Reset Data", key='reset1'): | |
| # Clear values from *all* all in-memory and on-disk data caches: | |
| # i.e. clear values from both square and cube | |
| st.cache_data.clear() | |
| roo_data, sd_roo_data, timestamp = init_baselines() | |
| dk_lineups = init_DK_lineups() | |
| fd_lineups = init_FD_lineups() | |
| hold_display = roo_data | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| st.write(timestamp) | |
| options_container = st.empty() | |
| hold_container = st.empty() | |
| with options_container: | |
| col1, col2 = st.columns([4, 4]) | |
| with col1: | |
| site_var = st.selectbox("Select a Site", ["Draftkings", "FanDuel"]) | |
| with col2: | |
| type_var = st.selectbox("Select a Type", ["Full Slate", "Showdown"]) | |
| with hold_container: | |
| if type_var == "Full Slate": | |
| display = hold_display[hold_display['Site'] == site_var] | |
| elif type_var == "Showdown": | |
| display = sd_roo_data | |
| st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True) | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(display), | |
| file_name='PGA_DFS_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| roo_data, sd_roo_data, timestamp = init_baselines() | |
| hold_display = roo_data | |
| dk_lineups = init_DK_lineups() | |
| fd_lineups = init_FD_lineups() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1) | |
| if site_var1 == 'Draftkings': | |
| raw_baselines = hold_display | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings'] | |
| # Get the minimum and maximum ownership values from dk_lineups | |
| min_own = np.min(dk_lineups[:,8]) | |
| max_own = np.max(dk_lineups[:,8]) | |
| column_names = dk_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| elif site_var1 == 'Fanduel': | |
| raw_baselines = hold_display | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel'] | |
| min_own = np.min(fd_lineups[:,8]) | |
| max_own = np.max(fd_lineups[:,8]) | |
| column_names = fd_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='NBA_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| with col2: | |
| if site_var1 == 'Draftkings': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif site_var1 == 'Fanduel': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| export_file = st.session_state.data_export_display.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| with st.container(): | |
| if st.button("Reset Optimals", key='reset3'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if site_var1 == 'Draftkings': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| elif site_var1 == 'Fanduel': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| if 'data_export_display' in st.session_state: | |
| st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True) | |
| st.download_button( | |
| label="Export display optimals", | |
| data=convert_df(export_file), | |
| file_name='NBA_display_optimals.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| if 'working_seed' in st.session_state: | |
| # Create a new dataframe with summary statistics | |
| if site_var1 == 'Draftkings': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| elif site_var1 == 'Fanduel': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| # Set the index of the summary dataframe as the "Metric" column | |
| summary_df = summary_df.set_index('Metric') | |
| # Display the summary dataframe | |
| st.subheader("Optimal Statistics") | |
| st.dataframe(summary_df.style.format({ | |
| 'Salary': '{:.2f}', | |
| 'Proj': '{:.2f}', | |
| 'Own': '{:.2f}' | |
| }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True) | |
| with st.container(): | |
| tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"]) | |
| with tab1: | |
| if 'data_export_display' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.values.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / lineup_num_var * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| # Display the table | |
| st.write("Player Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export player frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='PGA_player_frequency.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| if 'working_seed' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| # Display the table | |
| st.write("Seed Frame Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export seed frame frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='PGA_seed_frame_frequency.csv', | |
| mime='text/csv', | |
| ) |