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| 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 gspread | |
| import random | |
| import gc | |
| tab1, tab2 = st.tabs(['Uploads', 'Manage Portfolio']) | |
| 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)) | |
| with col2: | |
| st.info("The Portfolio file must contain only columns in order and explicitly named: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. 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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] | |
| split_portfolio = portfolio_dataframe | |
| split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True) | |
| split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True) | |
| split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True) | |
| split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True) | |
| split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True) | |
| split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True) | |
| split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True) | |
| split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True) | |
| split_portfolio['PG'] = split_portfolio['PG'].str.strip() | |
| split_portfolio['SG'] = split_portfolio['SG'].str.strip() | |
| split_portfolio['SF'] = split_portfolio['SF'].str.strip() | |
| split_portfolio['PF'] = split_portfolio['PF'].str.strip() | |
| split_portfolio['C'] = split_portfolio['C'].str.strip() | |
| split_portfolio['G'] = split_portfolio['G'].str.strip() | |
| split_portfolio['F'] = split_portfolio['F'].str.strip() | |
| split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip() | |
| split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), | |
| split_portfolio['SG'].map(player_salary_dict), | |
| split_portfolio['SF'].map(player_salary_dict), | |
| split_portfolio['PF'].map(player_salary_dict), | |
| split_portfolio['C'].map(player_salary_dict), | |
| split_portfolio['G'].map(player_salary_dict), | |
| split_portfolio['F'].map(player_salary_dict), | |
| split_portfolio['UTIL'].map(player_salary_dict)]) | |
| split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), | |
| split_portfolio['SG'].map(player_proj_dict), | |
| split_portfolio['SF'].map(player_proj_dict), | |
| split_portfolio['PF'].map(player_proj_dict), | |
| split_portfolio['C'].map(player_proj_dict), | |
| split_portfolio['G'].map(player_proj_dict), | |
| split_portfolio['F'].map(player_proj_dict), | |
| split_portfolio['UTIL'].map(player_proj_dict)]) | |
| split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), | |
| split_portfolio['SG'].map(player_own_dict), | |
| split_portfolio['SF'].map(player_own_dict), | |
| split_portfolio['PF'].map(player_own_dict), | |
| split_portfolio['C'].map(player_own_dict), | |
| split_portfolio['G'].map(player_own_dict), | |
| split_portfolio['F'].map(player_own_dict), | |
| split_portfolio['UTIL'].map(player_own_dict)]) | |
| st.table(split_portfolio.head(10)) | |
| except: | |
| portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] | |
| split_portfolio = portfolio_dataframe | |
| split_portfolio[['PG_ID', 'PG']] = split_portfolio.PG.str.split(":", n=1, expand = True) | |
| split_portfolio[['SG_ID', 'SG']] = split_portfolio.SG.str.split(":", n=1, expand = True) | |
| split_portfolio[['SF_ID', 'SF']] = split_portfolio.SF.str.split(":", n=1, expand = True) | |
| split_portfolio[['PF_ID', 'PF']] = split_portfolio.PF.str.split(":", n=1, expand = True) | |
| split_portfolio[['C_ID', 'C']] = split_portfolio.C.str.split(":", n=1, expand = True) | |
| split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True) | |
| split_portfolio[['F_ID', 'F']] = split_portfolio.F.str.split(":", n=1, expand = True) | |
| split_portfolio[['UTIL_ID', 'UTIL']] = split_portfolio.UTIL.str.split(":", n=1, expand = True) | |
| split_portfolio['PG'] = split_portfolio['PG'].str.strip() | |
| split_portfolio['SG'] = split_portfolio['SG'].str.strip() | |
| split_portfolio['SF'] = split_portfolio['SF'].str.strip() | |
| split_portfolio['PF'] = split_portfolio['PF'].str.strip() | |
| split_portfolio['C'] = split_portfolio['C'].str.strip() | |
| split_portfolio['G'] = split_portfolio['G'].str.strip() | |
| split_portfolio['F'] = split_portfolio['F'].str.strip() | |
| split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip() | |
| split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), | |
| split_portfolio['SG'].map(player_salary_dict), | |
| split_portfolio['SF'].map(player_salary_dict), | |
| split_portfolio['PF'].map(player_salary_dict), | |
| split_portfolio['C'].map(player_salary_dict), | |
| split_portfolio['G'].map(player_salary_dict), | |
| split_portfolio['F'].map(player_salary_dict), | |
| split_portfolio['UTIL'].map(player_salary_dict)]) | |
| split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), | |
| split_portfolio['SG'].map(player_proj_dict), | |
| split_portfolio['SF'].map(player_proj_dict), | |
| split_portfolio['PF'].map(player_proj_dict), | |
| split_portfolio['C'].map(player_proj_dict), | |
| split_portfolio['G'].map(player_proj_dict), | |
| split_portfolio['F'].map(player_proj_dict), | |
| split_portfolio['UTIL'].map(player_proj_dict)]) | |
| split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), | |
| split_portfolio['SG'].map(player_own_dict), | |
| split_portfolio['SF'].map(player_own_dict), | |
| split_portfolio['PF'].map(player_own_dict), | |
| split_portfolio['C'].map(player_own_dict), | |
| split_portfolio['G'].map(player_own_dict), | |
| split_portfolio['F'].map(player_own_dict), | |
| split_portfolio['UTIL'].map(player_own_dict)]) | |
| st.table(split_portfolio.head(10)) | |
| except: | |
| split_portfolio = portfolio_dataframe | |
| split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), | |
| split_portfolio['SG'].map(player_salary_dict), | |
| split_portfolio['SF'].map(player_salary_dict), | |
| split_portfolio['PF'].map(player_salary_dict), | |
| split_portfolio['C'].map(player_salary_dict), | |
| split_portfolio['G'].map(player_salary_dict), | |
| split_portfolio['F'].map(player_salary_dict), | |
| split_portfolio['UTIL'].map(player_salary_dict)]) | |
| split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), | |
| split_portfolio['SG'].map(player_proj_dict), | |
| split_portfolio['SF'].map(player_proj_dict), | |
| split_portfolio['PF'].map(player_proj_dict), | |
| split_portfolio['C'].map(player_proj_dict), | |
| split_portfolio['G'].map(player_proj_dict), | |
| split_portfolio['F'].map(player_proj_dict), | |
| split_portfolio['UTIL'].map(player_proj_dict)]) | |
| split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), | |
| split_portfolio['SG'].map(player_own_dict), | |
| split_portfolio['SF'].map(player_own_dict), | |
| split_portfolio['PF'].map(player_own_dict), | |
| split_portfolio['C'].map(player_own_dict), | |
| split_portfolio['G'].map(player_own_dict), | |
| split_portfolio['F'].map(player_own_dict), | |
| split_portfolio['UTIL'].map(player_own_dict)]) | |
| display_portfolio = split_portfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'Salary', 'Projection', 'Ownership']] | |
| hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False) | |
| gc.collect() | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| display_portfolio = hold_portfolio | |
| if st.button("Trim Lineups", key='trim1'): | |
| max_proj = 10000 | |
| max_own = 10000 | |
| x = 0 | |
| for index, row in display_portfolio.iterrows(): | |
| if row['Ownership'] > max_own: | |
| max_own = row['Ownership'] | |
| display_portfolio.drop(index, inplace=True) | |
| with col2: | |
| with st.container(): | |
| st.dataframe(display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |