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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:
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:
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)
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)])
display_portfolio = split_portfolio[["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST", 'Salary', 'Projection', 'Ownership']]
st.session_state.display_portfolio = display_portfolio
hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:9].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
gc.collect()
with tab2:
with st.container():
hold_container = st.empty()
col1, col2, col3 = st.columns([3, 3, 3])
with col1:
if st.button("Load/Reset Data", key='reset1'):
for key in st.session_state.keys():
del st.session_state[key]
display_portfolio = hold_portfolio
st.session_state.display_portfolio = display_portfolio
st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
with col2:
if st.button("Trim Lineups", key='trim1'):
max_proj = 10000
max_own = display_portfolio['Ownership'].iloc[0]
x = 0
for index, row in display_portfolio.iterrows():
if row['Ownership'] > max_own:
display_portfolio.drop(index, inplace=True)
elif row['Ownership'] <= max_own:
max_own = row['Ownership']
st.session_state.display_portfolio = display_portfolio
st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
with col3:
player_check = st.selectbox('Select player to create comps', options = proj_dataframe['Player'].unique(), key='dk_player')
if st.button('Simulate appropriate pivots'):
with hold_container:
working_roo = proj_dataframe
own_dict = dict(zip(working_roo.Player, working_roo.Own))
team_dict = dict(zip(working_roo.Player, working_roo.Team))
opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
total_sims = 1000
player_var = working_roo.loc[working_roo['Player'] == player_check]
player_var = player_var.reset_index()
working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
working_roo = working_roo[working_roo['Team'].isin(team_var1)]
working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
flex_file['Floor_raw'] = flex_file['Median'] * .20
flex_file['Ceiling_raw'] = flex_file['Median'] * 1.9
flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
flex_file['Floor'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * .15), flex_file['Floor_raw'])
flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
flex_file['Ceiling'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
flex_file['STD'] = flex_file['Median'] / 4
flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
hold_file = flex_file
overall_file = flex_file
salary_file = flex_file
overall_players = overall_file[['Player']]
for x in range(0,total_sims):
salary_file[x] = salary_file['Salary']
salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
salary_file.astype('int').dtypes
salary_file = salary_file.div(1000)
for x in range(0,total_sims):
overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
overall_file.astype('int').dtypes
players_only = hold_file[['Player']]
raw_lineups_file = players_only
for x in range(0,total_sims):
maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
players_only[x] = raw_lineups_file[x].rank(ascending=False)
players_only=players_only.drop(['Player'], axis=1)
players_only.astype('int').dtypes
salary_2x_check = (overall_file - (salary_file*2))
salary_3x_check = (overall_file - (salary_file*3))
salary_4x_check = (overall_file - (salary_file*4))
players_only['Average_Rank'] = players_only.mean(axis=1)
players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
players_only['Player'] = hold_file[['Player']]
final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
final_Proj['Own'] = final_Proj['Player'].map(own_dict)
final_Proj['Team'] = final_Proj['Player'].map(team_dict)
final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
final_Proj['LevX'] = 0
final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
final_Proj['CPT_Own'] = final_Proj['Own'] / 4
final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
final_Proj['Player_swap'] = player_check
st.session_state.final_Proj = final_Proj
hold_container = st.empty()
with st.container():
col1, col2 = st.columns([7, 2])
with col1:
if 'display_portfolio' in st.session_state:
st.dataframe(st.session_state.display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
# with display_container:
# display_container = st.empty()
# if 'final_Proj' in st.session_state:
# st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
with col2:
if 'player_freq' in st.session_state:
st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |