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import streamlit as st
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import pymongo
import unicodedata
st.set_page_config(layout="wide")
@st.cache_resource
def init_conn():
uri = st.secrets['mongo_uri']
client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
db = client["NBA_DFS"]
wnba_db = client["WNBA_DFS"]
return db, wnba_db
db, wnba_db = init_conn()
dk_nba_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
dk_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_nba_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
dk_wnba_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
dk_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_wnba_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
st.markdown("""
<style>
/* Tab styling */
.stTabs [data-baseweb="tab-list"] {
gap: 8px;
padding: 4px;
}
.stTabs [data-baseweb="tab"] {
height: 50px;
white-space: pre-wrap;
background-color: #DAA520;
color: white;
border-radius: 10px;
gap: 1px;
padding: 10px 20px;
font-weight: bold;
transition: all 0.3s ease;
}
.stTabs [aria-selected="true"] {
background-color: #DAA520;
border: 3px solid #FFD700;
color: white;
}
.stTabs [data-baseweb="tab"]:hover {
background-color: #FFD700;
cursor: pointer;
}
</style>""", unsafe_allow_html=True)
@st.cache_data(ttl=60)
def load_overall_stats(league: str):
if league == 'NBA':
collection = db["DK_Player_Stats"]
elif league == 'WNBA':
collection = wnba_db["DK_Player_Stats"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
elif league == 'WNBA':
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
dk_raw = raw_display.sort_values(by='Median', ascending=False)
if league == 'NBA':
collection = db["FD_Player_Stats"]
elif league == 'WNBA':
collection = wnba_db["FD_Player_Stats"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
elif league == 'WNBA':
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
fd_raw = raw_display.sort_values(by='Median', ascending=False)
if league == 'NBA':
collection = db["Secondary_DK_Player_Stats"]
elif league == 'WNBA':
collection = wnba_db["Secondary_DK_Player_Stats"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
elif league == 'WNBA':
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
if league == 'NBA':
collection = db["Secondary_FD_Player_Stats"]
elif league == 'WNBA':
collection = wnba_db["Secondary_FD_Player_Stats"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
elif league == 'WNBA':
raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
if league == 'NBA':
collection = db["Player_SD_Range_Of_Outcomes"]
elif league == 'WNBA':
collection = wnba_db["Player_SD_Range_Of_Outcomes"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float)
raw_display = raw_display.rename(columns={"player_id": "player_ID"})
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
sd_raw = raw_display.sort_values(by='Median', ascending=False)
dk_sd_raw = sd_raw[sd_raw['site'] == 'Draftkings']
fd_sd_raw = sd_raw[sd_raw['site'] == 'Fanduel']
fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].astype(str)
fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].str.rsplit('-', n=1).str[0].astype(str)
print(sd_raw.head(10))
if league == 'NBA':
collection = db["Player_Range_Of_Outcomes"]
elif league == 'WNBA':
collection = wnba_db["Player_Range_Of_Outcomes"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
try:
raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
except:
raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
raw_display = raw_display.rename(columns={"player_id": "player_ID"})
raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float)
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
roo_raw = raw_display.sort_values(by='Median', ascending=False)
timestamp = raw_display['timestamp'].values[0]
return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp
@st.cache_data(ttl = 60)
def init_DK_lineups(slate_desig: str, league: str):
if slate_desig == 'Main Slate':
if league == 'NBA':
collection = db['DK_NBA_name_map']
elif league == 'WNBA':
collection = wnba_db['DK_WNBA_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
if league == 'NBA':
collection = db["DK_NBA_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["DK_WNBA_seed_frame"]
cursor = collection.find().limit(10000)
elif slate_desig == 'Secondary':
if league == 'NBA':
collection = db['DK_NBA_Secondary_name_map']
elif league == 'WNBA':
collection = wnba_db['DK_WNBA_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
if league == 'NBA':
collection = db["DK_NBA_Secondary_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["DK_WNBA_Secondary_seed_frame"]
cursor = collection.find().limit(10000)
elif slate_desig == 'Auxiliary':
collection = db['DK_NBA_Auxiliary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db["DK_NBA_Auxiliary_seed_frame"]
cursor = collection.find().limit(10000)
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
elif league == 'WNBA':
raw_display = raw_display[['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
for col in dict_columns:
raw_display[col] = raw_display[col].map(names_dict)
DK_seed = raw_display.to_numpy()
return DK_seed
@st.cache_data(ttl = 60)
def init_DK_SD_lineups(slate_desig: str, league: str):
if slate_desig == 'Main Slate':
if league == 'NBA':
collection = db["DK_NBA_SD_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["DK_WNBA_SD_seed_frame"]
elif slate_desig == 'Secondary':
if league == 'NBA':
collection = db["DK_NBA_Secondary_SD_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["DK_WNBA_Secondary_SD_seed_frame"]
elif slate_desig == 'Auxiliary':
collection = db["DK_NBA_Auxiliary_SD_seed_frame"]
cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000)
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
DK_seed = raw_display.to_numpy()
return DK_seed
@st.cache_data(ttl = 60)
def init_FD_lineups(slate_desig: str, league: str):
if slate_desig == 'Main Slate':
if league == 'NBA':
collection = db['FD_NBA_name_map']
elif league == 'WNBA':
collection = wnba_db['FD_WNBA_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
if league == 'NBA':
collection = db["FD_NBA_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["FD_WNBA_seed_frame"]
cursor = collection.find().limit(10000)
elif slate_desig == 'Secondary':
if league == 'NBA':
collection = db['FD_NBA_Secondary_name_map']
elif league == 'WNBA':
collection = wnba_db['FD_WNBA_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
if league == 'NBA':
collection = db["FD_NBA_Secondary_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["FD_WNBA_Secondary_seed_frame"]
cursor = collection.find().limit(10000)
elif slate_desig == 'Auxiliary':
collection = db['FD_NBA_Auxiliary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db["FD_NBA_Auxiliary_seed_frame"]
cursor = collection.find().limit(10000)
raw_display = pd.DataFrame(list(cursor))
if league == 'NBA':
raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
elif league == 'WNBA':
raw_display = raw_display[['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
for col in dict_columns:
raw_display[col] = raw_display[col].map(names_dict)
FD_seed = raw_display.to_numpy()
return FD_seed
@st.cache_data(ttl = 60)
def init_FD_SD_lineups(slate_desig: str, league: str):
if slate_desig == 'Main Slate':
if league == 'NBA':
collection = db["FD_NBA_SD_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["FD_WNBA_SD_seed_frame"]
elif slate_desig == 'Secondary':
if league == 'NBA':
collection = db["FD_NBA_Secondary_SD_seed_frame"]
elif league == 'WNBA':
collection = wnba_db["FD_WNBA_Secondary_SD_seed_frame"]
elif slate_desig == 'Auxiliary':
collection = db["FD_NBA_Auxiliary_SD_seed_frame"]
cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000)
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
DK_seed = raw_display.to_numpy()
return DK_seed
def normalize_special_characters(text):
"""Convert accented characters to their ASCII equivalents"""
if pd.isna(text):
return text
# Normalize unicode characters to their closest ASCII equivalents
normalized = unicodedata.normalize('NFKD', str(text))
# Remove diacritics (accents, umlauts, etc.)
ascii_text = ''.join(c for c in normalized if not unicodedata.combining(c))
return ascii_text
def convert_df_to_csv(df):
df_clean = df.copy()
for col in df_clean.columns:
if df_clean[col].dtype == 'object':
df_clean[col] = df_clean[col].apply(normalize_special_characters)
return df_clean.to_csv(index=False).encode('utf-8')
@st.cache_data
def convert_df(array):
array = pd.DataFrame(array, columns=column_names)
# Normalize special characters in the dataframe before export
for col in array.columns:
if array[col].dtype == 'object':
array[col] = array[col].apply(normalize_special_characters)
return array.to_csv(index=False).encode('utf-8')
@st.cache_data
def convert_pm_df(array):
array = pd.DataFrame(array)
# Normalize special characters in the dataframe before export
for col in array.columns:
if array[col].dtype == 'object':
array[col] = array[col].apply(normalize_special_characters)
return array.to_csv(index=False).encode('utf-8')
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
with st.container():
st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
reset_col, view_col, site_col, league_col = st.columns(4)
with reset_col:
# First row - timestamp and reset button
col1, col2 = st.columns([3, 3])
with col1:
st.info(t_stamp)
with col2:
if st.button("Load/Reset Data", key='reset1'):
st.cache_data.clear()
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
for key in st.session_state.keys():
del st.session_state[key]
with view_col:
view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
with site_col:
site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
with league_col:
league_var = st.radio("What League to load:", ('WNBA', 'NBA'), key='league_var')
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
with tab1:
with st.expander("Info and Filters"):
col1, col2, col3 = st.columns(3)
with col1:
slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
with col2:
slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
if slate_split == 'Main Slate':
if site_var2 == 'Draftkings':
if slate_type_var2 == 'Regular':
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
elif slate_type_var2 == 'Showdown':
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
elif site_var2 == 'Fanduel':
if slate_type_var2 == 'Regular':
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
elif slate_type_var2 == 'Showdown':
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
elif slate_split == 'Secondary':
if site_var2 == 'Draftkings':
if slate_type_var2 == 'Regular':
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
elif slate_type_var2 == 'Showdown':
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
elif site_var2 == 'Fanduel':
if slate_type_var2 == 'Regular':
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
elif slate_type_var2 == 'Showdown':
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
with col3:
split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
if split_var2 == 'Specific Games':
team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
else:
team_var2 = raw_baselines.Team.values.tolist()
pos_var2 = st.selectbox('Position Filter', options=['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2')
col1, col2 = st.columns(2)
with col1:
low_salary = st.number_input('Enter Lowest Salary', min_value=300, max_value=15000, value=300, step=100, key='low_salary')
with col2:
high_salary = st.number_input('Enter Highest Salary', min_value=300, max_value=25000, value=25000, step=100, key='high_salary')
display_container_1 = st.empty()
display_dl_container_1 = st.empty()
display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
display_proj = display_proj[display_proj['Salary'].between(low_salary, high_salary)]
if view_var2 == 'Advanced':
display_proj = display_proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
elif view_var2 == 'Simple':
display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
export_data = raw_baselines.copy()
export_data_pm = raw_baselines[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own']]
export_data_pm = export_data_pm.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'CPT_Own': 'captain ownership'})
# display_proj = display_proj.set_index('Player')
st.session_state.display_proj = display_proj.set_index('Player', drop=True)
reg_dl_col, pm_dl_col, blank_col = st.columns([2, 2, 6])
with reg_dl_col:
st.download_button(
label="Export ROO (Regular)",
data=convert_df_to_csv(export_data),
file_name='NBA_ROO_export.csv',
mime='text/csv',
)
with pm_dl_col:
st.download_button(
label="Export ROO (Portfolio Manager)",
data=convert_df_to_csv(export_data_pm),
file_name='NBA_ROO_export.csv',
mime='text/csv',
)
if 'display_proj' in st.session_state:
if pos_var2 == 'All':
st.session_state.display_proj = st.session_state.display_proj
elif pos_var2 != 'All':
st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2),
height=1000, use_container_width = True)
with tab2:
with st.expander("Info and Filters"):
if st.button("Load/Reset Data", key='reset2'):
st.cache_data.clear()
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
for key in st.session_state.keys():
del st.session_state[key]
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
with col2:
slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
with col3:
lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
with col4:
if site_var2 == 'Draftkings':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
column_names = dk_nba_columns
elif slate_type_var1 == 'Showdown':
column_names = dk_nba_sd_columns
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
column_names = dk_wnba_columns
elif slate_type_var1 == 'Showdown':
column_names = dk_wnba_sd_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 = dk_raw['Player'].unique())
elif player_var1 == 'Full Slate':
player_var2 = dk_raw.Player.values.tolist()
elif site_var2 == 'Fanduel':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
column_names = fd_nba_columns
elif slate_type_var1 == 'Showdown':
column_names = fd_nba_sd_columns
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
column_names = fd_wnba_columns
elif slate_type_var1 == 'Showdown':
column_names = fd_wnba_sd_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 = fd_raw['Player'].unique())
elif player_var1 == 'Full Slate':
player_var2 = fd_raw.Player.values.tolist()
with col5:
if site_var2 == 'Draftkings':
salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 50000, value = 49000, step = 100, key = 'salary_min_var')
salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var')
elif site_var2 == 'Fanduel':
salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 40000, value = 39000, step = 100, key = 'salary_min_var')
salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 40000, value = 40000, step = 100, key = 'salary_max_var')
reg_dl_col, filtered_dl_col, blank_dl_col = st.columns([2, 2, 6])
with reg_dl_col:
if st.button("Prepare full data export", key='data_export'):
name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
if site_var2 == 'Draftkings':
if slate_type_var1 == 'Regular':
if league_var == 'NBA':
map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
elif league_var == 'WNBA':
map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
elif slate_type_var1 == 'Showdown':
map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
for col_idx in map_columns:
if slate_type_var1 == 'Regular':
data_export[col_idx] = data_export[col_idx].map(id_dict)
elif slate_type_var1 == 'Showdown':
data_export[col_idx] = data_export[col_idx].map(dk_id_dict_sd)
elif site_var2 == 'Fanduel':
if slate_type_var1 == 'Regular':
if league_var == 'NBA':
map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'C2', 'UTIL']
elif league_var == 'WNBA':
map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
elif slate_type_var1 == 'Showdown':
map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
for col_idx in map_columns:
if slate_type_var1 == 'Regular':
data_export[col_idx] = data_export[col_idx].map(id_dict)
elif slate_type_var1 == 'Showdown':
data_export[col_idx] = data_export[col_idx].map(fd_id_dict_sd)
pm_name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
pm_data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
reg_opt_col, pm_opt_col = st.columns(2)
with reg_opt_col:
st.download_button(
label="Export optimals set (IDs)",
data=convert_df(data_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
st.download_button(
label="Export optimals set (Names)",
data=convert_df(name_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
with pm_opt_col:
st.download_button(
label="Portfolio Manager Export (IDs)",
data=convert_pm_df(pm_data_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
st.download_button(
label="Portfolio Manager Export (Names)",
data=convert_pm_df(pm_name_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
with filtered_dl_col:
if st.button("Prepare full data export (Filtered)", key='data_export_filtered'):
name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
if site_var2 == 'Draftkings':
if slate_type_var1 == 'Regular':
if league_var == 'NBA':
map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
elif league_var == 'WNBA':
map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
elif slate_type_var1 == 'Showdown':
map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
elif site_var2 == 'Fanduel':
if slate_type_var1 == 'Regular':
if league_var == 'NBA':
map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'C2', 'UTIL']
elif league_var == 'WNBA':
map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
elif slate_type_var1 == 'Showdown':
map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
for col_idx in map_columns:
if slate_type_var1 == 'Regular':
data_export[col_idx] = data_export[col_idx].map(id_dict)
elif slate_type_var1 == 'Showdown':
data_export[col_idx] = data_export[col_idx].map(fd_id_dict_sd)
data_export = data_export[data_export['salary'] >= salary_min_var]
data_export = data_export[data_export['salary'] <= salary_max_var]
name_export = name_export[name_export['salary'] >= salary_min_var]
name_export = name_export[name_export['salary'] <= salary_max_var]
pm_name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
pm_data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
reg_opt_col, pm_opt_col = st.columns(2)
with reg_opt_col:
st.download_button(
label="Export optimals set (IDs)",
data=convert_df(data_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
st.download_button(
label="Export optimals set (Names)",
data=convert_df(name_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
with pm_opt_col:
st.download_button(
label="Portfolio Manager Export (IDs)",
data=convert_pm_df(pm_data_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
st.download_button(
label="Portfolio Manager Export (Names)",
data=convert_pm_df(pm_name_export),
file_name='NBA_optimals_export.csv',
mime='text/csv',
)
if site_var2 == '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 = st.session_state.working_seed
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:
if slate_type_var1 == 'Regular':
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
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':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
elif site_var2 == '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 = st.session_state.working_seed
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:
if slate_type_var1 == 'Regular':
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
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':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'].between(salary_min_var, salary_max_var)]
export_file = st.session_state.data_export_display.copy()
if site_var2 == 'Draftkings':
if slate_type_var1 == 'Regular':
for col_idx in range(8):
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
elif slate_type_var1 == 'Showdown':
for col_idx in range(6):
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd)
elif site_var2 == 'Fanduel':
if slate_type_var1 == 'Regular':
for col_idx in range(9):
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
elif slate_type_var1 == 'Showdown':
for col_idx in range(6):
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(fd_id_dict_sd)
with st.container():
if st.button("Reset Optimals", key='reset3'):
for key in st.session_state.keys():
del st.session_state[key]
if site_var2 == 'Draftkings':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = dk_nba_lineups.copy()
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = dk_nba_sd_lineups.copy()
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = dk_wnba_lineups.copy()
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = dk_wnba_sd_lineups.copy()
elif site_var2 == 'Fanduel':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = fd_nba_lineups.copy()
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = fd_nba_sd_lineups.copy()
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
st.session_state.working_seed = fd_wnba_lineups.copy()
elif slate_type_var1 == 'Showdown':
st.session_state.working_seed = fd_wnba_sd_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_var2 == 'Draftkings':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
summary_df = pd.DataFrame({
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
'Salary': [
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])
],
'Proj': [
np.min(st.session_state.working_seed[:,9]),
np.mean(st.session_state.working_seed[:,9]),
np.max(st.session_state.working_seed[:,9]),
np.std(st.session_state.working_seed[:,9])
],
'Own': [
np.min(st.session_state.working_seed[:,14]),
np.mean(st.session_state.working_seed[:,14]),
np.max(st.session_state.working_seed[:,14]),
np.std(st.session_state.working_seed[:,14])
]
})
elif slate_type_var1 == 'Showdown':
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[:,12]),
np.mean(st.session_state.working_seed[:,12]),
np.max(st.session_state.working_seed[:,12]),
np.std(st.session_state.working_seed[:,12])
]
})
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
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[:,12]),
np.mean(st.session_state.working_seed[:,12]),
np.max(st.session_state.working_seed[:,12]),
np.std(st.session_state.working_seed[:,12])
]
})
elif slate_type_var1 == 'Showdown':
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[:,12]),
np.mean(st.session_state.working_seed[:,12]),
np.max(st.session_state.working_seed[:,12]),
np.std(st.session_state.working_seed[:,12])
]
})
elif site_var2 == 'Fanduel':
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
summary_df = pd.DataFrame({
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
'Salary': [
np.min(st.session_state.working_seed[:,9]),
np.mean(st.session_state.working_seed[:,9]),
np.max(st.session_state.working_seed[:,9]),
np.std(st.session_state.working_seed[:,9])
],
'Proj': [
np.min(st.session_state.working_seed[:,10]),
np.mean(st.session_state.working_seed[:,10]),
np.max(st.session_state.working_seed[:,10]),
np.std(st.session_state.working_seed[:,10])
],
'Own': [
np.min(st.session_state.working_seed[:,15]),
np.mean(st.session_state.working_seed[:,15]),
np.max(st.session_state.working_seed[:,15]),
np.std(st.session_state.working_seed[:,15])
]
})
elif slate_type_var1 == 'Showdown':
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[:,12]),
np.mean(st.session_state.working_seed[:,12]),
np.max(st.session_state.working_seed[:,12]),
np.std(st.session_state.working_seed[:,12])
]
})
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
summary_df = pd.DataFrame({
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
'Salary': [
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])
],
'Proj': [
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])
],
'Own': [
np.min(st.session_state.working_seed[:,13]),
np.mean(st.session_state.working_seed[:,13]),
np.max(st.session_state.working_seed[:,13]),
np.std(st.session_state.working_seed[:,13])
]
})
elif slate_type_var1 == 'Showdown':
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[:,12]),
np.mean(st.session_state.working_seed[:,12]),
np.max(st.session_state.working_seed[:,12]),
np.std(st.session_state.working_seed[:,12])
]
})
# 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 league_var == 'NBA':
if slate_type_var1 == 'Regular':
if site_var2 == 'Draftkings':
player_columns = st.session_state.data_export_display.iloc[:, :8]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.data_export_display.iloc[:, :9]
elif slate_type_var1 == 'Showdown':
if site_var2 == 'Draftkings':
player_columns = st.session_state.data_export_display.iloc[:, :5]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.data_export_display.iloc[:, :5]
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
if site_var2 == 'Draftkings':
player_columns = st.session_state.data_export_display.iloc[:, :7]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.data_export_display.iloc[:, :8]
elif slate_type_var1 == 'Showdown':
if site_var2 == 'Draftkings':
player_columns = st.session_state.data_export_display.iloc[:, :5]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.data_export_display.iloc[:, :5]
# 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,
'Salary': [salary_dict.get(player, player) for player in 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}%'}, precision=2), height=500, use_container_width=True)
st.download_button(
label="Export player frequency",
data=convert_df_to_csv(summary_df),
file_name='NBA_player_frequency.csv',
mime='text/csv',
)
with tab2:
if 'working_seed' in st.session_state:
if league_var == 'NBA':
if slate_type_var1 == 'Regular':
if site_var2 == 'Draftkings':
player_columns = st.session_state.working_seed[:, :8]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.working_seed[:, :9]
elif slate_type_var1 == 'Showdown':
if site_var2 == 'Draftkings':
player_columns = st.session_state.working_seed[:, :5]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.working_seed[:, :5]
elif league_var == 'WNBA':
if slate_type_var1 == 'Regular':
if site_var2 == 'Draftkings':
player_columns = st.session_state.working_seed[:, :7]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.working_seed[:, :8]
elif slate_type_var1 == 'Showdown':
if site_var2 == 'Draftkings':
player_columns = st.session_state.working_seed[:, :5]
elif site_var2 == 'Fanduel':
player_columns = st.session_state.working_seed[:, :5]
# 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,
'Salary': [salary_dict.get(player, player) for player in 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}%'}, precision=2), height=500, use_container_width=True)
st.download_button(
label="Export seed frame frequency",
data=convert_df_to_csv(summary_df),
file_name='NBA_seed_frame_frequency.csv',
mime='text/csv',
)
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