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import pulp |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import gspread |
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from itertools import combinations |
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import time |
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st.set_page_config(layout="wide") |
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@st.cache_resource |
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def init_conn(): |
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scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] |
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credentials = { |
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"type": "service_account", |
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"project_id": "model-sheets-connect", |
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"private_key_id": st.secrets['model_sheets_connect_pk'], |
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"private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", |
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"client_email": "[email protected]", |
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"client_id": "100369174533302798535", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" |
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} |
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credentials2 = { |
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"type": "service_account", |
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"project_id": "sheets-api-connect-378620", |
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"private_key_id": st.secrets['sheets_api_connect_pk'], |
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"private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", |
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"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", |
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"client_id": "106625872877651920064", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" |
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} |
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NBA_Data = st.secrets['NBA_Data'] |
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gc = gspread.service_account_from_dict(credentials) |
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gc2 = gspread.service_account_from_dict(credentials2) |
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return gc, gc2, NBA_Data |
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gcservice_account, gcservice_account2, NBA_Data = init_conn() |
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', |
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} |
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team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', |
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'5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} |
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player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', |
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'4x%': '{:.2%}','GPP%': '{:.2%}'} |
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expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'} |
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@st.cache_resource(ttl=30) |
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def init_load(): |
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try: |
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sh = gcservice_account.open_by_url(NBA_Data) |
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except: |
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sh = gcservice_account2.open_by_url(NBA_Data) |
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worksheet = sh.worksheet('Player_Level_SD_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display.rename(columns={"Name": "Player", "Fantasy": "Median", "Minutes Proj": "Minutes"}, inplace = True) |
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timestamp = load_display['timestamp'].values[0] |
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roo_raw = load_display.loc[load_display['Median'] > 0] |
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worksheet = sh.worksheet('DK_SD_Build') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display.rename(columns={"Name": "Player", "Fantasy": "Median"}, inplace = True) |
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load_display = load_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own']] |
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dk_roo_raw = load_display.loc[load_display['Median'] > 0] |
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worksheet = sh.worksheet('FD_SD_Build') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display.rename(columns={"Name": "Player", "Fantasy": "Median"}, inplace = True) |
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load_display = load_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own']] |
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fd_roo_raw = load_display.loc[load_display['Median'] > 0] |
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worksheet = sh.worksheet('DK_SD2_Build') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display.rename(columns={"Name": "Player", "Fantasy": "Median"}, inplace = True) |
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load_display = load_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own']] |
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dk_roo_raw_2 = load_display.loc[load_display['Median'] > 0] |
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worksheet = sh.worksheet('FD_SD2_Build') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display.rename(columns={"Name": "Player", "Fantasy": "Median"}, inplace = True) |
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load_display = load_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'Minutes']] |
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fd_roo_raw_2 = load_display.loc[load_display['Median'] > 0] |
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return roo_raw, dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, timestamp |
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roo_raw, dk_roo_raw, dk_roo_raw_2, fd_roo_raw, fd_roo_raw_2, t_stamp = init_load() |
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@st.cache_data |
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def convert_df_to_csv(df): |
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return df.to_csv().encode('utf-8') |
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tab1, tab2 = st.tabs(['Range of Outcomes', 'Uploads and Info']) |
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with tab1: |
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col1, col2 = st.columns([1, 9]) |
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with col1: |
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st.info(t_stamp) |
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if st.button("Load/Reset Data", key='reset1'): |
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st.cache_data.clear() |
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dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = init_load() |
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t_stamp = f"Last Update: " + str(timestamp) + f" CST" |
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for key in st.session_state.keys(): |
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del st.session_state[key] |
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table_view2 = st.radio("What view would you like to display?", ('Simple', 'Advanced'), key='table_view2') |
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site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') |
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if site_var2 == 'Draftkings': |
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site_baselines = roo_raw[roo_raw['site'] == 'Draftkings'] |
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elif site_var2 == 'Fanduel': |
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site_baselines = roo_raw[roo_raw['site'] == 'Fanduel'] |
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slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Showdown #1', 'Showdown #2'), key='slate_split') |
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if slate_split == 'Showdown #1': |
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raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1'] |
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elif slate_split == 'Showdown #2': |
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raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2'] |
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split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2') |
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if split_var2 == 'Specific Games': |
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team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2') |
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elif split_var2 == 'Full Slate Run': |
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team_var2 = raw_baselines.Team.values.tolist() |
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pos_var2 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2') |
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with col2: |
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display_container_1 = st.empty() |
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display_dl_container_1 = st.empty() |
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display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)] |
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display_proj = display_proj.drop(columns=['site', 'version', 'slate', 'timestamp']) |
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st.session_state.display_proj = display_proj |
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with display_container_1: |
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display_container = st.empty() |
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if 'display_proj' in st.session_state: |
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if pos_var2 == 'All': |
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st.session_state.display_proj = st.session_state.display_proj |
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elif pos_var2 != 'All': |
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st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)] |
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if table_view2 == 'Simple': |
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st.session_state.display_proj = st.session_state.display_proj[['Player', 'Team', 'Position', 'Median', 'GPP%', 'Own']] |
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st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True) |
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elif table_view2 == 'Advanced': |
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st.session_state.display_proj = st.session_state.display_proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', |
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'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own']] |
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st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True) |
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with display_dl_container_1: |
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display_dl_container = st.empty() |
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if 'display_proj' in st.session_state: |
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st.download_button( |
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label="Export Tables", |
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data=convert_df_to_csv(st.session_state.display_proj), |
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file_name='NBA_SD_ROO_export.csv', |
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mime='text/csv', |
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) |
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with tab2: |
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st.write("Yeah man idk that's crazy") |
