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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| from itertools import combinations | |
| scope = ['https://www.googleapis.com/auth/spreadsheets', | |
| "https://www.googleapis.com/auth/drive"] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| gc = gspread.service_account_from_dict(credentials) | |
| st.set_page_config(layout="wide") | |
| american_format = {'OwnAvg': '{:.2%}'} | |
| mma_format = {'ML_perc': '{:.2%}', 'Min_%': '{:.2%}', 'Med_%': '{:.2%}', } | |
| def init_baselines(): | |
| sh = gc.open_by_url("https://docs.google.com/spreadsheets/d/17OAf4OAfW92-loMNUFvIubNmgF9111dsObybo6xhtYY/edit?gid=1468336051#gid=1468336051") | |
| worksheet = sh.worksheet('QB') | |
| all_values = worksheet.get_all_values() | |
| cell_vals = [row[0:11] for row in all_values[2:500]] | |
| frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank']) | |
| frame_hold['PointsAvg'] = frame_hold['PointsAvg'].astype(float) | |
| frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100 | |
| frame_hold['Floor'] = frame_hold['PointsAvg'] * .15 | |
| frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85 | |
| qb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']] | |
| string_cols = ['Team'] | |
| qb_frame = qb_frame.drop_duplicates(subset='Player') | |
| qb_frame = qb_frame.set_index('Player') | |
| for col in qb_frame.columns: | |
| if col not in string_cols: | |
| try: | |
| qb_frame[col] = pd.to_numeric(qb_frame[col], errors='coerce') | |
| except ValueError: | |
| pass # Ignore columns that cannot be converted | |
| qb_frame = qb_frame.sort_values(by='GPP Rank', ascending=False) | |
| worksheet = sh.worksheet('RB') | |
| all_values = worksheet.get_all_values() | |
| cell_vals = [row[0:11] for row in all_values[2:500]] | |
| frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank']) | |
| frame_hold['PointsAvg'] = frame_hold['PointsAvg'].astype(float) | |
| frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100 | |
| frame_hold['Floor'] = frame_hold['PointsAvg'] * .15 | |
| frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85 | |
| rb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']] | |
| string_cols = ['Team'] | |
| rb_frame = rb_frame.drop_duplicates(subset='Player') | |
| rb_frame = rb_frame.set_index('Player') | |
| for col in rb_frame.columns: | |
| if col not in string_cols: | |
| try: | |
| rb_frame[col] = pd.to_numeric(rb_frame[col], errors='coerce') | |
| except ValueError: | |
| pass # Ignore columns that cannot be converted | |
| rb_frame = rb_frame.sort_values(by='GPP Rank', ascending=False) | |
| worksheet = sh.worksheet('WR') | |
| all_values = worksheet.get_all_values() | |
| cell_vals = [row[0:11] for row in all_values[2:500]] | |
| frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank']) | |
| frame_hold['PointsAvg'] = frame_hold['PointsAvg'].astype(float) | |
| frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100 | |
| frame_hold['Floor'] = frame_hold['PointsAvg'] * .15 | |
| frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85 | |
| wr_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']] | |
| string_cols = ['Team'] | |
| wr_frame = wr_frame.drop_duplicates(subset='Player') | |
| wr_frame = wr_frame.set_index('Player') | |
| for col in wr_frame.columns: | |
| if col not in string_cols: | |
| try: | |
| wr_frame[col] = pd.to_numeric(wr_frame[col], errors='coerce') | |
| except ValueError: | |
| pass # Ignore columns that cannot be converted | |
| wr_frame = wr_frame.sort_values(by='GPP Rank', ascending=False) | |
| worksheet = sh.worksheet('Flex') | |
| all_values = worksheet.get_all_values() | |
| cell_vals = [row[0:11] for row in all_values[2:500]] | |
| frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank']) | |
| frame_hold['PointsAvg'] = frame_hold['PointsAvg'].astype(float) | |
| frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100 | |
| frame_hold['Floor'] = frame_hold['PointsAvg'] * .15 | |
| frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85 | |
| flex_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']] | |
| string_cols = ['Team'] | |
| flex_frame = flex_frame.drop_duplicates(subset='Player') | |
| flex_frame = flex_frame.set_index('Player') | |
| for col in flex_frame.columns: | |
| if col not in string_cols: | |
| try: | |
| flex_frame[col] = pd.to_numeric(flex_frame[col], errors='coerce') | |
| except ValueError: | |
| pass # Ignore columns that cannot be converted | |
| flex_frame = flex_frame.sort_values(by='GPP Rank', ascending=False) | |
| return qb_frame, rb_frame, wr_frame, flex_frame | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| qb_frame, rb_frame, wr_frame, flex_frame = init_baselines() | |
| tab1, tab2, tab3, tab4 = st.tabs(['QB data', 'RB data', 'WR data', 'Flex data']) | |
| with tab1: | |
| with st.container(): | |
| st.dataframe(qb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(qb_frame), | |
| file_name='NCAAF_QB_model_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| with st.container(): | |
| st.dataframe(rb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(rb_frame), | |
| file_name='NCAAF_RB_model_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| with st.container(): | |
| st.dataframe(wr_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(wr_frame), | |
| file_name='NCAAF_WR_model_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab4: | |
| with st.container(): | |
| st.dataframe(flex_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(flex_frame), | |
| file_name='NCAAF_Flex_model_export.csv', | |
| mime='text/csv', | |
| ) |