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import numpy as np
import pandas as pd
import streamlit as st
import pymongo
import os
st.set_page_config(layout="wide")
@st.cache_resource
def init_conn():
uri = os.getenv('mongo_uri')
client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
db = client["MLB_Database"]
return db
db = init_conn()
@st.cache_resource(ttl = 60)
def init_baselines():
collection = db["HR_Table"]
cursor = collection.find()
raw_display = pd.DataFrame(cursor)
raw_display.rename(columns={"Names": "Player"}, inplace = True)
raw_frame = raw_display.drop(columns=['_id']).drop_duplicates(subset='Player')
return raw_frame
hr_frame = init_baselines()
st.title("HR Finder Table")
disp_options = st.radio("Display options:", options = ['Basics', 'Exclude DFS Info', 'Include DFS Info'], key='display_options')
if disp_options == 'Basics':
st.session_state['disp_frame'] = hr_frame[['Player', 'xHR/PA', 'Opp_xHR/PA', 'BP_Binom_xHR', 'Binom_xHR']]
st.session_state['disp_frame'] = st.session_state['disp_frame'].rename(columns={'xHR/PA': 'Hitter', 'Opp_xHR/PA': 'SP', 'BP_Binom_xHR': 'BP', 'Binom_xHR': 'xHRs'})
elif disp_options == 'Exclude DFS Info':
st.session_state['disp_frame'] = hr_frame.drop(columns=['Salary', 'Position', 'FD_Position', 'Order'])
else:
st.session_state['disp_frame'] = hr_frame.copy()
st.session_state['disp_frame'] = st.session_state['disp_frame'].sort_values(by='Binom_xHR', ascending=False)
if 'disp_frame' in st.session_state:
st.dataframe(st.session_state['disp_frame'].style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(precision=2), height=1200, use_container_width = True, hide_index = True) |