Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import datetime as dt
+
+#### TITLE/PAGE
+st.set_page_config(layout="wide", page_icon="⚽", page_title="FPL Stats")
+st.title("⚽ FPL Lab")
+st.caption("A live dashboard of the top 200 Premiere League players across all positions. Metrics plotted include Cost, Points Earned, Form, Selection Rate, etc. Use the Position Analysis tab to pick cheap/high value players.")
+st.toast('Includes all data through W5', icon='⚽')
+
+
+#### DATA PROCESSING
+df = pd.read_csv('week5_fpl.csv')
+df.fillna(0, inplace=True)
+pattern = r'(.+)\r\n(.{3})(.+)'
+df[['name', 'team', 'position']] = df['Player'].str.extract(pattern)
+df = df.rename(columns={'Seelection Rate': 'Selection Rate',})
+df[['Cost (M)','Form','Points Earned']] = df[['Cost (M)','Form','Points Earned']].astype(float)
+df['P/C'] = df['Points Earned'] / df['Cost (M)']
+df[['Status','Player','name','team','position']] = df[['Status','Player','name','team','position']].astype(str)
+df['Selection Rate'] = df['Selection Rate'].str.rstrip('%').astype('float')
+
+#### SIDEBAR
+with st.sidebar:
+    st.title("Player Search")
+    options = st.multiselect(
+    'Choose Position',
+    ['FWD','MID','DEF','GKP'])
+    price = st.slider(
+    "Pick a price range:",
+    min_value=0,
+    max_value=20,
+    value=(0, 15))
+    st.write("Price Range: $",price)
+    new_df = df[df['position'].isin(options)]
+    new_df = new_df[(new_df['Cost (M)'] >= price[0]) & (new_df['Cost (M)'] <= price[1])]
+    new_df = new_df.rename(columns={'Points Earned': 'Points',})
+    st.dataframe(new_df[['name','position','Points','Cost (M)','P/C']],hide_index=True) 
+
+#### TABS
+tab1, tab2,tab3 = st.tabs(["Position Analysis", "Total Metrics","Player Utility"])
+with tab1: 
+    st.caption("These plots look at points earned and cost of the players. We want players closer to the bottom right of the graphs meaning they earn points but are cheap.")
+    position_scatter_fig = px.scatter(df, y="Cost (M)", x="Points Earned", color="Form", template="plotly_dark", facet_col="position",hover_data=['name','team','position','Form'])
+    position_scatter_fig.update_xaxes(showgrid=False,showline=False)
+    position_scatter_fig.update_yaxes(showgrid=False)
+    st.plotly_chart(position_scatter_fig, use_container_width=True)
+
+    heatmap_fig = px.density_heatmap(df, x="Points Earned", y="Cost (M)",template="plotly_dark",facet_col="position")
+    heatmap_fig.update_xaxes(showgrid=False,showline=False)
+    heatmap_fig.update_yaxes(showgrid=False)
+    st.plotly_chart(heatmap_fig, use_container_width=True)
+
+with tab2: 
+    st.caption("Here we have a consolidated scatter plot of the data on the other page, and a look at team point contributions ")
+    summary_fig = px.scatter(df, x="Cost (M)", y="Points Earned", size="Selection Rate", color="Form",hover_data=['name','team','position','Form'], template="plotly_dark", title="Player Cost vs Points Earned (sized by selection rate and colored by form)",size_max=12) #text="name",
+    summary_fig.update_xaxes(showgrid=False,showline=False)
+    summary_fig.update_yaxes(showgrid=False)
+    st.plotly_chart(summary_fig, use_container_width=True)    
+
+    fig_bar = px.bar(df, x="team", y="Points Earned",template="plotly_dark", color='Cost (M)', title="Points Contributed and Cost by Team",hover_data=['name','team','position','Form']) #color_continuous_scale='YlOrRd'
+    fig_bar.update_layout(barmode='stack', xaxis={'categoryorder':'total descending'})
+    fig_bar.update_xaxes(showgrid=False,showline=False)
+    fig_bar.update_yaxes(showgrid=False)
+    st.plotly_chart(fig_bar, use_container_width=True)
+
+
+with tab3: 
+    st.caption("We introduce a new features here to measure player utility. This is calculated by dividing points earned by cost referred to as P/C. Outliers in these plots could indicate high utility players for your roster.")
+    utility_scatter = px.scatter(df, x="Form", y="P/C", color="Cost (M)",hover_data=['name','team','position','Form'], template="plotly_dark", title="Player Utility vs Form, colored by cost",size_max=12) #text="name",
+    utility_scatter.update_xaxes(showgrid=False,showline=False)
+    utility_scatter.update_yaxes(showgrid=False)
+    st.plotly_chart(utility_scatter, use_container_width=True) 
+
+    utility_bar = px.violin(df, x="position", y="P/C", points='all', color='position',hover_data=['name','team','position','Form'], template="plotly_dark", title="Utility Distribution Across Positions") #text="name",
+    utility_bar.update_xaxes(showgrid=False,showline=False)
+    utility_bar.update_yaxes(showgrid=False)  
+    st.plotly_chart(utility_bar, use_container_width=True)
+