Enhance date filtering options in team analysis. Added functionality to select date ranges for data analysis, including a "Last Year" option and a custom date range input. Updated init_team_data function to filter game logs based on selected dates, improving data relevance for analysis.

app.py

@@ -4,6 +4,7 @@ import numpy as np
 import pandas as pd
 import pymongo
 import time
+from datetime import datetime, timedelta
 
 @st.cache_resource
 def init_conn():
@@ -13,18 +14,48 @@ def init_conn():
         db = client["League_of_Legends_Database"]
 
         collection = db["gamelogs"] 
+        min_date = collection.find_one({}, sort=[("date", 1)])["date"]
+        max_date = collection.find_one({}, sort=[("date", -1)])["date"]
         team_names = collection.distinct("teamname")
         player_names = collection.distinct("playername")
 
-        return db, team_names, player_names
+        return db, team_names, player_names, min_date, max_date
     
-db, team_names, player_names = init_conn()
+db, team_names, player_names, min_date, max_date = init_conn()
 
 display_formats = {'wKill%': '{:.2%}', 'wDeath%': '{:.2%}', 'wAssist%': '{:.2%}', 'lKill%': '{:.2%}', 'lDeath%': '{:.2%}', 'lAssist%': '{:.2%}'}
 
 # Create sidebar container for options
 with st.sidebar:
     st.header("Team Analysis Options")
+
+    # Date filtering options
+    st.subheader("Date Range")
+    date_filter = st.radio(
+        "Select Date Range",
+        ["Last Year", "Custom Range"]
+    )
+    
+    if date_filter == "Last Year":
+        end_date = max_date
+        start_date = end_date - timedelta(days=365)
+    else:
+        col1, col2 = st.columns(2)
+        with col1:
+            start_date = st.date_input(
+                "Start Date",
+                value=max_date - timedelta(days=365),
+                min_value=min_date,
+                max_value=max_date
+            )
+        with col2:
+            end_date = st.date_input(
+                "End Date",
+                value=max_date,
+                min_value=min_date,
+                max_value=max_date
+            )
+
     selected_team = st.selectbox(
         "Select Team",
         options=team_names,
@@ -53,10 +84,10 @@ with st.sidebar:
     )
 
 @st.cache_data(ttl = 60)
-def init_team_data(team, win_loss, kill_prediction, death_prediction):  
+def init_team_data(team, win_loss, kill_prediction, death_prediction, start_date, end_date):  
         
         collection = db["gamelogs"] 
-        cursor = collection.find({"teamname": team})
+        cursor = collection.find({"teamname": team, "date": {"$gte": start_date, "$lte": end_date}})
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['playername', 'teamname', 'playername_avg_kill_share_win', 'playername_avg_death_share_win', 'playername_avg_assist_share_win', 'playername_avg_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_loss']]
@@ -77,4 +108,4 @@ def init_team_data(team, win_loss, kill_prediction, death_prediction):
         return team_data.dropna().reset_index(drop=True)
 
 if st.button("Run"):
-    st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)
+    st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction, start_date, end_date).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)