Update app.py

app.py

@@ -8,6 +8,7 @@ for name in dir():
 import pulp
 import numpy as np
 import pandas as pd
+import polars as pl
 import streamlit as st
 import gspread
 import time
@@ -174,42 +175,42 @@ def create_stack_options(player_data, wr_var):
     
         return correl_dict
 
+@st.cache_data
+def apply_range(s: pl.Series) -> pl.Series:
+    return pl.Series("Var", list(range(s.len())))
+
 def create_overall_dfs(pos_players, table_name, dict_name, pos):
     if pos == "FLEX":
-        pos_players = pos_players.sort_values(by='Value', ascending=False)
-        table_name_raw = pos_players.reset_index(drop=True)
-        overall_table_name = table_name_raw.head(round(len(table_name_raw)))
-        overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
-        overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
+        pos_players = pos_players.sort("Value", reverse=True)
+        overall_table_name = pos_players.slice(0, round(pos_players.shape[0]))
+        overall_table_name = overall_table_name.with_column(pl.col("Var").apply_range())
+        overall_dict_name = {row[0]: row[1] for row in overall_table_name.select(["Var", "Player"]).collect()}
         
         del pos_players
-        del table_name_raw
     elif pos != "FLEX":
-        table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True)
-        overall_table_name = table_name_raw.head(round(len(table_name_raw)))
-        overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
-        overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
+        table_name_raw = pos_players.filter(pl.col("Position").str_contains(pos))
+        overall_table_name = table_name_raw.slice(0, round(table_name_raw.shape[0]))
+        overall_table_name = overall_table_name.with_column(pl.col("Var").apply_range())
+        overall_dict_name = {row[0]: row[1] for row in overall_table_name.select(["Var", "Player"]).collect()}
         
         del pos_players
-        del table_name_raw
     
     return overall_table_name, overall_dict_name
 
 
 def get_overall_merged_df():
     ref_dict = {
-        'pos':['RB', 'WR', 'TE', 'FLEX'],
-        'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'],
-        'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict']
-        }
+        'pos': ['RB', 'WR', 'TE', 'FLEX'],
+        'pos_dfs': ['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'],
+        'pos_dicts': ['rb_dict', 'wr_dict', 'te_dict', 'flex_dict']
+    }
     
-    for i in range(0,4):
-        ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
+    for i in range(0, 4):
+        ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] = \
             create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
-        
-    df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
     
-    return df_out, ref_dict
+    # Assuming ref_dict['pos_dfs'] is a list of polars.Dataframe
+    df_out = pl.concat(ref_dict['pos_dfs'], rechunk=True)
 
 def calculate_range_var(count, min_val, FieldStrength, field_growth):
     var = round(len(count[0]) * FieldStrength)