75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
|
|
||
|
|
import sqlite3
|
||
|
|
import pandas as pd
|
||
|
|
import os
|
||
|
|
|
||
|
|
# Config
|
||
|
|
L2_DB_PATH = r'database/L2/L2_Main.sqlite'
|
||
|
|
L3_DB_PATH = r'database/L3/L3_Features.sqlite'
|
||
|
|
|
||
|
|
def analyze_team_dmg_per_1k():
|
||
|
|
if not os.path.exists(L3_DB_PATH):
|
||
|
|
print(f"Error: L3 DB not found at {L3_DB_PATH}")
|
||
|
|
return
|
||
|
|
|
||
|
|
conn_l3 = sqlite3.connect(L3_DB_PATH)
|
||
|
|
conn_l2 = sqlite3.connect(L2_DB_PATH)
|
||
|
|
|
||
|
|
print("--- Analysis: Team Dmg/$1k (Economy Efficiency) ---")
|
||
|
|
|
||
|
|
try:
|
||
|
|
# 1. Get all L3 features
|
||
|
|
query = """
|
||
|
|
SELECT f.steam_id_64, f.eco_avg_damage_per_1k, p.username
|
||
|
|
FROM dm_player_features f
|
||
|
|
LEFT JOIN dim_players p ON f.steam_id_64 = p.steam_id_64
|
||
|
|
ORDER BY f.eco_avg_damage_per_1k DESC
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Attach L2 for username lookup
|
||
|
|
# We can't attach across connections easily in sqlite python without ATTACH DATABASE command
|
||
|
|
# So let's fetch L3 first, then map names from L2
|
||
|
|
|
||
|
|
df_l3 = pd.read_sql_query("SELECT steam_id_64, eco_avg_damage_per_1k FROM dm_player_features", conn_l3)
|
||
|
|
|
||
|
|
if df_l3.empty:
|
||
|
|
print("No data in L3 Features.")
|
||
|
|
return
|
||
|
|
|
||
|
|
# Fetch names
|
||
|
|
ids = tuple(df_l3['steam_id_64'].tolist())
|
||
|
|
placeholders = ','.join(['?'] * len(ids))
|
||
|
|
q_names = f"SELECT steam_id_64, username FROM dim_players WHERE steam_id_64 IN ({placeholders})"
|
||
|
|
df_names = pd.read_sql_query(q_names, conn_l2, params=ids)
|
||
|
|
|
||
|
|
# Merge
|
||
|
|
df = df_l3.merge(df_names, on='steam_id_64', how='left')
|
||
|
|
|
||
|
|
# Sort
|
||
|
|
df = df.sort_values('eco_avg_damage_per_1k', ascending=False)
|
||
|
|
|
||
|
|
print(f"{'Rank':<5} {'Player':<20} {'Dmg/$1k':<10}")
|
||
|
|
print("-" * 40)
|
||
|
|
|
||
|
|
for idx, row in df.iterrows():
|
||
|
|
rank = idx + 1 # This index is not rank if we iterated row by row after sort, wait.
|
||
|
|
# reset_index to get rank
|
||
|
|
pass
|
||
|
|
|
||
|
|
df = df.reset_index(drop=True)
|
||
|
|
for idx, row in df.iterrows():
|
||
|
|
name = row['username'] if row['username'] else row['steam_id_64']
|
||
|
|
val = row['eco_avg_damage_per_1k']
|
||
|
|
print(f"#{idx+1:<4} {name:<20} {val:.2f}")
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
print(f"Error: {e}")
|
||
|
|
import traceback
|
||
|
|
traceback.print_exc()
|
||
|
|
finally:
|
||
|
|
conn_l2.close()
|
||
|
|
conn_l3.close()
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
analyze_team_dmg_per_1k()
|