Example skeleton:
df.to_parquet('sc2_actions.parquet', compression='snappy') If you control the game (bot development): preparing game data starcraft 2
data = [] for event in replay.events: if event.name in ['UnitBornEvent', 'UpgradeCompleteEvent'] and event.second <= 180: data.append( 'time': event.second, 'type': event.name, 'unit': getattr(event, 'unit_type_name', None), 'upgrade': getattr(event, 'upgrade_type_name', None), 'player_race': event.player.play_race, 'winner': 1 if event.player == replay.winner else 0 ) Example skeleton: df
| Source | Format | Use Case | |--------|--------|----------| | | Binary / MPQ archive | Full game state reconstruction, player actions, timings | | Live game state (via API) | JSON (via SC2API) | Real-time bot development, decision-making models | | Match history (Blizzard API) | JSON | Win rates, map stats, ladder ranking | Example skeleton: df.to_parquet('sc2_actions.parquet'
build_order_vector = [] for second in [60, 120, 180, 240, 300]: units_at_time = [e for e in replay.events if e.second <= second and e.name == 'UnitBornEvent'] build_order_vector.append(len([u for u in units_at_time if 'Zergling' in u.unit_type_name])) Goal: Predict race & opening from first 3 minutes. Extraction Code import sc2reader import pandas as pd replay = sc2reader.load_file("replay.SC2Replay")
