mirror of
https://github.com/Steffo99/unimore-bda-3.git
synced 2024-11-29 03:14:19 +00:00
61 lines
3.1 KiB
Python
61 lines
3.1 KiB
Python
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from unimore_bda_3.prelude import *
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import unimore_bda_3.loaders as loaders
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def process_game(name: str, path: Path) -> pd.DataFrame:
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steam_appid_path = path.joinpath("steam_appid.txt")
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gtrends_worldwide_path = path.joinpath("gtrends-worldwide.csv")
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steamdb_players_path = path.joinpath("steamdb-players.csv")
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steamdb_price_path = path.joinpath("steamdb-price.csv")
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itad_price_path = path.joinpath("itad-price.js")
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with open(steam_appid_path) as fd:
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steam_news: pd.DataFrame = loaders.steam.load(fd=fd)
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with open(gtrends_worldwide_path) as fd:
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google_trends: pd.DataFrame = loaders.gtrends.load(fd=fd, query_name=name)
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with open(steamdb_players_path) as fd:
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steamdb_players: pd.DataFrame = loaders.steamdb.load_players(fd=fd)
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with open(steamdb_price_path) as fd:
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steamdb_price: pd.DataFrame = loaders.steamdb.load_price(fd=fd)
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with open(itad_price_path) as fd:
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itad_prices: list[pd.DataFrame] = loaders.itad.load(fd=fd)
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dataframe: pd.DataFrame = utils.join_frames(steamdb_players, steamdb_price, google_trends, steam_news, *itad_prices)
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dataframe["SteamDB · Steam"].fillna(method="ffill", inplace=True)
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dataframe["Google Trends · Score"].fillna(method="ffill", inplace=True)
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dataframe["ITAD · Best Price"].fillna(method="ffill", inplace=True)
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dataframe["ITAD · Best Regular Price"].fillna(method="ffill", inplace=True)
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dataframe["ITAD · Worst Regular Price"].fillna(method="ffill", inplace=True)
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dataframe["ITAD · Historical Low"].fillna(method="ffill", inplace=True)
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news_columns = list(filter(lambda c: c.startswith("Steam · Count of News tagged"), dataframe.columns))
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for news_col_name in news_columns:
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dataframe[news_col_name].fillna(0, inplace=True)
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dataframe["Steam · Is there News?"] = pd.Series(data=False, dtype=bool)
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dataframe["Steam · Is there News?"] = (dataframe[news_columns] > 0).any(axis="columns")
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dataframe["SteamDB · Relative concurrent players"] = dataframe["SteamDB · Peak concurrent players"] / dataframe["SteamDB · Peak concurrent players"].max()
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dataframe["SteamDB · Relative Steam price"] = dataframe["SteamDB · Steam"] / dataframe["SteamDB · Steam"].max()
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dataframe["ITAD · Relative Best Price"] = dataframe["ITAD · Best Price"] / dataframe["ITAD · Best Price"].max()
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dataframe["ITAD · Best price change from previous day"] = dataframe["ITAD · Best Price"].diff()
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dataframe["SteamDB · Steam price change from previous day"] = - dataframe["SteamDB · Steam"].diff()
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dataframe["SteamDB · Is there a discount?"] = dataframe["SteamDB · Steam price change from previous day"] < 0
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dataframe["ITAD · Is there a discount?"] = dataframe["ITAD · Best price change from previous day"] < 0
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dataframe["Cumulative · Is something happening on Steam?"] = dataframe["Steam · Is there News?"] + dataframe["SteamDB · Is there a discount?"]
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dataframe["Cumulative · Is something happening?"] = dataframe["Steam · Is there News?"] + dataframe["SteamDB · Is there a discount?"] + dataframe["ITAD · Is there a discount?"]
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return dataframe
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__all__ = (
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"process_game",
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)
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