pygambit.catalog.games

pygambit.catalog.games(n_actions: int | None = None, n_contingencies: int | None = None, n_infosets: int | None = None, is_const_sum: bool | None = None, is_perfect_recall: bool | None = None, is_tree: bool | None = None, min_payoff: float | None = None, max_payoff: float | None = None, n_nodes: int | None = None, n_outcomes: int | None = None, n_players: int | None = None, n_strategies: int | None = None, include_descriptions: bool = False) → DataFrame

List games available in the package catalog.

Most arguments are treated as filters on the attributes of the Game objects.

Parameters:

• n_actions (int, optional) – The number of actions in the game. Only extensive games are returned.

• n_contingencies (int, optional) – The number of contingencies in the game.

• n_infosets (int, optional) – The number of information sets in the game. Only extensive games are returned.

• is_const_sum (bool, optional) – Whether the game is constant-sum.

• is_perfect_recall (bool, optional) – Whether the game has perfect recall.

• is_tree (bool, optional) – Whether the game is an extensive game (a tree).

• min_payoff (float, optional) – The minimum payoff in the game. Games returned have min_payoff >= value.

• max_payoff (float, optional) – The maximum payoff in the game. Games returned have max_payoff <= value.

• n_nodes (int, optional) – The number of nodes in the game. Only extensive games are returned.

• n_outcomes (int, optional) – The number of outcomes in the game.

• n_players (int, optional) – The number of players in the game.

• n_strategies (int, optional) – The number of pure strategies in the game.

• include_descriptions (bool, optional) – Whether to include the description of each game in the returned DataFrame. Defaults to False.

Returns:

A DataFrame with columns “Game” and “Title”, where “Game” is the slug to load the game. If include_descriptions=True, the DataFrame will also include a “Description” column.

Return type:

pd.DataFrame