Classes

This chapter documents the Classes used in this package.

Poker

Class object for running the package.

Poker(repo_location, grouped, money_multi):

Calculate stats for all games and players.

Parameters:

• repo_location (str) – Location of data folder.

• grouped (str) – List of lists, filled with unique player Ids that are related to the same person. Optional

• money_multi (int) – Multiple to divide the money amounts to translate them to dollars Optional

Example:

from poker.poker_class import Poker
repo = 'location of your previous game'
grouped = [['YEtsj6CMK4', 'M_ODMJ-3Je', 'DZy-22KNBS'],
            ['48QVRRsiae', 'u8_FUbXpAz']]
poker = Poker(repo_location=repo, grouped=grouped)

Note:

Grouped will need to be figured out by the player. The grouped stats are only taken into account within this class

Poker Attributes:

Name	Type	Description
Poker.files	List[str]	Returns list of data files
Poker.matches	dict	Returns list of data files
Poker.players_money_overview	pd.DataFrame	Returns summary info for each player across games
Poker.card_distribution	pd.DataFrame	Returns count and percent for each card that showed up across games
Poker.winning_hand_distribution	pd.DataFrame	Returns count and percent of each type of winning hand across games
Poker.players_history	dict	Collects player stats for all matches and groups based on grouper input

DocumentFilter

Class for getting data from Poker.

The class_lst can have any of the following:

• Requests

• Approved

• Joined

• MyCards

• SmallBlind

• BigBlind

• Folds

• Calls

• Raises

• Checks

• Wins

• Shows

• Quits

• Flop

• Turn

• River

• Undealt

• StandsUp

• SitsIn

• PlayerStacks

The position_lst can have any of the following:

• ‘Pre Flop’

• ‘Post Flop’

• ‘Post Turn’

• ‘Post River’

The win_loss_all can be one of the following:

• ‘Win’

• ‘Loss’

• ‘All’

The column_lst can have any of the following:

• ‘All In’

• ‘Bet Amount’

• ‘Class’

• ‘End Time’

• ‘From Person’

• ‘Game Id’

• ‘Player Current Chips’

• ‘Player Index’

• ‘Player Name’

• ‘Player Starting Chips’

• ‘Position’

• ‘Pot Size’

• ‘Previous Time’

• ‘Remaining Players’

• ‘Round’

• ‘Start Time’

• ‘Time’

• ‘Win’

• ‘Win Hand’

• ‘Win Stack’

• ‘Winner’

DocumentFilter(data):

Get a selection from the Poker Object. Uses a set of filters to return a desired set of data to be used in later analysis.

Parameters:

• data (Poker) – Input Poker object to be filtered.

• game_id_lst (Union[List[str], str, None]) – Game Id filter, default is None. Optional

• player_index_lst (Union[List[str], str, None]) – Player Index filter, default is None. Optional

• player_name_lst (Union[List[str], str, None]) – Player Name filter, default is None. Optional

• class_lst (Union[List[str], str, None]) – Filter by class objects, default is None. Optional

• position_lst (Union[List[str], str, None]) – Filter by position, default is None. Optional

• win_loss_all (Union[str, None]) – Filter by Win, Loss or All, default is None. Optional

• column_lst (Union[List[str], str, None]) – Filter by column name, default is None. Optional

Example:

None

Note:

All inputs, except data, are Optional and defaults are set to None. Any str inputs are placed in a list.

DocumentFilter Attributes:

Name	Type	Description
DocumentFilter.df	pd.DataFrame	Returns a DataFrame of requested items
DocumentFilter.game_id_lst	Union[List[str], None]	Returns game id input
DocumentFilter.player_index_lst	Union[List[str], None]	Returns player index input
DocumentFilter.player_name_lst	Union[List[str], None]	Returns player name input
DocumentFilter.class_lst	Union[List[str], None]	Returns class input
DocumentFilter.position_lst	Union[List[str], None]	Returns position input
DocumentFilter.win_loss_all	Union[List[str], None]	Returns win loss or all input
DocumentFilter.column_lst	Union[List[str], None]	Returns column input

Game

Class object used for getting specific game stats.

Game(hand_lst, file_id, players_data):

Calculate stats for a game.

Parameters:

• hand_lst (List[dict]) – List of dict’s from the csv.

• file_id (str) – Name of file.

• players_data (dict) – A dict of player data.

Example:

None

Note:

This class is intended to be used internally.

Game Attributes:

Name	Type	Description
Game.file_name	str	Returns name of data file
Game.hands_lst	List[Hand]	Returns list of hands in the game
Game.card_distribution	dict	Returns count of each card that showed up
Game.winning_hand_distribution	dict	Returns count of winning hands
Game.players_data	dict	Returns Player stats for players across hands
Game.game_stats	dict	Returns Mean stats for Game across hands

Player

Class object used for getting specific player stats.

Player(player_index):

Calculate stats for a player.

Parameters:

player_index (str or List[str]) – A unique player ID.

Example:

None

Note:

This class is intended to be used internally.

Player Attributes:

Name	Type	Description
Player.win_percent	dict	Returns player win percent
Player.win_count	dict	Returns player win count
Player.largest_win	dict	Returns players largest win
Player.largest_loss	dict	Returns players largest loss
Player.hand_count	dict	Returns total hand count when player involved
Player.all_in	dict	Returns a dict documenting when the player went all in
Player.player_index	List[str]	Returns player index or indexes
Player.player_name	List[str]	Returns player name or names
Player.player_money_info	dict	Returns a dict of DataFrames documenting player buy-in and loss counts
Player.hand_dic	dict	Returns a dict of DataFrames documenting hands when the player won
Player.card_dic	dict	Returns a dict of DataFrames documenting card appearances
Player.line_dic	dict	Returns a dict with a list of objects where player involved
Player.moves_dic	dict	Returns a players moves on the table
Player.merged_moves	dict	Returns a combined dict of player moves

Hand

Class object used for getting specific hand stats.

Hand(lst_hand_objects, file_id, player_dic):

Organizes a hand with a class and adds the stands to the player_dic.

Parameters:

• lst_hand_objects (list) – A list of Class Objects connected to a hand.

• file_id (str) – Unique file name.

• player_dic (dict) – Dict of players.

Example:

None

Note:

This class is intended to be used internally.

Hand Attributes:

Name	Type	Description
Hand.merged_moves	list	Returns a list of actions as objects
Hand.small_blind	SmallBlind	Returns SmallBlind Class
Hand.big_blind	BigBlind	Returns BigBlind Class
Hand.winner	Wins	Returns Wins Class or list of Wins Classes
Hand.starting_players	dict	Returns dict of name and ID for each player that was present at the hand start
Hand.starting_players_chips	dict	Returns dict of name and stack amount for each player that was present at the hand start
Hand.flop_cards	Flop	Returns Flop Class
Hand.turn_card	Turn	Returns Turn Class
Hand.river_card	River	Returns River Class
Hand.my_cards	MyCards	Returns MyCards Class
Hand.chips_on_board	int	Returns the count of chips on the table
Hand.gini_coef	float	Returns the gini coef for the board
Hand.pot_size_lst	List[int]	Returns pot size over course of hand
Hand.players	dict	Returns dict of player moves
Hand.start_time	TimeStamp	Returns time of first hand item
Hand.end_time	TimeStamp	Returns time of last hand item
Hand.win_stack	Union[int, None]	Returns win amount for the hand
Hand.bet_lst	List[int]	Returns Raise amounts for the hand

Processor

Class object for holding information from lines.

The following child classes use this framework:

• Requests

• Approved

• Joined

• MyCards

• SmallBlind

• BigBlind

• Folds

• Calls

• Raises

• Checks

• Wins

• Shows

• Quits

• Flop

• Turn

• River

• Undealt

• StandsUp

• SitsIn

• PlayerStacks

LineAttributes:

Applies attributes to a respective Class object.

Parameters:

text (str) – A line of text from the data.

Example:

None

Note:

This class is intended to be used internally. All values are set to None or 0 by default.

LineAttributes Attributes:

Name	Type	Description
LineAttributes.text	Union[str, None]	Text input
LineAttributes.player_name	Union[str, None]	Player Name
LineAttributes.player_index	Union[str, None]	Player Id
LineAttributes.stack	Union[int, None]	Amount offered to the table
LineAttributes.position	Union[str, None]	Position of move in relation to table cards being drawn
LineAttributes.winning_hand	Union[str, None]	Winning hand
LineAttributes.cards	Union[str, list, None]	Card or cards
LineAttributes.current_round	Union[int, None]	Round number within the game
LineAttributes.pot_size	Union[int, None]	Size of pot when move happens
LineAttributes.remaining_players	Union[List[str], None]	Players left in hand
LineAttributes.action_from_player	Union[str, None]	Who bet previously
LineAttributes.action_amount	Union[int, None]	Previous bet amount
LineAttributes.all_in	Union[bool, None]	Notes if player when all-in
LineAttributes.game_id	Union[str, None]	File name
LineAttributes.starting_chips	Union[int, None]	Player’s chip count at start of hand
LineAttributes.current_chips	Union[int, None]	Player’s chip count at time of move
LineAttributes.winner	Union[str, None]	Player Name who wins the hand
LineAttributes.win_stack	Union[int, None]	Amount won at end of hand
LineAttributes.time	TimeStamp	Timestamp of action
LineAttributes.previous_time	TimeStamp	Timestamp of previous action
LineAttributes.start_time	TimeStamp	Timestamp of the start of the hand
LineAttributes.end_time	TimeStamp	Timestamp of the end of the hand

TSanalysis

Class for Time Series Analysis. The ts_analysis function in Analysis does not compute running values.

TSanalysis:

Calculate Time Series stats for a player.

Parameters:

• data (DocumentFilter) – Input DocumentFilter.

• upper_q (float) – Upper Quantile percent, default is 0.841. Optional

• lower_q (float) – Lower Quantile percent, default is 0.159. Optional

• window (int) – Rolling window, default is 5. Optional

Example:

>>> from poker.time_series_class import TSanalysis
>>> docu_filter = DocumentFilter(data=poker, player_index_lst=['DZy-22KNBS'])
>>> TSanalysis(data=docu_filter)

Note:

This class expects a DocumentFilter with only one player_index used.

TSanalysis Attributes:

Name	Type	Description
TSanalysis.ts_hand	pd.DataFrame	Hand Related base data
TSanalysis.ts_hand_mean	pd.DataFrame	Hand Related mean data
TSanalysis.ts_hand_std	pd.DataFrame	Hand Related std data
TSanalysis.ts_hand_median	pd.DataFrame	Hand Related median data
TSanalysis.ts_hand_upper_quantile	pd.DataFrame	Hand Related upper quantile data
TSanalysis.ts_hand_lower_quantile	pd.DataFrame	Hand Related lower quantile data
TSanalysis.ts_position	pd.DataFrame	Position Related base data
TSanalysis.ts_position_mean	pd.DataFrame	Position Related mean data
TSanalysis.ts_position_std	pd.DataFrame	Position Related std data
TSanalysis.ts_position_median	pd.DataFrame	Position Related median data
TSanalysis.ts_position_upper_quantile	pd.DataFrame	Position Related upper quantile data
TSanalysis.ts_position_lower_quantile	pd.DataFrame	Position Related lower quantile data
TSanalysis.ts_class	pd.DataFrame	Class Related base data
TSanalysis.ts_class_mean	pd.DataFrame	Class Related mean data
TSanalysis.ts_class_std	pd.DataFrame	Class Related std data
TSanalysis.ts_class_median	pd.DataFrame	Class Related median data
TSanalysis.ts_class_upper_quantile	pd.DataFrame	Class Related upper quantile data
TSanalysis.ts_class_lower_quantile	pd.DataFrame	Class Related lower quantile data

Plot Classes

Plot Class objects.

Possible Font Size Strings:

• ‘xx-small’

• ‘x-small’

• ‘small’

• ‘medium’

• ‘large’

• ‘x-large’

• ‘xx-large’

Possible Legend Locations:

• ‘best’

• ‘upper right’

• ‘upper left’

• ‘lower left’

• ‘lower right’

• ‘right’

• ‘center left’

• ‘center right’

• ‘lower center’

• ‘upper center’

• ‘center

Line

Line(data):

Class for Line plots.

Parameters:

• data (pd.DataFrame) – Input data.

• limit (int) – Limit the length of data. Optional

• label_lst (List[str]) – List of labels to include, if None will include all columns. Optional

• color_lst (List[str]) – List of colors to graph, needs to be same length as label_lst. Optional

• normalize_x (List[str]) – List of columns to normalize. Optional

• running_mean_x (List[str]) – List of columns to calculate running mean. Optional

• running_mean_value (int) – Value used when calculating running mean, default = 50. Optional

• cumulative_mean_x (List[str]) – List of columns to calculate cumulative mean. Optional

• fig_size (tuple) – Figure size, default = (10, 7). Optional

• ylabel (str) – Y axis label. Optional

• ylabel_color (str) – Y axis label color, default = ‘black’. Optional

• ylabel_size (str) – Y label size, default = ‘medium’. Optional

• xlabel (str) – X axis label. Optional

• xlabel_color (str) – X axis label color, default = ‘black’. Optional

• xlabel_size (str) – X label size, default = ‘medium’. Optional

• title (str) – Graph title, default = ‘Line Plot’. Optional

• title_size (str) – Title size, default = ‘xx-large’. Optional

• grid (bool) – If True will show grid, default = true. Optional

• grid_alpha (float) – Grid alpha, default = 0.75. Optional

• grid_dash_sequence (tuple) – Grid dash sequence, default = (3, 3). Optional

• grid_lineweight (float) – Grid lineweight, default = 0.5. Optional

• legend_fontsize (str) – Legend fontsize, default = ‘medium’. Optional

• legend_transparency (float) – Legend transparency, default = 0.75. Optional

• legend_location (str) – legend location, default = ‘lower right’. Optional

• corr (List[str]) – Pass two strings to return the correlation. Optional

Example:

from poker.plot import Line
Line(data=val[['Pot Size', 'Win Stack']],
     normalize_x=['Pot Size', 'Win Stack'],
     color_lst=['tab:orange', 'tab:blue'],
     title='Pot Size and Winning Stack Amount (Player: ' + key + ')',
     ylabel='Value',
     xlabel='Time Periods',
     corr=['Pot Size', 'Win Stack'])
plt.show()

Note:

None

Line Attributes:

Name	Type	Description
Line.ax	plt	Returns a Line Plot

Scatter

Scatter(data):

Class for Scatter plots.

Parameters:

• data (pd.DataFrame,) – Input data.

• limit (int) – Limit the length of data. Optional

• label_lst (List[str]) – List of labels to include, if None will include all columns. Optional

• color_lst (List[str]) – List of colors to graph. Optional

• normalize_x (List[str]) – List of columns to normalize. Optional

• regression_line (List[str]) – If included, requires a column str or List[str], default = None. Optional

• regression_line_color (str) – Color of regression line, default = ‘red’. Optional

• regression_line_lineweight (float) – Regression lineweight, default = 2.0. Optional

• running_mean_x (List[str]) – List of columns to calculate running mean. Optional

• running_mean_value (Optional[int] = 50,) – List of columns to calculate running mean. Optional

• cumulative_mean_x (List[str]) – List of columns to calculate cumulative mean. Optional

• fig_size (tuple) – default = (10, 7), Optional

• ylabel (str) – Y axis label. Optional

• ylabel_color (str) – Y axis label color, default = ‘black’. Optional

• ylabel_size (str) – Y label size, default = ‘medium’. Optional

• xlabel (str) – X axis label. Optional

• xlabel_color (str) – X axis label color, default = ‘black’. Optional

• xlabel_size (str) – X label size, default = ‘medium’. Optional

• title (str) – Graph title, default = ‘Scatter Plot’. Optional

• title_size (str) – Title size, default = ‘xx-large’. Optional

• grid (bool) – If True will show grid, default = true. Optional

• grid_alpha (float) – Grid alpha, default = 0.75. Optional

• grid_dash_sequence (tuple) – Grid dash sequence, default = (3, 3). Optional

• grid_lineweight (float) – Grid lineweight, default = 0.5. Optional

• legend_fontsize (str) – Legend fontsize, default = ‘medium’. Optional

• legend_transparency (float) – Legend transparency, default = 0.75. Optional

• legend_location (str) – legend location, default = ‘lower right’. Optional

• compare_two (List[str]) – If given will return a scatter comparing two variables, default is None. Optional

• y_limit (float) – If given will limit the y axis, default is None. Optional

Example:

from poker.plot import Scatter
Scatter(data=val,
        compare_two=['Round Seconds', 'Player Reserve'],
        normalize_x=['Round Seconds', 'Player Reserve'],
        color_lst=['tab:orange'],
        regression_line=['Player Reserve'],
        regression_line_color='tab:blue',
        title='Time per Hand vs Player Reserve (Player: ' + key + ')',
        ylabel='Player Chip Count',
        xlabel='Total Round Seconds')
plt.show()

Note:

Slope of the regression line is noted in he legend.

Scatter Attributes:

Name	Type	Description
Scatter.ax	plt	Returns a Scatter Plot

Histogram

Histogram(data):

Class for Histogram plots.

Parameters:

• data (pd.DataFrame,) – Input data.

• limit (int) – Limit the length of data. Optional

• label_lst (List[str]) – List of labels to include, if None will include all columns. Optional

• color_lst (List[str]) – List of colors to graph. Optional

• include_norm (str) – Include norm. If included, requires a column str, default = None. Optional

• norm_color (str) – Norm color, default = ‘red’. Optional

• norm_lineweight (float) – Norm lineweight, default = 1.0. Optional

• norm_ylabel (str) – Norm Y axis label. Optional

• norm_legend_location (str) – Location of norm legend, default = ‘upper right’. Optional

• fig_size (tuple) – default = (10, 7), Optional

• bins (str) – Way of calculating bins, default = ‘sturges’. Optional

• hist_type (str) – Type of histogram, default = ‘bar’. Optional

• stacked (bool) – If True, will stack histograms, default = False. Optional

• ylabel (str) – Y axis label. Optional

• ylabel_color (str) – Y axis label color, default = ‘black’. Optional

• ylabel_size (str) – Y label size, default = ‘medium’. Optional

• ytick_rotation (Optional[int] = 0,) –

• xlabel (str) – X axis label. Optional

• xlabel_color (str) – X axis label color, default = ‘black’. Optional

• xlabel_size (str) – X label size, default = ‘medium’. Optional

• xtick_rotation (Optional[int] = 0,) –

• title (str) – Graph title, default = ‘Histogram’. Optional

• title_size (str) – Title size, default = ‘xx-large’. Optional

• grid (bool) – If True will show grid, default = true. Optional

• grid_alpha (float) – Grid alpha, default = 0.75. Optional

• grid_dash_sequence (tuple) – Grid dash sequence, default = (3, 3). Optional

• grid_lineweight (float) – Grid lineweight, default = 0.5. Optional

• legend_fontsize (str) – Legend fontsize, default = ‘medium’. Optional

• legend_transparency (float) – Legend transparency, default = 0.75. Optional

• legend_location (str) – legend location, default = ‘lower right’. Optional

Example:

from poker.plot import Histogram
Histogram(data=val,
          label_lst=['Move Seconds'],
          include_norm='Move Seconds',
          title='Move Second Histogram (Player: ' + key + ')')
plt.show()

Note:

None

Histogram Attributes:

Name	Type	Description
Histogram.ax	plt	Returns a Histogram Plot