Classes¶

This chapter documents the classes used.

CallofDuty¶

CallofDuty(hacker_data, squad_data, streamer_mode):

Calculate stats for all maps/modes for each squad member.

Parameters

user_input_dict (dict) – A dict of user inputs.
squad_data (bool) – If True, will build the Squad class. default is True. Optional
hacker_data (bool) – This Requires a seperate csv with hacker data saved. This data can be collected by finding hackers after the fact and scraping there data from CodTracker, this can then be used to find hackers in other games. Default is False. Optional
streamer_mode (bool) – If True, will hide User inputted Gamertag’s and Uno’s. default is False. Optional

Example

>>> from warzone.call_of_duty import CallofDuty
    >>> inputs = {'repo': 'local data directory',
    >>>        'gamertag': 'your gamertag',
    >>>        'squad': ['friend gamertag1', 'friend gamertag2', '... etc'],
    >>>        'file_name': 'match_data.csv'}
    >>> cod = CallofDuty(user_input_dict=inputs, squad_data=True, hacker_data=False, streamer_mode=False)
        >>> from call_of_duty import CallofDuty
>>> inputs = {'repo': 'local data directory',
>>>        'gamertag': 'your gamertag',
>>>        'squad': ['friend gamertag1', 'friend gamertag2', '... etc'],
>>>        'file_name': 'match_data.csv'}
>>> cod = CallofDuty(user_input_dict=inputs, squad_data=True, hacker_data=False, streamer_mode=False)

Note

This will calculate and build the CallofDuty class.

DocumentFilter¶

DocumentFilter class objects.

DocumentFilter(hacker_data, squad_data, streamer_mode):

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

Parameters

original_df (pd.DataFrame) – Input DataFrame to be filtered.
map_choice (str) – Map filter. Either ‘mp_e’ for Rebirth and ‘mp_d’ for Verdansk. Optional
mode_choice (str) – Mode filter. Either ‘solo’, ‘duo’, ‘trio’, or ‘quad’. Optional
username (str) – Filter by a players username. Can cause errors if same username as another player. Optional
uno (str) – Filter by a players uno. Optional
username_dic (dict) – Required if ‘username’ or ‘username_lst’ is used. {username1: uno1, username2: uno2, etc}. Optional
username_lst (List[str]) – Filter using a list of usernames. Optional

Example

>>> from warzone.document_filter import DocumentFilter
    >>> doc = DocumentFilter(original_df=cod.our_df, map_choice='mp_e', mode_choice='quad')
        >>> from document_filter import DocumentFilter
>>> doc = DocumentFilter(original_df=cod.our_df, map_choice='mp_e', mode_choice='quad')

Note

All inputs, except original_df, are Optional amd defaults are set to None. This will return any data with map = rebirth and mode = Quads. By specifiying ‘cod.our_df’, this will only return data related to the user.

Plot¶

Plot class objects.

font size = [‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’]

Legend location = [‘best’, ‘upper right’, ‘upper left’, ‘lower left’, ‘lower right’, ‘right’, ‘center left’, ‘center right’, ‘lower center’, ‘upper center’, ‘center’]

Line:

Class for plotting 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. 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

Example

None

Note

None

Scatter:

Class for plotting 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.

Example

None

Note

None

Histogram:

Class for plotting histograms.

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

None

Note

None

Table:

Class for plotting tables.

Parameters

data (pd.DataFrame) – Input data.
label_lst (List[str]) – List of labels to include, if None will include all columns. Optional
fig_size (tuple) – default = (10, 10), Optional
font_size (str) – Font size inside cells, default = ‘medium’. Optional
col_widths (float) – Width of columns, default = 0.30. Optional
row_colors (str) – Color of rows. Optional
header_colors (str) – Header of table color. Optional
edge_color (str) – Color of cell edges, default = ‘w’. Optional
sequential_cells (bool) – If True will color ever other row. Optional
color_map (str) – Color map used in cells, default = ‘Greens’. Optional

Example

None

Note

None

Regression¶

Regression class object.

Regression:

Calculate a linear regression.

Parameters

doc_filter (DocumentFilter) – Input DocumentFilter.
x_column (str, or List[str]) – Name of column or columns to be used in regression analysis.
y_column (str) – Name of column to be used as y variable in regression.

Example

>>> from warzone.document_filter import DocumentFilter
    >>> from warzone.regression import Regression
    >>> doc = DocumentFilter(original_df=cod.our_df, map_choice='mp_e', mode_choice='quad')
    >>> model = Regression(doc_filter=doc, x_column='kills', y_column='placementPercent')
        >>> from document_filter import DocumentFilter
>>> from regression import Regression
>>> doc = DocumentFilter(original_df=cod.our_df, map_choice='mp_e', mode_choice='quad')
>>> model = Regression(doc_filter=doc, x_column='kills', y_column='placementPercent')

Note

This will return a Regression object with regression result information.

Squad¶

Squad class objects.

Performance:

The Performance class is used to evaluate a players performance on a given map and mode

Parameters

original_df (pd.DataFrame) – Input data.
nap_choice – Map filter. Either ‘mp_e’ for Rebirth and ‘mp_d’ for Verdansk.
mode_choice (str) – Mode filter. Either ‘solo’, ‘duo’, ‘trio’, or ‘quad’.
uno (str) – Input person uno Id.

Example

None

Note

None

Person:

The Person class is used to gather all map/mode stats for a given player

Parameters

original_df (pd.DataFrame) – Input data.
uno (str) – Input person uno Id.
gamertag (str) – Input person’s gamertag.

Example

None

Note

None

Squad:

Calculate stats for all maps/modes for each squad memeber.

Parameters

squad_lst (List[str]) – List of gamertags. Include your gamertag in the list.
original_df (pd.DataFrame) – Original DataFrame for stats to be calculated from.
uno_name_dic (dict) – A dict of all gamertags and respective unos.

Example

>>> from warzone.credentials import user_inputs
        >>> from warzone.user import User
        >>> from warzone.squad import Squad
        >>> _User = User(info=user_inputs)
        >>> _Squad = Squad(squad_lst=_User.squad_lst, original_df=cod.our_df, uno_name_dic=cod.name_uno_dict)
            >>> from credentials import user_inputs
    >>> from warzone.user import User
    >>> from warzone.squad import Squad
    >>> _User = User(info=user_inputs)
    >>> _Squad = Squad(squad_lst=_User.squad_lst, original_df=cod.our_df, uno_name_dic=cod.name_uno_dict)
        >>> from warzone.credentials import user_inputs
    >>> from user import User
    >>> from squad import Squad
    >>> _User = User(info=user_inputs)
    >>> _Squad = Squad(squad_lst=_User.squad_lst, original_df=cod.our_df, uno_name_dic=cod.name_uno_dict)
        >>> from credentials import user_inputs
>>> from user import User
>>> from squad import Squad
>>> _User = User(info=user_inputs)
>>> _Squad = Squad(squad_lst=_User.squad_lst, original_df=cod.our_df, uno_name_dic=cod.name_uno_dict)

Note

This will calculate and return the stats for all squad members.

User¶

User class objects.

User:

Organizes the Users input data.

Parameters

info (dict) – User input dict.

Example

>>> from warzone.user import User
    >>> inputs = {'repo': 'local data directory',
    >>>        'gamertag': 'your gamertag',
    >>>        'squad': ['friend gamertag1', 'friend gamertag2', '... etc'],
    >>>        'file_name': 'match_data.csv'}
    >>> user = User(info=inputs)
        >>> from user import User
>>> inputs = {'repo': 'local data directory',
>>>        'gamertag': 'your gamertag',
>>>        'squad': ['friend gamertag1', 'friend gamertag2', '... etc'],
>>>        'file_name': 'match_data.csv'}
>>> user = User(info=inputs)

Note

None