SnapShots class

class zilean.SnapShots.SnapShots(timelines, frames=[8], creep_score=True, porportion=True, verbose=False)

Bases: object

SnapShots is used for extracting interesting player data from Riot MatchTimelineDto s. SnapShots is a helper object that facilitates data analysis on League of Legends matches.

The reason for the name is because SnapShots can extract player data from a match at specific time intervals, or frames (in minutes). Data at frames of interest can be used to, for example, predict the result of a match.

timelines
The source data. It can be either a:

  • String, a file name where either a list of MatchTimelineDto s (in JSON format) or the computed summary statistics (csv) is stored. The computed summary statistics (csv) should be an earlier saved DataFrame using the SnapShots.to_disk() method.

  • List. A list of MatchTimelineDto s.

  • Dict. A single MatchTimelineDto.

Type

str | list | dict

frames

Integers indicating the frames (in minutes) of interest. Default [8]. This argument does nothing if the specified input timelines file is a stored summary file in csv.

Type

list

creep_score

Compute the creep score for the players, then drop the minionKilled and jungleMinionKilled feature of the players. Defaults to True.

Type

bool

porportion

Add goldPorportion and xpPorportion as features to the players. Defaults to True.

Type

bool

verbose

Print out the progress of loading the source data, defaults to False.

Type

bool

agg(type, func: ~typing.Callable[[...], float] = <built-in function sum>) → list

Aggregate summary statistics either by team or by frame. If by team, the statistics across all five lanes are aggregated. If by frame, the statistics across all frames are aggregated. The default aggregate function is the summation.

Parameters

  • type (str) –

    Either “team” or “frame”. - “team”: Aggregate summary statistics

    by team. A team consist of five lanes, marked by number 0 to 4 in the feature names. For example: totalGold_0_frame8 , totalGold_1_frame8 , …, totalGold_4_frame8 will be aggregated into a single feature totalGold_frame8 .

    • ”frame”: Aggregate summary statistics by frame. Frames are usually marked by a number after “frame” the feature names. For example: totalGold_0_frame8 and totalGold_0_frame12 will be aggregated into a single feature totalGold_0 .

  • func (function) – A function to perform the aggregation. Defaults to sum (the summation function).

Returns

Summary statistics after aggregation.

Return type

list

Notes

This method is not compatible with per frame summary. This feature will be developed in future versions.

subset(features=[], lanes=[], frames=[])

Return a new SnapShots with only a subset of the original summary statistics. Features of the original summary statistics contain information that is seperated by underscores. The generic format of a feature is “FEATURE_LANE_frame#”. For example, totalGold_0_frame8 is the total gold difference between the TOP players at 8 minutes mark. This method will return a copy of the original SnapShots instance if no argument were provided.

Parameters

  • features (list, optional) – Features of interest. For available options, please refer to the .feature_info_ variable.

  • lanes (list, optional) – Lane options are any of {“TOP”, “JUG”, “MID”, “BOT”, “SUP”} or their corresponding index {0, 1, 2, 3, 4}.

  • frames (list, optional) – Frames options can only be chosen from the frames specified during the initiation of this SnapShots instance.

Returns

A new SnapShots with only a subset of the original summary statistics.

Return type

zilean.SnapShots

Notes

This method is not compatible does not support subsetting the per frame summary. Only the per match summary will be subsetted. Support of per frame summary will be developed in future versions.

summary(per_frame=False) → list

Return the summary for all the matches (MatchTimelineDto). For each match, summary statistics of every time frame of interest is returned. The summary is ready for further data analysis.

Parameters

per_frame (bool) – If False (default), each match (MatchTimelineDto) is one dictionary. If True, each frame (in minutes) of a match is one dictionary. Defaults to False.

Returns

A list of dictionaries, ready for further data analysis. Each dictionary is either a match or a frame (see per_frame).

Return type

list

to_disk(path='data/', verbose=True) → None

Save the summaries to disk as csv files using pandas.DataFrame.to_csv()

Parameters

  • path (str) – Path name relative to your working directory. Defaults to data/

  • verbose (bool) – Print the directory where of the saved file. Defaults to True