DataFactory

class Event_data(data_provider='datafactory', event_path=datafactory_path).load_data() → pd.DataFrame

Load the datafactory event data from a JSON file and convert it into a DataFrame.

Parameters:

event_path (str) – Path to the JSON file containing event data.

Returns:

DataFrame containing event data with additional columns for analysis.

Return type:

pd.DataFrame

Example

import pandas as pd
from preprocessing import Event_data

datafactory_path = 'path/to/event_data.json'
datafactory_df = Event_data(data_provider='datafactory', event_path=datafactory_path).load_data()
print(datafactory_df.head())

Details

This function performs the following steps:

  1. Loads the event data from a JSON file specified by event_path.

  2. Extracts match ID and team names.

  3. Iterates over different types of events and individual events within each type.

  4. Extracts various event details such as period, minute, second, event type, team, player, and coordinates.

  5. Converts extracted data into a list of events.

  6. Converts the event list into a DataFrame.

  7. Adds a seconds column to the DataFrame by converting minutes and seconds to total seconds.

  8. Reorders the columns and sorts the DataFrame by the seconds column.

The returned DataFrame includes the following columns:

  • match_id: Match identifier

  • period: Period of the match (e.g., first half, second half)

  • minute: Minute of the event

  • second: Second of the event

  • seconds: Total seconds calculated from minute and second

  • event_type: Type of the event

  • event_type_2: Subtype of the event

  • team: Team associated with the event

  • player: Player ID associated with the event

  • start_x: Starting x-coordinate of the event

  • start_y: Starting y-coordinate of the event

  • start_z: Starting z-coordinate of the event (if available)

  • end_x: Ending x-coordinate of the event (if available)

  • end_y: Ending y-coordinate of the event (if available)

  • end_z: Ending z-coordinate of the event (if available)