Source code for pm4py.statistics.sojourn_time.pandas.get

from enum import Enum

from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util.business_hours import soj_time_business_hours_diff

DIFF_KEY = "@@diff"
[docs]def apply(dataframe, parameters=None): """ Gets the sojourn time per activity on a Pandas dataframe Parameters -------------- dataframe Pandas dataframe parameters Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => activity key - Parameters.START_TIMESTAMP_KEY => start timestamp key - Parameters.TIMESTAMP_KEY => timestamp key - Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time. Default: False - Parameters.WORKTIMING => work schedule of the company (provided as a list where the first number is the start of the work time, and the second number is the end of the work time), if business hours are enabled Default: [7, 17] (work shift from 07:00 to 17:00) - Parameters.WEEKENDS => indexes of the days of the week that are weekend Default: [6, 7] (weekends are Saturday and Sunday) Returns -------------- soj_time_dict Sojourn time dictionary """ if parameters is None: parameters = {} business_hours = exec_utils.get_param_value(Parameters.BUSINESS_HOURS, parameters, False) worktiming = exec_utils.get_param_value(Parameters.WORKTIMING, parameters, [7, 17]) weekends = exec_utils.get_param_value(Parameters.WEEKENDS, parameters, [6, 7]) activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters, xes_constants.DEFAULT_TIMESTAMP_KEY) timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes_constants.DEFAULT_TIMESTAMP_KEY) if business_hours: dataframe[DIFF_KEY] = dataframe.apply( lambda x: soj_time_business_hours_diff(x[start_timestamp_key], x[timestamp_key], worktiming, weekends), axis=1) else: dataframe[DIFF_KEY] = ( dataframe[timestamp_key] - dataframe[start_timestamp_key]).astype('timedelta64[s]') dataframe = dataframe.reset_index() ret_dict = dataframe.groupby(activity_key)[DIFF_KEY].mean().to_dict() # assure to avoid problems with np.float64, by using the Python float type for el in ret_dict: ret_dict[el] = float(ret_dict[el]) return ret_dict