if True:
# ignore this part in true PowerBI executions
import os
import pandas as pd
from pm4py.objects.log.util import dataframe_utils
log_path = os.path.join("..", "..", "tests", "input_data", "running-example.csv")
dataset = pd.read_csv(log_path)
dataset = dataframe_utils.convert_timestamp_columns_in_df(dataset)
import pandas as pd
# this part is required because the dataframe provided by PowerBI has strings
dataset["time:timestamp"] = pd.to_datetime(dataset["time:timestamp"])
dataset = dataset.sort_values("time:timestamp")
from pm4py.statistics.traces.generic.pandas import case_statistics
from pm4py.visualization.graphs import visualizer as graphs_visualizer
x_cases, y_cases = case_statistics.get_kde_caseduration(dataset)
graph_cases = graphs_visualizer.apply(x_cases, y_cases, variant=graphs_visualizer.Variants.CASES,
parameters={graphs_visualizer.Variants.CASES.value.Parameters.FORMAT: "png"})
graphs_visualizer.matplotlib_view(graph_cases)
if True:
# ignore this part in true PowerBI executions
from pm4py.objects.log.adapters.pandas import csv_import_adapter
dataset = csv_import_adapter.import_dataframe_from_path(
"C:/running-example.csv")
import pandas as pd
# this part is required because the dataframe provided by PowerBI has strings
dataset["time:timestamp"] = pd.to_datetime(dataset["time:timestamp"])
from pm4py.statistics.attributes.pandas import get as attributes_filter
from pm4py.visualization.graphs import visualizer as graphs_visualizer
# visualize events over time graph
x_dates, y_dates = attributes_filter.get_kde_date_attribute(dataset)
graph_dates = graphs_visualizer.apply(
x_dates,
y_dates,
variant=graphs_visualizer.Variants.DATES,
parameters={
graphs_visualizer.Variants.DATES.value.Parameters.FORMAT: "png"
})
graphs_visualizer.matplotlib_view(graph_dates)