import matplotlib.pyplot as pltimport numpy as npimport scipy.stats as statsdef confidence(data):? result = stats.t.interval(? alpha = 0.95,? df = len(data) - 1,? loc = np.mean(data),? scale=stats.sem(data))? error = (result[1] - result[0]) / 2? return errordef visualise_data():? # read the data from powerbi into a data frame? data_frame = pd.DataFrame(dataset)? # define the columns? column_names = [? 'Agitation Acceleration', 'Agitation Speed',? 'Diluent Amount', 'Initial Soil Amount',? 'Interval between Images', 'Number of Agitation Actions',? 'Number of Cycles', 'Number of Images',? 'Temperature', 'Total Concentration',? 'CONTEXT_STRING_REPRESENTATION', 'Y_RESULT_TEMPLATE',? 'Name', 'Name.1'? ]? data_frame = data_frame.groupby((column_names), as_index=False).agg({'Y_VALUE':['mean','std', confidence]})? data_frame.columns = [' '.join(col).strip() for col in data_frame.columns.values]? y_error = data_frame['Y_VALUE confidence']? xtic = data_frame["CONTEXT_STRING_REPRESENTATION"].unique()? data_frame.sort_values(? by='CONTEXT_STRING_REPRESENTATION',? inplace=True)? fig, axes = plt.subplots(figsize=(18,10))? data_frame.groupby(['Name', 'Y_RESULT_TEMPLATE']).plot(? kind = 'line',? x = 'CONTEXT_STRING_REPRESENTATION',? y = 'Y_VALUE mean',? yerr = y_error,? marker = 'x',? markersize = '15',? ax = axes,? fontsize=18? )? plt.xlabel('Cycle Number', fontsize=18)? plt.ylabel('Measure Value', fontsize=18)? axes.xaxis.set_ticks(xtic)? axes.legend(data_frame['Name'], prop={'size': 15})? plt.show()visualise_data()

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