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python script 95% interval
发布时间：2025/3/18
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python script 95% interval
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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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