def export_normal_distribution(self, column, date):
path = DataFrame.get_export_path(date)
mean = MathsUtil.arr_mean(self.data[self.headers[column]])
median = MathsUtil.arr_median(self.data[self.headers[column]])
mode = MathsUtil.arr_mode(self.data[self.headers[column]])
GraphUtils.export_distribution_graph(self.data[self.headers[column]], self.headers[column],
mean, median, mode, path)
print(f"exported {path}dist_{self.headers[column]}")
def plot_normal_distribution(self, column):
mean = MathsUtil.arr_mean(self.data[self.headers[column]])
median = MathsUtil.arr_median(self.data[self.headers[column]])
mode = MathsUtil.arr_mode(self.data[self.headers[column]])
GraphUtils.show_distribution_graph(self.data[self.headers[column]], self.headers[column], mean, median, mode)
def print_deviation_calculations(self, column):
rows = self.data[self.headers[column]]
DataFrame.print_deviation_calculation("Mean", rows, MathsUtil.arr_mean(rows))
DataFrame.print_deviation_calculation("Median", rows, MathsUtil.arr_median(rows))
DataFrame.print_deviation_calculation("Mode", rows, MathsUtil.arr_mode(rows))
def test_arr_standard_deviation(self):
self.assertEqual(MathsUtil.arr_standard_deviation([3, 9], MathsUtil.arr_mean([3, 9])), 3)
def test_arr_variance(self):
self.assertEqual(MathsUtil.arr_variance([1, 2, 3, 4], MathsUtil.arr_mean([1, 2, 3, 4])), 1.25)
self.assertEqual(MathsUtil.arr_variance([-10, 1, 2, 3, 4], MathsUtil.arr_mean([-10, 1, 2, 3, 4])), 26)
def test_mean_of_array(self):
self.assertEqual(MathsUtil.arr_mean([1, 2, 3]), 2)
self.assertEqual(MathsUtil.arr_mean([14, -2, -3]), 3)
self.assertEqual(MathsUtil.arr_mean([1, 2.6, 3.07, 2.3524]), 2.2556)