def test(self):
d1 = [
2.0, 3.0, 5.0, 7.0, 11.0, 13.0, 17.0, 19.0, 23.0, 29.0, 31.0, 37.0,
43.0, 47.0
]
d2 = [
2.5, 3.0, 5.0, 7.0, None, 13.0, 17.0, 19.0, 23.0, 29.0, 31.0, 37.0,
43.0, 46.5
]
t1 = pd.DataFrame({'d1': d1, 'd2': d2})
print(t1)
stats1 = ['sum', 'avg', 'variance', 'std', 'max', 'min', 'range']
stats2 = ['q1', 'percentile', 'trimmed_mean']
stats3 = [
'max', 'min', 'range', 'sum', 'avg', 'variance', 'stddev',
'skewness', 'kurtosis', 'nrow', 'num_of_value', 'null_count',
'mode', 'median', 'q1', 'q3', 'iqr', 'percentile', 'trimmed_mean'
]
pa_list = [0.25, 0.25]
ta_list = [0.20, 0.20]
out1 = statistic_summary(t1,
input_cols=['d1', 'd2'],
statistics=stats1)
print(out1['out_table'])
out2 = statistic_summary(t1,
input_cols=['d1', 'd2'],
statistics=stats2,
percentile_amounts=pa_list,
trimmed_mean_amounts=ta_list)
print(out2['out_table'])
out3 = statistic_summary(t1,
input_cols=['d1', 'd2'],
statistics=stats3,
percentile_amounts=[0.25, 0.75],
trimmed_mean_amounts=[0.15, 0.45])
print(out3['out_table'])
print(out3['out_table'].columns.values)
d_out1 = statistic_derivation(t1,
input_cols=['d1', 'd2'],
statistics=stats1)
print(d_out1['out_table'])
print(d_out1['out_table'].columns.values)
d_out2 = statistic_derivation(t1,
input_cols=['d1', 'd2'],
statistics=stats2,
percentile_amounts=pa_list,
trimmed_mean_amounts=ta_list)
print(d_out2['out_table'])
print(d_out2['out_table'].columns.values)
def test_first(self):
sd = statistic_derivation(self.testdata,
input_cols=['sepal_length'],
statistics=['max'])
DF1 = sd['out_table'].values
# print(DF1)
np.testing.assert_almost_equal(DF1[0][5], 7.9, 10)
np.testing.assert_almost_equal(DF1[1][5], 7.9, 10)
np.testing.assert_almost_equal(DF1[2][5], 7.9, 10)
np.testing.assert_almost_equal(DF1[3][5], 7.9, 10)
np.testing.assert_almost_equal(DF1[4][5], 7.9, 10)
def test_deriv1(self):
d = {
'd1': [2.0, 3.0, 5.0, 7.0, 11.0, 13.0, 17.0, 19.0],
'd2': [2.7, 3.7, 5.7, 7.7, 11.7, 13.7, 17.7, 19.7],
'd3': [2.7, 3.7, None, 7.7, np.nan, 13.7, 17.7, 19.7],
}
df = pd.DataFrame(d)
#out = statistic_derivation(df, ['d1','d2','d3'], ['min', 'max', 'mode', 'percentile'], [0.1, 0.75], [0.1, 0.3])['out_table']
out = statistic_derivation(df, ['d1', 'd2', 'd3'], ['min', 'mode'],
[0.1, 0.75], [0.1, 0.3])['out_table']
print(out)
def test_second(self):
sd = statistic_derivation(
self.testdata,
input_cols=['petal_width'],
statistics=['sum', 'variance', 'trimmed_mean'],
trimmed_mean_amounts=[0.2])
DF2 = sd['out_table'].values
# print(DF2)
np.testing.assert_array_almost_equal(
DF2[0][5:8], [179.8, 0.5824143176733784, 1.2022222222222223], 10)
np.testing.assert_array_almost_equal(
DF2[1][5:8], [179.8, 0.5824143176733784, 1.2022222222222223], 10)
np.testing.assert_array_almost_equal(
DF2[2][5:8], [179.8, 0.5824143176733784, 1.2022222222222223], 10)
np.testing.assert_array_almost_equal(
DF2[3][5:8], [179.8, 0.5824143176733784, 1.2022222222222223], 10)
np.testing.assert_array_almost_equal(
DF2[4][5:8], [179.8, 0.5824143176733784, 1.2022222222222223], 10)