def test_outliers_z_score_info():
actual_df = source_df.outliers.z_score('height(ft)', 0.5).info()
actual_df = json_enconding(actual_df)
expected_value = json_enconding({
'count_outliers': 2,
'count_non_outliers': 3,
'max_z_score': 1.76684
})
assert (expected_value == actual_df)
def test_outliers_mad_info():
actual_df = source_df.outliers.mad('height(ft)', 0.5, 10000).info()
actual_df = json_enconding(actual_df)
expected_value = json_enconding({
'count_outliers': 3,
'count_non_outliers': 2,
'lower_bound': 12.5,
'upper_bound': 21.5
})
assert (expected_value == actual_df)
def test_outliers_tukey_whiskers():
actual_df = source_df.outliers.tukey('height(ft)').whiskers()
actual_df = json_enconding(actual_df)
expected_value = json_enconding({
'lower_bound': -6.5,
'upper_bound': 45.5,
'iqr1': 13,
'iqr3': 26
})
assert (expected_value == actual_df)
def test_outliers_modified_z_score_info():
actual_df = source_df.outliers.modified_z_score(
'height(ft)', 0.5, 10000).info()
actual_df = json_enconding(actual_df)
expected_value = json_enconding({
'count_outliers': 3,
'count_non_outliers': 2,
'max_m_z_score': 21.20928
})
assert (expected_value == actual_df)
def test_outliers_tukey_info():
actual_df = source_df.outliers.tukey('height(ft)').info()
actual_df = json_enconding(actual_df)
expected_value = json_enconding({
'count_outliers': 2,
'count_non_outliers': 3,
'lower_bound': -6.5,
'lower_bound_count': 1,
'upper_bound': 45.5,
'upper_bound_count': 1,
'q1': 13,
'median': 17,
'q3': 26,
'iqr': 13
})
assert (expected_value == actual_df)
def test_n_gram_fingerprint_cluster():
actual_df =keyCol.n_gram_fingerprint_cluster(source_df,'STATE',2)
actual_df =json_enconding(actual_df)
expected_value =json_enconding({'Distrito Federal': {'similar': {'Distrito Federal': 10}, 'count': 1, 'sum': 10}})
assert(expected_value == actual_df)
def test_outliers_mad_count():
actual_df = source_df.outliers.mad('height(ft)', 0.5, 10000).count()
actual_df = json_enconding(actual_df)
expected_value = json_enconding(3)
assert (expected_value == actual_df)
def test_outliers_z_score_non_outliers_count():
actual_df = source_df.outliers.z_score('height(ft)',
0.5).non_outliers_count()
actual_df = json_enconding(actual_df)
expected_value = json_enconding(3)
assert (expected_value == actual_df)
def test_outliers_tukey_non_outliers_count():
actual_df = source_df.outliers.tukey('height(ft)').non_outliers_count()
actual_df = json_enconding(actual_df)
expected_value = json_enconding(3)
assert (expected_value == actual_df)
def test_columns_agg():
actual_df =p.columns_agg(source_df,'*')
actual_df =json_enconding(actual_df)
expected_value =json_enconding({'names': {'count_uniques': 5, 'min': 'Jazz', 'max': 'ironhide&', 'count_na': 1, 'stddev': None, 'kurtosis': None, 'mean': None, 'skewness': None, 'sum': None, 'variance': None, 'zeros': 0}, 'height(ft)': {'count_uniques': 5, 'min': -28, 'max': 300, 'count_na': 2, 'stddev': 132.66612, 'kurtosis': 0.13863, 'mean': 65.6, 'skewness': 1.4049, 'sum': 328, 'variance': 17600.3, 'zeros': 0, 'percentile': {'0.75': 26, '0.95': 300, '0.05': -28, '0.25': 13, '0.5': 17}, 'hist': [{'count': 4.0, 'lower': -28.0, 'upper': 54.0}, {'count': 0.0, 'lower': 54.0, 'upper': 136.0}, {'count': 0.0, 'lower': 136.0, 'upper': 218.0}, {'count': 0.0, 'lower': 218.0, 'upper': 300.0}]}, 'function': {'count_uniques': 6, 'min': 'Battle Station', 'max': 'Security', 'count_na': 1, 'stddev': None, 'kurtosis': None, 'mean': None, 'skewness': None, 'sum': None, 'variance': None, 'zeros': 0}, 'rank': {'count_uniques': 3, 'min': 7, 'max': 10, 'count_na': 1, 'stddev': 1.36626, 'kurtosis': -1.5, 'mean': 8.33333, 'skewness': 0.3818, 'sum': 50, 'variance': 1.86667, 'zeros': 0, 'percentile': {'0.75': 10, '0.95': 10, '0.05': 7, '0.25': 7, '0.5': 8}, 'hist': [{'count': 4.0, 'lower': 7.0, 'upper': 8.5}, {'count': 0.0, 'lower': 8.5, 'upper': 10.0}]}, 'age': {'count_uniques': 1, 'min': 5000000, 'max': 5000000, 'count_na': 1, 'stddev': 0.0, 'kurtosis': nan, 'mean': 5000000.0, 'skewness': nan, 'sum': 30000000, 'variance': 0.0, 'zeros': 0, 'percentile': {'0.75': 5000000, '0.95': 5000000, '0.05': 5000000, '0.25': 5000000, '0.5': 5000000}, 'hist': [{'count': 6, 'lower': 5000000, 'upper': 5000001}]}, 'weight(t)': {'count_uniques': 5, 'min': 1.8, 'max': 5.7, 'count_na': 2, 'stddev': 1.64712, 'kurtosis': -1.43641, 'mean': 3.56, 'skewness': 0.06521, 'sum': 17.8, 'variance': 2.713, 'zeros': 0, 'percentile': {'0.75': 4.300000190734863, '0.95': 5.699999809265137, '0.05': 1.7999999523162842, '0.25': 2.0, '0.5': 4.0}, 'hist': [{'count': 1.0, 'lower': 1.8, 'upper': 2.78}, {'count': 0.0, 'lower': 2.78, 'upper': 3.75}, {'count': 2.0, 'lower': 3.75, 'upper': 4.73}, {'count': 1.0, 'lower': 4.73, 'upper': 5.7}]}, 'japanese name': {'count_uniques': 6, 'min': ['Bumble', 'Goldback'], 'max': ['Roadbuster'], 'count_na': 1}, 'last position seen': {'count_uniques': 4, 'min': '10.642707,-71.612534', 'max': '37.789563,-122.400356', 'count_na': 3, 'stddev': None, 'kurtosis': None, 'mean': None, 'skewness': None, 'sum': None, 'variance': None, 'zeros': 0}, 'date arrival': {'count_uniques': 1, 'min': '1980/04/10', 'max': '1980/04/10', 'count_na': 1, 'stddev': None, 'kurtosis': None, 'mean': None, 'skewness': None, 'sum': None, 'variance': None, 'zeros': 0}, 'last date seen': {'count_uniques': 6, 'min': '2011/04/10', 'max': '2016/09/10', 'count_na': 1, 'stddev': None, 'kurtosis': None, 'mean': None, 'skewness': None, 'sum': None, 'variance': None, 'zeros': 0}, 'attributes': {'count_uniques': 6, 'min': [None, 5700.0], 'max': [91.44000244140625, None], 'count_na': 1}, 'Date Type': {'count_uniques': 6, 'min': datetime.date(2011, 4, 10), 'max': datetime.date(2016, 9, 10), 'count_na': 1}, 'timestamp': {'count_uniques': 1, 'min': datetime.datetime(2014, 6, 24, 0, 0), 'max': datetime.datetime(2014, 6, 24, 0, 0), 'count_na': 1}, 'Cybertronian': {'count_uniques': 1, 'min': 1, 'max': 1, 'count_na': 1}, 'function(binary)': {'count_uniques': 6, 'min': bytearray(b'Battle Station'), 'max': bytearray(b'Security'), 'count_na': 1}, 'NullType': {'count_uniques': 0, 'min': None, 'max': None, 'count_na': 7}, 'p_count_na': 100.0, 'p_count_uniques': 0.0, 'range': 3.9000000000000004, 'median': 4.0, 'interquartile_range': 2.3000001907348633, 'coef_variation': 0.46267, 'mad': 1.7})
assert(expected_value == actual_df)
