def test_get_perc_works_as_expected(self):
float_nums = [(0.123, '12.30%'), (3.1243453, '312.43%'),
(213.12312, '21,312.31%')]
int_nums = [(0.14, '14%'), (1.300, '130%')]
for num, expected in float_nums:
self.assertEqual(DataFrameSummary._percent(num), expected)
for num, expected in int_nums:
self.assertEqual(DataFrameSummary._percent(num), expected)
def test_get_perc_works_as_expected(self):
float_nums = [(0.123, '12.30%'),
(3.1243453, '312.43%'),
(213.12312, '21,312.31%')]
int_nums = [(0.14, '14%'),
(1.300, '130%')]
for num, expected in float_nums:
self.assertEqual(DataFrameSummary._percent(num), expected)
for num, expected in int_nums:
self.assertEqual(DataFrameSummary._percent(num), expected)
def test_bool1_summary(self):
count_values = self.df['dbool1'].value_counts()
total_count = self.df['dbool1'].count()
count0 = count_values[0]
count1 = count_values[1]
perc0 = DataFrameSummary._percent(count0 / total_count)
perc1 = DataFrameSummary._percent(count1 / total_count)
expected = pd.Series(index=['"0" count', '"0" perc', '"1" count', '"1" perc',
'counts', 'uniques', 'missing', 'missing_perc', 'types'],
data=[str(count0), perc0, str(count1), perc1,
self.size, 2, 0, '0%', DataFrameSummary.TYPE_BOOL],
name='dbool1',
dtype=object).sort_index()
assert_series_equal(self.dfs['dbool1'].sort_index(), expected)
def test_numerics_summary(self):
num1 = self.df['dnumerics1']
dm, dmp = self.dfs._get_deviation_of_mean(num1)
dam, damp = self.dfs._get_median_absolute_deviation(num1)
expected = pd.Series(index=['mean', 'std', 'variance', 'min', 'max', '5%', '25%', '50%',
'75%', '95%', 'iqr', 'kurtosis', 'skewness', 'sum', 'mad', 'cv',
'zeros_num', 'zeros_perc', 'deviating_of_mean',
'deviating_of_mean_perc', 'deviating_of_median',
'deviating_of_median_perc', 'top_correlations', 'counts',
'uniques', 'missing', 'missing_perc', 'types'],
data=[num1.mean(), num1.std(), num1.var(), num1.min(), num1.max(),
num1.quantile(0.05), num1.quantile(
0.25), num1.quantile(0.5),
num1.quantile(0.75), num1.quantile(0.95),
num1.quantile(0.75) - num1.quantile(0.25),
num1.kurt(), num1.skew(), num1.sum(), num1.mad(),
num1.std() / num1.mean() if num1.mean() else np.nan,
self.size - np.count_nonzero(num1),
DataFrameSummary._percent(
(self.size - np.count_nonzero(num1))/self.size),
dm, dmp, dam, damp, 'dnumerics2: 100%', self.size, self.size,
0, '0%', DataFrameSummary.TYPE_NUMERIC],
name='dnumerics1',
dtype=object)
assert_series_equal(self.dfs['dnumerics1'], expected)
def test_bool1_summary(self):
count_values = self.df['dbool1'].value_counts()
total_count = self.df['dbool1'].count()
count0 = count_values[0]
count1 = count_values[1]
perc0 = DataFrameSummary._percent(count0 / total_count)
perc1 = DataFrameSummary._percent(count1 / total_count)
expected = pd.Series(index=[
'"0" count', '"0" perc', '"1" count', '"1" perc', 'counts',
'uniques', 'missing', 'missing_perc', 'types'
],
data=[
str(count0), perc0,
str(count1), perc1, self.size, 2, 0, '0%',
DataFrameSummary.TYPE_BOOL
],
name='dbool1',
dtype=object)
assert_series_equal(self.dfs['dbool1'], expected)
def test_numerics_summary(self):
num1 = self.df['dnumerics1']
dm, dmp = self.dfs._get_deviation_of_mean(num1)
dam, damp = self.dfs._get_median_absolute_deviation(num1)
expected = pd.Series(
index=[
'mean', 'std', 'variance', 'min', 'max', 'mode', '5%', '25%',
'50%', '75%', '95%', 'iqr', 'kurtosis', 'skewness', 'sum',
'mad', 'cv', 'zeros_num', 'zeros_perc', 'deviating_of_mean',
'deviating_of_mean_perc', 'deviating_of_median',
'deviating_of_median_perc', 'top_correlations', 'counts',
'uniques', 'missing', 'missing_perc', 'types'
],
data=[
num1.mean(),
num1.std(),
num1.var(),
num1.min(),
num1.max(),
num1.mode()[0],
num1.quantile(0.05),
num1.quantile(0.25),
num1.quantile(0.5),
num1.quantile(0.75),
num1.quantile(0.95),
num1.quantile(0.75) - num1.quantile(0.25),
num1.kurt(),
num1.skew(),
num1.sum(),
num1.mad(),
num1.std() / num1.mean() if num1.mean() else np.nan,
self.size - np.count_nonzero(num1),
DataFrameSummary._percent(
(self.size - np.count_nonzero(num1)) / self.size), dm, dmp,
dam, damp, 'dnumerics2: 100%', self.size, self.size, 0, '0%',
DataFrameSummary.TYPE_NUMERIC
],
name='dnumerics1',
dtype=object)
assert_series_equal(self.dfs['dnumerics1'], expected)