def test_pandas_summary_on_excel_df(self):
#: fixme: this returns error
xdfs = DataFrameSummary(self.xdf)
print(xdfs.get_numeric_summary())
def test_pandas_summary_on_csv_df(self):
#: this works great!
cdfs = DataFrameSummary(self.cdf)
print(cdfs.get_numeric_summary())
class DataFrameSummaryTest(unittest.TestCase):
"""
Test the new methods added by Alfonso R. Reyes.
Dataframe has been expanded to show more columns of the same type.
Needed for the summary.
"""
def setUp(self):
self.size = 1000
missing = [np.nan] * (self.size // 10) + list(range(10)) * (
(self.size - self.size // 10) // 10)
shuffle(missing)
self.types = [
DataFrameSummary.TYPE_NUMERIC, DataFrameSummary.TYPE_BOOL,
DataFrameSummary.TYPE_CATEGORICAL, DataFrameSummary.TYPE_CONSTANT,
DataFrameSummary.TYPE_UNIQUE, DataFrameSummary.TYPE_DATE
]
self.columns = [
'dbool1', 'dbool2', 'duniques1', 'duniques2', 'dcategoricals1',
'dcategoricals2', 'dnumerics1', 'dnumerics2', 'dnumerics3',
'dmissing', 'dconstant', 'ddates1', 'ddates2'
]
self.df = pd.DataFrame(
dict(
dbool1=np.random.choice([0, 1], size=self.size),
dbool2=np.random.choice(['a', 'b'], size=self.size),
duniques1=['x{}'.format(i) for i in range(self.size)],
duniques2=['y{}'.format(i) for i in range(self.size)],
dcategoricals1=[
'a'.format(i) if i % 2 == 0 else 'b'.format(i) if i %
3 == 0 else 'c'.format(i) for i in range(self.size)
],
dcategoricals2=[
'x'.format(i) if i % 2 == 0 else 'y'.format(i) if i %
3 == 0 else 'z'.format(i) for i in range(self.size)
],
dnumerics1=range(self.size),
dnumerics2=range(self.size, 2 * self.size),
dnumerics3=list(range(self.size - self.size // 10)) +
list(range(-self.size // 10, 0)),
dmissing=missing,
dconstant=['a'] * self.size,
ddates1=pd.date_range('2010-01-01',
periods=self.size,
freq='1M'),
ddates2=pd.date_range('2000-01-01',
periods=self.size,
freq='1W'),
))
self.dfs = DataFrameSummary(self.df)
def test_columns_stats(self):
"""
Test the columns_stats instance variable and the columns of the test dataframe.
:return:
"""
columns_stats = self.dfs.columns_stats
print(type(columns_stats))
self.assertIsInstance(columns_stats, pd.core.frame.DataFrame)
expected = [
'dbool1', 'dbool2', 'dcategoricals1', 'dcategoricals2',
'dconstant', 'ddates1', 'ddates2', 'dmissing', 'dnumerics1',
'dnumerics2', 'dnumerics3', 'duniques1', 'duniques2'
]
result = columns_stats.columns.tolist()
print(result)
self.assertEqual(expected, result)
def test__is_all_numeric_false(self):
"""
Test that not all the columns provided in the list are "numeric".
It must return "False"
:return:
"""
columns = [
'dbool1', 'dbool2', 'dcategoricals', 'dconstant', 'ddates',
'dmissing', 'dnumerics1', 'dnumerics2', 'dnumerics3', 'duniques'
]
result = self.dfs._is_all_numeric(columns)
print(result)
self.assertFalse(result)
def test__is_all_numeric_true(self):
"""
Test that all columns passed are "numeric".
It must be "True"
:return:
"""
columns = ['dnumerics1', 'dnumerics2', 'dnumerics3']
result = self.dfs._is_all_numeric(columns)
print(result)
self.assertTrue(result)
def test__is_all_numeric_true_missing(self):
"""
Numeric columns provided this time included NaNs.
It muest be "True"
:return:
"""
#: includes missing nan column, which is numeric as well
columns = ['dnumerics1', 'dnumerics2', 'dnumerics3', 'dmissing']
result = self.dfs._is_all_numeric(columns)
print(result)
self.assertTrue(result)
def test__get_list_of_type_numeric(self):
"""
Test that a list of numeric columns matches the test dataframe
:return:
"""
expected = ['dmissing', 'dnumerics1', 'dnumerics2', 'dnumerics3']
result = self.dfs._get_list_of_type("numeric")
print(result)
print(self.dfs[result])
self.assertTrue(expected == result)
def test__get_list_of_type_numeric_generic(self):
"""
Test that all the columns returning are all of the same `numeric` type
:return:
"""
the_type = "numeric"
columns = self.dfs._get_list_of_type(the_type)
frame = self.dfs[columns]
print(frame)
types = frame.ix['types']
set_of_types = set(types.tolist())
result = the_type in set_of_types
print(result)
self.assertTrue(result)
def test_get_numeric_summary(self):
"""
Test that the columns types reduce to a unique numeric value and matches.
:return:
"""
frame = self.dfs.get_numeric_summary()
print(frame)
result = self.dfs.TYPE_NUMERIC in set(frame.ix['types'])
print(result)
self.assertTrue(result)
def test__get_list_of_type_boolean(self):
"""
Test that boolean columns match the type `bool`
:return:
"""
expected = ['dbool1', 'dbool2']
result = self.dfs._get_list_of_type("bool")
print(result)
self.assertTrue(expected == result)
def test_show_dataframe_per_type(self):
"""
Shows a column, one by one grouping by column type
:return:
"""
for column in self.types:
print(column)
columns = self.dfs._get_list_of_type(column)
# print(self.dfs[columns])
list_of = columns
for col in list_of:
print(self.dfs[col])
def test__get_list_of_type_bool_generic(self):
"""
This is an OLD behavior. Now corrected.
There is a problem when the list of columns specified is not numeric: what returns when
dfs[columns] is specified could be a list of the columns values.
No what we are looking for.
"""
the_type = "bool"
columns = self.dfs._get_list_of_type(the_type)
print(columns)
df = self.dfs[['dbool1', 'dbool2']]
print(df)
self.assertTrue(df.shape[1] == 2)
def test_get_all_series_bool(self):
"""
Test that boolean summary return the same number of rows.
WIth the new behavior the number of rows must be 9 in the case of booleans
:return:
"""
list_of = ['dbool1', 'dbool2']
for col in list_of:
ser = self.dfs[col]
print ser
print ser.shape[0]
self.assertTrue(ser.shape[0] == 9)
def test_show_columns_types(self):
"""
Test that the columns in the test dataframe is a subset of the class variable "types"
:return:
"""
self.assertTrue(
set(self.dfs.columns_types.index).issubset(self.dfs.types))
def test__is_type_the_same_bool(self):
"""
Test that the columns passed are of the same type
:return:
"""
columns = ['dbool1', 'dbool2']
list_of_types = self.dfs._is_type_the_same(columns)
self.assertTrue(list_of_types)
def test__is_type_the_same_many_false(self):
"""
Tests that the columns passed are NOT all of the same type
:return:
"""
columns = ['dbool1', 'dbool2', 'dnumerics1']
list_of_types = self.dfs._is_type_the_same(columns)
self.assertFalse(list_of_types)
def test__is_type_the_same_numeric(self):
"""
Test that the columns passed are all the same
:return:
"""
columns = ['dnumerics1', 'dnumerics2', 'dnumerics3', 'dmissing']
list_of_types = self.dfs._is_type_the_same(columns)
self.assertTrue(list_of_types)
def test_get_all_the_same_unique(self):
"""
Test that the unique columns passed are all unique
:return:
"""
columns = ['duniques1', 'duniques2']
self.assertTrue(
set(self.dfs[columns].loc['types'].tolist()) == {'unique'})
def test_get_all_the_same_numeric(self):
"""
Test that all the numeric columns are all numeric
"""
columns = ['dnumerics1', 'dnumerics2', 'dnumerics3', 'dmissing']
self.assertTrue(
set(self.dfs[columns].loc['types'].tolist()) == {'numeric'})
def test_get_all_the_same_categorical(self):
"""
Tests that all categorical columns reduce to `categorical`
:return:
"""
columns = ['dcategoricals1', 'dcategoricals2']
self.assertTrue(
set(self.dfs[columns].loc['types'].tolist()) == {'categorical'})
def test_get_all_the_same_dates(self):
"""
Test that all the ``date columns reduce to a unique type `date`
:return:
"""
columns = ['ddates1', 'ddates2']
self.assertTrue(
set(self.dfs[columns].loc['types'].tolist()) == {'date'})