class Test_attach(unittest.TestCase):
def test0(self):
self.df1 = DataFrame()
self.df1.read_tbl('data/words~ageXcondition.csv')
with self.assertRaises(Exception) as cm:
self.df1.attach('s')
self.assertEqual(str(cm.exception),
'second argument must be a DataFrame')
def test1(self):
self.df1 = DataFrame()
self.df2 = DataFrame()
self.df1.read_tbl('data/words~ageXcondition.csv')
self.df2.read_tbl('data/words~ageXcondition.csv')
# add an extra key to df1
self.df1['EXTRA'] = [5 for a in self.df1['AGE']]
with self.assertRaises(Exception) as cm:
self.df1.attach(self.df2)
self.assertEqual(str(cm.exception),
'self and other must have the same columns')
def test2(self):
df1 = DataFrame()
df2 = DataFrame()
df1.read_tbl('data/words~ageXcondition.csv')
df2.read_tbl('data/words~ageXcondition.csv')
M = df1.shape()[1]
# this should work
df1.attach(df2)
# df1 should have twice as many rows now
self.assertEqual(df1.shape()[1] / 2, df2.shape()[1])
# go through and check data
for i in range(M):
for n in list(df1.keys()):
if _isfloat(df1[n][i]):
self.assertAlmostEqual(df1[n][i], df1[n][M + i])
else:
self.assertEqual(df1[n][i], df1[n][M + i])
class Test_attach(unittest.TestCase):
def test0(self):
self.df1=DataFrame()
self.df1.read_tbl('data/words~ageXcondition.csv')
with self.assertRaises(Exception) as cm:
self.df1.attach('s')
self.assertEqual(str(cm.exception),
'second argument must be a DataFrame')
def test1(self):
self.df1=DataFrame()
self.df2=DataFrame()
self.df1.read_tbl('data/words~ageXcondition.csv')
self.df2.read_tbl('data/words~ageXcondition.csv')
# add an extra key to df1
self.df1['EXTRA'] = [5 for a in self.df1['AGE']]
with self.assertRaises(Exception) as cm:
self.df1.attach(self.df2)
self.assertEqual(str(cm.exception),
'self and other must have the same columns')
def test2(self):
df1=DataFrame()
df2=DataFrame()
df1.read_tbl('data/words~ageXcondition.csv')
df2.read_tbl('data/words~ageXcondition.csv')
M=df1.shape()[1]
# this should work
df1.attach(df2)
# df1 should have twice as many rows now
self.assertEqual(df1.shape()[1]/2,df2.shape()[1])
# go through and check data
for i in range(M):
for n in df1.keys():
if _isfloat(df1[n][i]):
self.assertAlmostEqual(df1[n][i],df1[n][M+i])
else:
self.assertEqual(df1[n][i],df1[n][M+i])
def test2(self):
df1 = DataFrame()
df2 = DataFrame()
df1.read_tbl('data/words~ageXcondition.csv')
df2.read_tbl('data/words~ageXcondition.csv')
M = df1.shape()[1]
# this should work
df1.attach(df2)
# df1 should have twice as many rows now
self.assertEqual(df1.shape()[1] / 2, df2.shape()[1])
# go through and check data
for i in range(M):
for n in list(df1.keys()):
if _isfloat(df1[n][i]):
self.assertAlmostEqual(df1[n][i], df1[n][M + i])
else:
self.assertEqual(df1[n][i], df1[n][M + i])
def test2(self):
df1=DataFrame()
df2=DataFrame()
df1.read_tbl('data/words~ageXcondition.csv')
df2.read_tbl('data/words~ageXcondition.csv')
M=df1.shape()[1]
# this should work
df1.attach(df2)
# df1 should have twice as many rows now
self.assertEqual(df1.shape()[1]/2,df2.shape()[1])
# go through and check data
for i in range(M):
for n in df1.keys():
if _isfloat(df1[n][i]):
self.assertAlmostEqual(df1[n][i],df1[n][M+i])
else:
self.assertEqual(df1[n][i],df1[n][M+i])
