def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df = mkdf(10, 3)
df.columns = ['a', 'a', 'b']
cols = ['b', 'a']
result = df[['b', 'a']].columns
expected = Index(['b', 'a', 'a'])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1, 2, 1., 2., 3., 'foo', 'bar']],
columns=list('aaaaaaa'))
df.head()
str(df)
result = DataFrame([[1, 2, 1., 2., 3., 'foo', 'bar']])
result.columns = list('aaaaaaa')
df_v = df.iloc[:, 4]
res_v = result.iloc[:, 4]
assert_frame_equal(df, result)
# GH 3561, dups not in selected order
ind = ['A', 'A', 'B', 'C']
df = DataFrame({'test': range(len(ind))}, index=ind)
rows = ['C', 'B']
res = df.ix[rows]
self.assert_(rows == list(res.index))
res = df.ix[Index(rows)]
self.assert_(Index(rows).equals(res.index))
rows = ['C', 'B', 'E']
res = df.ix[rows]
self.assert_(rows == list(res.index))
# inconcistent returns for unique/duplicate indices when values are missing
df = DataFrame(randn(4, 3), index=list('ABCD'))
expected = df.ix[['E']]
dfnu = DataFrame(randn(5, 3), index=list('AABCD'))
result = dfnu.ix[['E']]
assert_frame_equal(result, expected)
def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df = mkdf(10, 3)
df.columns = ["a", "a", "b"]
cols = ["b", "a"]
result = df[["b", "a"]].columns
expected = Index(["b", "a", "a"])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("aaaaaaa"))
df.head()
str(df)
result = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]])
result.columns = list("aaaaaaa")
df_v = df.iloc[:, 4]
res_v = result.iloc[:, 4]
assert_frame_equal(df, result)
# GH 3561, dups not in selected order
ind = ["A", "A", "B", "C"]
df = DataFrame({"test": range(len(ind))}, index=ind)
rows = ["C", "B"]
res = df.ix[rows]
self.assert_(rows == list(res.index))
res = df.ix[Index(rows)]
self.assert_(Index(rows).equals(res.index))
rows = ["C", "B", "E"]
res = df.ix[rows]
self.assert_(rows == list(res.index))
# inconcistent returns for unique/duplicate indices when values are missing
df = DataFrame(randn(4, 3), index=list("ABCD"))
expected = df.ix[["E"]]
dfnu = DataFrame(randn(5, 3), index=list("AABCD"))
result = dfnu.ix[["E"]]
assert_frame_equal(result, expected)
def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df= mkdf(10, 3)
df.columns = ['a','a','b']
cols = ['b','a']
result = df[['b','a']].columns
expected = Index(['b','a','a'])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
df.head()
str(df)
result = DataFrame([[1,2,1.,2.,3.,'foo','bar']])
result.columns = list('aaaaaaa')
df_v = df.iloc[:,4]
res_v = result.iloc[:,4]
assert_frame_equal(df,result)
# GH 3561, dups not in selected order
ind = ['A', 'A', 'B', 'C']
df = DataFrame({'test':range(len(ind))}, index=ind)
rows = ['C', 'B']
res = df.ix[rows]
self.assert_(rows == list(res.index))
res = df.ix[Index(rows)]
self.assert_(Index(rows).equals(res.index))
rows = ['C','B','E']
res = df.ix[rows]
self.assert_(rows == list(res.index))
# inconcistent returns for unique/duplicate indices when values are missing
df = DataFrame(randn(4,3),index=list('ABCD'))
expected = df.ix[['E']]
dfnu = DataFrame(randn(5,3),index=list('AABCD'))
result = dfnu.ix[['E']]
assert_frame_equal(result, expected)
def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df= mkdf(10, 3)
df.columns = ['a','a','b']
cols = ['b','a']
result = df[['b','a']].columns
expected = Index(['b','a','a'])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
df.head()
str(df)
result = DataFrame([[1,2,1.,2.,3.,'foo','bar']])
result.columns = list('aaaaaaa')
df_v = df.iloc[:,4]
res_v = result.iloc[:,4]
assert_frame_equal(df,result)
def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df = mkdf(10, 3)
df.columns = ['a', 'a', 'b']
cols = ['b', 'a']
result = df[['b', 'a']].columns
expected = Index(['b', 'a', 'a'])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1, 2, 1., 2., 3., 'foo', 'bar']],
columns=list('aaaaaaa'))
df.head()
str(df)
result = DataFrame([[1, 2, 1., 2., 3., 'foo', 'bar']])
result.columns = list('aaaaaaa')
df_v = df.iloc[:, 4]
res_v = result.iloc[:, 4]
assert_frame_equal(df, result)
def test_dups_fancy_indexing(self):
# GH 3455
from pandas.util.testing import makeCustomDataframe as mkdf
df= mkdf(10, 3)
df.columns = ['a','a','b']
cols = ['b','a']
result = df[['b','a']].columns
expected = Index(['b','a','a'])
self.assert_(result.equals(expected))
# across dtypes
df = DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
df.head()
str(df)
result = DataFrame([[1,2,1.,2.,3.,'foo','bar']])
result.columns = list('aaaaaaa')
df_v = df.iloc[:,4]
res_v = result.iloc[:,4]
assert_frame_equal(df,result)
# GH 3561, dups not in selected order
df = DataFrame({'test': [5,7,9,11]}, index=['A', 'A', 'B', 'C'])
rows = ['C', 'B']
expected = DataFrame({'test' : [11,9]},index=rows)
result = df.ix[rows]
assert_frame_equal(result, expected)
result = df.ix[Index(rows)]
assert_frame_equal(result, expected)
rows = ['C','B','E']
expected = DataFrame({'test' : [11,9,np.nan]},index=rows)
result = df.ix[rows]
assert_frame_equal(result, expected)
# inconsistent returns for unique/duplicate indices when values are missing
df = DataFrame(randn(4,3),index=list('ABCD'))
expected = df.ix[['E']]
dfnu = DataFrame(randn(5,3),index=list('AABCD'))
result = dfnu.ix[['E']]
assert_frame_equal(result, expected)
# GH 4619; duplicate indexer with missing label
df = DataFrame({"A": [0, 1, 2]})
result = df.ix[[0,8,0]]
expected = DataFrame({"A": [0, np.nan, 0]},index=[0,8,0])
assert_frame_equal(result,expected)
df = DataFrame({"A": list('abc')})
result = df.ix[[0,8,0]]
expected = DataFrame({"A": ['a', np.nan, 'a']},index=[0,8,0])
assert_frame_equal(result,expected)
# non unique with non unique selector
df = DataFrame({'test': [5,7,9,11]}, index=['A','A','B','C'])
expected = DataFrame({'test' : [5,7,5,7,np.nan]},index=['A','A','A','A','E'])
result = df.ix[['A','A','E']]
assert_frame_equal(result, expected)
