def test_non_unique_idvars(self):
# GH16382
# Raise an error message if non unique id vars (i) are passed
df = pd.DataFrame({
'A_A1': [1, 2, 3, 4, 5],
'B_B1': [1, 2, 3, 4, 5],
'x': [1, 1, 1, 1, 1]
})
with pytest.raises(ValueError):
wide_to_long(df, ['A_A', 'B_B'], i='x', j='colname')
def test_invalid_suffixtype(self):
# If all stubs names end with a string, but a numeric suffix is
# assumed, an empty data frame is returned
df = pd.DataFrame({
'Aone': [1.0, 2.0],
'Atwo': [3.0, 4.0],
'Bone': [5.0, 6.0],
'X': ['X1', 'X2']
})
df['id'] = df.index
exp_data = {
'X': '',
'Aone': [],
'Atwo': [],
'Bone': [],
'id': [],
'year': [],
'A': [],
'B': []
}
exp_frame = pd.DataFrame(exp_data)
exp_frame = exp_frame.set_index(
['id', 'year'])[['X', 'Aone', 'Atwo', 'Bone', 'A', 'B']]
exp_frame.index.set_levels([[0, 1], []], inplace=True)
long_frame = wide_to_long(df, ['A', 'B'], i='id', j='year')
tm.assert_frame_equal(long_frame.sort_index(axis=1),
exp_frame.sort_index(axis=1))
def test_num_string_disambiguation(self):
# Test that we can disambiguate number value_vars from
# string value_vars
df = pd.DataFrame({
'A11': ['a11', 'a22', 'a33'],
'A12': ['a21', 'a22', 'a23'],
'B11': ['b11', 'b12', 'b13'],
'B12': ['b21', 'b22', 'b23'],
'BB11': [1, 2, 3],
'BB12': [4, 5, 6],
'Arating': [91, 92, 93],
'Arating_old': [91, 92, 93]
})
df['id'] = df.index
exp_frame = pd.DataFrame({
'Arating': [91, 92, 93, 91, 92, 93],
'Arating_old': [91, 92, 93, 91, 92, 93],
'A': ['a11', 'a22', 'a33', 'a21', 'a22', 'a23'],
'B': ['b11', 'b12', 'b13', 'b21', 'b22', 'b23'],
'BB': [1, 2, 3, 4, 5, 6],
'id': [0, 1, 2, 0, 1, 2],
'year': ['11', '11', '11', '12', '12', '12']
})
exp_frame = exp_frame.set_index(
['id', 'year'])[['Arating', 'Arating_old', 'A', 'B', 'BB']]
long_frame = wide_to_long(df, ['A', 'B', 'BB'], i='id', j='year')
tm.assert_frame_equal(long_frame.sort_index(axis=1),
exp_frame.sort_index(axis=1))
def test_invalid_separator(self):
# if an invalid separator is supplied a empty data frame is returned
sep = 'nope!'
df = pd.DataFrame({
'A2010': [1.0, 2.0],
'A2011': [3.0, 4.0],
'B2010': [5.0, 6.0],
'X': ['X1', 'X2']
})
df['id'] = df.index
exp_data = {
'X': '',
'A2010': [],
'A2011': [],
'B2010': [],
'id': [],
'year': [],
'A': [],
'B': []
}
exp_frame = pd.DataFrame(exp_data)
exp_frame = exp_frame.set_index(
['id', 'year'])[['X', 'A2010', 'A2011', 'B2010', 'A', 'B']]
exp_frame.index.set_levels([[0, 1], []], inplace=True)
long_frame = wide_to_long(df, ['A', 'B'], i='id', j='year', sep=sep)
tm.assert_frame_equal(long_frame.sort_index(axis=1),
exp_frame.sort_index(axis=1))
def test_character_overlap(self):
# Test we handle overlapping characters in both id_vars and value_vars
df = pd.DataFrame({
'A11': ['a11', 'a22', 'a33'],
'A12': ['a21', 'a22', 'a23'],
'B11': ['b11', 'b12', 'b13'],
'B12': ['b21', 'b22', 'b23'],
'BB11': [1, 2, 3],
'BB12': [4, 5, 6],
'BBBX': [91, 92, 93],
'BBBZ': [91, 92, 93]
})
df['id'] = df.index
exp_frame = pd.DataFrame({
'BBBX': [91, 92, 93, 91, 92, 93],
'BBBZ': [91, 92, 93, 91, 92, 93],
'A': ['a11', 'a22', 'a33', 'a21', 'a22', 'a23'],
'B': ['b11', 'b12', 'b13', 'b21', 'b22', 'b23'],
'BB': [1, 2, 3, 4, 5, 6],
'id': [0, 1, 2, 0, 1, 2],
'year': ['11', '11', '11', '12', '12', '12']
})
exp_frame = exp_frame.set_index(['id', 'year'
])[['BBBX', 'BBBZ', 'A', 'B', 'BB']]
long_frame = wide_to_long(df, ['A', 'B', 'BB'], i='id', j='year')
tm.assert_frame_equal(long_frame.sort_index(axis=1),
exp_frame.sort_index(axis=1))
def test_unbalanced(self):
# test that we can have a varying amount of time variables
df = pd.DataFrame({
'A2010': [1.0, 2.0],
'A2011': [3.0, 4.0],
'B2010': [5.0, 6.0],
'X': ['X1', 'X2']
})
df['id'] = df.index
exp_data = {
'X': ['X1', 'X1', 'X2', 'X2'],
'A': [1.0, 3.0, 2.0, 4.0],
'B': [5.0, np.nan, 6.0, np.nan],
'id': [0, 0, 1, 1],
'year': ['2010', '2011', '2010', '2011']
}
exp_frame = pd.DataFrame(exp_data)
exp_frame = exp_frame.set_index(['id', 'year'])[["X", "A", "B"]]
long_frame = wide_to_long(df, ['A', 'B'], i='id', j='year')
tm.assert_frame_equal(long_frame, exp_frame)
def test_escapable_characters(self):
np.random.seed(123)
x = np.random.randn(3)
df = pd.DataFrame({
"A(quarterly)1970": {
0: "a",
1: "b",
2: "c"
},
"A(quarterly)1980": {
0: "d",
1: "e",
2: "f"
},
"B(quarterly)1970": {
0: 2.5,
1: 1.2,
2: .7
},
"B(quarterly)1980": {
0: 3.2,
1: 1.3,
2: .1
},
"X": dict(zip(range(3), x))
})
df["id"] = df.index
exp_data = {
"X": x.tolist() + x.tolist(),
"A(quarterly)": ['a', 'b', 'c', 'd', 'e', 'f'],
"B(quarterly)": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
"year": ['1970', '1970', '1970', '1980', '1980', '1980'],
"id": [0, 1, 2, 0, 1, 2]
}
exp_frame = DataFrame(exp_data)
exp_frame = exp_frame.set_index(
['id', 'year'])[["X", "A(quarterly)", "B(quarterly)"]]
long_frame = wide_to_long(df, ["A(quarterly)", "B(quarterly)"],
i="id",
j="year")
tm.assert_frame_equal(long_frame, exp_frame)
def test_multiple_id_columns(self):
# Taken from http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm
df = pd.DataFrame({
'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
})
exp_frame = pd.DataFrame({
'ht': [
2.8, 3.4, 2.9, 3.8, 2.2, 2.9, 2.0, 3.2, 1.8, 2.8, 1.9, 2.4,
2.2, 3.3, 2.3, 3.4, 2.1, 2.9
],
'famid': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
'birth': [1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3],
'age': [
'1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2',
'1', '2', '1', '2', '1', '2'
]
})
exp_frame = exp_frame.set_index(['famid', 'birth', 'age'])[['ht']]
long_frame = wide_to_long(df, 'ht', i=['famid', 'birth'], j='age')
tm.assert_frame_equal(long_frame, exp_frame)