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CLN test_reshape
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‎pandas/tests/test_reshape.py

+135-104
Original file line numberDiff line numberDiff line change
@@ -20,110 +20,141 @@
2020
_multiprocess_can_split_ = True
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def test_melt():
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df = tm.makeTimeDataFrame()[:10]
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df['id1'] = (df['A'] > 0).astype(np.int64)
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df['id2'] = (df['B'] > 0).astype(np.int64)
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var_name = 'var'
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value_name = 'val'
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# Default column names
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result = melt(df)
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result1 = melt(df, id_vars=['id1'])
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result2 = melt(df, id_vars=['id1', 'id2'])
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result3 = melt(df, id_vars=['id1', 'id2'],
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value_vars='A')
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result4 = melt(df, id_vars=['id1', 'id2'],
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value_vars=['A', 'B'])
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expected4 = DataFrame({'id1': df['id1'].tolist() * 2,
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'id2': df['id2'].tolist() * 2,
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'variable': ['A']*10 + ['B']*10,
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'value': df['A'].tolist() + df['B'].tolist()},
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columns=['id1', 'id2', 'variable', 'value'])
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tm.assert_frame_equal(result4, expected4)
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# Supply custom name for the 'variable' column
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result5 = melt(df, var_name=var_name)
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result6 = melt(df, id_vars=['id1'], var_name=var_name)
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result7 = melt(df, id_vars=['id1', 'id2'], var_name=var_name)
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result8 = melt(df, id_vars=['id1', 'id2'],
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value_vars='A', var_name=var_name)
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result9 = melt(df, id_vars=['id1', 'id2'],
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value_vars=['A', 'B'], var_name=var_name)
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expected9 = DataFrame({'id1': df['id1'].tolist() * 2,
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'id2': df['id2'].tolist() * 2,
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var_name: ['A']*10 + ['B']*10,
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'value': df['A'].tolist() + df['B'].tolist()},
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columns=['id1', 'id2', var_name, 'value'])
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tm.assert_frame_equal(result9, expected9)
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# Supply custom name for the 'value' column
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result10 = melt(df, value_name=value_name)
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result11 = melt(df, id_vars=['id1'], value_name=value_name)
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result12 = melt(df, id_vars=['id1', 'id2'], value_name=value_name)
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result13 = melt(df, id_vars=['id1', 'id2'],
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value_vars='A', value_name=value_name)
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result14 = melt(df, id_vars=['id1', 'id2'],
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value_vars=['A', 'B'], value_name=value_name)
71-
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expected14 = DataFrame({'id1': df['id1'].tolist() * 2,
73-
'id2': df['id2'].tolist() * 2,
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'variable': ['A']*10 + ['B']*10,
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value_name: df['A'].tolist() + df['B'].tolist()},
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columns=['id1', 'id2', 'variable', value_name])
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tm.assert_frame_equal(result14, expected14)
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# Supply custom names for the 'variable' and 'value' columns
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result15 = melt(df, var_name=var_name, value_name=value_name)
81-
result16 = melt(df, id_vars=['id1'], var_name=var_name, value_name=value_name)
82-
result17 = melt(df, id_vars=['id1', 'id2'],
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var_name=var_name, value_name=value_name)
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result18 = melt(df, id_vars=['id1', 'id2'],
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value_vars='A', var_name=var_name, value_name=value_name)
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result19 = melt(df, id_vars=['id1', 'id2'],
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value_vars=['A', 'B'], var_name=var_name, value_name=value_name)
88-
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expected19 = DataFrame({'id1': df['id1'].tolist() * 2,
90-
'id2': df['id2'].tolist() * 2,
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var_name: ['A']*10 + ['B']*10,
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value_name: df['A'].tolist() + df['B'].tolist()},
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columns=['id1', 'id2', var_name, value_name])
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tm.assert_frame_equal(result19, expected19)
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df1 = df.copy()
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df1.columns.name = 'foo'
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result20 = melt(df1)
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assert(result20.columns.tolist() == ['foo', 'value'])
100-
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def test_convert_dummies():
102-
df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
103-
'foo', 'bar', 'foo', 'foo'],
104-
'B': ['one', 'one', 'two', 'three',
105-
'two', 'two', 'one', 'three'],
106-
'C': np.random.randn(8),
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'D': np.random.randn(8)})
108-
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result = convert_dummies(df, ['A', 'B'])
110-
result2 = convert_dummies(df, ['A', 'B'], prefix_sep='.')
111-
112-
expected = DataFrame({'A_foo': [1, 0, 1, 0, 1, 0, 1, 1],
113-
'A_bar': [0, 1, 0, 1, 0, 1, 0, 0],
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'B_one': [1, 1, 0, 0, 0, 0, 1, 0],
115-
'B_two': [0, 0, 1, 0, 1, 1, 0, 0],
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'B_three': [0, 0, 0, 1, 0, 0, 0, 1],
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'C': df['C'].values,
118-
'D': df['D'].values},
119-
columns=result.columns, dtype=float)
120-
expected2 = expected.rename(columns=lambda x: x.replace('_', '.'))
121-
122-
tm.assert_frame_equal(result, expected)
123-
tm.assert_frame_equal(result2, expected2)
124-
125-
126-
class Test_lreshape(unittest.TestCase):
23+
class TestMelt(unittest.TestCase):
24+
25+
def setUp(self):
26+
self.df = tm.makeTimeDataFrame()[:10]
27+
self.df['id1'] = (self.df['A'] > 0).astype(np.int64)
28+
self.df['id2'] = (self.df['B'] > 0).astype(np.int64)
29+
30+
self.var_name = 'var'
31+
self.value_name = 'val'
32+
33+
def test_default_col_names(self):
34+
result = melt(self.df)
35+
self.assertEqual(result.columns.tolist(), ['variable', 'value'])
36+
37+
result1 = melt(self.df, id_vars=['id1'])
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self.assertEqual(result1.columns.tolist(), ['id1', 'variable', 'value'])
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40+
result2 = melt(self.df, id_vars=['id1', 'id2'])
41+
self.assertEqual(result2.columns.tolist(), ['id1', 'id2', 'variable', 'value'])
42+
43+
def test_value_vars(self):
44+
result3 = melt(self.df, id_vars=['id1', 'id2'], value_vars='A')
45+
self.assertEqual(len(result3), 10)
46+
47+
result4 = melt(self.df, id_vars=['id1', 'id2'], value_vars=['A', 'B'])
48+
expected4 = DataFrame({'id1': self.df['id1'].tolist() * 2,
49+
'id2': self.df['id2'].tolist() * 2,
50+
'variable': ['A']*10 + ['B']*10,
51+
'value': self.df['A'].tolist() + self.df['B'].tolist()},
52+
columns=['id1', 'id2', 'variable', 'value'])
53+
tm.assert_frame_equal(result4, expected4)
54+
55+
def test_custom_var_name(self):
56+
result5 = melt(self.df, var_name=self.var_name)
57+
self.assertEqual(result5.columns.tolist(), ['var', 'value'])
58+
59+
result6 = melt(self.df, id_vars=['id1'], var_name=self.var_name)
60+
self.assertEqual(result6.columns.tolist(), ['id1', 'var', 'value'])
61+
62+
result7 = melt(self.df, id_vars=['id1', 'id2'], var_name=self.var_name)
63+
self.assertEqual(result7.columns.tolist(), ['id1', 'id2', 'var', 'value'])
64+
65+
result8 = melt(self.df, id_vars=['id1', 'id2'],
66+
value_vars='A', var_name=self.var_name)
67+
self.assertEqual(result8.columns.tolist(), ['id1', 'id2', 'var', 'value'])
68+
69+
result9 = melt(self.df, id_vars=['id1', 'id2'],
70+
value_vars=['A', 'B'], var_name=self.var_name)
71+
expected9 = DataFrame({'id1': self.df['id1'].tolist() * 2,
72+
'id2': self.df['id2'].tolist() * 2,
73+
self.var_name: ['A']*10 + ['B']*10,
74+
'value': self.df['A'].tolist() + self.df['B'].tolist()},
75+
columns=['id1', 'id2', self.var_name, 'value'])
76+
tm.assert_frame_equal(result9, expected9)
77+
78+
def test_custom_value_name(self):
79+
result10 = melt(self.df, value_name=self.value_name)
80+
self.assertEqual(result10.columns.tolist(), ['variable', 'val'])
81+
82+
result11 = melt(self.df, id_vars=['id1'], value_name=self.value_name)
83+
self.assertEqual(result11.columns.tolist(), ['id1', 'variable', 'val'])
84+
85+
result12 = melt(self.df, id_vars=['id1', 'id2'], value_name=self.value_name)
86+
self.assertEqual(result12.columns.tolist(), ['id1', 'id2', 'variable', 'val'])
87+
88+
result13 = melt(self.df, id_vars=['id1', 'id2'],
89+
value_vars='A', value_name=self.value_name)
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self.assertEqual(result13.columns.tolist(), ['id1', 'id2', 'variable', 'val'])
91+
92+
result14 = melt(self.df, id_vars=['id1', 'id2'],
93+
value_vars=['A', 'B'], value_name=self.value_name)
94+
expected14 = DataFrame({'id1': self.df['id1'].tolist() * 2,
95+
'id2': self.df['id2'].tolist() * 2,
96+
'variable': ['A']*10 + ['B']*10,
97+
self.value_name: self.df['A'].tolist() + self.df['B'].tolist()},
98+
columns=['id1', 'id2', 'variable', self.value_name])
99+
tm.assert_frame_equal(result14, expected14)
100+
101+
def test_custom_var_and_value_name(self):
102+
103+
result15 = melt(self.df, var_name=self.var_name, value_name=self.value_name)
104+
self.assertEqual(result15.columns.tolist(), ['var', 'val'])
105+
106+
result16 = melt(self.df, id_vars=['id1'], var_name=self.var_name, value_name=self.value_name)
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self.assertEqual(result16.columns.tolist(), ['id1', 'var', 'val'])
108+
109+
result17 = melt(self.df, id_vars=['id1', 'id2'],
110+
var_name=self.var_name, value_name=self.value_name)
111+
self.assertEqual(result17.columns.tolist(), ['id1', 'id2', 'var', 'val'])
112+
113+
result18 = melt(df, id_vars=['id1', 'id2'],
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value_vars='A', var_name=self.var_name, value_name=self.value_name)
115+
self.assertEqual(result18.columns.tolist(), ['id1', 'id2', 'var', 'val'])
116+
117+
result19 = melt(self.df, id_vars=['id1', 'id2'],
118+
value_vars=['A', 'B'], var_name=self.var_name, value_name=self.value_name)
119+
expected19 = DataFrame({'id1': self.df['id1'].tolist() * 2,
120+
'id2': self.df['id2'].tolist() * 2,
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var_name: ['A']*10 + ['B']*10,
122+
value_name: self.df['A'].tolist() + self.df['B'].tolist()},
123+
columns=['id1', 'id2', self.var_name, self.value_name])
124+
tm.assert_frame_equal(result19, expected19)
125+
126+
def test_custom_var_and_value_name(self):
127+
self.df.columns.name = 'foo'
128+
result20 = melt(self.df)
129+
self.assertEqual(result20.columns.tolist(), ['foo', 'value'])
130+
131+
class TestConvertDummies(unittest.TestCase):
132+
def test_convert_dummies(self):
133+
df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
134+
'foo', 'bar', 'foo', 'foo'],
135+
'B': ['one', 'one', 'two', 'three',
136+
'two', 'two', 'one', 'three'],
137+
'C': np.random.randn(8),
138+
'D': np.random.randn(8)})
139+
140+
result = convert_dummies(df, ['A', 'B'])
141+
result2 = convert_dummies(df, ['A', 'B'], prefix_sep='.')
142+
143+
expected = DataFrame({'A_foo': [1, 0, 1, 0, 1, 0, 1, 1],
144+
'A_bar': [0, 1, 0, 1, 0, 1, 0, 0],
145+
'B_one': [1, 1, 0, 0, 0, 0, 1, 0],
146+
'B_two': [0, 0, 1, 0, 1, 1, 0, 0],
147+
'B_three': [0, 0, 0, 1, 0, 0, 0, 1],
148+
'C': df['C'].values,
149+
'D': df['D'].values},
150+
columns=result.columns, dtype=float)
151+
expected2 = expected.rename(columns=lambda x: x.replace('_', '.'))
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153+
tm.assert_frame_equal(result, expected)
154+
tm.assert_frame_equal(result2, expected2)
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156+
157+
class TestLreshape(unittest.TestCase):
127158

128159
def test_pairs(self):
129160
data = {'birthdt': ['08jan2009', '20dec2008', '30dec2008',

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