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TST: Test pivot with categorical data #14807

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TST: Test pivot with categorical data #14807

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69 changes: 69 additions & 0 deletions pandas/tools/tests/test_pivot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -854,6 +854,53 @@ def test_categorical_margins(self):
table = data.pivot_table('x', 'y', 'z', margins=True)
tm.assert_frame_equal(table, expected)

def test_categorical_aggfunc(self):
# GH 9534
df = pd.DataFrame({"C1": ["A", "B", "C", "C"],
"C2": ["a", "a", "b", "b"],
"V": [1, 2, 3, 4]})
df["C1"] = df["C1"].astype("category")
result = df.pivot_table("V", index="C1", columns="C2", aggfunc="count")

expected_index = pd.CategoricalIndex(['A', 'B', 'C'],
categories=['A', 'B', 'C'],
ordered=False,
name='C1')
expected_columns = pd.Index(['a', 'b'], name='C2')
expected_data = np.array([[1., np.nan],
[1., np.nan],
[np.nan, 2.]])
expected = pd.DataFrame(expected_data,
index=expected_index,
columns=expected_columns)
tm.assert_frame_equal(result, expected)

def test_categorical_pivot_index_ordering(self):
# GH 8731
df = pd.DataFrame({'Sales': [100, 120, 220],
'Month': ['January', 'January', 'January'],
'Year': [2013, 2014, 2013]})
months = ['January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November',
'December']
df['Month'] = df['Month'].astype('category').cat.set_categories(months)
result = df.pivot_table(values='Sales',
index='Month',
columns='Year',
aggfunc='sum')
expected_columns = pd.Int64Index([2013, 2014], name='Year')
expected_index = pd.CategoricalIndex(months,
categories=months,
ordered=False,
name='Month')
expected_data = np.empty((12, 2))
expected_data.fill(np.nan)
expected_data[0, :] = [320., 120.]
expected = pd.DataFrame(expected_data,
index=expected_index,
columns=expected_columns)
tm.assert_frame_equal(result, expected)


class TestCrosstab(tm.TestCase):

Expand Down Expand Up @@ -1212,6 +1259,28 @@ def test_crosstab_errors(self):
with tm.assertRaisesRegexp(ValueError, error):
pd.crosstab(df.a, df.b, normalize='all', margins=42)

def test_crosstab_with_categorial_columns(self):
# GH 8860
df = pd.DataFrame({'MAKE': ['Honda', 'Acura', 'Tesla',
'Honda', 'Honda', 'Acura'],
'MODEL': ['Sedan', 'Sedan', 'Electric',
'Pickup', 'Sedan', 'Sedan']})
categories = ['Sedan', 'Electric', 'Pickup']
df['MODEL'] = (df['MODEL'].astype('category')
.cat.set_categories(categories))
result = pd.crosstab(df['MAKE'], df['MODEL'])

expected_index = pd.Index(['Acura', 'Honda', 'Tesla'], name='MAKE')
expected_columns = pd.CategoricalIndex(categories,
categories=categories,
ordered=False,
name='MODEL')
expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
expected = pd.DataFrame(expected_data,
index=expected_index,
columns=expected_columns)
tm.assert_frame_equal(result, expected)


if __name__ == '__main__':
import nose
Expand Down