def test_basic_types(self):
# GH 10531
s_list = list('abc')
s_series = Series(s_list)
s_df = DataFrame({'a': [0, 1, 0, 1, 2],
'b': ['A', 'A', 'B', 'C', 'C'],
'c': [2, 3, 3, 3, 2]})
expected = DataFrame({'a': [1, 0, 0],
'b': [0, 1, 0],
'c': [0, 0, 1]},
dtype='uint8',
columns=list('abc'))
if not self.sparse:
compare = tm.assert_frame_equal
else:
expected = expected.to_sparse(fill_value=0, kind='integer')
compare = tm.assert_sp_frame_equal
result = get_dummies(s_list, sparse=self.sparse)
compare(result, expected)
result = get_dummies(s_series, sparse=self.sparse)
compare(result, expected)
result = get_dummies(s_df, sparse=self.sparse, columns=s_df.columns)
tm.assert_series_equal(result.get_dtype_counts(),
Series({'uint8': 8}))
result = get_dummies(s_df, sparse=self.sparse, columns=['a'])
expected = Series({'uint8': 3, 'int64': 1, 'object': 1}).sort_values()
tm.assert_series_equal(result.get_dtype_counts().sort_values(),
expected)
def test_dataframe_dummies_all_obj(self):
df = self.df[['A', 'B']]
result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'A_a': [1, 0, 1],
'A_b': [0, 1, 0],
'B_b': [1, 1, 0],
'B_c': [0, 0, 1]}, dtype=np.uint8)
assert_frame_equal(result, expected)
def test_just_na(self):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
just_na_series_index = Series(just_na_list, index=['A'])
res_list = get_dummies(just_na_list, sparse=self.sparse)
res_series = get_dummies(just_na_series, sparse=self.sparse)
res_series_index = get_dummies(just_na_series_index,
sparse=self.sparse)
self.assertEqual(res_list.empty, True)
self.assertEqual(res_series.empty, True)
self.assertEqual(res_series_index.empty, True)
self.assertEqual(res_list.index.tolist(), [0])
self.assertEqual(res_series.index.tolist(), [0])
self.assertEqual(res_series_index.index.tolist(), ['A'])
def test_just_na(self):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
just_na_series_index = Series(just_na_list, index=['A'])
res_list = get_dummies(just_na_list, sparse=self.sparse)
res_series = get_dummies(just_na_series, sparse=self.sparse)
res_series_index = get_dummies(just_na_series_index,
sparse=self.sparse)
assert res_list.empty
assert res_series.empty
assert res_series_index.empty
assert res_list.index.tolist() == [0]
assert res_series.index.tolist() == [0]
assert res_series_index.index.tolist() == ['A']
def test_basic_drop_first_one_level(self):
# Test the case that categorical variable only has one level.
s_list = list('aaa')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame(index=np.arange(3))
result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
expected = DataFrame(index=list('ABC'))
result = get_dummies(s_series_index, sparse=self.sparse,
drop_first=True)
assert_frame_equal(result, expected)
def test_basic_drop_first_one_level(self):
# Test the case that categorical variable only has one level.
s_list = list('aaa')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame(index=np.arange(3))
result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
expected = DataFrame(index=list('ABC'))
result = get_dummies(s_series_index, sparse=self.sparse,
drop_first=True)
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first(self):
df = self.df[['A', 'B']]
result = get_dummies(df, sparse=self.sparse, drop_first=True)
expected = DataFrame({
'A_b': [0, 1, 0],
'B_c': [0, 0, 1]
},
dtype=np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_str(self):
# not that you should do this...
df = self.df
result = get_dummies(df, prefix='bad', sparse=self.sparse)
expected = DataFrame(
[[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
columns=['C', 'bad_a', 'bad_b', 'bad_b', 'bad_c'],
dtype=np.uint8)
expected = expected.astype({"C": np.int64})
assert_frame_equal(result, expected)
def test_basic(self):
s_list = list('abc')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame({'a': {0: 1,
1: 0,
2: 0},
'b': {0: 0,
1: 1,
2: 0},
'c': {0: 0,
1: 0,
2: 1}}, dtype=np.uint8)
assert_frame_equal(get_dummies(s_list, sparse=self.sparse), expected)
assert_frame_equal(get_dummies(s_series, sparse=self.sparse), expected)
expected.index = list('ABC')
assert_frame_equal(
get_dummies(s_series_index, sparse=self.sparse), expected)
def test_dataframe_dummies_drop_first_with_na(self):
df = self.df
df.loc[3, :] = [np.nan, np.nan, np.nan]
result = get_dummies(df, dummy_na=True, sparse=self.sparse,
drop_first=True)
expected = DataFrame({'C': [1, 2, 3, np.nan],
'A_b': [0, 1, 0, 0],
'A_nan': [0, 0, 0, 1],
'B_c': [0, 0, 1, 0],
'B_nan': [0, 0, 0, 1]})
cols = ['A_b', 'A_nan', 'B_c', 'B_nan']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_b', 'A_nan', 'B_c', 'B_nan']]
assert_frame_equal(result, expected)
result = get_dummies(df, dummy_na=False, sparse=self.sparse,
drop_first=True)
expected = expected[['C', 'A_b', 'B_c']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_subset(self):
df = self.df
result = get_dummies(df, prefix=['from_A'], columns=['A'],
sparse=self.sparse)
expected = DataFrame({'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
cols = ['from_A_a', 'from_A_b']
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_sep(self):
df = self.df
result = get_dummies(df, prefix_sep='..', sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3],
'A..a': [1, 0, 1],
'A..b': [0, 1, 0],
'B..b': [1, 1, 0],
'B..c': [0, 0, 1]})
expected = expected[['C', 'A..a', 'A..b', 'B..b', 'B..c']]
cols = expected.columns[1:]
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep=['..', '__'], sparse=self.sparse)
expected = expected.rename(columns={'B..b': 'B__b', 'B..c': 'B__c'})
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep={'A': '..',
'B': '__'}, sparse=self.sparse)
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first_with_na(self):
df = self.df
df.loc[3, :] = [np.nan, np.nan, np.nan]
result = get_dummies(df, dummy_na=True, sparse=self.sparse,
drop_first=True)
expected = DataFrame({'C': [1, 2, 3, np.nan],
'A_b': [0, 1, 0, 0],
'A_nan': [0, 0, 0, 1],
'B_c': [0, 0, 1, 0],
'B_nan': [0, 0, 0, 1]})
cols = ['A_b', 'A_nan', 'B_c', 'B_nan']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_b', 'A_nan', 'B_c', 'B_nan']]
assert_frame_equal(result, expected)
result = get_dummies(df, dummy_na=False, sparse=self.sparse,
drop_first=True)
expected = expected[['C', 'A_b', 'B_c']]
assert_frame_equal(result, expected)
def test_basic(self):
s_list = list('abc')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame({'a': {0: 1,
1: 0,
2: 0},
'b': {0: 0,
1: 1,
2: 0},
'c': {0: 0,
1: 0,
2: 1}}, dtype=np.uint8)
assert_frame_equal(get_dummies(s_list, sparse=self.sparse), expected)
assert_frame_equal(get_dummies(s_series, sparse=self.sparse), expected)
expected.index = list('ABC')
assert_frame_equal(
get_dummies(s_series_index, sparse=self.sparse), expected)
def test_dataframe_dummies_prefix_str(self):
# not that you should do this...
df = self.df
result = get_dummies(df, prefix='bad', sparse=self.sparse)
expected = DataFrame([[1, 1, 0, 1, 0],
[2, 0, 1, 1, 0],
[3, 1, 0, 0, 1]],
columns=['C', 'bad_a', 'bad_b', 'bad_b', 'bad_c'],
dtype=np.uint8)
expected = expected.astype({"C": np.int64})
assert_frame_equal(result, expected)
def test_dataframe_dummies_subset(self):
df = self.df
result = get_dummies(df, prefix=['from_A'], columns=['A'],
sparse=self.sparse)
expected = DataFrame({'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
cols = ['from_A_a', 'from_A_b']
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_sep(self):
df = self.df
result = get_dummies(df, prefix_sep='..', sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3],
'A..a': [1, 0, 1],
'A..b': [0, 1, 0],
'B..b': [1, 1, 0],
'B..c': [0, 0, 1]})
expected = expected[['C', 'A..a', 'A..b', 'B..b', 'B..c']]
cols = expected.columns[1:]
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep=['..', '__'], sparse=self.sparse)
expected = expected.rename(columns={'B..b': 'B__b', 'B..c': 'B__c'})
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep={'A': '..',
'B': '__'}, sparse=self.sparse)
assert_frame_equal(result, expected)
def test_dataframe_dummies_mix_default(self):
df = self.df
result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3],
'A_a': [1, 0, 1],
'A_b': [0, 1, 0],
'B_b': [1, 1, 0],
'B_c': [0, 0, 1]})
cols = ['A_a', 'A_b', 'B_b', 'B_c']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_all_obj(self):
df = self.df[['A', 'B']]
result = get_dummies(df, sparse=self.sparse)
expected = DataFrame(
{
'A_a': [1, 0, 1],
'A_b': [0, 1, 0],
'B_b': [1, 1, 0],
'B_c': [0, 0, 1]
},
dtype=np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first_with_categorical(self):
df = self.df
df['cat'] = pd.Categorical(['x', 'y', 'y'])
result = get_dummies(df, sparse=self.sparse, drop_first=True)
expected = DataFrame({'C': [1, 2, 3],
'A_b': [0, 1, 0],
'B_c': [0, 0, 1],
'cat_y': [0, 1, 1]})
cols = ['A_b', 'B_c', 'cat_y']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_b', 'B_c', 'cat_y']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first_with_categorical(self):
df = self.df
df['cat'] = pd.Categorical(['x', 'y', 'y'])
result = get_dummies(df, sparse=self.sparse, drop_first=True)
expected = DataFrame({'C': [1, 2, 3],
'A_b': [0, 1, 0],
'B_c': [0, 0, 1],
'cat_y': [0, 1, 1]})
cols = ['A_b', 'B_c', 'cat_y']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_b', 'B_c', 'cat_y']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_mix_default(self):
df = self.df
result = get_dummies(df, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3],
'A_a': [1, 0, 1],
'A_b': [0, 1, 0],
'B_b': [1, 1, 0],
'B_c': [0, 0, 1]})
cols = ['A_a', 'A_b', 'B_b', 'B_c']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)
def test_include_na(self):
s = ['a', 'b', np.nan]
res = get_dummies(s, sparse=self.sparse)
exp = DataFrame({'a': {0: 1, 1: 0, 2: 0},
'b': {0: 0, 1: 1, 2: 0}}, dtype=np.uint8)
assert_frame_equal(res, exp)
# Sparse dataframes do not allow nan labelled columns, see #GH8822
res_na = get_dummies(s, dummy_na=True, sparse=self.sparse)
exp_na = DataFrame({nan: {0: 0, 1: 0, 2: 1},
'a': {0: 1, 1: 0, 2: 0},
'b': {0: 0, 1: 1, 2: 0}},
dtype=np.uint8)
exp_na = exp_na.reindex_axis(['a', 'b', nan], 1)
# hack (NaN handling in assert_index_equal)
exp_na.columns = res_na.columns
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse)
exp_just_na = DataFrame(Series(1, index=[0]), columns=[nan],
dtype=np.uint8)
tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
def test_include_na(self):
s = ['a', 'b', np.nan]
res = get_dummies(s, sparse=self.sparse)
exp = DataFrame({'a': {0: 1, 1: 0, 2: 0},
'b': {0: 0, 1: 1, 2: 0}}, dtype=np.uint8)
assert_frame_equal(res, exp)
# Sparse dataframes do not allow nan labelled columns, see #GH8822
res_na = get_dummies(s, dummy_na=True, sparse=self.sparse)
exp_na = DataFrame({nan: {0: 0, 1: 0, 2: 1},
'a': {0: 1, 1: 0, 2: 0},
'b': {0: 0, 1: 1, 2: 0}},
dtype=np.uint8)
exp_na = exp_na.reindex_axis(['a', 'b', nan], 1)
# hack (NaN handling in assert_index_equal)
exp_na.columns = res_na.columns
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse)
exp_just_na = DataFrame(Series(1, index=[0]), columns=[nan],
dtype=np.uint8)
tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
def test_dataframe_dummies_preserve_categorical_dtype(self):
# GH13854
for ordered in [False, True]:
cat = pd.Categorical(list("xy"), categories=list("xyz"),
ordered=ordered)
result = get_dummies(cat)
data = np.array([[1, 0, 0], [0, 1, 0]], dtype=np.uint8)
cols = pd.CategoricalIndex(cat.categories,
categories=cat.categories,
ordered=ordered)
expected = DataFrame(data, columns=cols)
tm.assert_frame_equal(result, expected)
def test_basic_drop_first(self):
# Basic case
s_list = list('abc')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame({'b': {0: 0,
1: 1,
2: 0},
'c': {0: 0,
1: 0,
2: 1}}, dtype=np.uint8)
result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
assert_frame_equal(result, expected)
expected.index = list('ABC')
result = get_dummies(s_series_index, sparse=self.sparse,
drop_first=True)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_dict(self):
prefixes = {'A': 'from_A', 'B': 'from_B'}
df = DataFrame({'A': ['a', 'b', 'a'],
'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
expected = DataFrame({'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'from_B_b': [1, 1, 0],
'from_B_c': [0, 0, 1],
'C': [1, 2, 3]})
cols = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_preserve_categorical_dtype(self):
# GH13854
for ordered in [False, True]:
cat = pd.Categorical(list("xy"), categories=list("xyz"),
ordered=ordered)
result = get_dummies(cat)
data = np.array([[1, 0, 0], [0, 1, 0]], dtype=np.uint8)
cols = pd.CategoricalIndex(cat.categories,
categories=cat.categories,
ordered=ordered)
expected = DataFrame(data, columns=cols)
tm.assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_dict(self):
prefixes = {'A': 'from_A', 'B': 'from_B'}
df = DataFrame({'A': ['a', 'b', 'a'],
'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
expected = DataFrame({'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'from_B_b': [1, 1, 0],
'from_B_c': [0, 0, 1],
'C': [1, 2, 3]})
cols = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
expected[cols] = expected[cols].astype(np.uint8)
assert_frame_equal(result, expected)
def test_basic_types(self):
# GH 10531
s_list = list('abc')
s_series = Series(s_list)
s_df = DataFrame({
'a': [0, 1, 0, 1, 2],
'b': ['A', 'A', 'B', 'C', 'C'],
'c': [2, 3, 3, 3, 2]
})
expected = DataFrame({
'a': [1, 0, 0],
'b': [0, 1, 0],
'c': [0, 0, 1]
},
dtype='uint8',
columns=list('abc'))
if not self.sparse:
compare = tm.assert_frame_equal
else:
expected = expected.to_sparse(fill_value=0, kind='integer')
compare = tm.assert_sp_frame_equal
result = get_dummies(s_list, sparse=self.sparse)
compare(result, expected)
result = get_dummies(s_series, sparse=self.sparse)
compare(result, expected)
result = get_dummies(s_df, sparse=self.sparse, columns=s_df.columns)
tm.assert_series_equal(result.get_dtype_counts(), Series({'uint8': 8}))
result = get_dummies(s_df, sparse=self.sparse, columns=['a'])
expected = Series({'uint8': 3, 'int64': 1, 'object': 1}).sort_values()
tm.assert_series_equal(result.get_dtype_counts().sort_values(),
expected)
def test_basic_drop_first_NA(self):
# Test NA hadling together with drop_first
s_NA = ['a', 'b', np.nan]
res = get_dummies(s_NA, sparse=self.sparse, drop_first=True)
exp = DataFrame({'b': {0: 0,
1: 1,
2: 0}}, dtype=np.uint8)
assert_frame_equal(res, exp)
res_na = get_dummies(s_NA, dummy_na=True, sparse=self.sparse,
drop_first=True)
exp_na = DataFrame({'b': {0: 0,
1: 1,
2: 0},
nan: {0: 0,
1: 0,
2: 1}}, dtype=np.uint8).reindex_axis(
['b', nan], 1)
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse,
drop_first=True)
exp_just_na = DataFrame(index=np.arange(1))
assert_frame_equal(res_just_na, exp_just_na)
def test_unicode(self
): # See GH 6885 - get_dummies chokes on unicode values
import unicodedata
e = 'e'
eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
s = [e, eacute, eacute]
res = get_dummies(s, prefix='letter', sparse=self.sparse)
exp = DataFrame({'letter_e': {0: 1,
1: 0,
2: 0},
u('letter_%s') % eacute: {0: 0,
1: 1,
2: 1}},
dtype=np.uint8)
assert_frame_equal(res, exp)
def test_basic_drop_first_NA(self):
# Test NA hadling together with drop_first
s_NA = ['a', 'b', np.nan]
res = get_dummies(s_NA, sparse=self.sparse, drop_first=True)
exp = DataFrame({'b': {0: 0,
1: 1,
2: 0}}, dtype=np.uint8)
assert_frame_equal(res, exp)
res_na = get_dummies(s_NA, dummy_na=True, sparse=self.sparse,
drop_first=True)
exp_na = DataFrame({'b': {0: 0,
1: 1,
2: 0},
nan: {0: 0,
1: 0,
2: 1}}, dtype=np.uint8).reindex_axis(
['b', nan], 1)
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse,
drop_first=True)
exp_just_na = DataFrame(index=np.arange(1))
assert_frame_equal(res_just_na, exp_just_na)
def test_unicode(self
): # See GH 6885 - get_dummies chokes on unicode values
import unicodedata
e = 'e'
eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
s = [e, eacute, eacute]
res = get_dummies(s, prefix='letter', sparse=self.sparse)
exp = DataFrame({'letter_e': {0: 1,
1: 0,
2: 0},
u('letter_%s') % eacute: {0: 0,
1: 1,
2: 1}},
dtype=np.uint8)
assert_frame_equal(res, exp)
def make_dummy_columns(mdc_df, column_name, prefix='', append_columns=[]):
dummies = reshape.get_dummies(mdc_df[column_name], prefix=prefix)
arr_append(append_columns, dummies.columns)
mdc_df = mdc_df.join(dummies)
return mdc_df
def test_dataframe_dummies_prefix_bad_length(self):
with pytest.raises(ValueError):
get_dummies(self.df, prefix=['too few'], sparse=self.sparse)
def test_dataframe_dummies_drop_first(self):
df = self.df[['A', 'B']]
result = get_dummies(df, sparse=self.sparse, drop_first=True)
expected = DataFrame({'A_b': [0, 1, 0],
'B_c': [0, 0, 1]}, dtype=np.uint8)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_sep_bad_length(self):
with pytest.raises(ValueError):
get_dummies(self.df, prefix_sep=['bad'], sparse=self.sparse)
def test_dataframe_dummies_prefix_bad_length(self):
with tm.assertRaises(ValueError):
get_dummies(self.df, prefix=['too few'], sparse=self.sparse)
def test_dataframe_dummies_prefix_sep_bad_length(self):
with tm.assertRaises(ValueError):
get_dummies(self.df, prefix_sep=['bad'], sparse=self.sparse)
