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ENH: Allow get_dummies to return SparseDataFrame #8823

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ENH: Allow get_dummies to return SparseDataFrame #8823

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.16.1.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,7 @@ Enhancements
df.drop(['A', 'X'], axis=1, errors='ignore')

- Allow conversion of values with dtype ``datetime64`` or ``timedelta64`` to strings using ``astype(str)`` (:issue:`9757`)
- ``get_dummies`` function now accepts ``sparse`` keyword. If set to ``True``, the return DataFrame is sparse. (:issue:`8823`)

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put backticks around DataFrame.
say "the return DataFrame is sparse (e.g. SparseDataFrame)"

.. _whatsnew_0161.api:

Expand Down
61 changes: 45 additions & 16 deletions pandas/core/reshape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,10 @@
from pandas.core.series import Series
from pandas.core.frame import DataFrame

from pandas.core.sparse import SparseDataFrame, SparseSeries
from pandas.sparse.array import SparseArray
from pandas._sparse import IntIndex

from pandas.core.categorical import Categorical
from pandas.core.common import (notnull, _ensure_platform_int, _maybe_promote,
isnull)
Expand Down Expand Up @@ -932,7 +936,7 @@ def melt_stub(df, stub, i, j):
return newdf.set_index([i, j])

def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
columns=None):
columns=None, sparse=False):
"""
Convert categorical variable into dummy/indicator variables

Expand All @@ -953,6 +957,8 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
Column names in the DataFrame to be encoded.
If `columns` is None then all the columns with
`object` or `category` dtype will be converted.
sparse : bool, default False
Whether the returned DataFrame should be sparse or not.

Returns
-------
Expand Down Expand Up @@ -1039,16 +1045,17 @@ def check_len(item, name):
with_dummies = [result]
for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):

dummy = _get_dummies_1d(data[col], prefix=pre,
prefix_sep=sep, dummy_na=dummy_na)
dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,
dummy_na=dummy_na, sparse=sparse)
with_dummies.append(dummy)
result = concat(with_dummies, axis=1)
else:
result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na)
result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
sparse=sparse)
return result


def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False, sparse=False):
# Series avoids inconsistent NaN handling
cat = Categorical.from_array(Series(data), ordered=True)
levels = cat.categories
Expand All @@ -1059,19 +1066,17 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
index = data.index
else:
index = np.arange(len(data))
return DataFrame(index=index)

number_of_cols = len(levels)
if dummy_na:
number_of_cols += 1

dummy_mat = np.eye(number_of_cols).take(cat.codes, axis=0)
if not sparse:
return DataFrame(index=index)
else:
return SparseDataFrame(index=index)

codes = cat.codes.copy()
if dummy_na:
codes[codes == -1] = len(cat.categories)
levels = np.append(cat.categories, np.nan)
else:
# reset NaN GH4446
dummy_mat[cat.codes == -1] = 0

number_of_cols = len(levels)

if prefix is not None:
dummy_cols = ['%s%s%s' % (prefix, prefix_sep, v)
Expand All @@ -1084,7 +1089,31 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False):
else:
index = None

return DataFrame(dummy_mat, index=index, columns=dummy_cols)
if sparse:
sparse_series = {}
N = len(data)
sp_indices = [ [] for _ in range(len(dummy_cols)) ]
for ndx, code in enumerate(codes):
if code == -1:
# Blank entries if not dummy_na and code == -1, #GH4446
continue
sp_indices[code].append(ndx)

for col, ixs in zip(dummy_cols, sp_indices):
sarr = SparseArray(np.ones(len(ixs)), sparse_index=IntIndex(N, ixs),
fill_value=0)
sparse_series[col] = SparseSeries(data=sarr, index=index)

return SparseDataFrame(sparse_series, index=index, columns=dummy_cols)

else:
dummy_mat = np.eye(number_of_cols).take(codes, axis=0)

if not dummy_na:
# reset NaN GH4446
dummy_mat[codes == -1] = 0

return DataFrame(dummy_mat, index=index, columns=dummy_cols)


def make_axis_dummies(frame, axis='minor', transform=None):
Expand Down
58 changes: 33 additions & 25 deletions pandas/tests/test_reshape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,6 +151,8 @@ def test_multiindex(self):

class TestGetDummies(tm.TestCase):

sparse = False

def setUp(self):
self.df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
Expand All @@ -163,20 +165,20 @@ def test_basic(self):
expected = DataFrame({'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0},
'c': {0: 0.0, 1: 0.0, 2: 1.0}})
assert_frame_equal(get_dummies(s_list), expected)
assert_frame_equal(get_dummies(s_series), expected)
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), expected)
assert_frame_equal(get_dummies(s_series_index, sparse=self.sparse), 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)
res_series = get_dummies(just_na_series)
res_series_index = get_dummies(just_na_series_index)
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)
Expand All @@ -188,20 +190,21 @@ def test_just_na(self):

def test_include_na(self):
s = ['a', 'b', np.nan]
res = get_dummies(s)
res = get_dummies(s, sparse=self.sparse)
exp = DataFrame({'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0}})
assert_frame_equal(res, exp)

res_na = get_dummies(s, dummy_na=True)
# 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: 0.0, 1: 0.0, 2: 1.0},
'a': {0: 1.0, 1: 0.0, 2: 0.0},
'b': {0: 0.0, 1: 1.0, 2: 0.0}}).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)
res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse)
exp_just_na = DataFrame(Series(1.0,index=[0]),columns=[nan])
assert_array_equal(res_just_na.values, exp_just_na.values)

Expand All @@ -210,21 +213,21 @@ def test_unicode(self): # See GH 6885 - get_dummies chokes on unicode values
e = 'e'
eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
s = [e, eacute, eacute]
res = get_dummies(s, prefix='letter')
res = get_dummies(s, prefix='letter', sparse=self.sparse)
exp = DataFrame({'letter_e': {0: 1.0, 1: 0.0, 2: 0.0},
u('letter_%s') % eacute: {0: 0.0, 1: 1.0, 2: 1.0}})
assert_frame_equal(res, exp)

def test_dataframe_dummies_all_obj(self):
df = self.df[['A', 'B']]
result = get_dummies(df)
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]})
assert_frame_equal(result, expected)

def test_dataframe_dummies_mix_default(self):
df = self.df
result = get_dummies(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]})
Expand All @@ -235,18 +238,18 @@ def test_dataframe_dummies_prefix_list(self):
prefixes = ['from_A', 'from_B']
df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
result = get_dummies(df, prefix=prefixes)
result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3], 'from_A_a': [1., 0, 1],
'from_A_b': [0., 1, 0], 'from_B_b': [1., 1, 0],
'from_B_c': [0., 0, 1]})
expected = expected[['C', 'from_A_a', 'from_A_b', 'from_B_b',
'from_B_c']]
assert_frame_equal(result, expected)

def test_datafrmae_dummies_prefix_str(self):
def test_dataframe_dummies_prefix_str(self):
# not that you should do this...
df = self.df
result = get_dummies(df, prefix='bad')
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.]],
Expand All @@ -256,40 +259,40 @@ def test_datafrmae_dummies_prefix_str(self):
def test_dataframe_dummies_subset(self):
df = self.df
result = get_dummies(df, prefix=['from_A'],
columns=['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]})
assert_frame_equal(result, expected)

def test_dataframe_dummies_prefix_sep(self):
df = self.df
result = get_dummies(df, prefix_sep='..')
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']]
assert_frame_equal(result, expected)

result = get_dummies(df, prefix_sep=['..', '__'])
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': '__'})
result = get_dummies(df, prefix_sep={'A': '..', 'B': '__'}, sparse=self.sparse)
assert_frame_equal(result, expected)

def test_dataframe_dummies_prefix_bad_length(self):
with tm.assertRaises(ValueError):
get_dummies(self.df, prefix=['too few'])
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'])
get_dummies(self.df, prefix_sep=['bad'], sparse=self.sparse)

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)
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]})
Expand All @@ -298,22 +301,22 @@ def test_dataframe_dummies_prefix_dict(self):
def test_dataframe_dummies_with_na(self):
df = self.df
df.loc[3, :] = [np.nan, np.nan, np.nan]
result = get_dummies(df, dummy_na=True)
result = get_dummies(df, dummy_na=True, sparse=self.sparse)
expected = DataFrame({'C': [1, 2, 3, np.nan], 'A_a': [1., 0, 1, 0],
'A_b': [0., 1, 0, 0], 'A_nan': [0., 0, 0, 1], 'B_b': [1., 1, 0, 0],
'B_c': [0., 0, 1, 0], 'B_nan': [0., 0, 0, 1]})
expected = expected[['C', 'A_a', 'A_b', 'A_nan', 'B_b', 'B_c',
'B_nan']]
assert_frame_equal(result, expected)

result = get_dummies(df, dummy_na=False)
result = get_dummies(df, dummy_na=False, sparse=self.sparse)
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)

def test_dataframe_dummies_with_categorical(self):
df = self.df
df['cat'] = pd.Categorical(['x', 'y', 'y'])
result = get_dummies(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], 'cat_x': [1., 0, 0],
Expand All @@ -322,6 +325,11 @@ def test_dataframe_dummies_with_categorical(self):
'cat_x', 'cat_y']]
assert_frame_equal(result, expected)


class TestGetDummiesSparse(TestGetDummies):
sparse = True


class TestLreshape(tm.TestCase):

def test_pairs(self):
Expand Down