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BUG: get_dummies not returning SparseDataFrame #10535

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BUG: get_dummies not returning SparseDataFrame #10535

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12 changes: 4 additions & 8 deletions doc/source/whatsnew/v0.17.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -377,16 +377,12 @@ Bug Fixes
- Bug in ``Series.plot(kind='hist')`` Y Label not informative (:issue:`10485`)










- Bug in operator equal on Index not being consistent with Series (:issue:`9947`)

- Reading "famafrench" data via ``DataReader`` results in HTTP 404 error because of the website url is changed (:issue:`10591`).

- Bug in `read_msgpack` where DataFrame to decode has duplicate column names (:issue:`9618`)


- Bug in `get_dummies` with `sparse=True` not returning SparseDataFrame (:issue:`10531`)

13 changes: 9 additions & 4 deletions pandas/core/reshape.py
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Expand Up @@ -957,13 +957,15 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
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.
Whether the dummy columns should be sparse or not. Returns
SparseDataFrame if `data` is a Series or if all columns are included.
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this is confusing, I think we should just always return a SparseDataFrame.

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What about the case when some of the blocks are dense ? e.g.

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'a':['A','B','C'],'b':['D','E','D']})

In [3]: pd.get_dummies(df, sparse=True, columns='b')
Out[3]: 
   a  b_D  b_E
0  A    1    0
1  B    0    1
2  C    1    0

Here a is still dense. Is a df where some blocks are dense and some sparse more accurately called a DataFrame or a SparseDataFrame?

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@jreback ?

Otherwise returns a DataFrame with some SparseBlocks.

.. versionadded:: 0.16.1

Returns
-------
dummies : DataFrame
dummies : DataFrame or SparseDataFrame

Examples
--------
Expand Down Expand Up @@ -1042,8 +1044,11 @@ def check_len(item, name):
elif isinstance(prefix_sep, dict):
prefix_sep = [prefix_sep[col] for col in columns_to_encode]

result = data.drop(columns_to_encode, axis=1)
with_dummies = [result]
if set(columns_to_encode) == set(data.columns):
with_dummies = []
else:
with_dummies = [data.drop(columns_to_encode, axis=1)]

for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):

dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,
Expand Down
28 changes: 28 additions & 0 deletions pandas/tests/test_reshape.py
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Expand Up @@ -8,6 +8,7 @@
import nose

from pandas import DataFrame, Series
from pandas.core.sparse import SparseDataFrame
import pandas as pd

from numpy import nan
Expand Down Expand Up @@ -171,6 +172,33 @@ def test_basic(self):
expected.index = list('ABC')
assert_frame_equal(get_dummies(s_series_index, sparse=self.sparse), 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]})

if not self.sparse:
exp_df_type = DataFrame
exp_blk_type = pd.core.internals.FloatBlock
else:
exp_df_type = SparseDataFrame
exp_blk_type = pd.core.internals.SparseBlock

self.assertEqual(type(get_dummies(s_list, sparse=self.sparse)), exp_df_type)
self.assertEqual(type(get_dummies(s_series, sparse=self.sparse)), exp_df_type)

r = get_dummies(s_df, sparse=self.sparse, columns=s_df.columns)
self.assertEqual(type(r), exp_df_type)

r = get_dummies(s_df, sparse=self.sparse, columns=['a'])
self.assertEqual(type(r[['a_0']]._data.blocks[0]), exp_blk_type)
self.assertEqual(type(r[['a_1']]._data.blocks[0]), exp_blk_type)
self.assertEqual(type(r[['a_2']]._data.blocks[0]), exp_blk_type)

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@jreback How about now?

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ok looks reasonable

ping when green

FYI - don't take lines out of the what's new I put them there on purpose - helps avoid merge conflicts (in fact u have one now because of that)

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@jreback OK , green now.
Sorry about the whatsnew newlines (though I don't think there's a merge conflict?) So, where is a good place to insert whatsnew notes? Somewhere between two newlines?


def test_just_na(self):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
Expand Down