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ENH - Modify Dataframe.select_dtypes to accept scalar values #16860

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Jul 10, 2017
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ENH - Modify Dataframe.select_dtypes to accept scalar values #16860

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ENH - Modify Dataframe.select_dtypes to accept scalar values
Jul 8, 2017
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Jul 8, 2017
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4 changes: 0 additions & 4 deletions doc/source/basics.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -2229,7 +2229,3 @@ All numpy dtypes are subclasses of ``numpy.generic``:

Pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
numpy hierarchy and wont show up with the above function.

.. note::

The ``include`` and ``exclude`` parameters must be non-string sequences.
2 changes: 1 addition & 1 deletion doc/source/style.ipynb
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Expand Up @@ -935,7 +935,7 @@
"\n",
"<span style=\"color: red\">*Experimental: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>\n",
"\n",
"Some support is available for exporting styled `DataFrames` to Excel worksheets using the `OpenPyXL` engine. CSS2.2 properties handled include:\n",
"Some support is available for exporting styled `DataFrames` to Excel worksheets using the `OpenPyXL` engine. CSS2.2 properties handled include:\n",
"\n",
"- `background-color`\n",
"- `border-style`, `border-width`, `border-color` and their {`top`, `right`, `bottom`, `left` variants}\n",
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.21.0.txt
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Expand Up @@ -39,6 +39,7 @@ Other Enhancements
- :func:`read_feather` has gained the ``nthreads`` parameter for multi-threaded operations (:issue:`16359`)
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a reason you changed style.ipynb?

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It got changed automatically by something as part of trying to build docs. I found it actually got deleted at some point and so reverted it from previous commits but the space was different for some reason.

- :func:`DataFrame.clip()` and :func:`Series.clip()` have gained an ``inplace`` argument. (:issue:`15388`)
- :func:`crosstab` has gained a ``margins_name`` parameter to define the name of the row / column that will contain the totals when ``margins=True``. (:issue:`15972`)
- :func:`Dataframe.select_dtypes` now accepts scalar values for include/exclude as well as list-like. (:issue:`16855`)

.. _whatsnew_0210.api_breaking:

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26 changes: 17 additions & 9 deletions pandas/core/frame.py
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Expand Up @@ -2285,18 +2285,16 @@ def select_dtypes(self, include=None, exclude=None):

Parameters
----------
include, exclude : list-like
A list of dtypes or strings to be included/excluded. You must pass
in a non-empty sequence for at least one of these.
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.

Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
TypeError
* If either of ``include`` or ``exclude`` is not a sequence

Returns
-------
Expand Down Expand Up @@ -2331,6 +2329,14 @@ def select_dtypes(self, include=None, exclude=None):
3 0.0764 False 2
4 -0.9703 True 1
5 -1.2094 False 2
>>> df.select_dtypes(include='bool')
c
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1
Expand All @@ -2348,10 +2354,12 @@ def select_dtypes(self, include=None, exclude=None):
4 True
5 False
"""
include, exclude = include or (), exclude or ()
if not (is_list_like(include) and is_list_like(exclude)):
raise TypeError('include and exclude must both be non-string'
' sequences')

if not is_list_like(include):
include = (include,) if include is not None else ()
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()

selection = tuple(map(frozenset, (include, exclude)))

if not any(selection):
Expand Down
130 changes: 111 additions & 19 deletions pandas/tests/frame/test_dtypes.py
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Expand Up @@ -104,7 +104,7 @@ def test_dtypes_are_correct_after_column_slice(self):
('b', np.float_),
('c', np.float_)])))

def test_select_dtypes_include(self):
def test_select_dtypes_include_using_list_like(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
Expand Down Expand Up @@ -145,14 +145,10 @@ def test_select_dtypes_include(self):
ei = df[['h', 'i']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(include=['timedelta'])
ei = df[['k']]
assert_frame_equal(ri, ei)

pytest.raises(NotImplementedError,
lambda: df.select_dtypes(include=['period']))

def test_select_dtypes_exclude(self):
def test_select_dtypes_exclude_using_list_like(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
Expand All @@ -162,7 +158,7 @@ def test_select_dtypes_exclude(self):
ee = df[['a', 'e']]
assert_frame_equal(re, ee)

def test_select_dtypes_exclude_include(self):
def test_select_dtypes_exclude_include_using_list_like(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
Expand All @@ -181,6 +177,114 @@ def test_select_dtypes_exclude_include(self):
e = df[['b', 'e']]
assert_frame_equal(r, e)

def test_select_dtypes_include_using_scalars(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})

ri = df.select_dtypes(include=np.number)
ei = df[['b', 'c', 'd', 'k']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(include='datetime')
ei = df[['g']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(include='datetime64')
ei = df[['g']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(include='category')
ei = df[['f']]
assert_frame_equal(ri, ei)

pytest.raises(NotImplementedError,
lambda: df.select_dtypes(include='period'))

def test_select_dtypes_exclude_using_scalars(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})

ri = df.select_dtypes(exclude=np.number)
ei = df[['a', 'e', 'f', 'g', 'h', 'i', 'j']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(exclude='category')
ei = df[['a', 'b', 'c', 'd', 'e', 'g', 'h', 'i', 'j', 'k']]
assert_frame_equal(ri, ei)

pytest.raises(NotImplementedError,
lambda: df.select_dtypes(exclude='period'))

def test_select_dtypes_include_exclude_using_scalars(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})

ri = df.select_dtypes(include=np.number, exclude='floating')
ei = df[['b', 'c', 'k']]
assert_frame_equal(ri, ei)

def test_select_dtypes_include_exclude_mixed_scalars_lists(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})

ri = df.select_dtypes(include=np.number,
exclude=['floating', 'timedelta'])
ei = df[['b', 'c']]
assert_frame_equal(ri, ei)

ri = df.select_dtypes(include=[np.number, 'category'],
exclude='floating')
ei = df[['b', 'c', 'f', 'k']]
assert_frame_equal(ri, ei)

def test_select_dtypes_not_an_attr_but_still_valid_dtype(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
Expand All @@ -205,18 +309,6 @@ def test_select_dtypes_empty(self):
'must be nonempty'):
df.select_dtypes()

def test_select_dtypes_raises_on_string(self):
df = DataFrame({'a': list('abc'), 'b': list(range(1, 4))})
with tm.assert_raises_regex(TypeError, 'include and exclude '
'.+ non-'):
df.select_dtypes(include='object')
with tm.assert_raises_regex(TypeError, 'include and exclude '
'.+ non-'):
df.select_dtypes(exclude='object')
with tm.assert_raises_regex(TypeError, 'include and exclude '
'.+ non-'):
df.select_dtypes(include=int, exclude='object')

def test_select_dtypes_bad_datetime64(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
Expand Down