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TST: better testing of Series.nlargest/nsmallest #15902

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56 changes: 45 additions & 11 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from pandas.types.common import (is_unsigned_integer_dtype,
is_signed_integer_dtype,
is_integer_dtype,
is_complex_dtype,
is_categorical_dtype,
is_extension_type,
is_datetimetz,
Expand Down Expand Up @@ -40,6 +41,44 @@
from pandas._libs.tslib import iNaT


# --------------- #
# dtype access #
# --------------- #

def _ensure_data_view(values):
"""
helper routine to ensure that our data is of the correct
input dtype for lower-level routines

Parameters
----------
values : array-like
"""

if needs_i8_conversion(values):
values = values.view(np.int64)
elif is_period_arraylike(values):
from pandas.tseries.period import PeriodIndex
values = PeriodIndex(values).asi8
elif is_categorical_dtype(values):
values = values.values.codes
elif isinstance(values, (ABCSeries, ABCIndex)):
values = values.values

if is_signed_integer_dtype(values):
values = _ensure_int64(values)
elif is_unsigned_integer_dtype(values):
values = _ensure_uint64(values)
elif is_complex_dtype(values):
values = _ensure_float64(values)
elif is_float_dtype(values):
values = _ensure_float64(values)
else:
values = _ensure_object(values)

return values


# --------------- #
# top-level algos #
# --------------- #
Expand Down Expand Up @@ -867,9 +906,7 @@ def nsmallest(arr, n, keep='first'):
narr = len(arr)
n = min(n, narr)

sdtype = str(arr.dtype)
arr = arr.view(_dtype_map.get(sdtype, sdtype))

arr = _ensure_data_view(arr)
kth_val = algos.kth_smallest(arr.copy(), n - 1)
return _finalize_nsmallest(arr, kth_val, n, keep, narr)

Expand All @@ -880,8 +917,7 @@ def nlargest(arr, n, keep='first'):

Note: Fails silently with NaN.
"""
sdtype = str(arr.dtype)
arr = arr.view(_dtype_map.get(sdtype, sdtype))
arr = _ensure_data_view(arr)
return nsmallest(-arr, n, keep=keep)


Expand Down Expand Up @@ -910,9 +946,10 @@ def select_n_series(series, n, keep, method):
nordered : Series
"""
dtype = series.dtype
if not issubclass(dtype.type, (np.integer, np.floating, np.datetime64,
np.timedelta64)):
raise TypeError("Cannot use method %r with dtype %s" % (method, dtype))
if not ((is_numeric_dtype(dtype) and not is_complex_dtype(dtype)) or
needs_i8_conversion(dtype)):
raise TypeError("Cannot use method '{method}' with "
"dtype {dtype}".format(method=method, dtype=dtype))

if keep not in ('first', 'last'):
raise ValueError('keep must be either "first", "last"')
Expand Down Expand Up @@ -964,9 +1001,6 @@ def _finalize_nsmallest(arr, kth_val, n, keep, narr):
return inds


_dtype_map = {'datetime64[ns]': 'int64', 'timedelta64[ns]': 'int64'}


# ------- #
# helpers #
# ------- #
Expand Down
180 changes: 106 additions & 74 deletions pandas/tests/series/test_analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1381,80 +1381,6 @@ def test_is_monotonic(self):
self.assertFalse(s.is_monotonic)
self.assertTrue(s.is_monotonic_decreasing)

def test_nsmallest_nlargest(self):
# float, int, datetime64 (use i8), timedelts64 (same),
# object that are numbers, object that are strings

base = [3, 2, 1, 2, 5]

s_list = [
Series(base, dtype='int8'),
Series(base, dtype='int16'),
Series(base, dtype='int32'),
Series(base, dtype='int64'),
Series(base, dtype='float32'),
Series(base, dtype='float64'),
Series(base, dtype='uint8'),
Series(base, dtype='uint16'),
Series(base, dtype='uint32'),
Series(base, dtype='uint64'),
Series(base).astype('timedelta64[ns]'),
Series(pd.to_datetime(['2003', '2002', '2001', '2002', '2005'])),
]

raising = [
Series([3., 2, 1, 2, '5'], dtype='object'),
Series([3., 2, 1, 2, 5], dtype='object'),
# not supported on some archs
# Series([3., 2, 1, 2, 5], dtype='complex256'),
Series([3., 2, 1, 2, 5], dtype='complex128'),
]

for r in raising:
dt = r.dtype
msg = "Cannot use method 'n(larg|small)est' with dtype %s" % dt
args = 2, len(r), 0, -1
methods = r.nlargest, r.nsmallest
for method, arg in product(methods, args):
with tm.assertRaisesRegexp(TypeError, msg):
method(arg)

for s in s_list:

assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]])
assert_series_equal(s.nsmallest(2, keep='last'), s.iloc[[2, 3]])

empty = s.iloc[0:0]
assert_series_equal(s.nsmallest(0), empty)
assert_series_equal(s.nsmallest(-1), empty)
assert_series_equal(s.nlargest(0), empty)
assert_series_equal(s.nlargest(-1), empty)

assert_series_equal(s.nsmallest(len(s)), s.sort_values())
assert_series_equal(s.nsmallest(len(s) + 1), s.sort_values())
assert_series_equal(s.nlargest(len(s)), s.iloc[[4, 0, 1, 3, 2]])
assert_series_equal(s.nlargest(len(s) + 1),
s.iloc[[4, 0, 1, 3, 2]])

s = Series([3., np.nan, 1, 2, 5])
assert_series_equal(s.nlargest(), s.iloc[[4, 0, 3, 2]])
assert_series_equal(s.nsmallest(), s.iloc[[2, 3, 0, 4]])

msg = 'keep must be either "first", "last"'
with tm.assertRaisesRegexp(ValueError, msg):
s.nsmallest(keep='invalid')
with tm.assertRaisesRegexp(ValueError, msg):
s.nlargest(keep='invalid')

# GH 13412
s = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
result = s.nlargest(3)
expected = s.sort_values(ascending=False).head(3)
assert_series_equal(result, expected)
result = s.nsmallest(3)
expected = s.sort_values().head(3)
assert_series_equal(result, expected)

def test_sort_index_level(self):
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
s = Series([1, 2], mi)
Expand Down Expand Up @@ -1729,3 +1655,109 @@ def test_value_counts_categorical_not_ordered(self):
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)


@pytest.fixture
def s_main_dtypes():
df = pd.DataFrame(
{'datetime': pd.to_datetime(['2003', '2002',
'2001', '2002',
'2005']),
'datetimetz': pd.to_datetime(
['2003', '2002',
'2001', '2002',
'2005']).tz_localize('US/Eastern'),
'timedelta': pd.to_timedelta(['3d', '2d', '1d',
'2d', '5d'])})

for dtype in ['int8', 'int16', 'int32', 'int64',
'float32', 'float64',
'uint8', 'uint16', 'uint32', 'uint64']:
df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)

return df


class TestNLargestNSmallest(object):

@pytest.mark.parametrize(
"r", [Series([3., 2, 1, 2, '5'], dtype='object'),
Series([3., 2, 1, 2, 5], dtype='object'),
# not supported on some archs
# Series([3., 2, 1, 2, 5], dtype='complex256'),
Series([3., 2, 1, 2, 5], dtype='complex128'),
Series(list('abcde'), dtype='category'),
Series(list('abcde'))])
def test_error(self, r):
dt = r.dtype
msg = ("Cannot use method 'n(larg|small)est' with "
"dtype {dt}".format(dt=dt))
args = 2, len(r), 0, -1
methods = r.nlargest, r.nsmallest
for method, arg in product(methods, args):
with tm.assertRaisesRegexp(TypeError, msg):
method(arg)

@pytest.mark.parametrize(
"s",
[v for k, v in s_main_dtypes().iteritems()])
def test_nsmallest_nlargest(self, s):
# float, int, datetime64 (use i8), timedelts64 (same),
# object that are numbers, object that are strings

assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]])
assert_series_equal(s.nsmallest(2, keep='last'), s.iloc[[2, 3]])

empty = s.iloc[0:0]
assert_series_equal(s.nsmallest(0), empty)
assert_series_equal(s.nsmallest(-1), empty)
assert_series_equal(s.nlargest(0), empty)
assert_series_equal(s.nlargest(-1), empty)

assert_series_equal(s.nsmallest(len(s)), s.sort_values())
assert_series_equal(s.nsmallest(len(s) + 1), s.sort_values())
assert_series_equal(s.nlargest(len(s)), s.iloc[[4, 0, 1, 3, 2]])
assert_series_equal(s.nlargest(len(s) + 1),
s.iloc[[4, 0, 1, 3, 2]])

def test_misc(self):

s = Series([3., np.nan, 1, 2, 5])
assert_series_equal(s.nlargest(), s.iloc[[4, 0, 3, 2]])
assert_series_equal(s.nsmallest(), s.iloc[[2, 3, 0, 4]])

msg = 'keep must be either "first", "last"'
with tm.assertRaisesRegexp(ValueError, msg):
s.nsmallest(keep='invalid')
with tm.assertRaisesRegexp(ValueError, msg):
s.nlargest(keep='invalid')

# GH 15297
s = Series([1] * 5, index=[1, 2, 3, 4, 5])
expected_first = Series([1] * 3, index=[1, 2, 3])
expected_last = Series([1] * 3, index=[5, 4, 3])

result = s.nsmallest(3)
assert_series_equal(result, expected_first)

result = s.nsmallest(3, keep='last')
assert_series_equal(result, expected_last)

result = s.nlargest(3)
assert_series_equal(result, expected_first)

result = s.nlargest(3, keep='last')
assert_series_equal(result, expected_last)

@pytest.mark.parametrize('n', range(1, 5))
def test_n(self, n):

# GH 13412
s = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
result = s.nlargest(n)
expected = s.sort_values(ascending=False).head(n)
assert_series_equal(result, expected)

result = s.nsmallest(n)
expected = s.sort_values().head(n)
assert_series_equal(result, expected)