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Merged
merged 1 commit into from
Oct 13, 2013
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TST: Groupby filter tests involved len, closing #4447 #5096

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20 changes: 16 additions & 4 deletions pandas/core/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from pandas.util.decorators import cache_readonly, Appender
import pandas.core.algorithms as algos
import pandas.core.common as com
from pandas.core.common import _possibly_downcast_to_dtype, notnull
from pandas.core.common import _possibly_downcast_to_dtype, isnull, notnull

import pandas.lib as lib
import pandas.algos as _algos
Expand Down Expand Up @@ -1605,8 +1605,19 @@ def filter(self, func, dropna=True, *args, **kwargs):
else:
wrapper = lambda x: func(x, *args, **kwargs)

indexers = [self.obj.index.get_indexer(group.index) \
if wrapper(group) else [] for _ , group in self]
# Interpret np.nan as False.
def true_and_notnull(x, *args, **kwargs):
b = wrapper(x, *args, **kwargs)
return b and notnull(b)
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should this and be &? can b be an array/frame/series?

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I think it has to be and in order to handle NaN, which we want to interpret as False. Thoughts, @cpcloud?

In [59]: np.nan & pd.notnull(np.nan)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-59-47e0df96d143> in <module>()
----> 1 np.nan & pd.notnull(np.nan)

TypeError: unsupported operand type(s) for &: 'float' and 'bool'

In [60]: np.nan and pd.notnull(np.nan)
Out[60]: False


try:
indexers = [self.obj.index.get_indexer(group.index) \
if true_and_notnull(group) else [] \
for _ , group in self]
except ValueError:
raise TypeError("the filter must return a boolean result")
except TypeError:
raise TypeError("the filter must return a boolean result")

if len(indexers) == 0:
filtered = self.obj.take([]) # because np.concatenate would fail
Expand Down Expand Up @@ -2124,7 +2135,8 @@ def add_indexer():
add_indexer()
else:
if getattr(res,'ndim',None) == 1:
if res.ravel()[0]:
val = res.ravel()[0]
if val and notnull(val):
add_indexer()
else:

Expand Down
53 changes: 52 additions & 1 deletion pandas/tests/test_groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2642,9 +2642,37 @@ def raise_if_sum_is_zero(x):
s = pd.Series([-1,0,1,2])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
self.assertRaises(ValueError,
self.assertRaises(TypeError,
lambda: grouped.filter(raise_if_sum_is_zero))

def test_filter_bad_shapes(self):
df = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)})
s = df['B']
g_df = df.groupby('B')
g_s = s.groupby(s)

f = lambda x: x
self.assertRaises(TypeError, lambda: g_df.filter(f))
self.assertRaises(TypeError, lambda: g_s.filter(f))

f = lambda x: x == 1
self.assertRaises(TypeError, lambda: g_df.filter(f))
self.assertRaises(TypeError, lambda: g_s.filter(f))

f = lambda x: np.outer(x, x)
self.assertRaises(TypeError, lambda: g_df.filter(f))
self.assertRaises(TypeError, lambda: g_s.filter(f))

def test_filter_nan_is_false(self):
df = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)})
s = df['B']
g_df = df.groupby(df['B'])
g_s = s.groupby(s)

f = lambda x: np.nan
assert_frame_equal(g_df.filter(f), df.loc[[]])
assert_series_equal(g_s.filter(f), s[[]])

def test_filter_against_workaround(self):
np.random.seed(0)
# Series of ints
Expand Down Expand Up @@ -2697,6 +2725,29 @@ def test_filter_against_workaround(self):
new_way = grouped.filter(lambda x: x['ints'].mean() > N/20)
assert_frame_equal(new_way.sort_index(), old_way.sort_index())

def test_filter_using_len(self):
# BUG GH4447
df = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)})
grouped = df.groupby('B')
actual = grouped.filter(lambda x: len(x) > 2)
expected = DataFrame({'A': np.arange(2, 6), 'B': list('bbbb'), 'C': np.arange(2, 6)}, index=np.arange(2, 6))
assert_frame_equal(actual, expected)

actual = grouped.filter(lambda x: len(x) > 4)
expected = df.ix[[]]
assert_frame_equal(actual, expected)

# Series have always worked properly, but we'll test anyway.
s = df['B']
grouped = s.groupby(s)
actual = grouped.filter(lambda x: len(x) > 2)
expected = Series(4*['b'], index=np.arange(2, 6))
assert_series_equal(actual, expected)

actual = grouped.filter(lambda x: len(x) > 4)
expected = s[[]]
assert_series_equal(actual, expected)

def test_groupby_whitelist(self):
from string import ascii_lowercase
letters = np.array(list(ascii_lowercase))
Expand Down