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BUG: Series.asof fails for all NaN Series (GH15713) #15758

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BUG: Series.asof fails for all NaN Series (GH15713) #15758

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676a4e5
Test
Mar 20, 2017
c78d687
BUG #15713 Series.asof return nan when series is all nans
Mar 21, 2017
4e26ab8
BUG #15713 Series.asof return nan when series is all nans.
Mar 21, 2017
17d1d77
BUG #15713 Series.asof return nan when series is all nans!
Mar 21, 2017
89fb6cf
BUG #15713 fixing failing tests
Mar 21, 2017
6b745af
Adding DataFrame tests & support, and optimizing the code
Mar 21, 2017
70c958f
Added tests for non-default indexes, scalar and multiple inputs, and …
Mar 23, 2017
3f9c7fd
Minor comments
Mar 23, 2017
04b7306
Removing .values and formating code PEP8
Mar 24, 2017
a080b9b
Making scalar input return in a Series
Mar 25, 2017
7448b96
Fixing scalar input
Mar 25, 2017
b8f078a
Small code standard change
Mar 25, 2017
af9a29b
Setting name of asof result when scalar input and all nan
Mar 25, 2017
bb63964
Propagating Series name
Mar 25, 2017
0765108
First simplification, code-block in the same place
Mar 25, 2017
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2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -930,3 +930,5 @@ Bug Fixes
- Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`)
- Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`)
- Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`)

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FYI in the future, if you put the whatnew notes in a blank space in Bug Fixes (these are on purpose), you wont' get merge conflicts

- Bug in ``Series.asof`` which raised an error if the series contained all ``nans`` (:issue:`15713`)
6 changes: 6 additions & 0 deletions pandas/core/generic.py
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Expand Up @@ -3972,6 +3972,12 @@ def asof(self, where, subset=None):
where = Index(where) if is_list else Index([where])

nulls = self.isnull() if is_series else self[subset].isnull().any(1)
if nulls.values.all():
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I don't think the .values is still needed.

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done

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The values is still here?

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Hi @jorisvandenbossche ... I removed then put it back because I thought it generated a backward compatibility error. Currently the build breaks for Python 2.7.9. Now I saw it has nothing to do with it in Travis CI log: it's a "ci/lint.sh" exiting 1.
I will remove it again and see where the code is unformatted. Thanks

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Removed the .values

if is_series:
return pd.Series(np.nan, index=where)
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this is not correct; should have name=self.name

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I thought about that, @jreback , but when I experimented with a non-null series, I saw that it has no name. I.e.:

result = Series(np.random.randn(4), index=[1, 2, 3, 4]).asof([4, 5])
print result

returns

4   -0.558532
5   -0.558532
dtype: float64
......

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and that not correct. we always want to propogate the names.

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ok, let me write the test case and fix for nan and non-nan inputs

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@jreback done here.. working on the request below, on simplifying the code

else:
return pd.DataFrame(np.nan, index=where, columns=self.columns)

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see if you can simplify this logic a bit (maybe set the name where is_list is used before)

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hey @jreback , I made a small simplification, pls check if that's ok... if it's ok, now I think everything is good to go

locs = self.index.asof_locs(where, ~(nulls.values))

# mask the missing
Expand Down
40 changes: 32 additions & 8 deletions pandas/tests/frame/test_asof.py
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Expand Up @@ -4,7 +4,6 @@
from pandas import (DataFrame, date_range, Timestamp, Series,
to_datetime)

from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas.util.testing as tm

from .common import TestData
Expand All @@ -14,9 +13,9 @@ class TestFrameAsof(TestData, tm.TestCase):

def setUp(self):
self.N = N = 50
rng = date_range('1/1/1990', periods=N, freq='53s')
self.rng = date_range('1/1/1990', periods=N, freq='53s')
self.df = DataFrame({'A': np.arange(N), 'B': np.arange(N)},
index=rng)
index=self.rng)

def test_basic(self):

Expand Down Expand Up @@ -51,19 +50,19 @@ def test_subset(self):
# with a subset of A should be the same
result = df.asof(dates, subset='A')
expected = df.asof(dates)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# same with A/B
result = df.asof(dates, subset=['A', 'B'])
expected = df.asof(dates)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# B gives self.df.asof
result = df.asof(dates, subset='B')
expected = df.resample('25s', closed='right').ffill().reindex(dates)
expected.iloc[20:] = 9

assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

def test_missing(self):
# GH 15118
Expand All @@ -75,9 +74,34 @@ def test_missing(self):
result = df.asof('1989-12-31')

expected = Series(index=['A', 'B'], name=Timestamp('1989-12-31'))
assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)

result = df.asof(to_datetime(['1989-12-31']))
expected = DataFrame(index=to_datetime(['1989-12-31']),
columns=['A', 'B'], dtype='float64')
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

def test_all_nans(self):
# GH 15713
# DataFrame is all nans
result = DataFrame([np.nan]).asof([0])
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try these with non-defualt indexes and see what happens (your test will break)

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Indeed, and also, when you have a DataFrame with multiple columns, those columns should be preserved in the result

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done

expected = DataFrame([np.nan])
tm.assert_frame_equal(result, expected)

# testing non-default indexes, multiple inputs
dates = date_range('1/1/1990', periods=self.N * 3, freq='25s')
result = DataFrame(np.nan, index=self.rng, columns=['A']).asof(dates)
expected = DataFrame(np.nan, index=dates, columns=['A'])
tm.assert_frame_equal(result, expected)

# testing multiple columns
dates = date_range('1/1/1990', periods=self.N * 3, freq='25s')
result = DataFrame(np.nan, index=self.rng, columns=['A', 'B', 'C']).asof(dates)
expected = DataFrame(np.nan, index=dates, columns=['A', 'B', 'C'])
tm.assert_frame_equal(result, expected)

# testing scalar input
date = date_range('1/1/1990', periods=self.N * 3, freq='25s')[0]
result = DataFrame(np.nan, index=self.rng, columns=['A']).asof(date)
expected = DataFrame(np.nan, index=[date], columns=['A'])
tm.assert_frame_equal(result, expected)
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I think a scalar input should result in a Series. That is at least the current behaviour for the working non-NaN case:

In [37]: df = pd.DataFrame(np.random.randn(2,2), index=[1,2], columns=['A', 'B'])

In [38]: df
Out[38]: 
          A         B
1 -0.643872  1.375342
2 -0.223192  0.231439

In [39]: df.asof([3])
Out[39]: 
          A         B
3 -0.223192  0.231439

In [40]: df.asof(3)
Out[40]: 
A   -0.223192
B    0.231439
Name: 3, dtype: float64

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done

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What you added is not the correct result I think. It are the original columns that are the index of the resulting series, not [where]. The where does become the name of the Series (i.e. as if you access a row from the dataframe)

Can you add the example above (but then with NaNs instead of the random data) as a test case? The it is really clear what the expected behaviour is.

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Done, added the tests

21 changes: 21 additions & 0 deletions pandas/tests/series/test_asof.py
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Expand Up @@ -148,3 +148,24 @@ def test_errors(self):
s = Series(np.random.randn(N), index=rng)
with self.assertRaises(ValueError):
s.asof(s.index[0], subset='foo')

def test_all_nans(self):
# GH 15713
# series is all nans
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can you add the issue number as a comment

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done

result = Series([np.nan]).asof([0])
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Can you make this a separate test? (as it is not related to errors). Eg test_all_nans

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done

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Can you also add a case not using zero as the argument?
And can you also add the case of a scalar, and of multiple values? (eg s.asof(10) and s.asof([10, 11])

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done

expected = Series([np.nan])
tm.assert_series_equal(result, expected)

# testing non-default indexes
N = 50
rng = date_range('1/1/1990', periods=N, freq='53s')

dates = date_range('1/1/1990', periods=N * 3, freq='25s')
result = Series(np.nan, index=rng).asof(dates)
expected = Series(np.nan, index=dates)
tm.assert_series_equal(result, expected)

# testing scalar input
date = date_range('1/1/1990', periods=N * 3, freq='25s')[0]
result = Series(np.nan, index=rng).asof(date)
assert isnull(result)