ENH: Dataframe should have a .isin() method

[TomAugspurger]

Any reason not to give DataFrame a .isin() method like Series?

The new wrinkle is that the user needs to specify if they want a logical OR or AND.

e.g.

In [26]: df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'f'], 
'ids2': ['e', 'f', 'c', 'f']})

In [27]: df
Out[27]: 
  ids ids2  vals
0   a    e     1
1   b    f     2
2   f    c     3
3   f    f     4

In [1]: other_ids = pd.Series(['a', 'b', 'c', 'c'])

df.isin(other_ids, how='or')

  ids ids2  vals
0   a    e     1
1   b    f     2
2   f    c     3

df.isin(other_ids, how='and')

See this SO post maybe.

If someone else wants to take this, feel free. Can't promise a PR any time soon, but maybe in the fall :)

[cpcloud]

i like this idea but it should return a bool not the frame indexed by and-ing or or-ing the isins, because the bool version is consistent with Series.isin

[TomAugspurger]

Absolutely on returning the bool. Mistake on my part.

The most obvious uses are how='and' and how='or', which will return a 1-d bool that can be indexed into.

Would it also be useful to have some way to get just the locations where the value is in the array?

#pseduocode

df.isin(['a', 'b', 'c'], how='any')  # any probably not the right argument name.

     ids   ids2   vals
0   True  False  False
1   True  False  False
2  False   True  False
3  False  False  False

This can be achieved by applying .isin() to each series and then concatenating together, or even using df.applymap(lamda x: x in arr). Not sure we'd need a third way. I'd say just 'and' and 'or' for now.

[TomAugspurger]

If no one else minds, I can try to take a crack at this. I haven't done much with cython so you might want to label it someday. It looks like the Series.isin() uses ismember from lib.pyx. I was thinking about either using that for each column of df we're checking for, or writing something similar for the DataFrame version.

Is there any preference for a default on and vs. or for the how parameter?

Also for the examples I've been giving it should be df[['ids', 'ids2']].isin(), not df.isin() since the vals column won't be in the reference array.

[jreback]

u can prob just do this is python
iterate over columns and use isin

if its not ast enough could change (but also good to get algo right first)

[TomAugspurger]

Yep. Should be much easier than I was thinking. I'll try to get this done today. Going to (possibly) be without internet until Sunday, but I should have it done by then.

[TomAugspurger]

So right now I'm thinking about going with @jreback's idea and calling isin on each column.

I'm getting one failure locally... (unreleated to what I've change, I think.)

FAIL: test_convert_objects (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/Users/tom/pandas/pandas/tests/test_frame.py", line 5486, in test_convert_objects
    assert_frame_equal(result, expected)
  File "/Users/tom/pandas/pandas/util/testing.py", line 238, in assert_frame_equal
    check_less_precise=check_less_precise)
  File "/Users/tom/pandas/pandas/util/testing.py", line 197, in assert_series_equal
    assert_almost_equal(left.values, right.values, check_less_precise)
  File "/Users/tom/pandas/pandas/util/testing.py", line 141, in assert_almost_equal
    assert_almost_equal(a[i], b[i], check_less_precise)
  File "/Users/tom/pandas/pandas/util/testing.py", line 129, in assert_almost_equal
    assert a == b, "%s != %s" % (a, b)
AssertionError: na != nan

I've got it pushed to my branch here. I'll do a proper PR in a sec, but I forgot to add the changes the release notes. Can I still make update the release notes and everything into the same commit without screwing up world? I know you aren't supposed to rebase changes pushed to a remote, but in this case is remote pandas/pandas or does TomAugspurger/pandas count too?

[cpcloud]

I get this but only when I use tox. Weird

[TomAugspurger]

I think I'm also going to add an ordered argument. If you pass that as True then the function will expect a sequence of tuples. That may be more common than just a flat array.

[TomAugspurger]

Design question for you all. If we have

In [43]: df = DataFrame({'A': ['a', 'b', 'c', 'd'], 'B': [1, 2, 3, 4]})

In [44]: df
Out[44]: 
   A  B
0  a  1
1  b  2
2  c  3
3  d  4

with something like other = [('a'), (1, 3)]. I'd like to compare column 'A' to 'a' and column 'B' to (1, 3). Should we insist that each sequence be a tuple or list? The way I've done it so far requires that each sub-sequence in other be iterable. So other =['a', (1, 3)] would work, but other = [('a'), 1] would fail since ints are not iterable. Not a great outcome.

FWIW, Series will fail on something like .isin(1) which makes since because in that case you'd just do series == 1.

[cpcloud]

hm do you mean other = [('a',), 1]? [('a'), 1] == ['a', 1] since parens with no commas forms an expression.

if it's a scalar then make it a list and it will be fine. OTOH it might be useful to make s.isin(some_scalar) == s == some_scalar where s is a Series object. Then you wouldn't have to worry about forming a list since it would be done in Series

[jreback]

@hayd I see u waited a long time to merge after 0.12 :)

[hayd]

@jreback I figured best to get in early on the 0.13 merge-storm...