Indicate index of rows for which an apply() statement fails

[hammer]

I'm writing some code to transform a column of my data frame. I expect all string values in this column, and I'm using x.startswith() on the column contents as part of the transformation logic. When I try to apply this transformation to each column in the data frame using df.apply(), the transformation is failing, claiming that it's trying to look up the startswith attribute on a float object. It would be useful for me to know on which row this transformation is failing; could that information be added to the traceback?

[wesm]

I like it, an easy change too. Here a self-contained example:

data = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
                         'bar', 'bar', 'bar', 'bar',
                         'foo', 'foo', 'foo'],
                  'B' : ['one', 'one', 'one', 'two',
                         'one', 'one', 'one', 'two',
                         'two', 'two', 'one'],
                  'C' : ['dull', 'dull', 'shiny', 'dull',
                         'dull', 'shiny', 'shiny', 'dull',
                         'shiny', 'shiny', 'shiny'],
                  'D' : np.random.randn(11),
                  'E' : np.random.randn(11),
                  'F' : np.random.randn(11)})

data['C'][4] = np.nan

def transform(row):
    if row['C'].startswith('shin') and row['A'] == 'foo':
        row['D'] = 7
    return row

def transform2(row):
    if (notnull(row['C']) and  row['C'].startswith('shin')
        and row['A'] == 'foo'):
        row['D'] = 7
    return row

then the traceback would look be:

In [2]: data.apply(transform, axis=1)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-2-cae6247f68ab> in <module>()
----> 1 data.apply(transform, axis=1)

/home/wesm/code/pandas/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
   2665                     return self._apply_raw(f, axis)
   2666                 else:
-> 2667                     return self._apply_standard(f, axis)
   2668             else:
   2669                 return self._apply_broadcast(f, axis)

/home/wesm/code/pandas/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
   2719             try:
   2720                 for k, v in series_gen:
-> 2721                     results[k] = func(v)
   2722             except Exception, e:
   2723                 if hasattr(e, 'args'):

/home/wesm/code/pandas/<ipython-input-1-696d0fa0400e> in transform(row)
     15 
     16 def transform(row):
---> 17         if row['C'].startswith('shin') and row['A'] == 'foo':
     18                 row['D'] = 7
     19         return row

AttributeError: ("'float' object has no attribute 'startswith'", 'occurred at index 4')

I also noticed that if you apply a function to the rows on a mixed-type DataFrame (like with the above example) that you lose the type information. I added a small type inference hack to convert things back. df.apply(f, axis=1) isn't especially fast right now, something I should fix at some point

[wesm]

Aside: it'd be nice to add vectorized string functions to pandas, similar to hadley's stringr package. They could also be made NA-friendly

[hammer]

Great! I should note that, looking into the problem a bit further, the float values were np.nan, which were generated when I imported a CSV with some blank values in a column. In other words, I suspect anyone applying string functions to a column with missing values will hit this issue. Which brings me to another potential improvement: verbose data import that indicates the number of missing values automatically filled in.

[ghost]

"Aside: it'd be nice to add vectorized string functions to pandas, similar to hadley's stringr package. They could also be made NA-friendly"
This would be great.

[wesm]

@hammer, OK I'll bite on that (this would have been useful information to me in the past). Hard to add a lot of verbosity without sacrificing performance but getting a basic NA count for non-numeric columns seems pretty useful:

from pandas import *
from StringIO import StringIO

def f(verbose=True):
    text = """a,b,c,d
one,1,2,3
one,1,2,3
,1,2,3
one,1,2,3
,1,2,3
,1,2,3
one,1,2,3
two,1,2,3"""
    data = StringIO(text)
    result = read_csv(data, verbose=verbose)
    return result

result = f()

looks like

In [2]: result = read_csv(data, verbose=True)
Filled 3 NA values in column a
Out[2]: 
  a    b  c  d
0 one  1  2  3
1 one  1  2  3
2 NaN  1  2  3
3 one  1  2  3
4 NaN  1  2  3
5 NaN  1  2  3
6 one  1  2  3
7 two  1  2  3

Something that can definitely be fleshed out over time