Should this work?
In [70]: cat = pd.Categorical(['a', 'b', 'c'])
In [71]: obj = pd.Series(['a', 'b', 'c'])
In [72]: num = pd.Series([1, 2, 3])
In [73]: df = pd.concat([pd.Series(cat), obj, num], axis=1, keys=['cat', 'obj', 'num'])
In [74]: df
Out[74]:
cat obj num
0 a a 1
1 b b 2
2 c c 3
In [75]: df.dtypes
Out[75]:
cat category
obj object
num int64
dtype: object
In [76]: df.dtypes == 'category'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-76-48fb3d0c49cf> in <module>()
----> 1 df.dtypes == 'category'
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas-0.14.1_291_g7a8a030-py3.4-macosx-10.9-x86_64.egg/pandas/core/ops.py in wrapper(self, other)
579
580 # scalars
--> 581 res = na_op(values, other)
582 if np.isscalar(res):
583 raise TypeError('Could not compare %s type with Series'
/Users/tom/Envs/py3/lib/python3.4/site-packages/pandas-0.14.1_291_g7a8a030-py3.4-macosx-10.9-x86_64.egg/pandas/core/ops.py in na_op(x, y)
526 msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
527 "compare values, use 'series <op> np.asarray(cat)'."
--> 528 raise TypeError(msg.format(op=op,typ=type(y)))
529 if x.dtype == np.object_:
530 if isinstance(y, list):
TypeError: Cannot compare a Categorical for op <built-in function eq> with type <class 'str'>. If you want to
compare values, use 'series <op> np.asarray(cat)'.
For comparison
In [77]: df.dtypes == 'object' Out[77]: cat False obj True num False dtype: bool In [78]: df.dtypes == 'int64' Out[78]: cat False obj False num True dtype: bool
This doesn't work either: In [87]: df.dtypes == df.cat.dtype (raise the same TypeError)
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