string、dict、list 的dtype 始终为object，为了测试type，需要选择列的一些值，例如iat:
type(resultstatsDF['file'].iat[0])
示例：
resultstatsDF = pd.DataFrame({'file':['a','d','f']})
print (resultstatsDF)
file
0 a
1 d
2 f
print (type(resultstatsDF['file'].iloc[0]))
<class 'str'>
print (resultstatsDF['file'].apply(type))
0 <class 'str'>
1 <class 'str'>
2 <class 'str'>
Name: file, dtype: object
示例：
df = pd.DataFrame({'strings':['a','d','f'],
'dicts':[{'a':4}, {'c':8}, {'e':9}],
'lists':[[4,8],[7,8],[3]],
'tuples':[(4,8),(7,8),(3,)],
'sets':[set([1,8]), set([7,3]), set([0,1])] })
print (df)
dicts lists sets strings tuples
0 {'a': 4} [4, 8] {8, 1} a (4, 8)
1 {'c': 8} [7, 8] {3, 7} d (7, 8)
2 {'e': 9} [3] {0, 1} f (3,)
所有值都具有相同的dtypes:
print (df.dtypes)
dicts object
lists object
sets object
strings object
tuples object
dtype: object
但type不一样，如果需要循环检查：
for col in df:
print (df[col].apply(type))
0 <class 'dict'>
1 <class 'dict'>
2 <class 'dict'>
Name: dicts, dtype: object
0 <class 'list'>
1 <class 'list'>
2 <class 'list'>
Name: lists, dtype: object
0 <class 'set'>
1 <class 'set'>
2 <class 'set'>
Name: sets, dtype: object
0 <class 'str'>
1 <class 'str'>
2 <class 'str'>
Name: strings, dtype: object
0 <class 'tuple'>
1 <class 'tuple'>
2 <class 'tuple'>
Name: tuples, dtype: object
或列的第一个值：
print (type(df['strings'].iat[0]))
<class 'str'>
print (type(df['dicts'].iat[0]))
<class 'dict'>
print (type(df['lists'].iat[0]))
<class 'list'>
print (type(df['tuples'].iat[0]))
<class 'tuple'>
print (type(df['sets'].iat[0]))
<class 'set'>
如果可能，使用boolean indexing 混合列（然后可以破坏一些熊猫功能）可以通过type 过滤：
df = pd.DataFrame({'mixed':['3', 5, 9,'2']})
print (df)
mixed
0 3
1 5
2 9
3 2
print (df.dtypes)
mixed object
dtype: object
for col in df:
print (df[col].apply(type))
0 <class 'str'>
1 <class 'int'>
2 <class 'int'>
3 <class 'str'>
Name: mixed, dtype: object
#python 3 - string
#python 2 - basestring
mask = df['mixed'].apply(lambda x: isinstance(x,str))
print (mask)
0 True
1 False
2 False
3 True
Name: mixed, dtype: bool
df = df[mask]
print (df)
mixed
0 3
3 2