Given the file:

$ cat test.csv 
a,b,c,NULL,d
e,f,g,h,i
j,k,l,m,n

Where the 3rd column is to be treated as str.

When I did a string function on the column, pandas has read the NULL str as a NaN float:

>>> import pandas as pd
>>> df = pd.read_csv('test.csv', names=[0,1,2,3,4], dtype={0:str, 1:str, 2:str, 3:str, 4:str})>>> df[3].apply(str.strip)
Traceback (most recent call last):File "<stdin>", line 1, in <module>File "/usr/local/lib/python3.5/site-packages/pandas/core/series.py", line 2355, in applymapped = lib.map_infer(values, f, convert=convert_dtype)File "pandas/_libs/src/inference.pyx", line 1569, in pandas._libs.lib.map_infer (pandas/_libs/lib.c:66440)
TypeError: descriptor 'strip' requires a 'str' object but received a 'float'

To verify:

>>> for i in df[3]:
...    print (type(i), i)
... 
<class 'float'> nan
<class 'str'> h
<class 'str'> m

I've specified the dtype at initialization but somehow it got overriden.

How do I force the type of a specific column to be fixed?

Is there a way of automatically finding these abnormal NaN floats and change then back to 'NULL' string?

For me works astype:

df[3] = df[3].astype(str)for i in df[3]:print (type(i), i)<class 'str'> nan
<class 'str'> h
<class 'str'> m

Another solution is use keep_default_na=False in read_csv:

import pandas as pd
from pandas.compat import StringIOtemp=u"""a,b,c,NULL,d
e,f,g,h,i
j,k,l,m,n"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),  names=[0,1,2,3,4], keep_default_na=False)
print (df)0  1  2     3  4
0  a  b  c  NULL  d
1  e  f  g     h  i
2  j  k  l     m  nfor i in df[3]:print (type(i), i)
<class 'str'> NULL
<class 'str'> h
<class 'str'> m

Then is possible use na_values parameter if need parse NaN in numeric columns, but it has to be different e.g. NA:

import pandas as pd
from pandas.compat import StringIOtemp=u"""a,b,c,NULL,1
e,f,g,h,2
j,k,l,m,NA"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),  names=[0,1,2,3,4], keep_default_na=False, na_values=['NA'])
print (df)0  1  2     3    4
0  a  b  c  NULL  1.0
1  e  f  g     h  2.0
2  j  k  l     m  NaNfor i in df[3]:print (type(i), i)
<class 'str'> NULL
<class 'str'> h
<class 'str'> mfor i in df[4]:print (type(i), i)
<class 'numpy.float64'> 1.0
<class 'numpy.float64'> 2.0
<class 'numpy.float64'> nan