Import pandas dataframe column as string not int

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Just want to reiterate this will work in pandas >= 0.9.1:

In [2]: read_csv('sample.csv', dtype={'ID': object})
Out[2]: 
                           ID
0  00013007854817840016671868
1  00013007854817840016749251
2  00013007854817840016754630
3  00013007854817840016781876
4  00013007854817840017028824
5  00013007854817840017963235
6  00013007854817840018860166

I’m creating an issue about detecting integer overflows also.

EDIT: See resolution here: https://github.com/pydata/pandas/issues/2247

Update as it helps others:

To have all columns as str, one can do this (from the comment):

pd.read_csv('sample.csv', dtype = str)

To have most or selective columns as str, one can do this:

# lst of column names which needs to be string
lst_str_cols = ['prefix', 'serial']
# use dictionary comprehension to make dict of dtypes
dict_dtypes = {x : 'str'  for x in lst_str_cols}
# use dict on dtypes
pd.read_csv('sample.csv', dtype=dict_dtypes)

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