If you want a list of columns of a certain type, you can use groupby: >>> df = pd.DataFrame([[1, 2.3456, ‘c’, ‘d’, 78]], columns=list(“ABCDE”)) >>> df A B C D E 0 1 2.3456 c d 78 [1 rows x 5 columns] >>> df.dtypes A int64 B float64 C object D object E int64 dtype: … Read more
Update: this has been fixed: from 0.11.1 you passing str/np.str will be equivalent to using object. Use the object dtype: In [11]: pd.read_csv(‘a’, dtype=object, index_col=0) Out[11]: A B 1A 0.35633069074776547 0.745585398803751 1B 0.20037376323337375 0.013921830784260236 or better yet, just don’t specify a dtype: In [12]: pd.read_csv(‘a’, index_col=0) Out[12]: A B 1A 0.356331 0.745585 1B 0.200374 0.013922 … Read more
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 … Read more
The lack of NaN rep in integer columns is a pandas “gotcha”. The usual workaround is to simply use floats.