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
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
In version 0.24.+ pandas has gained the ability to hold integer dtypes with missing values. Nullable Integer Data Type. Pandas can represent integer data with possibly missing values using arrays.IntegerArray. This is an extension types implemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass … Read more