Types of columns can be checked by .dtypes atrribute of DataFrames.
In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 'C': [True, False, True]})
In [2]: df
Out[2]:
A B C
0 1 1.0 True
1 2 2.0 False
2 3 3.0 True
In [3]: df.dtypes
Out[3]:
A int64
...
astype() method changes the dtype of a Series and returns a new Series.
In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0],
'C': ['1.1.2010', '2.1.2011', '3.1.2011'],
'D': ['1 days', '2 days', '3 days'],
...
select_dtypes method can be used to select columns based on dtype.
In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 'C': ['a', 'b', 'c'],
'D': [True, False, True]})
In [2]: df
Out[2]:
A B C D
0 1 1.0 a True
1 2 2.0 b False
2...
get_dtype_counts method can be used to see a breakdown of dtypes.
In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 'C': ['a', 'b', 'c'],
'D': [True, False, True]})
In [2]: df.get_dtype_counts()
Out[2]:
bool 1
float64 1
int64 1
obje...
