Selecting Pandas Columns by dtype

Question

I was wondering if there is an elegant and shorthand way in Pandas DataFrames to select columns by data type (dtype). i.e. Select only int64 columns from a DataFrame.

To elaborate, something along the lines of

df.select_columns(dtype=float64) 

Thanks in advance for the help

Answer 1

Since 0.14.1 there's a select_dtypes method so you can do this more elegantly/generally.

In [11]: df = pd.DataFrame([[1, 2.2, 'three']], columns=['A', 'B', 'C'])  In [12]: df.select_dtypes(include=['int']) Out[12]:    A 0  1 

To select all numeric types use the numpy dtype numpy.number

In [13]: df.select_dtypes(include=[np.number]) Out[13]:    A    B 0  1  2.2  In [14]: df.select_dtypes(exclude=[object]) Out[14]:    A    B 0  1  2.2 

Answer 2

df.loc[:, df.dtypes == np.float64]