pandas.DataFrame.select_dtypes — pandas 0.16.2 documentation (original) (raw)
DataFrame.select_dtypes(include=None, exclude=None)¶
Return a subset of a DataFrame including/excluding columns based on their dtype.
| Parameters: | include, exclude : list-like A list of dtypes or strings to be included/excluded. You must pass in a non-empty sequence for at least one of these. |
|---|---|
| Returns: | subset : DataFrame The subset of the frame including the dtypes in include and excluding the dtypes in exclude. |
| Raises: | ValueError If both of include and exclude are empty If include and exclude have overlapping elements If any kind of string dtype is passed in. TypeError If either of include or exclude is not a sequence |
Notes
- To select all numeric types use the numpy dtype numpy.number
- To select strings you must use the object dtype, but note that this will return all object dtype columns
- See the numpy dtype hierarchy
- To select Pandas categorical dtypes, use ‘category’
Examples
df = pd.DataFrame({'a': np.random.randn(6).astype('f4'), ... 'b': [True, False] * 3, ... 'c': [1.0, 2.0] * 3}) df a b c 0 0.3962 True 1 1 0.1459 False 2 2 0.2623 True 1 3 0.0764 False 2 4 -0.9703 True 1 5 -1.2094 False 2 df.select_dtypes(include=['float64']) c 0 1 1 2 2 1 3 2 4 1 5 2 df.select_dtypes(exclude=['floating']) b 0 True 1 False 2 True 3 False 4 True 5 False