mars.dataframe.DataFrame.select_dtypes#

DataFrame.select_dtypes(include=None, exclude=None)#

Return a subset of the DataFrame’s columns based on the column dtypes.

Parameters
  • include (scalar or list-like) – A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

  • exclude (scalar or list-like) – A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns

The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Return type

DataFrame

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.

See also

DataFrame.dtypes

Return Series with the data type of each column.

Notes

  • To select all numeric types, use np.number or '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 datetimes, use np.datetime64, 'datetime' or 'datetime64'

  • To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'

  • To select Pandas categorical dtypes, use 'category'

  • To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0) or 'datetime64[ns, tz]'

Examples

>>> import mars.dataframe as md
>>> df = md.DataFrame({'a': [1, 2] * 3,
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df.execute()
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0
>>> df.select_dtypes(include='bool').execute()
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64']).execute()
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['int64']).execute()
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0