pandas.api.types.infer_dtype

pandas.api.types.infer_dtype()

Efficiently infer the type of a passed val, or list-like array of values. Return a string describing the type.

Parameters:	value : scalar, list, ndarray, or pandas type skipna : bool, default False Ignore NaN values when inferring the type. New in version 0.21.0.
Returns:	string describing the common type of the input data. Results can include: - string - unicode - bytes - floating - integer - mixed-integer - mixed-integer-float - decimal - complex - categorical - boolean - datetime64 - datetime - date - timedelta64 - timedelta - time - period - mixed
Raises:	TypeError if ndarray-like but cannot infer the dtype

Notes

• ‘mixed’ is the catchall for anything that is not otherwise specialized
• ‘mixed-integer-float’ are floats and integers
• ‘mixed-integer’ are integers mixed with non-integers

Examples

>>> infer_dtype(['foo', 'bar'])
'string'

>>> infer_dtype(['a', np.nan, 'b'], skipna=True)
'string'

>>> infer_dtype(['a', np.nan, 'b'], skipna=False)
'mixed'

>>> infer_dtype([b'foo', b'bar'])
'bytes'

>>> infer_dtype([1, 2, 3])
'integer'

>>> infer_dtype([1, 2, 3.5])
'mixed-integer-float'

>>> infer_dtype([1.0, 2.0, 3.5])
'floating'

>>> infer_dtype(['a', 1])
'mixed-integer'

>>> infer_dtype([Decimal(1), Decimal(2.0)])
'decimal'

>>> infer_dtype([True, False])
'boolean'

>>> infer_dtype([True, False, np.nan])
'mixed'

>>> infer_dtype([pd.Timestamp('20130101')])
'datetime'

>>> infer_dtype([datetime.date(2013, 1, 1)])
'date'

>>> infer_dtype([np.datetime64('2013-01-01')])
'datetime64'

>>> infer_dtype([datetime.timedelta(0, 1, 1)])
'timedelta'

>>> infer_dtype(pd.Series(list('aabc')).astype('category'))
'categorical'