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

valuescalar, list, ndarray, or pandas type

skipnabool, default True

Ignore NaN values when inferring the type.

Returns

str

Describing the common type of the input data.

Results can include:

• string

• 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'

pandas.api.types.union_categoricals pandas.api.types.pandas_dtype