pandas.api.types.infer_dtype

pandas.api.types.infer_dtype(value, skipna=True)

Return a string label of the type of a scalar or list-like of values.

Parameters:

value:scalar, list, ndarray, or pandas type

skipna:bool, 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

• unknown-array

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

• ‘unknown-array’ is the catchall for something that is an array (has a dtype attribute), but has a dtype unknown to pandas (e.g. external extension array)

Examples

>>> from pandas.api.types import infer_dtype
>>> 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'

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

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

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

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

>>> import 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'