pandas.api.types.infer_dtype#
- pandas.api.types.infer_dtype()#
Return a string label of the type of a scalar or list-like of values.
- 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
- 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
>>> import datetime >>> 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]) 'boolean'
>>> 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'