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'

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