# 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. The default of `False` will be deprecated in a later version of pandas.

  > 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'

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