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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'

© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.api.types.infer_dtype.html