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:

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 otherwisespecialized

• ‘mixed-integer-float’ are floats and integers

• ‘mixed-integer’ are integers mixed with non-integers

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

Examples

>>>frompandas.api.typesimportinfer_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'

>>>fromdecimalimportDecimal>>>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'

>>>importdatetime>>>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'