pandas.api.types.infer_dtype — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

pandas.api.types.infer_dtype(value, skipna=True)#

Return a string label of the type of the elements in a list-like input.

This method inspects the elements of the provided input and determines classification of its data type. It is particularly useful for handling heterogeneous data inputs where explicit dtype conversion may not be possible or necessary.

Parameters:

valuelist, ndarray, or pandas type

The input data to infer the dtype.

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

See also

api.types.is_scalar

Check if the input is a scalar.

api.types.is_list_like

Check if the input is list-like.

api.types.is_integer

Check if the input is an integer.

api.types.is_float

Check if the input is a float.

api.types.is_bool

Check if the input is a boolean.

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

from pandas.api.types import infer_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'

from decimal import Decimal 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'

import 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'