pandas.Series.convert_dtypes¶

Series.convert_dtypes(self: ~FrameOrSeries, infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True) → ~FrameOrSeries[source]¶

Convert columns to best possible dtypes using dtypes supporting pd.NA.

New in version 1.0.0.

Parameters

infer_objectsbool, default True: Whether object dtypes should be converted to the best possible types.
convert_stringbool, default True: Whether object dtypes should be converted to StringDtype().
convert_integerbool, default True: Whether, if possible, conversion can be done to integer extension types.
convert_booleanbool, defaults True: Whether object dtypes should be converted to BooleanDtypes().

Returns

Series or DataFrame: Copy of input object with new dtype.

See also

infer_objects: Infer dtypes of objects.
to_datetime: Convert argument to datetime.
to_timedelta: Convert argument to timedelta.
to_numeric: Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types or BooleanDtype, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples

>>> df = pd.DataFrame(
...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10    NaN
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0

>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string