DataFrame.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)[source]

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

New in version 1.0.0.

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().

convert_floatingbool, defaults True

Whether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.

New in version 1.2.0.

Series or DataFrame

Copy of input object with new dtype.

See also


Infer dtypes of objects.


Convert argument to datetime.


Convert argument to timedelta.


Convert argument to a numeric type.


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, convert_boolean and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, 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 or floating 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. Otherwise, convert to an appropriate floating extension type.

Changed in version 1.2: Starting with pandas 1.2, this method also converts float columns to the nullable floating 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.


>>> 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   <NA>
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