pandas.DataFrame.convert_dtypes   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.   Parameters 
 
infer_objects:bool, default True


Whether object dtypes should be converted to the best possible types.  
convert_string:bool, default True


Whether object dtypes should be converted to StringDtype().  
convert_integer:bool, default True


Whether, if possible, conversion can be done to integer extension types.  
convert_boolean:bool, defaults True


Whether object dtypes should be converted to BooleanDtypes().  
convert_floating:bool, 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.     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, 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. 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   <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