pandas.DataFrame.convert_dtypes#

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

Convert columns to the best possible dtypes using dtypes supportingpd.NA.

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 toStringDtype().

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 toBooleanDtypes().

convert_floatingbool, defaults True

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

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultantDataFrame(still experimental). Behaviour is as follows:

• "numpy_nullable": returns nullable-dtype-backedDataFrame(default).

• "pyarrow": returns pyarrow-backed nullableArrowDtypeDataFrame.

Added in version 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 eachSeries in a DataFrame) to dtypes that supportpd.NA. By using the optionsconvert_string,convert_integer,convert_boolean andconvert_floating, it is possible to turn off individual conversionstoStringDtype, the integer extension types,BooleanDtypeor floating extension types, respectively.

For object-dtyped columns, ifinfer_objects isTrue, use the inferencerules as during normal Series/DataFrame construction. Then, if possible,convert toStringDtype,BooleanDtype or an appropriate integeror floating extension type, otherwise leave asobject.

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

If the dtype is numeric, and consists of all integers, convert to anappropriate integer extension type. Otherwise, convert to anappropriate floating extension type.

In the future, as new dtypes are added that supportpd.NA, the resultsof 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      f0  1  x   True    h  10.0    NaN1  2  y  False    i   NaN  100.52  3  z    NaN  NaN  20.0  200.0

>>>df.dtypesa      int32b     objectc     objectd     objecte    float64f    float64dtype: object

Convert the DataFrame to use best possible dtypes.

>>>dfn=df.convert_dtypes()>>>dfn   a  b      c     d     e      f0  1  x   True     h    10   <NA>1  2  y  False     i  <NA>  100.52  3  z   <NA>  <NA>    20  200.0

>>>dfn.dtypesa             Int32b    string[python]c           booleand    string[python]e             Int64f           Float64dtype: object

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

>>>s=pd.Series(["a","b",np.nan])>>>s0      a1      b2    NaNdtype: object

Obtain a Series with dtypeStringDtype.

>>>s.convert_dtypes()0       a1       b2    <NA>dtype: string