modin.pandas.DataFrame.astype¶

DataFrame.astype(dtype: str | type | pd.Series | dict[str, type], copy: bool = True, errors: Literal['raise', 'ignore'] = 'raise') → pd.DataFrame | pd.Series[source]¶

Cast a pandas object to a specified dtype dtype.

Parameters:

  • dtype (str, data type, Series or Mapping of column name -> data type) – Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

  • copy (bool, default True) – Return a copy (i.e., a new object) when copy=True; otherwise, astype operates inplace (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

  • errors ({'raise', 'ignore'}, default 'raise') –

    Control raising of exceptions on invalid data for provided dtype.

    • raise : allow exceptions to be raised

    • ignore : suppress exceptions. On error return original object.

Return type:

same type as caller (Snowpark pandas DataFrame or Snowpark pandas Series)

Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object
Copy

Cast all columns to int32 (dtypes will be int64 since Snowpark pandas API will cast all integers to int64):

>>> df.astype('int32').dtypes
col1    int64
col2    int64
dtype: object
Copy

Cast col1 to float64 using a dictionary:

>>> df.astype({'col1': 'float64'}).dtypes
col1    float64
col2      int64
dtype: object
Copy

Create a series:

>>> ser = pd.Series([1, 2], dtype=str)
>>> ser
0    1
1    2
dtype: object
>>> ser.astype('float64')
0    1.0
1    2.0
dtype: float64
Copy
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