Pandas DataFrame.astype()-Python

Last Updated : 26 Jun, 2025

DataFrame.astype() function in pandas cast a pandas object such as a DataFrame or Series to a specified data type. This is especially useful when you need to ensure that columns have the correct type, such as converting strings to integers or floats to strings.

For example:

Output

 A B
0 1 3.5
1 2 4.5
A string[python]
B string[python]
dtype: object

Explanation: astype('string') converts all DataFrame columns to string type, ensuring that all values, whether integers or floats, are treated as strings.

Syntax

DataFrame.astype(dtype, copy=True, errors='raise')

Parameters:

dtype: Target data type and it can be a single type or a dict of column names to types.
copy: If True (default), returns a copy and if False, changes in-place if possible.
error: How to handle invalid data: 'raise' (default) or 'ignore'.

Returns: A new DataFrame or Series with updated data types.

Examples

Example 1: In this, we convert column 'A' to integer, while column 'B' remains unchanged.

Output

 A B
0 1 3.0
1 2 4.0
A int64
B object
dtype: object

Explanation: astype() method is applied to convert column 'A' from string to integer type. Column 'B' is left out, so it remains as object (string).

Example 2: In this, we convert column 'A' to integer and column 'B' to float.

Output

 A B
0 1 3.5
1 2 4.5
A int64
B float64
dtype: object

Explanation: astype() method is used with a dictionary to convert 'A' to integer and 'B' to float. Both columns are now in numeric form, suitable for calculations.

Example 3: In this, we try to convert column 'A' to integer, but due to a non-numeric value, the conversion is skipped and original types are retained.

Output

 A B
0 1 3.0
1 two 4.5
A object
B object
dtype: object

Explanation: astype() method tries to convert column 'A' to integer, but the value 'two' is not numeric. Since errors='ignore' is used, pandas skips the conversion and retains the original data types.

Example 4:

Output

 A B
0 1.1 3
1 2.2 4
A float64
B object
dtype: object

Example 4: In this, we convert column 'A' to float with copy=False to avoid creating a new object.

Output

 A B
0 1.1 3
1 2.2 4
A float64
B object
dtype: object

Explanation: By setting copy=False, pandas attempts to perform the conversion without creating a new object. Column 'B' remains unchanged as object.