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:

Python

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3.5, 4.5]})

res = df.astype('string')
print(res)
print(res.dtypes)

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.

Python

import pandas as pd
df = pd.DataFrame({'A': ['1', '2'], 'B': ['3.0', '4.0']})

res = df.astype({'A': int})
print(res)
print(res.dtypes)

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.

Python

import pandas as pd
df = pd.DataFrame({'A': ['1', '2'], 'B': ['3.5', '4.5']})

res = df.astype({'A': int, 'B': float})
print(res)
print(res.dtypes)

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.

Python

import pandas as pd
df = pd.DataFrame({'A': ['1', 'two'], 'B': ['3.0', '4.5']})

res = df.astype({'A': int}, errors='ignore')
print(res)
print(res.dtypes)

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:

Python

import pandas as pd
df = pd.DataFrame({'A': ['1.1', '2.2'], 'B': ['3', '4']})

res = df.astype({'A': float}, copy=False)
print(res)
print(res.dtypes)

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.

Python

import pandas as pd
df = pd.DataFrame({'A': ['1.1', '2.2'], 'B': ['3', '4']})

res = df.astype({'A': float}, copy=False)
print(res)
print(res.dtypes)

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.