Pandas DataFrame.astype()Python (original) (raw)

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.

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