How to Change Column Data Type in pandas DataFrame

To change a column's data type in pandas, use the astype() method on the DataFrame column, like df['col'] = df['col'].astype(new_dtype). This converts the column to the specified type such as int, float, or str.

Syntax

The basic syntax to change a column's data type is:

• df['column_name']: Selects the column to convert.
• astype(new_dtype): Converts the column to the new data type.
• new_dtype: The target data type, e.g., 'int', 'float', 'str', or 'category'.

python

df['column_name'] = df['column_name'].astype(new_dtype)

Example

This example shows how to convert a column from float to integer and from integer to string.

python

import pandas as pd

data = {'age': [25.0, 30.0, 22.0], 'score': [88, 92, 85]}
df = pd.DataFrame(data)

# Convert 'age' from float to int
df['age'] = df['age'].astype(int)

# Convert 'score' from int to string
df['score'] = df['score'].astype(str)

print(df)
print(df.dtypes)

Output

   age score
0   25    88
1   30    92
2   22    85
age       int64
score    object
dtype: object

Common Pitfalls

Common mistakes include:

• Trying to convert non-numeric strings to numbers without cleaning data first, which causes errors.
• Not assigning the result back to the DataFrame column, so the change is lost.
• Using astype() on columns with missing values without specifying nullable types.

python

import pandas as pd

data = {'value': ['10', '20', 'thirty']}
df = pd.DataFrame(data)

# Wrong: This will raise an error because 'thirty' is not numeric
# df['value'] = df['value'].astype(int)

# Right: Use pd.to_numeric with errors='coerce' to handle non-numeric
df['value'] = pd.to_numeric(df['value'], errors='coerce')

print(df)

Output

   value
0   10.0
1   20.0
2    NaN

Quick Reference

Method	Description	Example
astype()	Convert column to specified dtype	df['col'] = df['col'].astype('int')
pd.to_numeric()	Convert to numeric, handle errors	df['col'] = pd.to_numeric(df['col'], errors='coerce')
astype('category')	Convert column to categorical type	df['col'] = df['col'].astype('category')

Key Takeaways

Use df['col'] = df['col'].astype(new_dtype) to change a column's data type.

Always assign the result back to the DataFrame column to keep changes.

Use pd.to_numeric() with errors='coerce' to safely convert non-numeric strings to numbers.

Be careful converting columns with missing values; consider nullable types.

Common target types include int, float, str, and category.