Converting Data Types in a DataFrame Using astype() in Pandas

Last update on April 09 2025 12:54:15 (UTC/GMT +8 hours)

9. Replacing Missing Data with Mean Value

Write a Pandas program to convert data types using astype().

In this exercise, we have converted the data types of columns in a DataFrame using the astype() method.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with mixed types
df = pd.DataFrame({
    'ID': ['1', '2', '3'],
    'Price': ['10.5', '20.0', '30.5']
})

# Convert 'ID' to integer and 'Price' to float
df['ID'] = df['ID'].astype(int)
df['Price'] = df['Price'].astype(float)

# Output the result
print(df)

Output:

   ID  Price
0   1   10.5
1   2   20.0
2   3   30.5

Explanation:

• Created a DataFrame with columns in string format.
• Used astype() to convert 'ID' to integer and 'Price' to float.
• Returned the DataFrame with updated data types.

For more Practice: Solve these Related Problems:

• Write a Pandas program to replace missing values in a numeric column with the mean of that column.
• Write a Pandas program to replace missing values in several columns with their respective means simultaneously.
• Write a Pandas program to perform mean imputation and then validate the replacement by comparing before-and-after statistics.
• Write a Pandas program to visualize the effect of mean imputation on the distribution of a numeric column.

Python-Pandas Code Editor:

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