Ensuring Consistent Data Types in a Pandas DataFrame

Last update on December 21 2024 07:41:21 (UTC/GMT +8 hours)

Pandas: Data Validation Exercise-2 with Solution

Write a Pandas program to check and ensure that the data types of all columns are consistent.

The following exercise demonstrates how to check and ensure that the data types of all columns are consistent using dtypes.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with different data types
df = pd.DataFrame({
    'Name': ['Orville', 'Arturo', 'Ruth', None],
    'Age': [25, '30', 22, 35],
    'Salary': [50000, 60000, '70000', 80000]
})

# Check the data types of each column
data_types = df.dtypes

# Output the result
print(data_types)

Output:

Name      object
Age       object
Salary    object
dtype: object

Explanation:

• Created a DataFrame with mixed data types (string and integers).
• Used dtypes to check the data type of each column.
• Name: The Name column contains strings (or None, which is interpreted as NaN), so its data type is object.
• Age: The Age column contains mixed data types—both integers (25, 22, 35) and a string ('30'). Pandas automatically converts the entire column to object because it cannot store mixed types in a numerical column.
• Salary: Similarly, the Salary column contains integers (50000, 60000, 80000) and a string ('70000'). Pandas converts this column to object as well because of the string.

Python-Pandas Code Editor:

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