Ensuring Consistent Data Types in a Pandas DataFrame

Last update on April 09 2025 12:55:11 (UTC/GMT +8 hours)

2. Ensuring Consistent Data Types of All Columns

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.

For more Practice: Solve these Related Problems:

• Write a Pandas program to check that all columns in a DataFrame have consistent data types and list any anomalies.
• Write a Pandas program to enforce data type consistency by converting columns to their expected types and reporting changes.
• Write a Pandas program to validate data types across multiple DataFrames and compare their schemas for consistency.
• Write a Pandas program to generate a report of data types for each column and flag columns that deviate from a predefined standard.

Python-Pandas Code Editor:

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