Change Data Type

Change Data Type

What is this problem about?

The "Change Data Type interview question" is a fundamental data engineering task involving schema modification. In real-world data pipelines, you often receive data in a format that doesn't match your analysis requirements (e.g., numbers stored as strings). Your goal is to transform a specific column in a dataset (usually a pandas DataFrame or a SQL table) to a different data type, such as converting an object/string column to an integer or float.

Why is this asked in interviews?

Google and other data-driven companies use the "Change Data Type coding problem" to verify a candidate's basic data manipulation skills. It ensures you know how to handle data cleaning and preparation, which often takes up 80% of a data scientist's or engineer's time. It also tests your awareness of potential data loss (e.g., converting float to int) and your ability to handle non-numeric values during conversion.

Algorithmic pattern used

This problem follows the Data Transformation and Type Casting pattern.

1. Selection: Identify the target column(s).
2. Casting: Use built-in functions (like .astype() in pandas or CAST() in SQL) to perform the conversion.
3. Handling Anomalies: Ensure that values that cannot be converted (like "N/A" or "NaN") are handled appropriately (e.g., filled with zero or dropped) to avoid runtime errors.

Example explanation

Suppose you have a table of product prices where the price column is stored as strings: ["10.5", "20.0", "15"].

1. To perform calculations, you need these as floats.
2. You apply a casting operation: df['price'] = df['price'].astype(float).
3. The new column is: [10.5, 20.0, 15.0]. Now you can perform mathematical operations like sum() or mean().

Common mistakes candidates make

• Ignoring Nulls: Attempting to cast a column with NaN values to an integer type without handling the nulls first, which often results in an error in many libraries.
• Data Loss: Converting floats to integers without realizing that decimal parts will be truncated.
• String Formatting: Failing to remove symbols (like '$' or ',') from a string before attempting to convert it to a number.

Interview preparation tip

Master the data manipulation libraries specific to your role (e.g., pandas for Data Science, SQL for Backend/Data Engineering). Practice handling "dirty" data—columns that look like numbers but contain hidden spaces or special characters.