Top Important Python Questions for Data Science Interviews [Data Cleaning and Preprocessing]

Proficiency in Python is a cornerstone skill for data science and machine learning. Data science interviews often delve into not just the practical coding aspects but also the conceptual understanding of Python’s features and functionalities.

This blog post aims to explore and elucidate key Python questions related to data preprocessing and data clearing. Starting from how to detect missing data and handle them. After that, we will discuss data transformation and feature engineering. In addition to that, we will also cover how to detect outliers and handle them and more.

Join us in this insightful expedition where we decipher Python’s prowess in the context of data science, unveiling the methodologies that elevate your proficiency and deepen your understanding of the data preprocessing and cleansing domain.

Table of Contents:

1. Handling Missing Data Questions:

• How do you identify and handle missing values in a Pandas DataFrame?
• What is imputation, and why might it be useful in dealing with missing data?

2. Data Transformation Questions:

• How can you encode categorical variables in a Pandas DataFrame?
• What is one-hot encoding, and when would you use it in data preprocessing?

3. Removing Duplicates Questions:

• How do you identify and remove duplicate rows from a DataFrame?
• Can you explain the difference between the duplicated() and drop_duplicates() methods in Pandas?

4. Data Scaling and Normalization Questions:

• Discuss the importance of feature scaling in machine learning.
• Explain the difference between min-max scaling and z-score normalization.

5. Handling Outliers Questions:

• What are outliers, and why might they impact machine learning models?
• Describe different methods for detecting outliers in a dataset in Python
• How can you handle outliers in a continuous numerical variable in Python?