Python Interview Questions for Data Science

Python is considered the primary language in every Data Scientist’s toolkit. It’s not just a tool, but a full environment for solving tasks related to modeling, data preprocessing, visualization, hypothesis evaluation, and pipeline building. That’s why technical Python questions make up a significant part of interviews — especially for Data Scientist roles. Employers expect candidates to confidently use libraries like Pandas, NumPy, Scikit-learn, and to work with time series, text data, complex features, and non-standard distributions.

It’s important not just to write code, but to understand how models work, which parameters affect training, how to structure a pipeline, validate hypotheses, prevent overfitting, and ensure interpretability.

Data Science Python Interview Questions

A Data Scientist’s tasks go far beyond writing a model — they start with understanding the data and end with deploying the solution to production. In interviews, it’s important to demonstrate not just Python syntax knowledge but also the ability to think like an engineer and analyst: choose the right libraries, select appropriate metrics, and explain why one model is better than another. Interviewers expect reasoning, not just terminology.

1. What is your main Python toolset for Data Science and why?

In daily work, I primarily use a stack of Pandas, NumPy, Scikit-learn, and Matplotlib/Seaborn. Pandas is the foundation for loading, cleaning, and transforming data. NumPy is used for lower-level array and matrix operations, especially in linear algebra tasks. For modeling — Scikit-learn, which covers nearly all essential algorithms: from regression and decision trees to clustering. I visualize with Seaborn (quick and pretty) or Matplotlib if I need fine control over the chart. I also use XGBoost, LightGBM, and for deep learning — TensorFlow and PyTorch, depending on the project. The key is that all these tools integrate well with each other, and Python allows me to cover the entire workflow — from EDA to production.

2. How do you handle categorical features when preparing data for modeling?

It depends on the task and model. For simple models like logistic regression or decision trees, I usually use One-Hot Encoding for features with a small number of unique values. In Pandas, that’s easily done with get_dummies(). If there are many categories — I prefer Frequency Encoding or Target Encoding. For tree models, Ordinal Encoding is acceptable, as they can handle order. But I make sure not to introduce false order where it doesn’t exist. I also group rare categories into 'Other' to reduce dimensionality. The most important thing is to avoid data leakage and maintain interpretability.

3. How do you choose the evaluation metric for a model and why?

I always start from the business problem. For binary classification, I check class balance. If the data is balanced — I use accuracy. If not — I focus on precision, recall, and F1. If minimizing false positives is crucial — I prioritize precision; if minimizing false negatives — recall. I often plot the ROC curve and calculate AUC. For regression tasks — I typically use RMSE or MAE. RMSE is more sensitive to outliers, so if they’re present — I go with MAE. I always discuss with the team which business error matters more. A metric is not just a number — it’s a way to understand where the model fails and how it affects the product. I calculate everything in Scikit-learn — it has convenient functions for all metrics.

4. How do you determine feature importance and perform feature selection?

I always start with basic correlation checks — df.corr() for numeric features, Cramér's V for categorical ones. Then I run a simple model — logistic regression or RandomForest — and look at feature_importances_ or coefficients. I also use SelectKBest from Scikit-learn for automated selection. For complex models, I use SHAP or Permutation Importance — they provide a transparent view of feature contributions. But I always apply logic: if a feature is technically important but makes no real-world sense — it may be an artifact. And conversely, a weak but interpretable feature might stay in the model. The goal is not only automation but understanding what exactly the model is learning.

5. How do you handle overfitting in models using Python?

First, I always split data into train/validation/test. If the model performs well on training but poorly on validation — that’s a red flag. I apply regularization: L1 or L2, depending on the model. In Scikit-learn, it’s easily adjustable via parameters. I also control model complexity: avoid overly deep trees or too many features. If I use gradient boosting — I enable early stopping. For neural networks — Dropout, normalization, and early stopping as well. Cross-validation is a must. I always watch for a gap between training and validation metrics — it’s the clearest signal of overfitting.

6. How do you interpret a model if it works like a “black box”?

If the model is opaque — like XGBoost or a neural network — I use SHAP values. They show how much each feature contributes to a specific prediction. It’s helpful for explaining why one client received a high score and another a low one. I also analyze global patterns using Partial Dependence Plots, Feature Importance, and sliced prediction analysis. Even if the metric is good, if the client doesn’t understand how the model works — it’s useless. That’s why I always try not just to show “here’s the accuracy,” but to explain what influences the result, how it influences it, and where the model fails.

7. How do you select and process data before training a model?

I start with EDA: explore distributions, correlations, missing values, and outliers. I remove useless features — like constants or duplicates. Missing values — I either fill or drop them depending on the context. It’s critical to avoid data leakage — for example, not using features generated after the event. I apply scaling when needed — usually with StandardScaler for models sensitive to feature scale. I also create new features — aggregates, logs, binary flags. In general, a good model starts not with training but with clean and meaningful data. That’s 80% of success.

8. What do you use to build models in Python and why?

In 90% of cases — Scikit-learn. It covers everything I need: logistic regression, trees, boosting, clustering, cross-validation. The interface is stable and predictable. For advanced boosting — I use XGBoost or LightGBM. They offer better performance and more control. For text-related tasks — I often use TfidfVectorizer with logistic regression. If I need neural networks — I switch to PyTorch or Keras, depending on the project. I care about good documentation and seamless integration with the rest of the pipeline. In this, Python is unbeatable.

9. How do you handle imbalanced data?

First, I analyze the imbalance — look at class ratios. If the skew is large, accuracy is useless. I start with basic methods: use class weighting (e.g. class_weight=balanced in logistic regression) or apply undersampling/oversampling. I often use SMOTE to synthetically generate samples. Tree-based models are also sensitive to imbalance — so I set scale_pos_weight in XGBoost. I focus on F1, precision, recall. I also assess predictions per class — not just the overall metric. It’s important to understand that correct performance on rare classes requires special attention.

10. How do you deploy a model to production? Do you use Python?

Yes, in most cases, we deploy models using Python. After training, I serialize the model using joblib or pickle. Then I wrap it in an API using FastAPI or Flask, add input validation, logging, and versioning. Everything is deployed as a Docker container. Sometimes models are used in batch processes — in that case, I just save a script that runs on schedule. It’s crucial that the model can be restarted easily, is reproducible, and well-logged. That’s why I keep the entire pipeline — from preprocessing to inference — in one place. Python is ideal for these tasks — both for training and integration.

11. How do you work with time series in Python?

When I deal with forecasting or time-based analysis tasks, the first step is converting the date column to datetime format using pd.to_datetime(), then setting it as the index. This enables use of .resample(), rolling(), and expanding() — for example, to calculate moving averages or windowed aggregations. For more advanced tasks, I use statsmodels or Prophet. Prophet is great for configuring seasonality, trends, and working with holidays. If I need low-level control — I use ARIMA from statsmodels. Overall, Python is an excellent choice for time series: you can go from analysis to forecasting and visualization within one environment.

12. What do you use for cross-validation and why is it important?

Cross-validation is a reliable way to evaluate a model, especially with limited data. In Python, I use cross_val_score() or cross_validate() from Scikit-learn. Usually I go with KFold, but if the data is time-based — I use TimeSeriesSplit. Cross-validation helps reduce dependence on a single train-test split and lowers the risk of overfitting. I always make sure validation sets don’t overlap with training sets. Sometimes I create a custom split if business logic requires training only on data before a specific date. Without cross-validation, a model might "peek into the future", especially with strong seasonality.

13. How do you handle multicollinearity in data?

Multicollinearity is when features are highly correlated with each other, which can cause instability in linear model coefficients. First, I compute a correlation matrix. If I see feature pairs with correlation above 0.9 — I remove one of them. I also use Variance Inflation Factor (VIF) — if VIF > 5 or 10, the feature is considered redundant. This is especially important when the model needs to be interpretable. In tree models, multicollinearity is less of an issue, but I still watch for it — as it can slow training and affect generalization.

14. How do you handle classification with imbalanced classes in Python?

First, I assess the imbalance: I calculate class ratios and build a confusion matrix. If imbalance is high — I don’t rely on accuracy, but rather use precision, recall, and F1. In Python, I use class_weight='balanced' in Scikit-learn or scale_pos_weight in XGBoost. I also apply resampling techniques: SMOTE, RandomUnderSampler from the imblearn library. Sometimes, I first train the model on imbalanced data, then manually adjust the decision threshold to achieve the right balance between FP and FN. The solution depends on the task: if it’s disease prediction — false negatives are critical; for fraud detection — false positives should be minimized.

15. How do you work with text data in Python?

For text, I use nltk, re, sklearn.feature_extraction.text, and for more power — spaCy. I start with cleaning: converting to lowercase, removing punctuation and stop words. Then I tokenize and apply stemming or lemmatization. For vectorization, I use CountVectorizer or TfidfVectorizer depending on the model. For large-scale data — I work with gensim and word2vec. For interpretability — I use logistic regression with explainable features. For more advanced tasks — I integrate BERT via transformers. Python is ideal for NLP — it allows going from raw text to features and metrics quickly.

16. What steps do you take if the model shows low accuracy?

First, I analyze where exactly it fails — I build a confusion matrix, examine FP/FN, and review the input data. Sometimes the issue is poor data preparation: missing values, class imbalance, or multicollinearity. It could also be that the model is too simple or too complex for the dataset. I try different algorithms, do feature engineering, or add external data. I also double-check the evaluation metric — maybe accuracy isn’t the right one. If nothing helps — I consult the team, as the problem setup itself may need adjustment. The key is not to panic but to systematically review each step of the pipeline.

17. Describe your experience building a data processing pipeline.

I always try to separate the code into stages: loading, cleaning, transformation, feature engineering, modeling, validation. I use Scikit-learn’s Pipeline when I need to standardize steps. It’s convenient since each step can be reused, validated, and deployed. For example: SimpleImputer, followed by StandardScaler, then LogisticRegression — all in one pipeline. I also add custom steps using FunctionTransformer. For more complex projects, I use MLflow for tracking and versioning models. Python makes it easy to organize pipelines both as scripts and APIs — flexibly and transparently.

18. What is overfitting and how do you detect it?

Overfitting happens when a model performs great on training data but poorly on new data. I detect it by comparing train and validation metrics. If the train score is close to 1.0 but validation lags significantly — the model has memorized the data instead of learning patterns. Causes can include redundant features, overly complex structures, or poor tuning. To fix it, I use regularization, simplify the model, apply cross-validation, reduce tree depth, or add Dropout in neural networks. The main goal is to prevent the model from simply memorizing — and instead help it generalize.

19. How do you evaluate model stability over time?

First, I compare metrics across different time periods — train on older data, validate on newer data. If performance drops significantly — I analyze drift: both feature drift and target drift. I use Evidently AI, or simply examine distributions with value_counts, hist, mean/median over time. I also monitor models in production — if possible, I log metrics and compare them to baseline values. Python offers tools to build stable models and monitoring systems: scikit-metrics, MLflow, Prometheus + Grafana, and much of this can be automated.

20. How do you document and share your code with the team?

I try to write clean code following PEP8 standards. All functions include a docstring describing inputs and outputs. In Jupyter notebooks, I add Markdown blocks with explanations so each step is clear. For larger projects, I use Sphinx to generate documentation. Structure also matters: everything organized into folders like data, notebooks, scripts, models, reports. Code goes into Git with pull requests. And of course, I write code in a way that I can understand it a month later. Python is great because all of this can be done natively and cleanly — the main principle is: write not just for yourself, but for the team.