Python Data Science Interview Questions

Basic Python & Data Structures:

1. What are the key differences between lists and tuples in Python?

Lists are mutable (can be changed after creation), while tuples are immutable. Lists use [] and tuples use (). Mutability makes lists suitable for dynamic data, while immutability makes tuples safer for representing fixed data.

2. Explain the concept of list comprehension. Provide an example.

List comprehension is a concise way to create lists. [x**2 for x in range(10)] creates a list of squares from 0 to 9. It’s more efficient and readable than traditional loops.

3. How do you remove duplicates from a list?

Convert the list to a set (which inherently removes duplicates) and then back to a list: list(set(my_list)). Note: This changes the original order.

4. What are dictionaries in Python, and how are they used?

Dictionaries store data in key-value pairs. They are used for efficient lookups. my_dict = {"name": "Alice", "age": 30}. Access via my_dict["name"].

5. Explain the difference between __init__ and __str__ in a Python class.

__init__ is the constructor, used to initialize object attributes. __str__ defines how the object is represented as a string (when you print it).

NumPy:

6. What is NumPy, and why is it important for data science?

NumPy is a fundamental library for numerical computing in Python. It provides powerful N-dimensional arrays and tools for working with them efficiently.

7. How do you create a NumPy array?

import numpy as np; arr = np.array([1, 2, 3])

8. What are some common NumPy array operations?

arr.shape (dimensions), arr.reshape() (change shape), arr.sum(), arr.mean(), arr.dot() (matrix multiplication).

9. Explain broadcasting in NumPy.

Broadcasting allows arithmetic operations between arrays of different shapes, as long as certain compatibility rules are met. It avoids explicit looping for efficiency.

10. How do you access elements in a NumPy array?

Similar to lists, using indexing: arr[0], arr[1:3], arr[row_index, column_index] (for 2D arrays).

Pandas:

11. What is Pandas, and what are its key data structures?

Pandas is a library for data manipulation and analysis. Its main structures are Series (1D labeled array) and DataFrame (2D labeled table).

12. How do you create a Pandas DataFrame?

From a dictionary, list of dictionaries, CSV file, etc. pd.DataFrame({"A": [1, 2], "B": [3, 4]})

13. How do you read and write data using Pandas?

pd.read_csv("file.csv"), df.to_csv("output.csv"), pd.read_excel(), etc.

14. Explain indexing and selection in Pandas DataFrames.

.loc (label-based) and .iloc (integer-based) are used for selecting rows and columns. df.loc[row_label, column_label], df.iloc[row_index, column_index]

15. How do you handle missing values in Pandas?

df.isnull(), df.fillna(), df.dropna()

16. What are some common Pandas operations for data cleaning?

Removing duplicates (df.drop_duplicates()), renaming columns (df.rename()), type conversion (df['col'].astype(int)).

17. How do you group data in Pandas?

df.groupby('column_name') allows you to perform operations on groups of data.

18. What are pivot tables in Pandas, and how are they used?

Pivot tables summarize data by creating a table with rows and columns based on different variables. pd.pivot_table(df, values='value_col', index='index_col', columns='columns_col')

19. How do you merge or join DataFrames in Pandas?

pd.merge(df1, df2, on='common_column', how='inner/outer/left/right')

20. What is the difference between concat and merge in Pandas?

concat stacks DataFrames along rows or columns. merge joins DataFrames based on common columns (like SQL joins).

Data Visualization (Matplotlib & Seaborn):

21. What is Matplotlib, and why is it used?

Matplotlib is a plotting library in Python used for creating static, interactive, and animated visualizations.

22. How do you create a basic line plot using Matplotlib?

import matplotlib.pyplot as plt; plt.plot(x, y); plt.show()

23. What are some other common plot types in Matplotlib?

Scatter plots (plt.scatter()), bar charts (plt.bar()), histograms (plt.hist()), pie charts (plt.pie()).

24. What is Seaborn, and how does it relate to Matplotlib?

Seaborn is built on top of Matplotlib and provides a higher-level interface for creating statistically informative and visually appealing plots.

25. How do you create a scatter plot with regression line using Seaborn?

import seaborn as sns; sns.regplot(x='x_col', y='y_col', data=df)

Machine Learning (Scikit-learn):

26. What is Scikit-learn, and what are its key functionalities?

Scikit-learn is a powerful library for machine learning in Python. It provides tools for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. 

27. What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data (input-output pairs) to train models. Unsupervised learning uses unlabeled data to find patterns.

28. Explain the concept of model training and evaluation.

Training involves fitting a model to the training data. Evaluation assesses the model’s performance on unseen data (test set).

29. What is the train-test split, and why is it important?

Dividing the data into training and testing sets prevents overfitting and allows for evaluating how well the model generalizes to new data.

30. What are some common classification algorithms in Scikit-learn?

Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests.

31. What are some common regression algorithms in Scikit-learn?

Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Random Forest Regression.

32. What is the purpose of feature scaling?

Feature scaling (e.g., standardization, normalization) ensures that features are on a similar scale, which can improve the performance of some machine learning algorithms.

33. What are some common evaluation metrics for classification?

Accuracy, precision, recall, F1-score, ROC curve, AUC.

34. What are some common evaluation metrics for regression?

Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared.

35. What is cross-validation, and why is it used?

Cross-validation involves splitting the data into multiple folds and training/evaluating the model on different combinations of folds. It provides a more robust estimate of model performance.

36. What is regularization, and how does it help prevent overfitting?

Regularization adds a penalty to the loss function to discourage complex models and prevent overfitting. L1 and L2 regularization are common techniques.

37. Explain the bias-variance tradeoff.

Bias refers to the error introduced by simplifying assumptions made by the model. Variance refers to the model’s sensitivity to fluctuations in the training data. Good models balance low bias and low variance.

38. What is hyperparameter tuning, and how is it done?

Hyperparameters are parameters that are not learned during training. Tuning involves finding the optimal values for these parameters, often using techniques like GridSearchCV or RandomizedSearchCV.

40. What is dimensionality reduction, and why is it used?

Dimensionality reduction reduces the number of features in a dataset while preserving important information. It’s used to simplify models, improve performance, and address the curse of dimensionality.

41. What are some common dimensionality reduction techniques?

Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-SNE.

42. What is PCA, and how does it work?

PCA finds the principal components (directions of maximum variance) in the data and projects the data onto these components, reducing the dimensionality.

43. What is the difference between PCA and LDA?

PCA finds components that maximize variance. LDA finds components that maximize the separation between classes (in supervised learning).

44. What is t-SNE, and what is it used for?

t-SNE is a non-linear dimensionality reduction technique used for visualizing high-dimensional data in lower dimensions (typically 2D or 3D). It’s particularly useful for visualizing clusters.

45. What is the purpose of feature engineering?

Feature engineering involves creating new features from existing ones to improve the performance of machine learning models.  

46. Explain the data science lifecycle.

It typically includes: Data collection, data cleaning/preprocessing, exploratory data analysis (EDA), feature engineering, model selection, model training, model evaluation, deployment, and monitoring.

47. What is exploratory data analysis (EDA), and why is it important?

EDA involves exploring and visualizing data to understand its characteristics, identify patterns, and uncover potential issues. It’s crucial for gaining insights and informing subsequent steps in the data science process.

48. What are some common techniques used in EDA?

Summary statistics, histograms, scatter plots, box plots, correlation matrices.

49. What is data cleaning, and why is it necessary?

Data cleaning involves handling missing values, outliers, and inconsistencies in the data. It’s essential because dirty data can lead to inaccurate or misleading results.

50. Explain the importance of data visualization in data science.

Data visualization helps communicate complex information effectively, reveals patterns and insights, and aids in understanding data. It’s a powerful tool for both exploration and presentation.