23 Must-Know Python Data Science Interview Questions for 2024

As a data science professional, being proficient in Python is an essential skill. Python’s extensive libraries and frameworks, such as NumPy, Pandas, Matplotlib, and Scikit-learn, have made it a go-to language for data manipulation, analysis, and machine learning tasks. Consequently, many companies are seeking candidates with strong Python skills for data science roles.

To help you prepare for your next data science interview, we’ve compiled a list of 23 Python interview questions that cover a range of topics, from basic Python concepts to advanced data science techniques. These questions are designed to test your understanding of Python and its applications in the data science domain.

1. What is the difference between a list and a tuple in Python?

Lists and tuples are both Python data structures used to store collections of items, but they differ in their mutability and syntax.

• Lists are mutable, meaning their elements can be modified, added, or removed after creation. Lists are defined using square brackets [ ].
• Tuples are immutable, meaning their elements cannot be changed after creation. Tuples are defined using parentheses ( ).

Example:

# Listmy_list = [1, 2, 3]my_list[0] = 4  # Allowed (mutable)# Tuplemy_tuple = (1, 2, 3)my_tuple[0] = 4  # Not allowed (immutable)

2. What is the purpose of the __init__ method in Python classes?

The __init__ method is a special method in Python classes that is automatically called when an object of the class is created. It is commonly known as the constructor method and is used to initialize the attributes of an object with initial values.

Here’s an example:

class Person:    def __init__(self, name, age):        self.name = name        self.age = ageperson = Person("John", 30)print(person.name)  # Output: Johnprint(person.age)   # Output: 30

3. How can you handle missing values in a Pandas DataFrame?

Pandas provides several methods to handle missing values in a DataFrame. Here are some common approaches:

• Drop rows/columns: Use df.dropna(axis=0) to drop rows with any missing values, or df.dropna(axis=1) to drop columns with any missing values.
• Fill missing values: Use df.fillna(value) to replace missing values with a specific value, or df.fillna(method='ffill') to forward-fill missing values.
• Replace with a statistical value: Replace missing values with the mean, median, or mode of the column using df.fillna(df.mean()), df.fillna(df.median()), or df.fillna(df.mode()).

4. What is the difference between merge, join, and concatenate operations in Pandas?

These three operations are used to combine DataFrames in Pandas:

• Merge: Combines DataFrames based on common columns or indices, similar to SQL joins (pd.merge()).
• Join: Combines DataFrames along rows or columns based on index labels (df1.join(df2)).
• Concatenate: Glues or stacks DataFrames vertically (rows) or horizontally (columns) (pd.concat([df1, df2])).

5. How can you create a new column in a Pandas DataFrame based on conditions?

You can create a new column in a Pandas DataFrame based on conditions using the np.where() function from NumPy. Here’s an example:

import pandas as pdimport numpy as npdf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})df['C'] = np.where(df['A'] > 1, 'High', 'Low')

This will create a new column ‘C’ in the DataFrame, where the values are ‘High’ if the corresponding value in column ‘A’ is greater than 1, and ‘Low’ otherwise.

6. What is the purpose of the apply() method in Pandas?

The apply() method in Pandas is used to apply a function along one or more axes of a DataFrame or Series. It can be used for various operations, such as:

• Performing element-wise operations
• Applying a custom function to each row or column
• Performing operations that cannot be expressed using built-in operations

Here’s an example of using apply() to square the values in a DataFrame:

import pandas as pddf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})df_squared = df.apply(lambda x: x**2)

7. How can you plot a histogram using Matplotlib in Python?

To plot a histogram using Matplotlib in Python, you can use the plt.hist() function. Here’s an example:

import matplotlib.pyplot as pltimport numpy as np# Generate random datadata = np.random.normal(0, 1, 1000)# Plot histogramplt.hist(data, bins=30, edgecolor='black')plt.title('Histogram of Random Data')plt.xlabel('Value')plt.ylabel('Frequency')plt.show()

This code will generate a histogram of random data with 30 bins and display the plot with appropriate labels.

8. What is the purpose of the train_test_split function in Scikit-learn?

The train_test_split function from the Scikit-learn library is used to split a dataset into training and testing subsets. It is commonly used to evaluate the performance of a machine learning model on unseen data.

Here’s an example of how to use train_test_split:

from sklearn.model_selection import train_test_splitX = data.drop('target', axis=1)y = data['target']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

In this example, the dataset is split into training and testing sets, with 80% of the data used for training and 20% for testing. The random_state parameter ensures reproducibility.

9. What is the difference between the fit and transform methods in Scikit-learn?

In Scikit-learn, the fit and transform methods are used for different purposes:

• The fit method is used to train a model or estimator on the training data. It calculates the necessary parameters or statistics from the data.
• The transform method is used to apply the trained model or estimator to new data, usually for making predictions or transforming the data.

For example, in the case of a linear regression model, fit calculates the coefficients based on the training data, while transform uses those coefficients to make predictions on new data.

10. How can you handle imbalanced datasets in machine learning?

Imbalanced datasets, where one class is significantly underrepresented compared to the other, can lead to biased models and poor performance. There are several techniques to handle imbalanced datasets:

• Oversampling: Duplicate instances from the minority class to increase its representation.
• Undersampling: Remove instances from the majority class to balance the class distribution.
• Class weights: Assign higher weights to the minority class during model training.
• Synthetic data generation: Create synthetic instances of the minority class using techniques like SMOTE.

The choice of technique depends on the specific problem and the available computational resources.

11. What is the purpose of the GridSearchCV function in Scikit-learn?

The GridSearchCV function in Scikit-learn is used for hyperparameter tuning, which is the process of finding the optimal values for the hyperparameters of a machine learning model.

GridSearchCV performs an exhaustive search over a specified parameter grid, training and evaluating the model for each combination of hyperparameters. It then reports the best combination of hyperparameters based on a specified scoring metric.

Here’s an example of using GridSearchCV to tune the hyperparameters of a random forest classifier:

from sklearn.model_selection import GridSearchCVfrom sklearn.ensemble import RandomForestClassifierparam_grid = {    'n_estimators': [100, 200, 300],    'max_depth': [4, 6, 8],    'max_features': ['sqrt', 'log2']}rf_model = RandomForestClassifier()grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='accuracy')grid_search.fit(X_train, y_train)print(f"Best parameters: {grid_search.best_params_}")

12. What is the difference between NumPy arrays and Pandas Series/DataFrames?

NumPy arrays and Pandas Series/DataFrames are both data structures used for numerical computations, but they differ in their features and use cases:

• NumPy arrays are homogeneous (containing elements of the same data type) and efficient for mathematical operations. They are the base data structure for Pandas.
• Pandas Series is a one-dimensional labeled array that can hold any data type, including NumPy arrays. It is designed for working with structured, labeled data.
• Pandas DataFrames are two-dimensional labeled data structures, similar to a spreadsheet or SQL table, with rows and columns. They can hold different data types in different columns.

While NumPy arrays are faster for numerical computations, Pandas Series and DataFrames provide more functionality for data manipulation, handling missing data, and integrating with other data sources.

13. How can you save and load a machine learning model in Python?

In Python, you can save and load a trained machine learning model using various libraries and techniques, such as:

• Pickle: The pickle module in Python can be used to serialize and deserialize Python objects, including trained models.
• Joblib: Part of the Scikit-learn library, joblib provides an efficient way to persist and load models, especially for large NumPy arrays.
• Keras/TensorFlow: Deep learning models can be saved and loaded using the model.save() and tf.keras.models.load_model() functions in TensorFlow/Keras.

Here’s an example of saving and loading a Scikit-learn model using joblib:

from sklearn.linear_model import LogisticRegressionimport joblib# Train the modelmodel = LogisticRegression()model.fit(X_train, y_train)# Save the modeljoblib.dump(model, 'model.joblib')# Load the modelloaded_model = joblib.load('model.joblib')

14. What is the purpose of the describe() method in Pandas?

The describe() method in Pandas is used to generate descriptive statistics for a DataFrame or Series. It provides a summary of the central tendency, dispersion, and distribution of the data.

For numeric data, describe() calculates the count, mean, standard deviation, minimum, maximum, and quartile values. For non-numeric data, it provides the count and unique values.

Here’s an example:

import pandas as pddf = pd.DataFrame({'Age': [25, 30, 35, 40, 45],                   'Gender': ['M', 'F', 'M', 'F', 'M']})print(df.describe())

This will output:

       Agecount  5.0mean   35.0std     7.905694900786194min    25.025%    30.050%    35.075%    40.0max    45.0

15. How can you handle categorical variables in machine learning?

Categorical variables, also known as nominal or discrete variables, cannot be directly used in most machine learning algorithms, which expect numerical input. There are several techniques to handle categorical variables:

• One-Hot Encoding: Convert each category into a binary vector, with one column per category.
• Label Encoding: Assign a unique numerical label to each category.
• Target Encoding: Replace categories with their corresponding target mean or likelihood.
• Ordinal Encoding: Replace categories with ordinal numbers, if there is an inherent ordering.

The choice of technique depends on the characteristics of the data and the machine learning algorithm being used.

16. What is the difference between pandas.Series and numpy.array?

While pandas.Series and numpy.array are both data structures used for numerical computations, they have some key differences:

• A pandas.Series is a one-dimensional labeled array that can hold any data type, including NumPy arrays, while a numpy.array is a homogeneous (single data type) and efficient structure for mathematical operations.
• pandas.Series provides additional functionality for handling missing data, data alignment, and integration with other Pandas data structures like DataFrames.
• numpy.array is generally faster for numerical computations due to its homogeneous nature and optimized C implementation.

In data analysis workflows, pandas.Series is often used for manipulating and analyzing data, while numpy.array is used for efficient numerical computations.

17. How can you perform data visualization using Matplotlib in Python?

Matplotlib is a popular data visualization library in Python that provides a wide range of plotting capabilities. Here’s an example of how to create a simple line plot using Matplotlib:

import matplotlib.pyplot as pltimport numpy as np# Generate some datax = np.linspace(0, 10, 100)y = np.sin(x)# Create a line plotplt.plot(x, y)plt.xlabel('X')plt.ylabel('sin(X)')plt.title('Sine Wave')plt.show()

This code generates a sine wave plot with labeled axes and a title.

Matplotlib also supports various other plot types, such as scatter plots, bar charts, histograms, and more complex visualizations like subplots and contour plots.

18. What is the purpose of the groupby function in Pandas?

The groupby function in Pandas is used to group a DataFrame or Series based on one or more keys, and then apply a function or operation to each group.

This is particularly useful for data aggregation, where you want to calculate statistics or apply transformations to subsets of the data based on certain criteria.

Here’s an example of using groupby to calculate the mean of a column grouped by another column:

import pandas as pddf = pd.DataFrame({'Name': ['Alice', 'Bob', 'Alice', 'Charlie', 'Bob'],                   'Age': [25, 30, 27, 35, 32],                   'Score': [80, 75, 85, 90, 70]})grouped = df.groupby('Name')mean_scores = grouped['Score'].mean()print(mean_scores)

This will output:

NameAlice    82.5Bob      72.5Charlie  90.0Name: Score, dtype: float64

19. How can you handle large datasets in Python?

When working with large datasets in Python, memory limitations and performance issues can become a concern. Here are some techniques to handle large datasets efficiently:

• Use Pandas chunking: Instead of loading the entire dataset into memory, you can read and process data in chunks using the chunksize parameter in Pandas functions like read_csv().
• Use NumPy memory-mapped files: Memory-mapped files allow you to work with large arrays on disk as if they were in memory, without actually loading the entire array.
• Use Dask or Vaex: These libraries provide out-of-core and lazy evaluation capabilities for working with larger-than-memory datasets.
• Use database integration: Instead of loading the entire dataset into memory, you can use Pandas’ database integration to query and process data directly from a database.
• Parallelize computations: Use libraries like Dask or Numba to parallelize computations across multiple cores or machines.

The choice of technique depends on the specific requirements of your project and the available computational resources.

20. What is the purpose of the isnull() and notnull() methods in Pandas?

The isnull() and notnull() methods in Pandas are used to check for null or missing values in a DataFrame or Series.

• isnull() returns a boolean DataFrame or Series of the same shape, indicating whether each element is null (True) or not null (False).
• notnull() is the opposite of isnull(), returning True for non-null values and False for null values.

These methods are often used in conjunction with other Pandas functions to filter, fill, or handle missing data.

Here’s an example:

import pandas as pddf = pd.DataFrame({'A': [1, 2, None], 'B': [None,