Preparing for a data science or Python developer interview? Then you’re likely to encounter questions on Pandas and NumPy—two of the most essential Python libraries for data manipulation and numerical computing.
In this guide, we’ll cover the most frequently asked Pandas and NumPy interview questions, complete with answers and code examples to help you understand each concept better.
🧠 Why Learn Pandas and NumPy?
- NumPy: Ideal for numerical computations, working with arrays, and linear algebra.
- Pandas: Built for structured data operations using DataFrames and Series.
Whether you’re applying for a data analyst, data scientist, or backend developer role, mastering these libraries is a must.
🧪 Pandas and NumPy Interview Questions and Answers
✅ 1. What is NumPy?
Answer:
NumPy (Numerical Python) is a Python library used for numerical computations. It provides a powerful object called ndarray for efficient array operations.
pythonCopyEditimport numpy as np
arr = np.array([1, 2, 3])
✅ 2. What is Pandas?
Answer:
Pandas is a Python library used for data manipulation and analysis. It provides two main data structures: Series and DataFrame.
pythonCopyEditimport pandas as pd
data = pd.DataFrame({'Name': ['John', 'Alice'], 'Age': [25, 30]})
✅ 3. What is the difference between Series and DataFrame?
- Series: A one-dimensional labeled array.
- DataFrame: A two-dimensional labeled data structure like a table or spreadsheet.
✅ 4. How do you handle missing data in Pandas?
df.isnull()– Checks for missing values.df.dropna()– Removes missing values.df.fillna(value)– Replaces missing values with a specified value.
✅ 5. What is broadcasting in NumPy?
Answer:
Broadcasting allows NumPy to perform arithmetic operations on arrays of different shapes.
pythonCopyEditimport numpy as np
a = np.array([1, 2, 3])
b = 2
print(a + b) # Output: [3 4 5]
✅ 6. How do you filter rows in a Pandas DataFrame?
pythonCopyEditdf[df['Age'] > 25]
Returns all rows where the Age column is greater than 25.
✅ 7. What is the difference between loc[] and iloc[]?
loc[]is label-based (column or row names).iloc[]is index-based (integer positions).
pythonCopyEditdf.loc[0, 'Name'] # First row, Name column
df.iloc[0, 0] # First row, first column
✅ 8. How do you merge two DataFrames in Pandas?
pythonCopyEditpd.merge(df1, df2, on='ID', how='inner')
✅ 9. What is axis in Pandas?
axis=0: Perform operation along rows (vertical)axis=1: Perform operation along columns (horizontal)
✅ 10. How do you generate random numbers using NumPy?
pythonCopyEditnp.random.randint(1, 10, size=5)
Generates 5 random integers between 1 and 9.
✅ 11. How to sort a DataFrame in Pandas?
pythonCopyEditdf.sort_values(by='Age', ascending=False)
Sorts the DataFrame based on the ‘Age’ column in descending order.
✅ 12. How do you select specific columns from a DataFrame?
pythonCopyEditdf[['Name', 'Age']]
✅ 13. What is vectorization in NumPy?
Vectorization allows array operations without writing loops, resulting in faster and cleaner code.
pythonCopyEdita = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b) # Output: [5 7 9]
✅ 14. How do you group data in Pandas?
pythonCopyEditdf.groupby('Department')['Salary'].mean()
Groups the DataFrame by ‘Department’ and calculates the mean salary.
✅ 15. What is the shape of a NumPy array?
pythonCopyEditarr = np.array([[1, 2], [3, 4]])
print(arr.shape) # Output: (2, 2)
❓ FAQs: Pandas and NumPy Interview Questions
Q1. Are Pandas and NumPy enough for a data science interview?
Yes, they are foundational. However, knowledge of visualization, SQL, and machine learning is also important for advanced roles.
Q2. Should I memorize code or understand concepts?
Focus on understanding. Interviewers often test how you think and debug, not just syntax.
Q3. What version of Python should I prepare for?
Most interviews use Python 3.8+. Make sure you’re familiar with its syntax and library changes.
Q4. Can I use NumPy and Pandas together?
Absolutely! They’re often used together to preprocess and analyze data effectively.
These Pandas and NumPy interview questions will help you feel confident and well-prepared for your next technical round. Practice these regularly, and don’t just read the answers—try running the code, tweak it, and understand the output.