Pandas and NumPy Interview Questions and Answers PDF 2025

If you’re preparing for a job as a data analyst or data engineer, Pandas and NumPy are two tools you must know really well. These Python libraries help you work with data efficiently, data cleaning, transforming also analyzing huge datasets.

Interviewers often ask practical questions that test how well you can handle real-world problems using these libraries.

In this guide, we have shared a wide range of Pandas and NumPy interview questions. These Q&As will help you get ready for your next technical interview with confidence.

What is Pandas in Python?

Pandas is an open source Python library, it helps organize data in rows and columns, just like Excel but much more powerful. It can read files, sort information, find patterns in numbers, and create charts.

The jobs that use Pandas most include data scientists, data analysts, business analysts, research scientists, financial analysts, and marketing analysts.

What is NumPy?

NumPy helps computers work with lots of numbers at the same time, kind of like having a magic math box that can solve thousands of math problems instantly. Instead of doing math problems one by one, NumPy can handle huge lists of numbers all at once, making everything much faster.

The jobs that use NumPy most include data scientists, engineers, researchers, machine learning specialists, financial analysts, and scientists who work with lots of calculations.

Pandas Interview Questions and Answers

Que 1. How would you handle a very large CSV file (5GB+) using Pandas without running into memory issues?

Answer:
To manage large files, use the chunksize parameter in read_csv() to load the data in parts. Process each chunk iteratively to avoid loading the entire dataset into memory.

for chunk in pd.read_csv('large_file.csv', chunksize=100000):
    # process each chunk

Also, consider using dtypes to reduce memory usage and usecols to load only required columns.

Que 2. Explain the difference between loc[], iloc[], and at[] in Pandas with examples.

Answer:

• loc[] is label-based indexing.
• iloc[] is integer-position based indexing.
• at[] is optimized for accessing a single scalar value via label (faster than loc).

df.loc[2, 'name']      # label-based
df.iloc[2, 1]          # position-based
df.at[2, 'name']       # fast scalar access

Que 3. What are the performance considerations when applying functions to Pandas DataFrames?

Answer:
Avoid using apply() with lambda for row-wise operations—it’s slow. Prefer vectorized operations or NumPy functions for better performance. If needed, use cython, numba, or convert to DataFrame.to_numpy() for faster computation.

Que 4. How can you merge two DataFrames with different keys, and ensure no row loss?

Answer:
Use merge() with how='outer' to retain all rows from both DataFrames, regardless of matching keys:

pd.merge(df1, df2, how='outer', left_on='id1', right_on='id2')

Que 5. Describe a scenario where groupby() can be misused or misunderstood.

Answer:
Misuse often happens when forgetting to reset index after groupby().agg(), causing unexpected results in downstream merges or operations. Also, using groupby on high-cardinality columns can degrade performance.

Que 6. How would you find and fill missing values in time series data while preserving trends?

Answer:
Use forward fill (ffill) or backward fill (bfill) with caution. Better approaches include interpolation:

df['value'].interpolate(method='time')

This keeps the temporal trend intact, especially useful in stock or IoT data.

Que 7. How do you handle multi-indexed DataFrames during analysis or export?

Answer:
Flatten the multi-index using:

df.reset_index()

Or combine the levels:

df.columns = ['_'.join(col).strip() for col in df.columns.values]

This ensures compatibility with Excel/CSV exports or further joins.

Que 8. Explain the difference between pivot() and pivot_table().

Answer:

• pivot() fails if there are duplicate entries for the index/column combo.
• pivot_table() can aggregate those duplicates using functions like mean(), sum(), etc.

Use pivot_table() for production-grade analytics where duplicate groups may exist.

Que 9. How would you optimize Pandas for processing high-frequency financial tick data?

Answer:

Optimization Technique
Use proper datetime64 indexing
Store data in HDF5/Parquet for better I/O
Use rolling window operations for speed
Minimize type casting and column expansion

Que 10. How do you compare two large DataFrames for differences row by row?

Answer:
Use:

df1.compare(df2)

Or boolean mask:

df1 != df2

For performance on large sets, align DataFrames and convert to NumPy arrays.

Que 11. How do you detect and remove duplicate rows based on specific columns?

Answer:

df.drop_duplicates(subset=['col1', 'col2'], keep='first')

Always inspect with df.duplicated() before dropping to avoid data loss.

Que 12. How do you handle categorical data in Pandas for ML pipelines?

Answer:
Convert categories using:

• astype('category') for memory efficiency.
• pd.get_dummies() or LabelEncoder for ML-ready formats.

For large datasets, prefer category type to reduce RAM usage significantly.

Que 13. Explain the role of eval() and query() in improving Pandas performance.

Answer:
Both use the numexpr engine internally, allowing faster execution of expressions compared to native Python loops.

df.eval('new_col = col1 + col2')
df.query('col1 > 10 and col2 < 50')

Que 14. How do you extract rows with top 3 highest values for each group?

Answer:

df.groupby('group').apply(lambda x: x.nlargest(3, 'value'))

Or use transform() to rank and filter accordingly.

Que 15. How do you deal with timezone-aware datetime columns?

Answer:
Ensure consistent timezone using tz_convert() or tz_localize():

df['timestamp'] = df['timestamp'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')

This avoids errors in comparisons, joins, or plotting.

Also Check: Python Interview Questions and Answers

NumPy Interview Questions and Answers

Que 16. How does NumPy handle memory efficiency compared to Python lists?

Answer:
NumPy uses contiguous memory blocks and a fixed data type, which allows it to be more memory-efficient and faster than Python lists that store pointers to objects.

Que 17. What are broadcasting rules in NumPy?

Answer:
Broadcasting allows NumPy to perform arithmetic operations on arrays of different shapes. It follows a set of rules to stretch the smaller array across the larger one without copying data.

Que 18. How would you avoid unnecessary memory duplication while slicing arrays?

Answer:
By default, slicing in NumPy returns a view, not a copy. To avoid duplication, continue using views unless a copy is explicitly required using .copy().

Que 19. What is the difference between np.array_equal() and np.allclose()?

Answer:
np.array_equal() checks for exact equality, including shape and values, while np.allclose() checks if values are approximately equal within a given tolerance, useful for floating-point comparisons.

Que 20. How do you improve the performance of mathematical operations on large NumPy arrays?

Answer:
Use vectorized operations, avoid explicit loops, leverage broadcasting, and use in-place operations where possible to reduce memory overhead and improve speed.

Que 21. Explain the use of np.where() in NumPy.

Answer:
np.where() is used for conditional filtering and element selection. It returns indices or elements based on a condition, useful in constructing conditional arrays.

Que 22. How would you flatten a multi-dimensional array, and what are the trade-offs?

Answer:
Use ravel() (returns a view if possible) or flatten() (always returns a copy). Choose ravel() when performance matters and copying isn’t needed.

Que 23. What’s the use of np.meshgrid() in numerical computations?

Answer:
np.meshgrid() creates coordinate matrices from coordinate vectors. It’s essential in evaluating functions over a grid in 2D/3D plotting and numerical simulations.

Que 24. Describe the impact of dtype in NumPy operations.

Answer:
dtype defines the type of data (int, float, etc.) and its precision. Choosing an appropriate dtype improves memory usage and computation speed.

Que 25. How does NumPy handle missing or NaN values?

Answer:
NumPy supports NaN via np.nan, but operations involving NaNs generally return NaN. Use functions like np.isnan(), np.nanmean(), or mask invalid entries for handling.

Que 26. What are structured arrays in NumPy?

Answer:
Structured arrays allow different data types in one array (like a table). They’re useful for datasets that contain mixed-type records similar to a database table.

Que 27. Explain how you would optimize matrix operations in NumPy.

Answer:
Use @ or np.dot() for matrix multiplication, prefer in-place operations, use BLAS/LAPACK-accelerated libraries, and avoid reshaping arrays unnecessarily.

Que 28. How does NumPy support linear algebra operations?

Answer:
NumPy provides np.linalg module with functions like inv(), eig(), svd(), and solve() for matrix inversion, eigenvalues, singular value decomposition, and solving linear systems.

Que 29. What is memory layout in NumPy (C vs Fortran order), and why does it matter?

Answer:
C-order is row-major, Fortran-order is column-major. It impacts performance in matrix operations. Choose the right layout to optimize cache usage and computation speed.

Que 30. How would you handle large datasets that don’t fit in memory using NumPy?

Answer:
Use memory-mapped files (np.memmap) to read large binary data in chunks, enabling efficient processing without loading everything into RAM.

Let me know if you’d like an intro, PDF section, or want to continue with another topic like Pandas, SciPy, or TensorFlow.

Pandas and NumPy Interview Questions and Answers for Data Analyst

Que 31. How would you handle missing data in a Pandas DataFrame?

Answer:
You can handle missing data using dropna() to remove rows/columns with null values, fillna() to replace them with a value like mean or median, or interpolate values using interpolate() for continuous data.

Que 32. What is the difference between .loc[], .iloc[], and .at[] in Pandas?

Answer:
.loc[] is label-based indexing, .iloc[] is integer-based indexing, and .at[] is optimized for accessing a single scalar value using label-based indexing, offering better performance than .loc[].

Que 33. How would you join two large DataFrames in Pandas efficiently?

Answer:
Use merge() with appropriate on and how parameters, and set indexes or sort keys beforehand. For very large datasets, consider chunking or using categorical data types to optimize memory.

Que 34. How do you identify and remove duplicate rows in a DataFrame?

Answer:
Use df.duplicated() to identify duplicates and df.drop_duplicates() to remove them. You can also specify columns with the subset parameter and control which duplicate to keep using keep.

Que 35. What are the key differences between NumPy arrays and Pandas Series?

Answer:
NumPy arrays are homogeneous and indexed by position, while Pandas Series are one-dimensional labeled arrays, allowing mixed data types and better alignment with real-world datasets.

Que 36. How do you calculate rolling statistics in Pandas?

Answer:
Use the rolling() method followed by an aggregation function. For example, df['sales'].rolling(window=7).mean() computes a 7-day moving average, useful for trend analysis.

Que 37. When would you use groupby() in Pandas?

Answer:
Use groupby() to split data into groups, apply aggregation functions (like sum(), mean()), and combine results. It’s powerful for summarizing and transforming data by category.

Que 38. How can you reshape data using NumPy and Pandas?

Answer:
Use NumPy’s reshape() and ravel() for multi-dimensional arrays. In Pandas, use pivot(), melt(), and stack()/unstack() to reshape data between wide and long formats.

Que 39. What’s the difference between np.mean() and np.average()?

Answer:
np.mean() computes the arithmetic mean, while np.average() allows weighting the values via a weights parameter. Use np.average() when values have different importances.

Que 40. How would you detect and handle outliers using Pandas and NumPy?

Answer:
You can use statistical methods like IQR (quantile()), Z-score (scipy.stats.zscore() or manual formula), or visual methods like boxplots to detect outliers. Handling involves filtering, capping, or transforming them based on context.

Also Check: Data Analyst Interview Questions and Answers

Pandas and NumPy Interview Questions and Answers for Data engineer

Que 41. How do you handle very large datasets in Pandas without running out of memory?

Answer:
Use chunksize while reading data (e.g., pd.read_csv()), downcast numeric data types to save memory, drop unused columns early, use category for repeated strings, and consider Dask or Vaex for distributed computation.

Que 42. How would you perform an efficient join of two large datasets using Pandas?

Answer:
Set appropriate indexes or keys before joining, reduce memory footprint by selecting only needed columns, and use merge() with sorted keys or hashed values. Also, consider merging in chunks if datasets are huge.

Que 43. Explain the difference between shallow copy and deep copy in NumPy and Pandas.

Answer:
A shallow copy creates a new object but refers to the same data (view in NumPy), while a deep copy creates a new object with a completely new copy of the data (copy() method in both libraries).

Que 44. How do you apply transformations efficiently to entire columns in Pandas?

Answer:
Use vectorized operations like df['col'] = df['col'] * 2 instead of row-wise loops. You can also use apply(), map(), or transform() when working with custom logic, but prefer native vectorized methods for speed.

Que 45. How would you optimize a DataFrame with 100+ million rows?

Answer:
Use dtype optimization (e.g., convert float64 to float32), use category types for repeated strings, drop null-heavy columns, use filters before joins, and avoid loading all data at once—use generators or Dask.

Que 46. How can you write and read compressed DataFrames in Pandas?

Answer:
Use compression parameter in read_csv() and to_csv(). For example:
df.to_csv('data.csv.gz', compression='gzip')
and
pd.read_csv('data.csv.gz', compression='gzip').

Que 47. How do you use NumPy broadcasting in matrix computations?

Answer:
Broadcasting allows arithmetic operations between arrays of different shapes. For example:

a = np.array([1, 2, 3])
b = np.array([[1], [2], [3]])
result = a + b  # shape becomes (3, 3)

It’s memory efficient and avoids explicit looping.

Que 48. How can you handle datetime manipulation in large Pandas datasets?

Answer:
Use pd.to_datetime() to parse dates, then access components like .dt.year, .dt.month, etc. Set parse_dates=True when reading CSVs. Use .resample() for time series aggregation and downsample with asfreq().

Que 49. How do you detect data type mismatches in a DataFrame?

Answer:
Use df.dtypes to inspect column types. Combine with applymap() or pd.to_numeric(errors='coerce') to catch or convert incompatible types. Type mismatches can silently break joins, merges, and aggregations.

Que 50. What’s the role of np.where() in conditional operations?

Answer:
np.where() is used for element-wise conditional logic:

np.where(condition, value_if_true, value_if_false)

It is much faster than using for loops or apply() in Pandas for large arrays.

Que 51. How do you use .eval() and .query() in Pandas for performance?

Answer:
.eval() and .query() allow faster, memory-efficient operations by using a parser optimized for expressions.
Example:

df.eval('new_col = col1 + col2', inplace=True)  
df.query('col1 > 100 and col2 < 50')

These are useful for filtering and calculating on large datasets without creating intermediate copies.

Also Check: Data Engineer Interview Questions and Answers

Pandas and NumPy Interview Questions PDF

We have compiled all the Pandas and NumPy interview questions and answers in a PDF. It’s easy to read, covers all key topics, and perfect for last minute revision before your interview.

FAQs: Pandas and NumPy Interview Questions