top of page

Pandas Interview Questions Every Data Engineer Must Know

In today’s data-driven world, Pandas has become an essential tool for every Data Engineer. Whether you're building ETL pipelines, transforming large datasets, or preparing data for analytics and machine learning, Pandas plays a critical role in delivering clean, structured, and high-quality data


ree

1. What makes Pandas different from NumPy?

Answer:Pandas is built on top of NumPy but provides labeled, tabular, and time-series data structures like DataFrame and Series.It supports:

  • Missing value handling

  • Grouping

  • Joins

  • Indexing

  • Complex data manipulation

Pandas is preferred for structured data, whereas NumPy is ideal for numerical arrays.


2. What is a DataFrame? Why is it powerful?

Answer:A DataFrame is a 2D labeled table similar to SQL tables or Excel sheets.

Why powerful?

  • Heterogeneous data allowed

  • Fast filtering & grouping

  • Built-in I/O (CSV, SQL, Parquet)

  • Rich indexing features

Essential for ETL and analytics tasks.


3. What is the difference between .loc and .iloc?

Answer:

  • loc: Label-based indexing

df.loc["row_label", "column_label"]

  • iloc: Position-based indexing

df.iloc[3, 2]

loc is flexible for named indexes; iloc is used for fast numeric-based access.


4. How do you handle missing values in Pandas?

Answer:Most used methods:

df.isna()
df.dropna()
df.fillna(value)
df.fillna(method="ffill")  # forward fill

Data Engineers use these during data cleaning before loading to warehouses or ML pipelines.


5. Explain groupby() with a real example.

Answer:groupby() splits → applies function → combines results.

Example:

df.groupby("department")["salary"].mean()

Use case:Department-level aggregations in HR, Sales, Finance datasets.


6. How do you merge two DataFrames?

Answer:Using merge() with SQL-like joins:

pd.merge(df1, df2, on="id", how="left")

Supports: inner, left, right, outer joins.Critical for combining tables in ETL pipelines.


7. What is the difference between apply(), map(), and applymap()?

Answer:

Function

Works On

Purpose

map()

Series

Element-wise

apply()

Series/DataFrame

Row/column-wise

applymap()

DataFrame

Element-wise for full table

Use case:apply() is commonly used for feature engineering.


8. How do you improve performance in Pandas?

Answer:

  • Use vectorized operations (avoid Python loops)

  • Use categorical types for repeated values

  • Convert objects to numeric where possible

  • Use df.itertuples() for fast iteration

  • Use chunk processing for large files

Real life: Helps process millions of rows efficiently.


9. What is the difference between pivot() and pivot_table()?

Answer:

  • pivot(): Does not allow duplicates.

  • pivot_table(): Allows duplicates and supports aggregation functions.

Example:

df.pivot_table(values="sales", index="region", columns="month", aggfunc="sum")

10. How do you read large CSV files efficiently in Pandas?

Answer:Best practices:

pd.read_csv(..., chunksize=50000)
pd.read_csv(..., dtype={"col": "category"})
pd.read_csv(..., usecols=[...])

Chunking + dtype optimization is crucial for Data Engineers processing large datasets.


 
 
 

Comments

bottom of page