---
title: "Top 8 Python Data Engineering Interview Questions with SQL and Pandas Solutions"
description: "Prepare for Python Data Engineer interviews with these top SQL and Pandas exercises. Learn how to filter, aggregate, rank, and analyze data using MySQL CTEs and Pandas with step-by-step solutions and examples."
url: https://srinimf.com/2025/10/07/top-8-python-data-engineering-interview-questions-with-sql-and-pandas-solutions/
date: 2025-10-07
modified: 2026-10-10
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2025/10/pexels-photo-11674340.jpeg?fit=867%2C1300&ssl=1
categories: ["mysql", "Pandas", "Python"]
tags: ["Pandas exercises for Data Engineers", "Python Data Engineer Interview Questions"]
type: post
lang: en
---

# Top 8 Python Data Engineering Interview Questions with SQL and Pandas Solutions

Here are the SQL and Python(Pandas) [interview questions](https://srinimf.com/2024/05/11/10-python-interview-questions-slk-software/) recently asked in an interview.

## SQL interview Questions

## 1️⃣ Top 5 Customers with Highest Total Order Amount

**Question:**
Find the **top 5 customers** with the highest total order amount. Include the total sales for each customer and rank them using row numbers.

## **Table Details**

**Customers Table**

| Column | Type | Description |
| --- | --- | --- |
| CustomerID | INT | Unique identifier for the customer |
| CustomerName | NVARCHAR | Name of the customer |

**Orders Table**

| Column | Type | Description |
| --- | --- | --- |
| OrderID | INT | Unique identifier for the order |
| CustomerID | INT | Foreign key referencing Customers |
| OrderDate | DATETIME | Date and time when order was placed |

**OrderDetails Table**

| Column | Type | Description |
| --- | --- | --- |
| OrderDetailID | INT | Unique identifier for order detail |
| OrderID | INT | Foreign key referencing Orders |
| ProductID | INT | Foreign key referencing Products |
| Quantity | INT | Quantity of product ordered |
| UnitPrice | DECIMAL | Unit price of product |

**Products Table**

| Column | Type | Description |
| --- | --- | --- |
| ProductID | INT | Unique identifier for the product |
| ProductName | NVARCHAR | Name of the product |
| UnitPrice | DECIMAL | Unit price of the product |

Solution

```
WITH customer_totals AS (
    SELECT 
        c.CustomerID,
        c.CustomerName,
        SUM(od.Quantity * od.UnitPrice) AS TotalAmount
    FROM Customers c
    JOIN Orders o ON c.CustomerID = o.CustomerID
    JOIN OrderDetails od ON o.OrderID = od.OrderID
    GROUP BY c.CustomerID, c.CustomerName
),
ranked_customers AS (
    SELECT 
        CustomerID,
        CustomerName,
        TotalAmount,
        ROW_NUMBER() OVER (ORDER BY TotalAmount DESC) AS row_num
    FROM customer_totals
)
SELECT *
FROM ranked_customers
WHERE row_num <= 5;
```

## **2️⃣ List All Customers with Total Orders and Average Order Amount**

**Question:**
List **all customers** along with their **total number of orders** and **average order amount**. Include customers with zero orders.

```
WITH order_totals AS (
    SELECT 
        o.OrderID,
        o.CustomerID,
        SUM(od.Quantity * od.UnitPrice) AS OrderTotal
    FROM Orders o
    JOIN OrderDetails od ON o.OrderID = od.OrderID
    GROUP BY o.OrderID, o.CustomerID
)
SELECT 
    c.CustomerID,
    c.CustomerName,
    COUNT(DISTINCT ot.OrderID) AS TotalOrders,
    AVG(ot.OrderTotal) AS AvgOrderAmount
FROM Customers c
LEFT JOIN order_totals ot ON c.CustomerID = ot.CustomerID
GROUP BY c.CustomerID, c.CustomerName;
```

## **3️⃣ Sales by Month for a Specific Product in 2023**

**Question:**
Retrieve the **total sales by month** for a specific product in the year **2023**.

```
WITH product_sales AS (
    SELECT 
        od.ProductID,
        DATE_FORMAT(o.OrderDate, '%Y-%m') AS Month,
        SUM(od.Quantity * od.UnitPrice) AS TotalSales
    FROM OrderDetails od
    JOIN Orders o ON od.OrderID = o.OrderID
    WHERE od.ProductID = 123  -- Replace with your product ID
      AND YEAR(o.OrderDate) = 2023
    GROUP BY od.ProductID, DATE_FORMAT(o.OrderDate, '%Y-%m')
)
SELECT *
FROM product_sales
ORDER BY Month;
```

## **4️⃣ Customers Who Have Not Placed Any Orders in the Last 6 Months**

**Question:**
Retrieve a list of **customers who have not placed any orders in the last 6 months**.

```
WITH recent_orders AS (
    SELECT CustomerID
    FROM Orders
    WHERE OrderDate >= DATE_SUB(CURDATE(), INTERVAL 6 MONTH)
)
SELECT c.CustomerID, c.CustomerName
FROM Customers c
LEFT JOIN recent_orders ro 
ON c.CustomerID = ro.CustomerID
WHERE ro.CustomerID IS NULL;
```

## Pandas interview questions

```
data = [
    (1, "USA", "A", 200),
    (2, "Canada", "B", 300),
    (3, "India", "C", 50),
    (4, "USA", "D", 5000),
    (5, "India", "A", 800),
    (6, "Canada", "B", 9000),
    (7, "USA", "C", 2500),
    (8, "India", "D", 12000),
    (9, "Canada", "A", 500),
    (10, "USA", "B", 6000)
]

import pandas as pd

df = pd.DataFrame(data, columns=["ID", "Country", "Product", "Sales"])
```

## **1️⃣ Filter records where sales ≥ 100**

**Question:**
Filter out the records where the sales amount is less than 100.

**Solution:**

```
# Filter sales >= 100
df_filtered = df[df["Sales"] >= 100]
print(df_filtered)
```

Output:

```
ID Country Product  Sales
0   1     USA       A    200
1   2  Canada       B    300
3   4     USA       D   5000
4   5   India       A    800
5   6  Canada       B   9000
6   7     USA       C   2500
7   8   India       D  12000
8   9  Canada       A    500
9  10     USA       B   6000
```

## **2️⃣ Group by country and calculate total sales**

**Question:**
Group the sales by **country** and calculate the **total sales amount** for each country.

**Solution:**

```
df_grouped = df_filtered.groupby("Country", as_index=False)["Sales"].sum()
df_grouped.rename(columns={"Sales": "Total_Sales"}, inplace=True)
print(df_grouped)
```

Output:

```
Country  Total_Sales
0  Canada         9800
1   India        12800
2     USA        13700
```

## **3️⃣ Add a new column categorizing total sales**

**Question:**
Add a new column that categorizes **total sales** into `"High"`, `"Medium"`, and `"Low"`:

- `"High"` if total sales > 10,000
- `"Medium"` if total sales > 5,000 and ≤ 10,000
- `"Low"` if total sales ≤ 5,000

**Solution:**

```
def categorize(sales):
    if sales > 10000:
        return "High"
    elif sales > 5000:
        return "Medium"
    else:
        return "Low"

df_grouped["Category"] = df_grouped["Total_Sales"].apply(categorize)
print(df_grouped)
```

Output:

```
Country  Total_Sales Category
0  Canada         9800   Medium
1   India        12800     High
2     USA        13700     High
```

## **4️⃣ Optional: Rank countries by total sales**

**Question:**
Rank the countries by **total sales** using a numeric rank.

**Solution:**

```
df_grouped["Rank"] = df_grouped["Total_Sales"].rank(ascending=False, method="first")
print(df_grouped)
```

Output:

```
Country  Total_Sales Category  Rank
0  Canada         9800   Medium   3.0
1   India        12800     High   2.0
2     USA        13700     High   1.0
```

✅ **Notes:**

- Step 1 filters the dataset to remove small sales.
- Step 2 aggregates sales per country.
- Step 3 adds a categorical label based on thresholds.
- Step 4 shows [how to](https://srinimf.com/2022/11/14/how-to-convert-list-data-to-table-in-python/) rank items numerically in Pandas.

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