Nested Lists Basics Practice Problem

This data science coding problem helps you practice Lists & Basic Data Structures, nested lists basics, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Lists & Basic Data Structures.

• Problem ID: 73
• Problem key: 73-nested-lists-basics
• URL: https://datacrack.app/solve/73-nested-lists-basics
• Difficulty: easy
• Topic: Lists & Basic Data Structures
• Module: Python Fundamentals

Problem Statement

# 🧩 Nested Lists Basics

---

### 🎯 Goal
A **nested list** (list of lists) is Python's way of representing 2D data — tables, matrices, grids, and spreadsheets. Understanding how to navigate nested lists is the bridge to NumPy arrays and Pandas DataFrames.

---

### 🔍 How Nested Lists Work

```python
matrix = [
    [1, 2, 3],    # Row 0
    [4, 5, 6],    # Row 1
    [7, 8, 9]     # Row 2
]

matrix[0]       # [1, 2, 3]  — entire row 0
matrix[1][2]    # 6          — row 1, column 2
len(matrix)     # 3          — number of rows
len(matrix[0])  # 3          — number of columns
```

> 💡 Think of it as: **first index = row**, **second index = column**.

---

### 💻 Task
Implement `explore_matrix(matrix, row, col)` that returns a dictionary with:
- `element`: the value at `matrix[row][col]`
- `row_data`: the entire row at `matrix[row]`
- `num_rows`: total number of rows
- `num_cols`: number of columns (using the first row)

---

### 📥 Input
- `matrix`: list of lists (a 2D grid, guaranteed rectangular)
- `row`: int — the row index
- `col`: int — the column index

### 📤 Output
- A dictionary: `{"element": ..., "row_data": ..., "num_rows": ..., "num_cols": ...}`

---

### 🧩 Starter Code
```python
def explore_matrix(matrix, row, col):
    """
    Extract information from a 2D matrix (list of lists).

    Args:
        matrix (list[list]): A 2D grid of values
        row (int): Row index
        col (int): Column index

    Returns:
        dict: Dictionary with 'element', 'row_data', 'num_rows', 'num_cols'
    """
    # 🧠 TODO: Access the element at [row][col]
    # 🧠 TODO: Get the entire row
    # 🧠 TODO: Count rows and columns
    pass
```

---

### 💡 Example
```python
explore_matrix([[1,2,3],[4,5,6],[7,8,9]], 1, 2)
# Expected: {"element": 6, "row_data": [4, 5, 6], "num_rows": 3, "num_cols": 3}
```

---

### 🔑 Key Concepts
- `matrix[row]` returns a list (the entire row)
- `matrix[row][col]` chains two index operations to get a single element
- `len(matrix)` = number of rows, `len(matrix[0])` = number of columns
- This is the same `[row, col]` convention used by NumPy: `array[row, col]`

Starter Code

def explore_matrix(matrix, row, col):
    """
    Extract information from a 2D matrix (list of lists).

    Args:
        matrix (list[list]): A 2D grid of values
        row (int): Row index
        col (int): Column index

    Returns:
        dict: Dictionary with 'element', 'row_data', 'num_rows', 'num_cols'
    """
    # 🧠 TODO: Access the element at [row][col]
    # 🧠 TODO: Get the entire row
    # 🧠 TODO: Count rows and columns
    pass