Reshape and Flatten Practice Problem

This data science coding problem helps you practice Array Creation & Attributes, reshape and flatten, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Array Creation & Attributes.

• Problem ID: 48
• Problem key: 48-reshape-and-flatten
• URL: https://datacrack.app/solve/48-reshape-and-flatten
• Difficulty: easy
• Topic: Array Creation & Attributes
• Module: NumPy Foundations

Problem Statement

# 🧩 Reshape and Flatten

---

### 🎯 Goal
`reshape` is one of the most important NumPy operations in machine learning. Data often arrives in one shape (e.g., a 1D stream of pixels) and needs to be transformed into another (e.g., a 2D image matrix). Understanding reshape prepares you to handle the constant shape manipulation in ML pipelines.

---

### 🔍 The Rule
Reshaping works as long as the **total number of elements stays the same**:
```
[1, 2, 3, 4, 5, 6]  →  reshape(2, 3)  →  [[1, 2, 3], [4, 5, 6]]
Total: 6                                  Total: 2 × 3 = 6  ✓
```

Flattening is just reshaping to 1D.

---

### 💻 Task
Implement `reshape_array(data, new_shape)` using `np.array().reshape()`.

---

### 📥 Input
- `data`: a Python list (any shape)
- `new_shape`: list — the desired shape, e.g., `[2, 3]`

### 📤 Output
- The reshaped array as a nested Python list

---

### 🧩 Starter Code
```python
import numpy as np

def reshape_array(data, new_shape):
    """
    Reshape a NumPy array to the specified shape.

    Args:
        data (list): Input data (any shape)
        new_shape (list): Target shape, e.g. [2, 3]

    Returns:
        list: Reshaped array as a nested Python list
    """
    arr = np.array(data)
    # 🧠 TODO: 
    pass
```

---

### 💡 Example
```python
reshape_array([1, 2, 3, 4, 5, 6], [2, 3])
# Expected: [[1, 2, 3], [4, 5, 6]]

reshape_array([[1, 2, 3], [4, 5, 6]], [6])
# Expected: [1, 2, 3, 4, 5, 6]  (flatten)
```

---

### 🔑 Key Concepts
- `arr.reshape(-1)` or `arr.flatten()` — both flatten to 1D
- `-1` in shape: NumPy infers that dimension automatically, e.g., `reshape(-1, 3)` means "figure out the number of rows"
- `arr.reshape(new_shape)` does **not** copy data (view), `arr.flatten()` always copies

Starter Code

import numpy as np

def reshape_array(data, new_shape):
    """
    Reshape a NumPy array to the specified shape.

    Args:
        data (list): Input data (any shape)
        new_shape (list): Target shape, e.g. [2, 3]

    Returns:
        list: Reshaped array as a nested Python list
    """
    arr = np.array(data)
    # 🧠 TODO: 
    pass

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