Fancy Indexing Practice Problem

This data science coding problem helps you practice Indexing, Slicing & Filtering, fancy indexing, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Indexing, Slicing & Filtering.

• Problem ID: 52
• Problem key: 52-fancy-indexing
• URL: https://datacrack.app/solve/52-fancy-indexing
• Difficulty: easy
• Topic: Indexing, Slicing & Filtering
• Module: NumPy Foundations

Problem Statement

# 🧩 Fancy Indexing

---

### 🎯 Goal
**Fancy indexing** lets you select arbitrary elements by providing a list of indices — not necessarily contiguous. This is how you randomly sample rows from a dataset, reorder features, or select the top-K elements after sorting.

---

### 🔍 How It Works
```python
arr = np.array([10, 20, 30, 40, 50])
arr[[0, 2, 4]]    # → [10, 30, 50]   (elements at indices 0, 2, 4)
arr[[4, 1, 3]]    # → [50, 20, 40]   (arbitrary order — indices control order!)
arr[[0, 0, 2]]    # → [10, 10, 30]   (can repeat indices)
```

---

### 💻 Task
Implement `fancy_index(data, indices)` using NumPy fancy indexing.

---

### 📥 Input
- `data`: list of elements
- `indices`: list of integers — the positions to select

### 📤 Output
- A list of elements at the specified indices, in the specified order

---

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

def fancy_index(data, indices):
    """
    Select elements at specified indices using fancy indexing.

    Args:
        data (list): Input data
        indices (list[int]): Indices to select

    Returns:
        list: Selected elements in the order specified by indices
    """
    arr = np.array(data)
    idx = np.array(indices)
    # 🧠 TODO
    pass
```

---

### 💡 Example
```python
fancy_index([10, 20, 30, 40, 50, 60, 70, 80, 90, 100], [0, 2, 4, 6])
# Expected: [10, 30, 50, 70]
```

---

### 🔑 Key Concepts
- **Fancy indexing picks exact positions you ask for**  
  Unlike slicing (`arr[1:4]`), you choose any indices in any order. 

```python
arr = np.array([10, 20, 30, 40, 50])
arr[[0, 2, 4]]   # [10, 30, 50]
arr[[4, 1, 3]]   # [50, 20, 40]
```

* The result follows the indices list order  
  NumPy returns values in the same order as the indices you provide.

```python
arr = np.array([10, 20, 30, 40, 50])
arr[[4, 0, 2]]   # [50, 10, 30] # index 4 first, then 0, then 2
```

* Indices can repeat values

```python
arr[[1, 1, 3]]   # [20, 20, 40]
```

* `np.argsort()` gives indices that sort an array  
  It returns the positions of elements in sorted order.  
  Then fancy indexing uses those positions to rearrange the array.

```python
arr = np.array([40, 10, 30])
idx = np.argsort(arr)   # [1, 2, 0]
arr[idx]                # [10, 30, 40]
```

Starter Code

import numpy as np

def fancy_index(data, indices):
    """
    Select elements at specified indices using fancy indexing.

    Args:
        data (list): Input data
        indices (list[int]): Indices to select

    Returns:
        list: Selected elements in the order specified by indices
    """
    arr = np.array(data)
    idx = np.array(indices)
    # 🧠 TODO
    pass