Array Slicing Practice Problem

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

• Problem ID: 49
• Problem key: 49-array-slicing
• URL: https://datacrack.app/solve/49-array-slicing
• Difficulty: easy
• Topic: Indexing, Slicing & Filtering
• Module: NumPy Foundations

Problem Statement

# 🧩 Array Slicing

---

### 🎯 Goal
NumPy slicing is the foundation of all data selection. Before you learn Pandas `.loc[]` and `.iloc[]`, you need fluency in NumPy's slice notation — including 1D slicing, 2D (row, column) slicing, and reverse slicing.

---

### 🔍 Slice Notation: `start:stop:step`

**1D array:**
```python
arr = np.array([10, 20, 30, 40, 50])
arr[1:4]      # → [20, 30, 40]       (indices 1,2,3)
arr[::2]      # → [10, 30, 50]       (every 2nd)
arr[::-1]     # → [50, 40, 30, 20, 10]  (reversed)
```

**2D array (matrix):**
```python
M[row_start:row_stop, col_start:col_stop]
M[:2, 1:3]   # first 2 rows, cols 1 and 2
```

---

### 💻 Task
Implement two functions:
1. `slice_1d(data, start, stop, step)` — slice a 1D array
2. `slice_2d(data, row_start, row_stop, col_start, col_stop)` — slice a 2D array

The test harness calls either function based on which parameters are provided.

---

### 📥 Input

For 1D slicing:
- `data`: list of numbers
- `start`, `stop`, `step`: int

For 2D slicing:
- `data`: 2D list (matrix)
- `row_start`, `row_stop`, `col_start`, `col_stop`: int

### 📤 Output
- Sliced array as a Python list

---

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

def slice_data(data, **kwargs):
    """
    Handle array slicing for both 1D and 2D arrays in one function.
    Automatically detects dimensions and calls the appropriate slicer.
    """
    arr = np.array(data)

    def slice_1d(arr, start, stop, step):
        # 🧠 TODO:

    def slice_2d(arr, row_start, row_stop, col_start, col_stop):
        # 🧠 TODO:

    if arr.ndim == 1:
        return slice_1d(arr, kwargs['start'], kwargs['stop'], kwargs['step'])
    elif arr.ndim == 2:
        return slice_2d(arr, kwargs['row_start'], kwargs['row_stop'], kwargs['col_start'], kwargs['col_stop'])
    else:
        raise ValueError("Only 1D and 2D arrays are supported.")

# Example usage:
# res_1d = slice_data([1, 2, 3, 4, 5], 0, 3, 1)
# res_2d = slice_data([[1, 2], [3, 4]], 0, 1, 0, 1)

```

---

### 🔑 Key Concepts
- `step = -1` reverses the array
- In 2D: `arr[rows, cols]` — rows and columns are separate slice axes
- `arr[0, :]` = first row (all columns); `arr[:, 0]` = first column (all rows)

Starter Code

import numpy as np

def slice_data(data, **kwargs):
    """
    Handle array slicing for both 1D and 2D arrays in one function.
    Automatically detects dimensions and calls the appropriate slicer.
    """
    arr = np.array(data)

    def slice_1d(arr, start, stop, step):
        # 🧠 TODO:

    def slice_2d(arr, row_start, row_stop, col_start, col_stop):
        # 🧠 TODO:

    if arr.ndim == 1:
        return slice_1d(arr, kwargs['start'], kwargs['stop'], kwargs['step'])
    elif arr.ndim == 2:
        return slice_2d(arr, kwargs['row_start'], kwargs['row_stop'], kwargs['col_start'], kwargs['col_stop'])
    else:
        raise ValueError("Only 1D and 2D arrays are supported.")

# Example usage:
# res_1d = slice_data([1, 2, 3, 4, 5], 0, 3, 1)
# res_2d = slice_data([[1, 2], [3, 4]], 0, 1, 0, 1)