IQR Outlier Detection Practice Problem

This data science coding problem helps you practice Outlier Detection & Treatment, iqr outlier detection, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Outlier Detection & Treatment.

• Problem ID: 161
• Problem key: 161-iqr-outlier-detection
• URL: https://datacrack.app/solve/161-iqr-outlier-detection
• Difficulty: easy
• Topic: Outlier Detection & Treatment
• Module: Data Cleaning

Problem Statement

# IQR Outlier Detection

### 🎯 Goal
Detect outliers in a numeric column using the Interquartile Range (IQR) method.

### 💻 Task
Implement `detect_outliers_iqr(data, column)` that:
1. Converts the input dictionary to a DataFrame
2. Computes Q1 (25th percentile), Q3 (75th percentile), and IQR = Q3 − Q1
3. Defines outlier bounds as: lower = Q1 − 1.5 × IQR, upper = Q3 + 1.5 × IQR
4. Identifies values outside these bounds as outliers
5. Returns a dictionary with the bounds and detected outliers

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of numbers
- `column`: The name of the column to check for outliers

### 📤 Output
- A dictionary with:
  - `"lower_bound"` (float, rounded to 2 decimals)
  - `"upper_bound"` (float, rounded to 2 decimals)
  - `"outlier_indices"` (list of integer indices)
  - `"outliers"` (list of outlier values)

---

### 🧩 Starter Code

```python
import pandas as pd
import numpy as np

def detect_outliers_iqr(data, column):
    """
    Detect outliers using the IQR method.

    Args:
        data (dict): Input data as dictionary (from JSON)
        column (str): Column name to check for outliers

    Returns:
        dict: Dictionary with lower_bound, upper_bound, outlier_indices, outliers
    """
    # TODO: Convert input dictionary to a DataFrame
    # TODO: Compute Q1, Q3, and IQR
    # TODO: Calculate lower and upper bounds
    # TODO: Find outlier indices and values
    # TODO: Return result dictionary
    pass
```

---

### 💡 Examples

**Example 1:** Single upper outlier
```python
data = {"values": [1, 2, 3, 4, 5, 6, 7, 8, 9, 100]}
detect_outliers_iqr(data, "values")
```
```
{
    "lower_bound": -3.5,
    "upper_bound": 14.5,
    "outlier_indices": [9],
    "outliers": [100]
}
```

**Example 2:** Outliers on both ends
```python
data = {"values": [-100, 2, 4, 6, 8, 10, 12, 14, 16, 100]}
detect_outliers_iqr(data, "values")
```
```
{
    "lower_bound": -9.0,
    "upper_bound": 27.0,
    "outlier_indices": [0, 9],
    "outliers": [-100, 100]
}
```

Starter Code

import pandas as pd
import numpy as np

def detect_outliers_iqr(data, column):
    """
    Detect outliers using the IQR method.

    Args:
        data (dict): Input data as dictionary (from JSON)
        column (str): Column name to check for outliers

    Returns:
        dict: Dictionary with lower_bound, upper_bound, outlier_indices, outliers
    """
    # TODO: Convert input dictionary to a DataFrame
    # TODO: Compute Q1, Q3, and IQR
    # TODO: Calculate lower and upper bounds
    # TODO: Find outlier indices and values
    # TODO: Return result dictionary
    pass