Log Transformation Practice Problem

This data science coding problem helps you practice Feature Scaling & Transformation, log transformation, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Feature Scaling & Transformation.

• Problem ID: 34
• Problem key: 34-log-transformation
• URL: https://datacrack.app/solve/34-log-transformation
• Difficulty: medium
• Topic: Feature Scaling & Transformation
• Module: Data Cleaning

Problem Statement

# Log Transformation

### 🎯 Goal
Apply logarithmic transformations to reduce skewness in numeric data.

### 💻 Task
Implement `log_transform(data, method='log1p')` that:
1. Converts the input dictionary to a DataFrame
2. Applies the specified log method to all numeric columns:
   - `'log1p'` → `np.log1p(x)` — natural log of (1 + x), safe for zeros
   - `'log10'` → `np.log10(x)` — base-10 logarithm
   - `'log2'` → `np.log2(x)` — base-2 logarithm
3. Returns the transformed DataFrame rounded to 2 decimal places

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of numeric data
- `method`: One of `'log1p'`, `'log10'`, or `'log2'`

### 📤 Output
- A pandas DataFrame with log-transformed values, rounded to 2 decimals

---

### 🧩 Starter Code

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

def log_transform(data, method='log1p'):
    """
    Apply logarithmic transformation to reduce skewness.

    Args:
        data (dict): Input data as dictionary (from JSON)
        method (str): 'log1p', 'log10', or 'log2'

    Returns:
        pd.DataFrame: DataFrame with log-transformed values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply the appropriate log function based on method
    # TODO: Round the result to 2 decimal places and return
    pass
```

---

### 💡 Examples

**Example 1:** log1p (safe for zeros)
```python
data = {"A": [0.0, 1.0, 2.0, 3.0, 4.0]}
log_transform(data, method='log1p')
```
```
      A
0  0.00
1  0.69
2  1.10
3  1.39
4  1.61
```

**Example 2:** log10 (powers of 10)
```python
data = {"A": [1.0, 10.0, 100.0, 1000.0]}
log_transform(data, method='log10')
```
```
     A
0  0.0
1  1.0
2  2.0
3  3.0
```

**Example 3:** log2 (powers of 2)
```python
data = {"A": [1.0, 2.0, 4.0, 8.0, 16.0]}
log_transform(data, method='log2')
```
```
     A
0  0.0
1  1.0
2  2.0
3  3.0
4  4.0
```

Starter Code

import pandas as pd
import numpy as np

def log_transform(data, method='log1p'):
    """
    Apply logarithmic transformation to reduce skewness.

    Args:
        data (dict): Input data as dictionary (from JSON)
        method (str): 'log1p', 'log10', or 'log2'

    Returns:
        pd.DataFrame: DataFrame with log-transformed values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply the appropriate log function based on method
    # TODO: Round the result to 2 decimal places and return
    pass