Convert Boolean Values Practice Problem

This data science coding problem helps you practice Data Type Conversion & Validation, convert boolean values, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Data Type Conversion & Validation.

• Problem ID: 39
• Problem key: 39-convert-boolean-values
• URL: https://datacrack.app/solve/39-convert-boolean-values
• Difficulty: easy
• Topic: Data Type Conversion & Validation
• Module: Data Cleaning

Problem Statement

# Convert Boolean Values

### 🎯 Goal
Convert common string representations of boolean values into actual Python booleans.

### 💻 Task
Implement `convert_booleans(data, column)` that:
1. Converts the input dictionary to a DataFrame
2. Maps common boolean string representations to Python `True`/`False`
3. Handles case-insensitive matching for: `"Yes"/"No"`, `"True"/"False"`, `"T"/"F"`, `"1"/"0"`, `"Y"/"N"`
4. Returns the resulting DataFrame

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of boolean-like strings
- `column`: The name of the column to convert

### 📤 Output
- A pandas DataFrame with the specified column converted to Python booleans

---

### 🧩 Starter Code

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

def convert_booleans(data, column):
    """
    Convert common boolean string representations to Python booleans.

    Args:
        data (dict): Input data as dictionary with boolean-like strings
        column (str): Name of the column to convert

    Returns:
        pd.DataFrame: DataFrame with the column converted to booleans
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Define a mapping of common boolean string representations
    # TODO: Apply the mapping (case-insensitive) to the specified column
    # TODO: Return the resulting DataFrame
    pass
```

---

### 💡 Examples

**Example 1:** Yes/No values
```python
data = {"active": ["Yes", "No", "Yes", "No"]}
convert_booleans(data, "active")
```
```
   active
0    True
1   False
2    True
3   False
```

**Example 2:** T/F shorthand
```python
data = {"flag": ["T", "F", "T", "F", "T"]}
convert_booleans(data, "flag")
```
```
    flag
0   True
1  False
2   True
3  False
4   True
```

**Example 3:** Numeric 1/0 strings
```python
data = {"status": ["1", "0", "1", "1", "0"]}
convert_booleans(data, "status")
```
```
   status
0    True
1   False
2    True
3    True
4   False
```

Starter Code

import pandas as pd
import numpy as np

def convert_booleans(data, column):
    """
    Convert common boolean string representations to Python booleans.

    Args:
        data (dict): Input data as dictionary with boolean-like strings
        column (str): Name of the column to convert

    Returns:
        pd.DataFrame: DataFrame with the column converted to booleans
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Define a mapping of common boolean string representations
    # TODO: Apply the mapping (case-insensitive) to the specified column
    # TODO: Return the resulting DataFrame
    pass