Validate Email Addresses Practice Problem

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

• Problem ID: 42
• Problem key: 42-validate-email-addresses
• URL: https://datacrack.app/solve/42-validate-email-addresses
• Difficulty: hard
• Topic: Data Type Conversion & Validation
• Module: Data Cleaning

Problem Statement

# Validate Email Addresses

### 🎯 Goal
Validate email addresses using a regex pattern and flag each row as valid or invalid.

### 💻 Task
Implement `validate_emails(data, column)` that:
1. Converts the input dictionary to a DataFrame
2. Uses a regex pattern to validate email format (must match `text@text.text` with at least 2 characters in the domain extension)
3. Adds a new boolean column `"{column}_valid"` indicating whether each email is valid
4. Returns the resulting DataFrame

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of email strings
- `column`: The name of the column containing email addresses

### 📤 Output
- A pandas DataFrame with the original column plus a new `"{column}_valid"` boolean column

---

### 🧩 Starter Code

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

def validate_emails(data, column):
    """
    Validate email addresses using regex and add a validity column.

    Args:
        data (dict): Input data as dictionary with email strings
        column (str): Name of the column containing emails

    Returns:
        pd.DataFrame: DataFrame with original column and a new boolean validity column
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Define a regex pattern for valid email format
    # TODO: Apply the pattern to create a boolean validity column
    # TODO: Return the resulting DataFrame
    pass
```

---

### 💡 Examples

**Example 1:** Mix of valid and invalid emails
```python
data = {"email": ["user@example.com", "invalid-email", "test@domain.org", "@missing.com", "name@.com"]}
validate_emails(data, "email")
```
```
               email  email_valid
0  user@example.com         True
1     invalid-email        False
2   test@domain.org         True
3      @missing.com        False
4         name@.com        False
```

**Example 2:** Short domains and missing extensions
```python
data = {"email": ["a@b.co", "user.name@domain.com", "user@domain", "test@test.c"]}
validate_emails(data, "email")
```
```
                  email  email_valid
0                a@b.co         True
1  user.name@domain.com         True
2           user@domain        False
3           test@test.c        False
```

**Example 3:** Empty strings are invalid
```python
data = {"email": ["hello@world.com", "", "test@123.org"]}
validate_emails(data, "email")
```
```
             email  email_valid
0  hello@world.com         True
1                         False
2     test@123.org         True
```