Validate Phone Numbers Practice Problem

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

• Problem ID: 43
• Problem key: 43-validate-phone-numbers
• URL: https://datacrack.app/solve/43-validate-phone-numbers
• Difficulty: medium
• Topic: Data Type Conversion & Validation
• Module: Data Cleaning

Problem Statement

# Validate Phone Numbers

### 🎯 Goal
Validate phone numbers by checking that they contain between 10 and 15 digits after removing common separators.

### 💻 Task
Implement `validate_phone_numbers(data, column)` that:
1. Converts the input dictionary to a DataFrame
2. For each phone number, strips separators (`-`, spaces, parentheses, `+`, `.`) and checks if the remaining characters are 10–15 digits
3. Adds a new boolean column `"{column}_valid"` indicating validity
4. Returns the resulting DataFrame

---

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

### 📤 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_phone_numbers(data, column):
    """
    Validate phone numbers by digit count after removing separators.

    Args:
        data (dict): Input data as dictionary with phone number strings
        column (str): Name of the column containing phone numbers

    Returns:
        pd.DataFrame: DataFrame with original column and a new boolean validity column
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Define a function to validate a single phone number
    # TODO: Remove separators (-, spaces, parens, +, .) and check digit count (10-15)
    # TODO: Apply the validation function to create the validity column
    # TODO: Return the resulting DataFrame
    pass
```

---

### 💡 Examples

**Example 1:** Various formats
```python
data = {"phone": ["+1-555-123-4567", "(555) 123-4567", "5551234567", "123", "abc"]}
validate_phone_numbers(data, "phone")
```
```
             phone  phone_valid
0  +1-555-123-4567         True
1   (555) 123-4567         True
2       5551234567         True
3              123        False
4              abc        False
```

**Example 2:** International numbers
```python
data = {"phone": ["+44 20 7946 0958", "12345", "+91-9876543210"]}
validate_phone_numbers(data, "phone")
```
```
              phone  phone_valid
0  +44 20 7946 0958         True
1             12345        False
2    +91-9876543210         True
```

**Example 3:** Dot-separated and empty
```python
data = {"phone": ["555.123.4567", "+1 (555) 123-4567", ""]}
validate_phone_numbers(data, "phone")
```
```
               phone  phone_valid
0       555.123.4567         True
1  +1 (555) 123-4567         True
2                          False
```