String to Numeric Practice Problem

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

• Problem ID: 41
• Problem key: 41-string-to-numeric
• URL: https://datacrack.app/solve/41-string-to-numeric
• Difficulty: easy
• Topic: Data Type Conversion & Validation
• Module: Data Cleaning

Problem Statement

# String to Numeric

### 🎯 Goal
Convert string columns in a DataFrame to numeric types, gracefully handling non-numeric values by coercing them to `NaN`.

### 💻 Task
Implement `convert_to_numeric(data, columns)` that:
1. Converts the input dictionary to a DataFrame
2. Applies `pd.to_numeric` with `errors='coerce'` to each specified column
3. Returns the resulting DataFrame

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of string data
- `columns`: A list of column names to convert to numeric

### 📤 Output
- A pandas DataFrame with specified columns converted to numeric types (`NaN` for unconvertible values)

---

### 🧩 Starter Code

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

def convert_to_numeric(data, columns):
    """
    Convert string columns to numeric types using pd.to_numeric.

    Args:
        data (dict): Input data as dictionary with string values
        columns (list): List of column names to convert

    Returns:
        pd.DataFrame: DataFrame with specified columns converted to numeric
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Loop through each column and apply pd.to_numeric with errors='coerce'
    # TODO: Return the resulting DataFrame
    pass
```

---

### 💡 Examples

**Example 1:** Mixed valid and invalid strings
```python
data = {"price": ["10", "20.5", "thirty", "40"], "qty": ["5", "N/A", "15", "20"]}
convert_to_numeric(data, ["price", "qty"])
```
```
   price   qty
0   10.0   5.0
1   20.5   NaN
2    NaN  15.0
3   40.0  20.0
```

**Example 2:** All valid strings
```python
data = {"value": ["100", "200", "300"]}
convert_to_numeric(data, ["value"])
```
```
   value
0  100.0
1  200.0
2  300.0
```

**Example 3:** Placeholder strings become NaN
```python
data = {"score": ["85.5", "-", "90", "N/A", "78.3"]}
convert_to_numeric(data, ["score"])
```
```
   score
0   85.5
1    NaN
2   90.0
3    NaN
4   78.3
```

Starter Code

import pandas as pd
import numpy as np

def convert_to_numeric(data, columns):
    """
    Convert string columns to numeric types using pd.to_numeric.

    Args:
        data (dict): Input data as dictionary with string values
        columns (list): List of column names to convert

    Returns:
        pd.DataFrame: DataFrame with specified columns converted to numeric
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Loop through each column and apply pd.to_numeric with errors='coerce'
    # TODO: Return the resulting DataFrame
    pass