Z-Score Standardization Practice Problem

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

• Problem ID: 122
• Problem key: 122-z-score-standardization
• URL: https://datacrack.app/solve/122-z-score-standardization
• Difficulty: easy
• Topic: Feature Scaling & Transformation
• Module: Data Cleaning

Problem Statement

# Z-Score Standardization

### 🎯 Goal
Standardize numeric columns so they have zero mean and unit variance using Z-Score scaling.

### 💻 Task
Implement `z_score_standardize(data)` that:
1. Converts the input dictionary to a DataFrame
2. Applies the Z-Score formula to each numeric column: `(x - mean) / std`
3. Uses pandas default standard deviation (ddof=1, sample std)
4. Returns the standardized DataFrame rounded to 2 decimal places

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of numeric data

### 📤 Output
- A pandas DataFrame with all numeric columns standardized (mean ≈ 0, std ≈ 1), rounded to 2 decimals

---

### 🧩 Starter Code

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

def z_score_standardize(data):
    """
    Standardize numeric columns using Z-Score (mean=0, std=1).

    Args:
        data (dict): Input data as dictionary (from JSON)

    Returns:
        pd.DataFrame: DataFrame with standardized values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply (x - mean) / std to each numeric column
    # TODO: Round the result to 2 decimal places and return
    pass
```

---

### 💡 Examples

**Example 1:** Single column with 5 values
```python
data = {"A": [1.0, 2.0, 3.0, 4.0, 5.0]}
z_score_standardize(data)
```
```
      A
0 -1.26
1 -0.63
2  0.00
3  0.63
4  1.26
```

**Example 2:** Evenly spaced values
```python
data = {"X": [10.0, 20.0, 30.0]}
z_score_standardize(data)
```
```
     X
0 -1.0
1  0.0
2  1.0
```

**Example 3:** Scaled version of Example 1
```python
data = {"A": [100.0, 200.0, 300.0, 400.0, 500.0]}
z_score_standardize(data)
```
```
      A
0 -1.26
1 -0.63
2  0.00
3  0.63
4  1.26
```

Starter Code

import pandas as pd
import numpy as np

def z_score_standardize(data):
    """
    Standardize numeric columns using Z-Score (mean=0, std=1).

    Args:
        data (dict): Input data as dictionary (from JSON)

    Returns:
        pd.DataFrame: DataFrame with standardized values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply (x - mean) / std to each numeric column
    # TODO: Round the result to 2 decimal places and return
    pass

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