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Implement z-Score Standardization

Data Processing

Easy

Standardize features to zero mean and unit variance.

z = \frac{x - \mu}{\sigma}

with μ mean and σ standard deviation.

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Examples

Input: X = np.array([[1,2],[3,6],[5,10]])

Output: Standardized columns (mean≈0, std≈1)

Hint 1

Use np.mean() and np.std() with the appropriate axis and keepdims=True.

Hint 2

Use std + eps in the denominator to avoid division by zero when standard deviation is 0.

Requirements

Handle 1D and 2D arrays
Vectorized; avoid divide-by-zero (use eps)
Return np.ndarray (float)

Constraints

Up to 10⁶ elements
NumPy only

Try Similar Problems

Min Max Scaling Robust Scaling Log Transform Rank Transform Matrix Normalization

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Case 1

X =

Accepts: array

axis =

Accepts: number

eps =

Accepts: number

You must run your code first.

Implement z-Score Standardization

Data Processing

Easy

Standardize features to zero mean and unit variance.

z = \frac{x - \mu}{\sigma}

with μ mean and σ standard deviation.

Loading visualization...

Examples

Input: X = np.array([[1,2],[3,6],[5,10]])

Output: Standardized columns (mean≈0, std≈1)

Hint 1

Use np.mean() and np.std() with the appropriate axis and keepdims=True.

Hint 2

Use std + eps in the denominator to avoid division by zero when standard deviation is 0.

Requirements

Handle 1D and 2D arrays
Vectorized; avoid divide-by-zero (use eps)
Return np.ndarray (float)

Constraints

Up to 10⁶ elements
NumPy only

Try Similar Problems

Min Max Scaling Robust Scaling Log Transform Rank Transform Matrix Normalization

Sign in to take notes on this problem

Case 1

X =

Accepts: array

axis =

Accepts: number

eps =

Accepts: number

You must run your code first.