Here's an example implementation of Principal Component Analysis (PCA) using NumPy:

```
import numpy as np

def pca(X):
    # subtract the mean for each column
    X = X - np.mean(X, axis=0)

    # calculate the covariance matrix
    cov = np.cov(X, rowvar=False)

    # calculate the eigenvectors and eigenvalues of the covariance matrix
    eigenvectors = np.linalg. eigen(cov)
    eigenvalues = np.diag(eigenvectors)

    # sort the eigenvectors in descending order based on the corresponding eigenvalues
    sorted_eigenvectors = eigenvalues.sort_index(False)[:, np.newaxis]
    sorted_eigenvectors = sorted_eigenvectors[::-1]

    # return the sorted eigenvectors as the principal components
    return sorted_eigenvectors[:, :].reshape(-1,)
```

This implementation first subtracts the mean for each column of the input data X. Then it calculates the covariance matrix of X using the np.cov() function. Next, it calculates the eigenvectors and eigenvalues of the covariance matrix using the np.linalg. eigen() function. Finally, it sorts the eigenvectors based on their corresponding eigenvalues and returns the top m eigenvectors as the principal components, where m is the number of columns in the original data.
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