from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

X, _ = load_digits(return_X_y=True)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X_scaled)

print(f"original shape: {X.shape}")
print(f"scaled shape: {X_scaled.shape}")
print(f"reduced shape: {X_reduced.shape}")
print(f"components kept: {pca.n_components_}")
print(f"explained variance ratio: {pca.explained_variance_ratio_.round(4).tolist()}")
print(f"total explained variance: {pca.explained_variance_ratio_.sum():.4f}")
print(f"first reduced row: {X_reduced[0].round(3).tolist()}")