Interpreting the results of randomized PCA in scikit-learn
Yes, through the components_ property:

import numpy, seaborn, pandas, sklearn.decomposition
data = numpy.random.randn(1000, 3) @ numpy.random.randn(3,3)
seaborn.pairplot(pandas.DataFrame(data, columns=['x', 'y', 'z']));




sklearn.decomposition.RandomizedPCA().fit(data).components_

&gt; array([[ 0.43929754,  0.81097276,  0.38644644],
       [-0.54977152,  0.58291122, -0.59830243],
       [ 0.71047094, -0.05037554, -0.70192119]])

sklearn.decomposition.RandomizedPCA(2).fit(data).components_

&gt; array([[ 0.43929754,  0.81097276,  0.38644644],
       [-0.54977152,  0.58291122, -0.59830243]])


We see that the truncated decomposition is simply the truncation of the full decomposition. Each row contains the coefficients of the corresponding principal component.