Insert or delete a step in scikit-learn Pipeline

I see that everyone mentioned only the delete step. In case you want to also insert a step in the pipeline:

pipe.steps.append(['step name',transformer()])

pipe.steps works in the same way as lists do, so you can also insert an item into a specific location:

pipe.steps.insert(1,['estimator',transformer()]) #insert as second step

Based on rudimentary testing you can safely remove a step from a scikit-learn pipeline just like you would any list item, with a simple

clf_pipeline.steps.pop(n)

where n is the position of the individual estimator you are trying to remove.

Just chiming in because I feel like the other answers answered the question of adding steps to a pipeline really well, but didn't really cover how to delete a step from a pipeline.

Watch out with my approach though. Slicing lists in this instance is a bit weird.

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures

estimators = [('reduce_dim', PCA()), ('poly', PolynomialFeatures()), ('svm', SVC())]
clf = Pipeline(estimators)

If you want to create a pipeline with just steps PCA/Polynomial you can just slice the list step by indexes and pass it to Pipeline

clf1 = Pipeline(clf.steps[0:2])

Want to just use steps 2/3? Watch out these slices don't always make the most amount of sense

clf2 = Pipeline(clf.steps[1:3])

Want to just use steps 1/3? I can't seem to do using this approach

clf3 = Pipeline(clf.steps[0] + clf.steps[2]) # errors