General Purpose Pipeline library?

[Jason]

a pipeline can be described as a sequence of functions that are applied to an input with each subsequent function getting the output of the preceding function:

out = f6(f5(f4(f3(f2(f1(in))))))

However this isn't very readable and does not support conditionals.

Tensorflow has tensor-focused pipepines:
    fc1 = layers.fully_connected(x, 256, activation_fn=tf.nn.relu, scope='fc1')
    fc2 = layers.fully_connected(fc1, 256, activation_fn=tf.nn.relu, scope='fc2')
    out = layers.fully_connected(fc2, 10, activation_fn=None, scope='out')

I have some code which allows me to mimic this, but with an implied parameter.

def executePipeline(steps, collection_funcs = [map, filter, reduce]):
	results = None
	for step in steps:
		func = step[0]
		params = step[1]
		if func in collection_funcs:
			print func, params[0]
			results = func(functools.partial(params[0], *params[1:]), results)
		else:
			print func
			if results is None:
				results = func(*params)
			else:
				results = func(*(params+(results,)))
	return results

executePipeline( [
				(read_rows, (in_file,)),
				(map, (lower_row, field)),
				(stash_rows, ('stashed_file', )),
				(map, (lemmatize_row, field)),
				(vectorize_rows, (field, min_count,)),
				(evaluate_rows, (weights, None)),
				(recombine_rows, ('stashed_file', )),
				(write_rows, (out_file,))
			]
)

Which gets me close, but I can't control where rows gets passed in. In the above code, it is always the last parameter.

I feel like I'm reinventing a wheel here.  I was wondering if there's already something that exists?