How to break a line of chained methods in Python?

天大地大妈咪最大 提交于 2019-11-26 15:56:39

You could use additional parenthesis:

subkeyword = (
        Session.query(Subkeyword.subkeyword_id, Subkeyword.subkeyword_word)
        .filter_by(subkeyword_company_id=self.e_company_id)
        .filter_by(subkeyword_word=subkeyword_word)
        .filter_by(subkeyword_active=True)
        .one()
    )
Raymond Hettinger

This is a case where a line continuation character is preferred to open parentheses. The need for this style becomes more obvious as method names get longer and as methods start taking arguments:

subkeyword = Session.query(Subkeyword.subkeyword_id, Subkeyword.subkeyword_word) \
                    .filter_by(subkeyword_company_id=self.e_company_id)          \
                    .filter_by(subkeyword_word=subkeyword_word)                  \
                    .filter_by(subkeyword_active=True)                           \
                    .one()

PEP 8 is intend to be interpreted with a measure of common-sense and an eye for both the practical and the beautiful. Happily violate any PEP 8 guideline that results in ugly or hard to read code.

That being said, if you frequently find yourself at odds with PEP 8, it may be a sign that there are readability issues that transcend your choice of whitespace :-)

My personal choice would be:

subkeyword = Session.query(
    Subkeyword.subkeyword_id,
    Subkeyword.subkeyword_word,
).filter_by(
    subkeyword_company_id=self.e_company_id,
    subkeyword_word=subkeyword_word,
    subkeyword_active=True,
).one()

Just store the intermediate result/object and invoke the next method on it, e.g.

q = Session.query(Subkeyword.subkeyword_id, Subkeyword.subkeyword_word)
q = q.filter_by(subkeyword_company_id=self.e_company_id)
q = q.filter_by(subkeyword_word=subkeyword_word)
q = q.filter_by(subkeyword_active=True)
subkeyword = q.one()

It's a bit of a different solution than provided by others but a favorite of mine since it leads to nifty metaprogramming sometimes.

base = [Subkeyword.subkeyword_id, Subkeyword_word]
search = {
    'subkeyword_company_id':self.e_company_id,
    'subkeyword_word':subkeyword_word,
    'subkeyword_active':True,
    }
subkeyword = Session.query(*base).filter_by(**search).one()

This is a nice technique for building searches. Go through a list of conditionals to mine from your complex query form (or string-based deductions about what the user is looking for), then just explode the dictionary into the filter.

According to Python Language Reference
You can use a backslash.
Or simply break it. If a bracket is not paired, python will not treat that as a line. And under such circumstance, the indentation of following lines doesn't matter.

You seems using SQLAlchemy, if it is true, sqlalchemy.orm.query.Query.filter_by() method takes multiple keyword arguments, so you could write like:

subkeyword = Session.query(Subkeyword.subkeyword_id,
                           Subkeyword.subkeyword_word) \
                    .filter_by(subkeyword_company_id=self.e_company_id,
                               subkeyword_word=subkeyword_word,
                               subkeyword_active=True) \
                    .one()

But it would be better:

subkeyword = Session.query(Subkeyword.subkeyword_id,
                           Subkeyword.subkeyword_word)
subkeyword = subkeyword.filter_by(subkeyword_company_id=self.e_company_id,
                                  subkeyword_word=subkeyword_word,
                                  subkeyword_active=True)
subkeuword = subkeyword.one()

I like to indent the arguments by two blocks, and the statement by one block, like these:

for image_pathname in image_directory.iterdir():
    image = cv2.imread(str(image_pathname))
    input_image = np.resize(
            image, (height, width, 3)
        ).transpose((2,0,1)).reshape(1, 3, height, width)
    net.forward_all(data=input_image)
    segmentation_index = net.blobs[
            'argmax'
        ].data.squeeze().transpose(1,2,0).astype(np.uint8)
    segmentation = np.empty(segmentation_index.shape, dtype=np.uint8)
    cv2.LUT(segmentation_index, label_colours, segmentation)
    prediction_pathname = prediction_directory / image_pathname.name
    cv2.imwrite(str(prediction_pathname), segmentation)
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