How to refactor lengthy Python code

1. Convert the for loop into a list/dictionary/set expression

A situation we often encounter when doing this is to create a set of values.

For example, we can create a list of cubic numbers and fill it iteratively. The standard method in most languages ​​is as follows:

cubes = []
for i in range(20):
    cubes.append(i ** 3)

In Python, we can use list expressions to generate the required data. This code can be compressed into one line to avoid defining the list and tedious filling operations.

cubes = [i ** 3 for i in range(20)]

Look, we have converted three lines of code into one line, which is undoubtedly a good choice - your eyes don't have to check the code up, down, left, and right.

Compressing code to one line makes reading more difficult, but this does not apply to derivation expressions. Once you get familiar with the syntax, all the elements you need are presented very clearly, making it easier to read than the for loop version.

Another point is that assignment is now more like an atomic operation - we declare what cubes are, rather than giving instructions on how to build them. By doing this, the code is easier to read because we are focusing on what the variable cubes means rather than how it is constructed.

Finally, expressions are often faster than building a collection in a loop, which is also an important factor if performance is a consideration.

2. Replace assignment with incremental assignment

Incremental assignment is a fast and simple Python syntax.

Any time there is code like this:

count = count + other_value

can be replaced with the following code:

count += other_value

The code is short and clear - we don't need to think about counting the variable twice. There are also a few operators available, including minus equals (-=), bitwise AND equals (&=), divide equals (/=), and multiply equals (*=).

You need to be careful because the type you assign to must have the appropriate operator defined. For example, numpy arrays do not support the /= operation.

3. Inline variables used only once

A situation we often see in people's code is to assign the result to a temporary variable and then immediately Return it.

def state_attributes(self):
    """Return the state attributes."""
    state_attr = {
        ATTR_CODE_FORMAT: self.code_format,
        ATTR_CHANGED_BY: self.changed_by,
    }
    return state_attr

In fact, a better way is to return the result directly instead of using a temporary variable to store the result

def state_attributes(self):
    """Return the state attributes."""
    return {
        ATTR_CODE_FORMAT: self.code_format,
        ATTR_CHANGED_BY: self.changed_by,
    }

This can shorten the code and delete unnecessary variables, thereby reducing the mental effort of reading the code consumption.

Temporary variables may be useful when they are used as parameters or conditions, and their names reflect their contents. In the above example, only the state attribute is returned, and state_attr does not provide any additional information. So it is not necessary to assign the result to a temporary variable.

4. Replace the if statement with an if expression

One situation you often encounter is that you often want to set a variable to two different values. one of.

if condition:
    x = 1
else:
    x = 2

This can be written on one line using Python's conditional expression syntax (python's version of the ternary operator):

x = 1 if condition else 2

This is definitely more concise, but it's a more controversial one Refactoring (like list expressions). Some programmers dislike this expression because they find it harder to understand than writing out the condition in its entirety in an if statement.

This improvement can improve efficiency if conditional expressions are shorter and can be combined, which is our opinion. Similar to the list expression example, when we read code we usually don't need to know how x is assigned, we just see it being assigned and then moving forward.

5. Use generators to replace unwanted expressions

Convert to simple sentences: You can use functions like any, all, and sum to use generators as Parameters, not collections. This means that instead of doing:

hat_found = any([is_hat(item) for item in wardrobe])

you can change the code to:

hat_found = any(is_hat(item) for item in wardrobe)

This will remove a pair of parentheses and make the code slightly clearer. If the any function finds a result, it returns immediately without having to build the entire list. This can lead to performance improvements.

Note that we are actually passing the generator into any(), strictly speaking the code should look like this:

hat_found = any(is_hat(item) for item in wardrobe)

But Python allows you to omit this pair of parentheses. The following are the standard library functions that accept generators:

'all', 'any', 'enumerate', 'frozenset', 'list', 'max', 'min', 'set', 'sum', 'tuple'

6. Simplify the condition into a return statement

The last refactoring technique introduced is, The function needs to return True or False. A common approach is:

def function():
    if isinstance(a, b) or issubclass(b, a):
        return True
    return False

However, it is more concise to return the result directly, as shown below:

def function():
    return isinstance(a, b) or issubclass(b, a)

This can only be done if the expression evaluates to a Boolean value. For example, in

def any_hats():
    hats = [item for item in wardrobe if is_hat(item)]
    if hats or self.wearing_hat():
        return True
    return False

, you can use bool() to combine hat and self.wearing_hat() into a bool list, thereby eliminating the if condition and simplifying the program.

def any_hats():
    hats = [item for item in wardrobe if is_hat(item)]
    return bool(hats or self.wearing_hat())

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