Howtime

Python tips

Exit early.

Try to leave a function as soon as you know it can do no more meaningful work. Doing this reduces the indentation of your program and makes it more readable. It also allows you to avoid nested if statements.

if positive_case:
  if particular_example: 
    do_something
else:
  raise exception

You can test the input in a few ways before carrying out your actions. Another approach is to raise the exception early and to carry out the main action in the else part of the loop.

if not positive_case:
  raise exception
if not particular_example:
  raise exception
do_something

Now you can see what this block of code is trying to achieve at first glance. You don’t need to follow the chain of logic in the conditionals. Also, you can clearly see when this function would raise an exception.

Try another way.

Once you’ve used a coding approach in your application, it can be easy to rely on that method again and again. However, experimenting can allow you to see which techniques are better. Not only will this keep you learning and thinking about the code you write, but it can also encourage you to be more innovative. Think about how you can creatively apply new coding techniques to get faster results in your application.

Use “while 1” for an infinite loop.

If you’re listening on a socket, then you’ll probably want to use an infinite loop. The normal route to achieve this is to use while True. This works, but you can achieve the same effect slightly faster by using while 1. This is a single jump operation, as it is a numerical comparison.

Keep up-to-date on the latest Python releases.

The Python maintainers are passionate about continually making the language faster and more robust. In general, each new release of the language has improved python performance and security. Just be sure that the libraries you want to use are compatible with the newest version before you make the leap.

Use join() to concatenate strings.

In Python, you can concatenate strings using “+”. However, strings in Python are immutable, and the “+” operation involves creating a new string and copying the old content at each step. A more efficient approach would be to use the array module to modify the individual characters and then use the join() function to re-create your final string.

new = "This" + "is" + "going" + "to" + "require" + "a" + "new" + "string" + "for" + "every" + "word"
print(new)

That code will print:

Thisisgoingtorequireanewstringforeveryword

On the other hand, this code:

new = " ".join(["This", "will", "only", "create", "one", "string", "and", "we", "can", "add", "spaces."])
print(new)

will print:

This will only create one string and we can add spaces.

This is cleaner, more elegant, and faster.

Avoid global variables.

Using few global variables is an effective design pattern because it helps you keep track of scope and unnecessary memory usage. Also, Python is faster retrieving a local variable than a global one. So, avoid that global keyword as much as you can.

Remember to use multiple assignment.

Python has an elegant way to assign the values of multiple variables.

first_name, last_name, city = "Kevin", "Cunningham", "Brighton"

You can use this method to swap the values of variables.

x, y = y, x

This approach is much quicker and cleaner than:

temp = x 
x = y
y = temp

Use sets and unions.

I’ve mentioned loops a few times in this list already. Most experts agree that too much looping puts unnecessary strain on your server. It’s rarely the most efficient approach. Say you wanted to get the overlapping values in two lists. You could do this using nested for loops, like this:

a = [1,2,3,4,5]
b = [2,3,4,5,6]

overlaps = []
for x in a:
  for y in b:
    if x==y:
      overlaps.append(x)

print(overlaps)

This will print the list [2, 3, 4, 5]. The number of comparisons here will get very large, very quickly. Another approach would be:

a = [1,2,3,4,5]
b = [2,3,4,5,6]

overlaps = set(a) & set(b)

print(overlaps)

This will print the dictionary {2, 3, 4, 5}. You’re leaning on the built-in functions and getting a big speed and memory bump as a result.

Be lazy with your module importing.

When you started learning Python, you probably got advice to import all the modules you’re using at the start of your program. Maybe you still sort these alphabetically. This approach makes it easier to keep track of what dependencies your program has. However, the disadvantage is that all your imports load at startup. Why not try a different approach? You can load the modules only when you need them. This technique helps distribute the loading time for modules more evenly, which may reduce peaks of memory usage.

Use “in” if possible.

To check if membership of a list, it’s generally faster to use the “in” keyword.

for name in member_list:
  print('{} is a member'.format(name))