Python is one of the most popular languages all over the world. Nowadays it is being used in competitive programming also because of its simple syntax and rich libraries. Most of us probably started coding with python. At first, everything goes simple and easy. But while solving a hard algorithmic problem, most of us suffer from Time Limit Exceeded. However, it is not a problem of python; it is the programmer's problem. I am not saying that language is not slow, but if a programmer writes an efficient programme, it will get Accepted for sure. Here are some tips to speed up your python programme.
Use proper data structure
Use of proper data structure has a significant effect on runtime. Python has list, tuple, set and dictionary as the built-in data structures. However, most of the people use the list in all cases. But it is not a right choice. Use proper data structures depending on your task. Especially use a tuple instead of a list. Because iterating over tuple is easier than iterating over a list.
Decrease the use of for loop
As for loop is dynamic in python, it takes more time than while loop. So, use while loop instead of for loop.
Use list comprehension
Do not use any other technique if you can use list comprehension. For example, here is a code to list all the numbers between 1 and 1000 that is the multiplier of 3:
L = []for i in range (1, 1000): if i%3 == 0: L.append (i)Using list comprehension, it would be:
L = [i for i in range (1, 1000) if i%3 == 0]List comprehension works faster than using the append method.
Use multiple assignments
Do not assaign variables like this:
a = 2b = 3c = 5d = 7Instead, assign variables like this:
a, b, c, d = 2, 3, 5, 7Do not use global variables
Python has global keyword to declare global variables. But global variables take higher time during operation than a local variable. So, do not use global variables if it is not necessary.
Use library function
Do not write your function (manually) if it is already in the library. Library functions are highly efficient, and you will probably won't be able to code with that efficiency.
Concatenate strings with join
In python, you can concatenate strings with + operation.
concatenatedString = "Programming " + "is " + "fun."It can also be done with join() method.
concatenatedString = " ".join (["Programming", "is", "fun."])join() concatenates strings faster than + operation because + operators create a new string and then copies the old content at each step. But join() doesn't work that way.
Use generators
If you have a large amount of data in your list and you need to use one data at a time and for once then use generators. It will save you time.
It may seem efficient, but it's not
See the below code:
L = []for element in set(L): ...The above code may seem efficient because it used set to delete duplicate data. But the reality is that the code is not efficient. Do not forget that converting a list into set takes time. So this code will work better than the previous:
for element in L: ...Do not use dot operation
Try to avoid dot operation. See the below programme.
import mathval = math.sqrt(60)Instead of the above style write code like this:
from math import sqrtval = sqrt(60)Because when you call a function using . (dot) it first calls __getattribute()__ or __getattr()__ which then use dictionary operation which costs time. So, try using from module import function.
Use 1 for infinity loops
Use while 1 instead of while True. It will reduce some runtime.
Try a different approach
Try new ways to write your code efficiently. See the below code.
if a_condition: if another_condition: do_somethingelse: raise exceptionInstead of the above code you can write:
if (not a_condition) or (not another_condition): raise exceptiondo_somethingUse speed up applications
For python's slow speed, some projects have been taken to decrease runtime. Pypy and Numba two of them. In most of the programming contests, you will see pypy if it allows python. These applications will reduce the runtime of your programme.
Use special libraries to process large datasets
C/C++ is faster than python. So, many packages and modules have been written in C/C++ that you can use in your python programme. Numpy, Scipy and Pandas are three of them and are popular for processing large datasets.
Use the latest release of python
Python is updated and upgraded regularly, and every release is faster and more optimized. So always use the latest version of python.
These were some of the tips to decrease the runtime of python code. There are a few more techniques that you can use. Use a search engine to find those and write efficient code!
