Fundamentals 9 min read

17 Must‑Know Python Tricks to Boost Your Coding Efficiency

This article presents 17 practical Python tips—from printing strings multiple times and returning multiple values to fast string reversal, duplicate removal, advanced unpacking, and modifying recursion limits—each illustrated with concise code examples for immediate use.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
17 Must‑Know Python Tricks to Boost Your Coding Efficiency

"Python is a truly wonderful language. When someone proposes a good idea, it takes about one minute and five lines of code to write something that almost does what you want. Then you can expand the script to 300 lines in an hour, and it still almost meets your needs." – Jack Jensen

Below are 17 useful Python development tricks.

1. Print a string N times

Use multiplication to repeat a string instead of a loop.

string = "Python "
ntimes = string * 3
print(ntimes)  # Python Python Python

2. Return multiple values from a function

def Mulvalues():
    return 1, 2, 3

a, b, c = Mulvalues()
print(a, b, c)  # 1 2 3

3. Get the file path of an imported module

import os
import json
import tkinter

print(os)
print(tkinter)
print(json)

4. Fast method to reverse a string

string1 = "Coder"
string2 = "Algorithm"
print(string1[::-1])  # redoC
print(string2[::-1])  # mhtiroglA

5. Multiple assignment

a, b, c = 1, 2, 3
print(a)  # 1
print(b)  # 2
print(c)  # 3

6. Quickly remove duplicate items

lst1 = [1, 3, 3, 4, 5, 1]
lst2 = ["A", "A", "B", "C", "D", "D"]
newlst1 = list(set(lst1))
newlst2 = list(set(lst2))
print(newlst1)  # [1, 3, 4, 5]
print(newlst2)  # ['C', 'D', 'A', 'B']

7. String formatting

name = "Haider"
skill = "Python"
# method 1
text = "My name is {n} and I'm a {s} Expert".format(**{"n": name, "s": skill})
print(text)
# method 2
text = "My name is {} and I'm a {} Expert".format(name, skill)
print(text)
# f‑string
text = f"My name is {name} and I'm a {skill} Expert"
print(text)

8. Check an object's memory usage

import sys
val = 500
print(sys.getsizeof(val))  # 28

9. Initialize empty containers

my_list = list()
my_dict = dict()
my_tuple = tuple()
my_set = set()
print(my_list)  # []
print(my_dict)  # {}

10. Reverse a list

my_list1 = [1, 2, 3, 4, 5, 100]
my_list2 = ["A", "B", "C"]
print(my_list1[::-1])  # [100, 5, 4, 3, 2, 1]
print(my_list2[::-1])  # ['C', 'B', 'A']

11. Reverse a dictionary

dict = {'x': 1, 'y': 2, 'z': 3}
new_dict = {value: key for key, value in dict.items()}
print(new_dict)  # {1: 'x', 2: 'y', 3: 'z'}

12. Advanced multiple assignment

a, *b, c, d = 3, 4, 5, 6, 7
print(a, b, c, d)  # 3 [4, 5] 6 7

13. Fast way to join strings

lst = ["I'm", "a", "Programmer"]
text = " ".join(lst)
print(text)  # I'm a Programmer

14. Merge two dictionaries

a = {"a": 1, "b": 2}
b = {"c": 3, "d": 4}
c = {**a, **b}
print(c)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4}

15. Change recursion limit

import sys
current_limit = sys.getrecursionlimit()
print(current_limit)  # 1000
sys.setrecursionlimit(5000)
print(sys.getrecursionlimit())  # 5000

16. Multi‑prefix search

string1 = "www.medium.com"
if string1.startswith(("www", "http")):
    print("True")
if string1.endswith(("com", "co.uk")):
    print("True")

17. IF statement tip

a = [1, 2, 3]
# slower way
if a[0] == 1 or a[1] == 1 or a[2] == 1:
    print("Number is present in the list")
# faster way
if 1 in a:
    print("Number is present in the list")
Original Source

Signed-in readers can open the original source through BestHub's protected redirect.

Sign in to view source
Republication Notice

This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactadmin@besthub.devand we will review it promptly.

Pythonprogrammingproductivitycode snippetsTips
Python Programming Learning Circle
Written by

Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

0 followers
Reader feedback

How this landed with the community

Sign in to like

Rate this article

Was this worth your time?

Sign in to rate
Discussion

0 Comments

Thoughtful readers leave field notes, pushback, and hard-won operational detail here.