1. String reversal
Use Python slicing to reverse the string:
# Reversing a string using slicing my_string = "ABCDE" reversed_string = my_string[::-1] print(reversed_string) # Output # EDCBA
2. Capitalize the first letter of each word
Use the title function Method:
my_string = "my name is chaitanya baweja" # using the title() function of string class new_string = my_string.title() print(new_string) # Output # My Name Is Chaitanya Baweja
3. Find unique elements in strings
Use the concept of sets to find unique elements in strings:
my_string = "aavvccccddddeee" # converting the string to a set temp_set = set(my_string) # stitching set into a string using join new_string = ''.join(temp_set) print(new_string) # output # cdvae
4. Repeat printing strings and lists n times
You can print a string or list multiple times using the multiplication sign (*):
n = 3 # number of repetitions my_string = "abcd" my_list = [1,2,3] print(my_string*n) # abcdabcdabcd print(my_list*n) # [1,2,3,1,2,3,1,2,3]
5. List generation
# Multiplying each element in a list by 2 original_list = [1,2,3,4] new_list = [2*x for x in original_list] print(new_list) # [2,4,6,8]
6. Variable exchange
a = 1 b = 2 a, b = b, a print(a) # 2 print(b) # 1
7. Split the string into a list of substrings
Use the .split() function:
string_1 = "My name is Chaitanya Baweja" string_2 = "sample/ string 2" # default separator ' ' print(string_1.split()) # ['My', 'name', 'is', 'Chaitanya', 'Baweja'] # defining separator as '/' print(string_2.split('/')) # ['sample', ' string 2']
8. Combine multiple strings into one string
list_of_strings = ['My', 'name', 'is', 'Chaitanya', 'Baweja'] # Using join with the comma separator print(','.join(list_of_strings)) # Output # My,name,is,Chaitanya,Baweja
9. Detect whether the string is a palindrome
my_string = "abcba" if my_string == my_string[::-1]: print("palindrome") else: print("not palindrome") # Output # palindrome
10. Count the number of elements in the list
# finding frequency of each element in a list from collections import Counter my_list = ['a','a','b','b','b','c','d','d','d','d','d'] count = Counter(my_list) # defining a counter object print(count) # Of all elements # Counter({'d': 5, 'b': 3, 'a': 2, 'c': 1}) print(count['b']) # of individual element # 3 print(count.most_common(1)) # most frequent element # [('d', 5)]
11. Determine whether two strings are Anagrams
The meaning of Anagrams If each English word (excluding uppercase and lowercase) appears the same number of times in the two words, use the Counter class to determine whether the two strings are Anagrams.
from collections import Counter str_1, str_2, str_3 = "acbde", "abced", "abcda" cnt_1, cnt_2, cnt_3 = Counter(str_1), Counter(str_2), Counter(str_3) if cnt_1 == cnt_2: print('1 and 2 anagram') if cnt_1 == cnt_3: print('1 and 3 anagram') # output # 1 and 2 anagram
12. Use the try-except-else-block module
except to get exception handling:
a, b = 1,0 try: print(a/b) # exception raised when b is 0 except ZeroDivisionError: print("division by zero") else: print("no exceptions raised") finally: print("Run this always") # output # division by zero # Run this always
13. Use the enumeration function to get the key/value pair
my_list = ['a', 'b', 'c', 'd', 'e'] for index, value in enumerate(my_list): print('{0}: {1}'.format(index, value)) # 0: a # 1: b # 2: c # 3: d # 4: e
14. Check the memory usage of the object
import sys num = 21 print(sys.getsizeof(num)) # In Python 2, 24 # In Python 3, 28
15. Merge dictionaries
dict_1 = {'apple': 9, 'banana': 6} dict_2 = {'banana': 4, 'orange': 8} combined_dict = {**dict_1, **dict_2} print(combined_dict) # Output # {'apple': 9, 'banana': 4, 'orange': 8}
16. Calculate the time it takes to execute a piece of code
Use the time class to calculate Time spent running a piece of code:
import time start_time = time.time() # Code to check follows for i in range(10**5): a, b = 1,2 c = a+ b # Code to check ends end_time = time.time() time_taken_in_micro = (end_time- start_time)*(10**6) print(time_taken_in_micro) # output # 18770.217895507812
17. List expansion
from iteration_utilities import deepflatten # if you only have one depth nested_list, use this def flatten(l): return [item for sublist in l for item in sublist] l = [[1,2,3],[3]] print(flatten(l)) # [1, 2, 3, 3] # if you don't know how deep the list is nested l = [[1,2,3],[4,[5],[6,7]],[8,[9,[10]]]] print(list(deepflatten(l, depth=3))) # [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
18. List sampling
import random my_list = ['a', 'b', 'c', 'd', 'e'] num_samples = 2 samples = random.sample(my_list,num_samples) print(samples) # [ 'a', 'e'] this will have any 2 random values
19. Digitization
Convert integers to List of numbers:
num = 123456 # using map list_of_digits = list(map(int, str(num))) print(list_of_digits) # [1, 2, 3, 4, 5, 6] # using list comprehension list_of_digits = [int(x) for x in str(num)] print(list_of_digits) # [1, 2, 3, 4, 5, 6]
20. Check the uniqueness of list elements
Check whether each element in the list is unique:
def unique(l): if len(l)==len(set(l)): print("All elements are unique") else: print("List has duplicates") unique([1,2,3,4]) # All elements are unique unique([1,1,2,3]) # List has duplicates
The above is the detailed content of What are the commonly used techniques in Python programming?. For more information, please follow other related articles on the PHP Chinese website!

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver CS6
Visual web development tools

WebStorm Mac version
Useful JavaScript development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Notepad++7.3.1
Easy-to-use and free code editor