Arrays
Lists
# Creating a list my_list = [] my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # List of different data types mixed_list = [1, "hello", 3.14, True] # Accessing elements print(my_list[0]) # Output: 1 print(my_list[-1]) # Output: 5 # Append to the end my_list.append(6) # Insert at a specific position my_list.insert(2, 10) # Find an element in an array index=my_list.find(element) # Remove by value my_list.remove(10) # Remove by index removed_element = my_list.pop(2) # Length of the list print(len(my_list)) # Slicing [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # sequence[start:stop:step] print(my_list[1:4]) # Output: [1, 2, 3] print(my_list[5:]) # Output: [5, 6, 7, 8, 9] print(my_list[:5]) # Output: [0, 1, 2, 3, 4] print(my_list[::2]) # Output: [0, 2, 4, 6, 8] print(my_list[-4:]) # Output: [6, 7, 8, 9] print(my_list[:-4]) # Output: [0, 1, 2, 3, 4, 5] print(my_list[::-1]) # Output: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0] print(my_list[8:2:-2]) # Output: [8, 6, 4] print(my_list[1:8:2]) # Output: [1, 3, 5, 7] print(my_list[-2:-7:-1]) # Output: [8, 7, 6, 5, 4] # Reversing a list my_list.reverse() # Sorting a list my_list.sort()
Permutation & Combination
import itertools # Example list data = [1, 2, 3] # Generating permutations of the entire list perms = list(itertools.permutations(data)) print(perms) # Output: [(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)] # Generating permutations of length 2 perms_length_2 = list(itertools.permutations(data, 2)) print(perms_length_2) # Output: [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)] combinations(iterable, r) #order does not matter
Generating Permutations Manually
You can also generate permutations manually using recursion. Here’s a simple implementation:
def permute(arr): result = [] # Base case: if the list is empty, return an empty list if len(arr) == 0: return [[]] # Recursive case for i in range(len(arr)): elem = arr[i] rest = arr[:i] + arr[i+1:] for p in permute(rest): result.append([elem] + p) return result
Stack
(list can be used as stack)
st=[] st.append() st.pop() top_element = stack[-1]
Tips
1) Strip:
It is used to remove leading and trailing whitespace (or other specified characters) from a string
#EX. (1,2) to 1,2 s.strip('()')
2) Don't use normal dictionary
from collections import defaultdict dictionary=defaultdict(int)
3) Important checking and convertion
s.isdigit() s.isalpha() s.isalnum() s.islower() s.isupper() s.lower() s.upper()
4) Non-Trivial
round(number, decimal_digits) ord(each)-ord('a')+1 # value of an alphabet #/ (Floating-Point Division) #// (Floor Division) maxim = float('-inf') minim = float('inf') unique_lengths.sort(reverse=True) s.count('x') list1 = [1, 2, 3] iterable = [4, 5, 6] list1.extend(iterable) position.replace('(', '').replace(')', '') expression = "2 + 3 * 4" result = eval(expression) print(result) #Determinant import numpy as arr=[[1,2,3],[3,4,5],[5,6,7]] print(np.linalg.det(np.array(arr)))
Sorted
my_list = [3, 1, 4, 1, 5] sorted_list = sorted(my_list) my_tuple = (3, 1, 4, 1, 5) sorted_list = sorted(my_tuple) my_dict = {'apple': 3, 'banana': 1, 'cherry': 2} sorted_keys = sorted(my_dict) my_list = [3, 1, 4, 1, 5] sorted_list = sorted(my_list, reverse=True)
Enumerate
my_list = ['a', 'b', 'c'] for index, value in enumerate(my_list): print(index, value)
Pass by Object Reference
Immutable Types (like integers, strings, tuples):
def modify_immutable(x): x = 10 # Rebinding the local variable to a new object print("Inside function:", x) a = 5 modify_immutable(a) #prints 10 print("Outside function:", a) #prints 5
Mutable Types (like lists, dictionaries, sets):
def modify_mutable(lst): lst.append(4) # Modifying the original list object print("Inside function:", lst) my_list = [1, 2, 3] modify_mutable(my_list) # [1,2,3] print("Outside function:", my_list) # [1,2,3,4]
Numpy arrays (for numerical operations)
import numpy as np # Creating a 1D array arr_1d = np.array([1, 2, 3, 4, 5]) # Creating a 2D array arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) # Creating an array filled with zeros zeros = np.zeros((3, 4)) # Creating an array filled with ones ones = np.ones((2, 3)) # Creating an array with a range of values range_arr = np.arange(0, 10, 2) # Creating an array with evenly spaced values linspace_arr = np.linspace(0, 1, 5) # Creating an identity matrix identity_matrix = np.eye(3) # Shape of the array shape = arr_2d.shape # Output: (2, 3) # Size of the array (total number of elements) size = arr_2d.size # Output: 6 # Element-wise addition arr_add = arr_1d + 5 # Output: array([6, 7, 8, 9, 10]) # Element-wise subtraction arr_sub = arr_1d - 2 # Output: array([ -1, 0, 1, 2, 3]) # Element-wise multiplication arr_mul = arr_1d * 2 # Output: array([ 2, 4, 6, 8, 10]) # Element-wise division arr_div = arr_1d / 2 # Output: array([0.5, 1. , 1.5, 2. , 2.5]) # Sum total_sum = np.sum(arr_2d) # Output: 21 # Mean mean_value = np.mean(arr_2d) # Output: 3.5 # Standard deviation std_dev = np.std(arr_2d) # Output: 1.707825127659933 # Maximum and minimum max_value = np.max(arr_2d) # Output: 6 min_value = np.min(arr_2d) # Output: 1 # Reshaping reshaped_arr = arr_1d.reshape((5, 1)) # Flattening flattened_arr = arr_2d.flatten() # Transposing transposed_arr = arr_2d.T # Indexing element = arr_2d[1, 2] # Output: 6 # Slicing subarray = arr_2d[0:2, 1:3] # Output: array([[2, 3], [5, 6]])
Astype
It is a function in NumPy used to convert a numpy array to different data type.
# Datatypes: np.int32,np.float32,np.float64,np.str_ import numpy as np # Create an integer array int_array = np.array([1, 2, 3, 4, 5], dtype=np.int32) # Convert to float float_array = int_array.astype(np.float32) print("Original array:", int_array) print("Converted array:", float_array)
Reshape
It is a powerful tool for changing the shape of an array without altering its data
import numpy as np # Create a 1D array array = np.arange(12) # Reshape to a 2D array (3 rows x 4 columns) reshaped_array = array.reshape((3, 4))
Matplotlib
import numpy as np import matplotlib.pyplot as plt # Create a random 2D array data = np.random.rand(10, 10) # Create a figure with a specific size and resolution plt.figure(figsize=(8, 6), dpi=100) # Display the 2D array as an image plt.imshow(data, cmap='viridis', interpolation='nearest') # Add a color bar to show the scale of values plt.colorbar() # Show the plot plt.show()
Dictionary
# Creating an empty dictionary # Maintains ascending order like map in cpp my_dict = {} # Creating a dictionary with initial values my_dict = {'name': 'Alice', 'age': 25, 'city': 'New York'} # Creating a dictionary using the dict() function my_dict = dict(name='Alice', age=25, city='New York') # Accessing a value by key name = my_dict['name'] # Output: 'Alice' # Using the get() method to access a value age = my_dict.get('age') # Output: 25 country = my_dict.get('country') # Output: None # Adding a new key-value pair my_dict['email'] = 'alice@example.com' # Updating an existing value my_dict['age'] = 26 # Removing a key-value pair using pop() age = my_dict.pop('age') # Removes 'age' and returns its value # Getting all keys in the dictionary keys = my_dict.keys() # Output: dict_keys(['name', 'email']) # Getting all values in the dictionary values = my_dict.values() # Output: dict_values(['Alice', 'alice@example.com']) # Iterating over keys for key in my_dict: print(key) # Iterating over values for value in my_dict.values(): print(value) # Iterating over key-value pairs for key, value in my_dict.items(): print(f"{key}: {value}")
Defaultdict
from collections import defaultdict d = defaultdict(int) # Initializes 0 to non-existent keys d['apple'] += 1 d['banana'] += 2
Set
# Creating an empty set my_set = set() # Creating a set with initial values my_set = {1, 2, 3, 4, 5} # Creating a set from a list my_list = [1, 2, 3, 4, 5] my_set = set(my_list) # Creating a set from a string my_set = set('hello') # Output: {'e', 'h', 'l', 'o'} # Adding an element to a set my_set.add(6) # my_set becomes {1, 2, 3, 4, 5, 6} # Removing an element from a set (raises KeyError if not found) my_set.remove(3) # my_set becomes {1, 2, 4, 5, 6} # Removing and returning an arbitrary element from the set element = my_set.pop() # Returns and removes an arbitrary element
String
# Single quotes str1 = 'Hello' # Double quotes str2 = "World" # Triple quotes for multi-line strings str3 = '''This is a multi-line string.''' # Raw strings (ignores escape sequences) raw_str = r'C:\Users\Name' str1 = 'Hello' # Accessing a single character char = str1[1] # 'e' # Accessing a substring (slicing) substring = str1[1:4] # 'ell' # Negative indexing last_char = str1[-1] # 'o' # Using + operator concatenated = 'Hello' + ' ' + 'World' # 'Hello World' # Using join method words = ['Hello', 'World'] concatenated = ' '.join(words) # 'Hello World' name = 'Alice' age = 25 # String formatting formatted_str = f'My name is {name} and I am {age} years old.' # Convert to uppercase upper_str = str1.upper() # 'HELLO WORLD' # Convert to lowercase lower_str = str1.lower() # 'hello world' # Convert to capitalize capital_str = str1.capitalize() # 'Hello world' str1 = ' Hello World ' # Remove leading and trailing whitespace trimmed = str1.strip() # 'Hello World' str1 = 'Hello World Python' # Split the string into a list of substrings split_list = str1.split() # ['Hello', 'World', 'Python'] # Split the string with a specific delimiter split_list = str1.split(' ') # ['Hello', 'World', 'Python'] # Join a list of strings into a single string joined_str = ' '.join(split_list) # 'Hello World Python' str1 = 'Hello World' # Find the position of a substring pos = str1.find('World') # 6 str1 = 'Hello123' # Check if all characters are alphanumeric is_alnum = str1.isalnum() # True # Check if all characters are alphabetic is_alpha = str1.isalpha() # False # Check if all characters are digits is_digit = str1.isdigit() # False # Check if all characters are lowercase is_lower = str1.islower() # False # Check if all characters are uppercase is_upper = str1.isupper() # False
Stay Connected!
If you enjoyed this post, don’t forget to follow me on social media for more updates and insights:
Twitter: madhavganesan
Instagram: madhavganesan
LinkedIn: madhavganesan
The above is the detailed content of Python Code Snippets. For more information, please follow other related articles on the PHP Chinese website!

Arraysarebetterforelement-wiseoperationsduetofasteraccessandoptimizedimplementations.1)Arrayshavecontiguousmemoryfordirectaccess,enhancingperformance.2)Listsareflexiblebutslowerduetopotentialdynamicresizing.3)Forlargedatasets,arrays,especiallywithlib

Mathematical operations of the entire array in NumPy can be efficiently implemented through vectorized operations. 1) Use simple operators such as addition (arr 2) to perform operations on arrays. 2) NumPy uses the underlying C language library, which improves the computing speed. 3) You can perform complex operations such as multiplication, division, and exponents. 4) Pay attention to broadcast operations to ensure that the array shape is compatible. 5) Using NumPy functions such as np.sum() can significantly improve performance.

In Python, there are two main methods for inserting elements into a list: 1) Using the insert(index, value) method, you can insert elements at the specified index, but inserting at the beginning of a large list is inefficient; 2) Using the append(value) method, add elements at the end of the list, which is highly efficient. For large lists, it is recommended to use append() or consider using deque or NumPy arrays to optimize performance.

TomakeaPythonscriptexecutableonbothUnixandWindows:1)Addashebangline(#!/usr/bin/envpython3)andusechmod xtomakeitexecutableonUnix.2)OnWindows,ensurePythonisinstalledandassociatedwith.pyfiles,oruseabatchfile(run.bat)torunthescript.

When encountering a "commandnotfound" error, the following points should be checked: 1. Confirm that the script exists and the path is correct; 2. Check file permissions and use chmod to add execution permissions if necessary; 3. Make sure the script interpreter is installed and in PATH; 4. Verify that the shebang line at the beginning of the script is correct. Doing so can effectively solve the script operation problem and ensure the coding process is smooth.

Arraysaregenerallymorememory-efficientthanlistsforstoringnumericaldataduetotheirfixed-sizenatureanddirectmemoryaccess.1)Arraysstoreelementsinacontiguousblock,reducingoverheadfrompointersormetadata.2)Lists,oftenimplementedasdynamicarraysorlinkedstruct

ToconvertaPythonlisttoanarray,usethearraymodule:1)Importthearraymodule,2)Createalist,3)Usearray(typecode,list)toconvertit,specifyingthetypecodelike'i'forintegers.Thisconversionoptimizesmemoryusageforhomogeneousdata,enhancingperformanceinnumericalcomp

Python lists can store different types of data. The example list contains integers, strings, floating point numbers, booleans, nested lists, and dictionaries. List flexibility is valuable in data processing and prototyping, but it needs to be used with caution to ensure the readability and maintainability of the code.


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

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Dreamweaver Mac version
Visual web development tools

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

Dreamweaver CS6
Visual web development tools

SublimeText3 Linux new version
SublimeText3 Linux latest version

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.
