search

Python Code Snippets

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!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Explain the performance differences in element-wise operations between lists and arrays.Explain the performance differences in element-wise operations between lists and arrays.May 06, 2025 am 12:15 AM

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

How can you perform mathematical operations on entire NumPy arrays efficiently?How can you perform mathematical operations on entire NumPy arrays efficiently?May 06, 2025 am 12:15 AM

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.

How do you insert elements into a Python array?How do you insert elements into a Python array?May 06, 2025 am 12:14 AM

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.

How can you make a Python script executable on both Unix and Windows?How can you make a Python script executable on both Unix and Windows?May 06, 2025 am 12:13 AM

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

What should you check if you get a 'command not found' error when trying to run a script?What should you check if you get a 'command not found' error when trying to run a script?May 06, 2025 am 12:03 AM

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.

Why are arrays generally more memory-efficient than lists for storing numerical data?Why are arrays generally more memory-efficient than lists for storing numerical data?May 05, 2025 am 12:15 AM

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

How can you convert a Python list to a Python array?How can you convert a Python list to a Python array?May 05, 2025 am 12:10 AM

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

Can you store different data types in the same Python list? Give an example.Can you store different data types in the same Python list? Give an example.May 05, 2025 am 12:10 AM

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.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

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

Hot Tools

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

DVWA

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

Dreamweaver CS6

Visual web development tools

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

Safe Exam Browser

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.