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Technical articles with clear explanations and examples
Find pairs with given sum such that pair elements lie in different BSTs in Python
Finding pairs with a given sum from two different Binary Search Trees is a classic problem that combines BST traversal with the two-pointer technique. The key insight is to use the sorted property of BST in-order traversals. Problem Statement Given two Binary Search Trees and a target sum, find all pairs where one element comes from the first BST and another from the second BST, and their sum equals the target. For example, with sum = 12: BST 1 ...
Read MoreFind pairs with given sum such that elements of pair are in different rows in Python
Finding pairs with a given sum from different rows in a matrix is a common problem in data structures. We need to find all pairs where one element comes from one row and the other from a different row, and their sum equals the target value. So, if the input matrix is ? 2 4 3 5 6 9 8 7 10 11 14 12 13 1 15 16 And sum = 13, then the output will be [(4, 9), (5, 8), (2, 11), (3, ...
Read MorePython - Write multiple files data to master file
When working with multiple data files, you often need to combine them into a single master file. Python provides several approaches to merge multiple files, from basic file operations to using pandas for structured data. Basic File Operations Approach This method reads multiple text files and writes their content to a master file using standard file operations ? import os # Create sample data files os.makedirs('data_files', exist_ok=True) # Create sample files with open('data_files/file1.txt', 'w') as f: f.write('John, Developer, 50000') f.write('Alice, Designer, 45000') with open('data_files/file2.txt', 'w') ...
Read MorePython - Working with .docx module
Word documents contain formatted text wrapped within three object levels. Lowest level − Run objects, Middle level − Paragraph objects and Highest level − Document object. So, we cannot work with these documents using normal text editors. But we can manipulate these word documents in Python using the python-docx module. Installation The first step is to install this third-party module python-docx. You can use pip ? pip install python-docx Important: After installation, import docx NOT python-docx. Use docx.Document class to start working with the word document. Creating a Basic Word Document ...
Read MorePython - Which is faster to initialize lists?
Python offers multiple ways to initialize lists, but their performance varies significantly. Understanding which method is fastest helps write more efficient code. This article compares four common list initialization methods: for loops, while loops, list comprehensions, and the star operator. Performance Comparison of List Initialization Methods Let's benchmark four different approaches to initialize a list with 10, 000 zeros and measure their execution times − import time # Initialize lists to store execution times for_loop_times = [] while_loop_times = [] list_comp_times = [] star_operator_times = [] # Run 500 iterations for reliable averages for ...
Read MorePython - Ways to merge strings into list
While developing an application, there are many scenarios when we need to operate on strings and convert them into mutable data structures like lists. Python provides several ways to merge string representations into a single list. Using ast.literal_eval() The ast.literal_eval() method safely evaluates string literals containing Python expressions. This is the recommended approach for converting string representations to lists ? import ast # Initialization of strings str1 = "'Python', 'for', 'fun'" str2 = "'vishesh', 'ved'" str3 = "'Programmer'" # Initialization of list result_list = [] # Extending into single list for x in ...
Read MorePython - Ways to iterate tuple list of lists
Lists are fundamental containers in Python programming used in day-to-day development. When dealing with nested structures like tuple lists of lists, knowing different iteration methods becomes essential for efficient data manipulation. Using zip_longest() with List Comprehension The zip_longest() function from itertools allows iteration across multiple lists of different lengths, filling missing values with None ? # using itertools.zip_longest from itertools import zip_longest # initialising list of lists test_list = [ [('11'), ('12'), ('13')], [('21'), ('22'), ('23')], [('31'), ('32'), ('33')] ] # printing ...
Read MorePython - Ways to invert mapping of dictionary
Dictionary is a collection which is unordered, changeable and indexed. In Python, dictionaries are written with curly brackets, and they have keys and values. Inverting a dictionary means swapping keys and values, so the original values become the new keys and vice versa. Using Dictionary Comprehension The most Pythonic way to invert a dictionary is using dictionary comprehension ? # initialising dictionary ini_dict = {101: "vishesh", 201: "laptop"} # print initial dictionary print("initial dictionary:", ini_dict) # inverse mapping using dict comprehension inv_dict = {v: k for k, v in ini_dict.items()} # print final ...
Read MorePython - Ways to format elements of given list
When working with lists containing numeric values, you often need to format them for better readability or presentation. Python provides several methods to format list elements, including list comprehension with string formatting, map() function, and modern f-string formatting. Using List Comprehension with % Formatting The traditional approach uses list comprehension with percentage formatting ? numbers = [100.7689454, 17.232999, 60.98867, 300.83748789] # Format to 2 decimal places using % formatting formatted = ["%.2f" % elem for elem in numbers] print(formatted) ['100.77', '17.23', '60.99', '300.84'] Using map() Function The map() function ...
Read MorePython - Ways to flatten a 2D list
Flattening a 2D list means converting a nested list structure into a single-dimensional list. Python provides several methods to accomplish this task, each with different performance characteristics and use cases. Using itertools.chain.from_iterable() The chain.from_iterable() function efficiently flattens a 2D list by chaining all sublists together − from itertools import chain nested_list = [[1, 2, 3], [3, 6, 7], [7, 5, 4]] print("Initial list:", nested_list) ...
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