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Python Logic to Find All Unique Pairs in an Array

- December 23, 2024

Here's the Python logic for finding all unique pairs in an array that sum up to a target value. Python Unique Pair Problem Write a Python function that finds all unique pairs in an array whose sum equals a target value. Avoid duplicates in the result. For example: Input: arr = [2, 4, 3, 5, 7, 8, 9] , target = 9 Output: [(2, 7), (4, 5)] Hints Use a set for tracking seen numbers. Check for complements efficiently. Example def find_unique_pairs(arr, target): """ Finds all unique pairs in the array that sum up to the target value. Parameters: arr (list): The input array of integers. target (int): The target sum value. Returns: list: A list of unique pairs that sum to the target value. """ seen = set() pairs = set() for num in arr: complement = target - num if complement in seen:...

How to Create a Symmetric Array in Python

- December 22, 2024

Here's a Python program that says to write a Symmetric array transformation. A top interview question. Symmetric Array Transformation Problem: Write a Python function that transforms a given array into a symmetric array by mirroring it around its center. For example: Input: [1, 2, 3] Output: [1, 2, 3, 2, 1] Hints: Use slicing for the reverse part. Concatenate the original array with its mirrored part. Example def symmetric_array(arr): """ Transforms the input array into a symmetric array by mirroring it around its center. Parameters: arr (list): The input array. Returns: list: The symmetric array. """ # Mirror the array by concatenating the original with its reverse (excluding the last element to avoid duplication) return arr + arr[-2::-1] # Example usage input_array = [1, 2, 3] symmetric_result = symmetric_array(input_array) print("Input Array:", input_arr...

15 Python Tips : How to Write Code Effectively

- November 02, 2024

Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code. Python Tips for Effective Coding 1. Code Readability and PEP 8 Always aim for clean and readable code by following PEP 8 guidelines. Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code. For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range When you need both the index ...

Python map() and lambda() Use Cases and Examples

- September 24, 2024

In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]...

How to Build CI/CD Pipeline: GitHub to AWS

- August 24, 2024

Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build pr...

5 SQL Queries That Popularly Used in Data Analysis

- August 03, 2024

Here are five popular SQL queries frequently used in data analysis. 1. SELECT with Aggregations Summarize data by calculating aggregates like counts, sums, averages, etc. SELECT department, COUNT(*) as employee_count, AVG(salary) as average_salary FROM employees GROUP BY department; 2. JOIN Operations Combine data from multiple tables based on a related column. SELECT e.employee_id, e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id; 3. WHERE Clause for Filtering Filter records based on specified conditions. SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31' AND amount > 1000; 4. ORDER BY Clause for Sorting Sort results in ascending or descending order based on one or more columns. SELECT product_name, price FROM products ORDER BY price DESC; 5. GROUP BY with HAVING Clause Group records and apply conditions to the aggregated results. SELECT department, SUM(salary) as total_salaries FROM employ...

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