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15 Python Tips : How to Write Code Effectively

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 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 ...

5 SQL Queries That Popularly Used in Data Analysis

 Here are five popular SQL queries frequently used in data analysis.


5 SQL Queries Popularly Used in Data Analytics




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 employees
GROUP BY department
HAVING SUM(salary) > 50000;

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