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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...

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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