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SQL Query: 3 Methods for Calculating Cumulative SUM

SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON >= GROUP BY, t1.value ORDER BY; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

Text Vs. Binary Vs. UTF-8 Top differences

Here are the differences between Text files, Binary files, and UTF-8. These would help understanding files correctly for beginners. Text File It contains plain text characters. When you open a text file in a text editor, it displays human-readable content.  The text may not be in a language you know or understand, but you will see mostly normal characters that you can type at any keyboard. Binary File It stores information in bytes that aren’t quite so human readable.  If you open the binary file in a text editor, it will not be readable. UTF-8 UTF-8 is short for Unicode Transformation Format, 8-bit, and is a standardized way to represent letters and numbers on computers. The original ASCII set of characters, which contains mostly uppercase and lowercase letters, numbers, and punctuation marks, worked okay in the early days of computing. But when other languages were brought into the mix, these characters were just not enough. Many standards for dealing with other languages have been p

Python Web data - How to Extract HTML Tags Easily

With BeautifulSoup you can extract HTML and XML tags easily that present in Web data. Here is the best example of how to remove these. The prime step of text analytics is cleaning . You can remove HTML tags using BeautifulSoup parser. Check out Python Logic and removing HTML tags. When analyzing web data, consider the below examples for your projects. Python Ideas to Remove HTML tags How I Removed Using BeautifulSoup Import BeautifulSoup Python Logic to Remove HTML tags Before and after executing the code 1. Import BeautifulSoup import  BeautifulSoup  from  bs4 2. Python BeautifulSoup: How to Remove HTML Tags from  bs4  import  BeautifulSoup soup =  BeautifulSoup ("<!DOCTYPE html><html><body><h1>My First Heading</h1><p>My first paragraph.</p></body></html>") text = soup. get_text() print (text) 3. Before and After Run Before the run see the below code. Before Executing the code After Run the tags are parsed. The means in