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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 and the value in a loop, enumerate is a more Pyth

Oracle fFlash Storage and its Top Features

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ORACLE flash storage is faster than other storage systems. This post tells why flash storage you need and the special features of flash storage from oracle. Customers today are facing a triad of issues requiring higher performance from their storage systems such as More server and application virtualization, Faster servers and scale-out applications and Latency-sensitive applications. Flash storage is seen as the solution to these issues because it delivers the desired higher performance.  But does all of your data need flash media?  What if there were “a smart and self-optimizing” storage system that automatically moves data across different media depending not only on frequency of usage, but also on the application’s performance requirements, media cost, and business value? Introducing Oracle FS1 Flash Storage System. How is Oracle FS1 Flash Storage System Different? Oracle FS1 intelligently delivers flash performance when you need it and reduces costs by moving data