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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 ...

What is so Trendy in Data Visualization and Reporting

Data Visualization: Data visualization is the process that defines any effort to assist people to understand the importance of data by placing it in a visual context.  Patterns, trends, and correlations that might be missed in text-based data can be represented and identified with data visualization software. It is a graphical representation of numerical data. This is one of the Hot skills in the market, you will get the highest salary. Types of data visualization Visual Reporting Visual reporting uses charts and graphics to represent business performance, usually defined by metrics and time-series information. The best dashboards and scorecards enable the users to drill down one or more levels to view more detailed information about a metric A dashboard is a visual exception report that signifies the ambiguities in performances using visualization techniques Visual Analysis Visual-analysis allows users to visually explore the data to observe the data and dis...

5 Emerging Trends in Data Visualization

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Nowadays, we deeply drowned in data of diverse kinds due to the increased computational power and accessibility. Specifically, in addition to public data available on the Internet (e.g., census, demographics, environmental data), data pertaining personal daily activities are now more easily collected. For example, through mobile devices that can log people's running distances and time or their manual record of nutrition consumption. Due to such expanded sources of data, there appear new applications that involve data collection, visualization, exploration, and distribution in daily contexts. These applications do, not only display static information but also let users navigate the data in forms of interactive visualizations. 5 Emerging Trends in Data Visualization. #1: This emerging trend has brought both opportunities and challenges to interaction designers to develop new approaches to designing data-based applications. #2: Conveying information has been one of main functio...