Published in Geek Culture·2 days ago12 Ways to Use Function Decorators to Improve Your Python CodeApplying separation of concerns, the easy way. Python decorators provide a simple way to modify or enhance existing functions. They are often used to add extra steps before or after a function in executed, which can be useful in many scenarios. There are other ways to use them too. In…Python9 min read
Published in Geek Culture·May 185 Advanced Vectorisation Techniques for Improved Python PerformanceUse NumPy to speed up your code. NumPy vectorisation applies a function to an entire array in one call. For example: a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) u = np.sqrt(a) # [1. 1.414 1.732] v = a + b # [5 7 9] Calling np.sqrt calculates…Python5 min read
Published in Geek Culture·Apr 19Structural Optimisation in PythonLooking at the bigger picture. We looked at low level otimisation previously. In this article we will look at structural optimisation. Structural optimisation takes a big picture approach. It attempts to optimise code by solving the problem in a different way, for example: Using a faster algorithm or more efficient…Python9 min read
Published in Geek Culture·Mar 319 Advanced Pie Chart Techniques in MatplotlibGoing beyond the basics. In this article, we will look at pie charts. A pie chart is used to compare the relative sizes of discrete categories, as a proportion of the total. A pie chart represents the entire data set as a circle and shows each category as a pie…Python7 min read
Published in Geek Culture·Mar 255 ways to make better bar charts with MatplotlibImproving your plots with extra information and styling. In this article, we will look at bar charts. A bar chart is used to compare discrete categories. Here are some examples: Looking at the annual sales for a company, we could create a bar chart of the total sales for each…Python7 min read
Published in Geek Culture·Mar 215 ways to Python efficiency with NumPyMemory, speed, and compatibilty improvements for large data sets. In this article, we will take a top-level look at the key advantages of using NumPy. What is NumPy, and why do we use it? NumPy is a Python library for manipulating large arrays of numerical data. It stores data internally as C-style arrays, and offers advanced vectorisation to process…Python8 min read
Published in Geek Culture·Mar 168 small changes to optimise your Python codeLow-level coding changes that can speed up execution This article looks at low-level code optimisation techniques. These techniques often involve changing individual lines of code to do the same thing in a slightly different way, with the aim of making the code run faster. Used with care, these techniques can…Python10 min read
Published in Geek Culture·Mar 10Chain of responsibility pattern in PythonDecoupling complex decision chains. Chain of responsibility is a behavioural design pattern. It is used to process command objects, where different types of command objects might need to be processed in different ways. It does this by allowing for multiple handler objects, each capable of handling a specific type of…Python5 min read
Published in Geek Culture·Mar 611 Pythonic Ways to Optimise your CodeGood practice that might also lead to good performance. When optimising Python code, it is generally best to concentrate your efforts on identifying the true bottlenecks in your code and speeding those areas up. But are is one exception. Certain coding techniques fall under the category of good practice and…Python5 min read
Published in Geek Culture·Feb 14How to Optimise Python PerformanceNo magic bullets, but a set of guiding principles Python is an interpreted language, which means it will generally be slower than compiled languages such as C++. For this reason, it is sometimes necessary to take additional steps to optimise Python code. In this article, we will look at some…Python6 min read
