Quick Tutorial for Python Numpy Arange Functions with Examples

What is Numpy Arange function in Python The Numpy Arange function is used to create a numpy array whose elements are evenly distributed within a given range.  In this tutorial, we will understand the syntax of np.arange() and go through multiple examples by using its various parameters. Numpy Arange : numpy.arange() Syntax numpy.arange(start=0, stop, step=1, dtype) start (optional) – It denotes the starting value of the range,    

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Agglomerative Hierarchical Clustering in Python Sklearn & Scipy

Introduction In this tutorial, we will see the implementation of Agglomerative Hierarchical Clustering in Python Sklearn and Scipy. First of all, we will understand in brief what is hierarchical clustering and how agglomerative clustering works. Then we will create our own sample dataset to show an example of creating a dendrogram graph in Scipy and implementing agglomerative clustering in sklearn. What is Hierarchical Clustering? Hierarchical clustering is an unsupervised clustering algorithm used to create clusters with a tree-like hierarchy. In […]

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YOLOv6 Explained with Tutorial and Example

Introduction In this article, we will introduce the new object detection model YOLOv6 which has been making buzz in the computer vision community ever since its GitHub was made public a few days back. We will take a brief look at its architecture and the improvement that its author claim. Then we will explain how to use YOLOv6 with step by step tutorial and example. What is YOLOv6? YOLOv6 is the object detection model created by a team at Meituan […]

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Split and Merge Image Color Space Channels in OpenCV and NumPy

Introduction In this tutorial, we will show you how to split the image into it’s multiple channels by using the OpenCV cv2.split() function and also with Numpy. We will also show you how we can merge the single channel images to generate the multi-channel image with the cv2.merge() function. Color Spaces and Channels The colors of images can be organized in many ways and this scheme is known as color space. For example, a popular way of representing the image […]

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How to Find an Absolute Value in Python

Absolute values are commonly used in mathematics, physics, and engineering. Although the school definition of an absolute value might seem straightforward, you can actually look at the concept from many different angles. If you intend to work with absolute values in Python, then you’ve come to the right place. In this tutorial, you’ll learn how to: Implement the absolute value function from scratch Use the built-in abs() function in Python Calculate the absolute values of numbers Call abs() on NumPy […]

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Caching in Python With lru_cache

There are many ways to achieve fast and responsive applications. Caching is one approach that, when used correctly, makes things much faster while decreasing the load on computing resources. Python’s functools module comes with the @lru_cache decorator, which gives you the ability to cache the result of your functions using the Least Recently Used (LRU) strategy. This is a simple yet powerful technique that you can use to leverage the power of caching in your code. In this video course, […]

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GitHub Copilot: Fly With Python at the Speed of Thought

GitHub Copilot is a thrilling new technology that promises to deliver to your code editor a virtual assistant powered by artificial intelligence, and it stirred up considerable controversy when it was released to the general public. Python is among the languages that are particularly well-supported by this tool. After reading this tutorial, you’ll know whether GitHub Copilot is a risk, a gimmick, or a true game changer in software engineering. To continue with this tutorial, you need to have a […]

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Finding performance problems: profiling or logging?

When your software is too slow in production, how can you figure out the source of the problem? One common starting point to improving production observability is logging, and ideally trace-based logging (tracing for short). For example, the OpenTelemetry standard and the libraries and backend services that work with it can help you collect metrics, logs, and traces. Tracing—both within and across processes—is the most general of these, immensely useful for identifying and debugging problems, including performance problems. But there’s […]

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Sorting a Python Dictionary: Values, Keys, and More

# samples.py dictionary_of_dictionaries = { 1: {“first_name”: “Dorthea”, “last_name”: “Emmanuele”, “age”: 29}, 2: {“first_name”: “Evelina”, “last_name”: “Ferras”, “age”: 91}, 3: {“first_name”: “Frederica”, “last_name”: “Livesay”, “age”: 99}, 4: {“first_name”: “Murray”, “last_name”: “Linning”, “age”: 36}, 5: {“first_name”: “Annette”, “last_name”: “Garioch”, “age”: 93},

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Exploring Special Function Parameters

Have you ever come across the forward slash (/) and asterisk (*) symbols in the documentation for your favorite libraries? These represent special parameters, which tell you what kinds of arguments you can use to call a given function. In Python, these parameters can help you ensure that your functions are used correctly. Maybe you’re a regular at Real Python’s weekly Office Hours meetup, or perhaps you’re curious about what happens there. You’ll get a glimpse in this Code Conversation, […]

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