Renaming columns is a common data-cleaning task. Pandas offers several flexible ways to rename columns depending on whether you want to rename a single column, many columns, use a function to transform all names, or do an in-place rename. Using DataFrame.rename() (recommended) rename() accepts a columns mapping (old_name → new_name) or a function. By default… Continue reading Rename columns in Pandas DataFrame
Day: October 23, 2025
Get Cell Value From Pandas DataFrame
Pandas is one of the most widely used Python libraries for data manipulation and analysis. A common operation when working with a DataFrame is retrieving individual cell values. For example, extracting a single record or checking a specific field. In this article, we’ll cover multiple ways to get cell values from a Pandas DataFrame using… Continue reading Get Cell Value From Pandas DataFrame
numpy.size() — Count Elements in a NumPy Array (With Examples)
The numpy.size() function in Python returns the number of elements in an array. It can return either the total count of elements in the entire array or, if an axis is specified, the number of elements along that axis. This makes it a simple yet powerful utility for understanding array structure, validating data shapes, and… Continue reading numpy.size() — Count Elements in a NumPy Array (With Examples)
Self-Supervised Learning
In recent years, self-supervised learning (SSL) has gained significant attention in the field of artificial intelligence (AI) and machine learning (ML). This approach to learning aims to extract useful information from unlabelled data by leveraging different techniques. In this article, we will delve deeper into self-supervised learning, its benefits, and how it has been applied… Continue reading Self-Supervised Learning
Adversarial Attacks and Defenses in Deep Learning
Deep learning has revolutionized the field of artificial intelligence, achieving state-of-the-art performance in a wide range of applications, from image recognition to natural language processing. However, deep learning models are vulnerable to adversarial attacks, where malicious actors can craft inputs that fool the model into making incorrect predictions. In this article, we will explore the… Continue reading Adversarial Attacks and Defenses in Deep Learning
numpy.percentile() in python
numpy.percentile()function used to compute the nth percentile of the given data (array elements) along the specified axis. Syntax : numpy.percentile(arr, n, axis=None, out=None) Parameters : arr :input array. n : percentile value. axis : axis along which we want to calculate the percentile value. Otherwise, it will consider arr to be flattened(works on all the… Continue reading numpy.percentile() in python
numpy.subtract() in Python
numpy.subtract() function is used when we want to compute the difference of two array.It returns the difference of arr1 and arr2, element-wise. Syntax : numpy.subtract(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj], ufunc ‘subtract’) Parameters : arr1 : [array_like or scalar]1st Input array. arr2 : [array_like or scalar]2nd Input array. dtype… Continue reading numpy.subtract() in Python
numpy.argmax() in Python
numpy.argmax() returns the index (or indices) of the maximum element along the specified axis. Syntax – numpy.argmax(a, axis=None, out=None) a : array_like — input array. axis : int or None — axis along which to find the indices of the maximum. None flattens. out : ndarray, optional — alternative output array to store result (must… Continue reading numpy.argmax() in Python