Python

The numpy.true_divide() function in Python is used to perform element-wise true division of two arrays and it is similar to the ‘/’ operator. However, it is designed to handle division with more flexibility and precision, especially when dealing with integer or boolean arrays. In this article, we will understand Python numpy.true_divide() in detail using its […]

The numpy.float_power() function in Python is part of the NumPy library that is used to compute the element-wise power of array elements. It allows us to raise each element of an array to a specified power, providing flexibility and efficiency for handling power operations on arrays. In this article, we will understand Python numpy.float_power() function, its syntax, and

As we know the Pandas DataFrame module is a data-structured tool for handling massive datasets in several dimensions, including CSV or Excel files. Since a DataFrame can contain a lot of data, we often face situations where we need to identify unique values from a dataset that may contain duplicate or redundant data. So to

The numpy.reciprocal() function in Python is used to compute the reciprocal of each element in an array. The reciprocal of a number is simply 1 divided by that number, which means let’s say we have a number ‘a’ so the reciprocal of ‘a’ will be ‘1/a’. This function is part of the NumPy library, which

In machine learning, one ought to deal with a lot of data for analysis. Handling such gargantuan amounts of data might be a bit tricky but with the right functions, it is just a matter of time before we end up right where we want to be. In this article, we shall explore one such

Python provides umpteen features to customise the visualisation of the data that is being analysed. In this article, we shall explore in great depth one such function that aids in the styling of the data in the MS Excel spreadsheet. A styler object is a high-potential tool that could be deployed for formatting the DataFrame,

The numpy.ones() function in Python’s NumPy library creates an array filled with ones. Similar to numpy.zeros(), it’s often used as a starting point for initializing arrays before populating them with actual data. This function is widely used in numerical and scientific computing. In this article, we will understand this function, and its syntax, and learn how to

In a Pandas DataFrame, every column has a label or name that indicates the kind of value it contains or represents. When we need to programmatically change the values of all columns or access all columns by name, getting the column names will be helpful. Also, we might need to retrieve the column names when

Mathematics has a handful of parameters using which we can gauge numbers. One among those is the square root. It might be a walk to find the square root when the given numbers are the product of themselves, but when it no longer seems to be the case, things get a tad bit difficult. This

The numpy library within Python contains a wide range of functions to modify or manipulate numerical data. Amongst those is a category of functions under einsum which are noteworthy for their manipulative capabilities in handling the N-dimensional arrays. They can seamlessly support adding, multiplying or rearranging any given input arrays in very short time periods