Priyanshu Singh

The numpy.modf() function in Python is a part of the NumPy library that can be used to separate the fractional and integer parts of an array of floating-point numbers. It returns two arrays – one containing the fractional parts and another containing the integer parts. In this article, we will understand Python numpy.modf() function, its syntax, and we […]

The numpy.floor_divide() function in Python is used to perform element-wise floor division of two arrays. It returns the largest integer that is less than or equal to each element in the resulting array. This function is particularly useful when we want to obtain the integer quotient after division, discarding any remainder. In this article, we

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

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

The numpy.where() function in Python’s NumPy library is used for element-wise conditional operations on arrays. It allows us to apply conditions to elements in an array and return elements from different arrays based on those conditions. Using numpy.where() function we can find indices where a condition is True which will help to locate a particular

Multiplication is the fundamental arithmetic operation that we utilize when we have two numbers or arrays and we need a product of both. In machine learning, we have large datasets to handle so we cannot do manual multiplication using calculators or basic operators. Here comes numpy.multiply() function. In this article, we will understand Python numpy.multiply() function, its syntax, and