In this article we will see method to replace NaN (Not a Number) value with the average of columns. If we talk about data analysis then handling NaN value is very crucial step. So, here you will learn about various methods using which we can replace NaN (Not a Number) value with the average of columns. Method 1: Using Numpy.nanmean(). Example import numpy as np arr = np.array([[1, 2, np.nan], [4, np.nan, 6], [np.nan, 8, 9]]) col_means = np.nanmean(arr, axis=0) arr_filled = np.where(np.isnan(arr), col_means, arr) print("Column mean: ", col_means) print("Final array: ... Read More

In this article we will see method to replace the multiple characters at once in any string. While working with programs where we face such situation when we will have to replace any particular character simultaneously at all of its occurences. Suppose a situation in which you have an article in which it contains one word named as “word” which is having multiple occurrences and you want to convert that “word” into “work” so you can do this manually but it is very bad practice of doing this work one by one. So, we will look at multiple methods using ... Read More

In this article we will learn how to replace any given list element with its ordinal number. We will see various methods to get this work done. Before that let’s know about ordinal numbers. Ordinal number − In mathematics we say ordinal number as number which represents the position or original order of list items present in a sequence. Take an example of a list: [ 6, 8, 5, 3 ] so ordinal value of these number would be − The first element (6) has ordinal value 1. The second element (8) has ordinal value 2. The third element ... Read More

In this article we will learn about the Jacobian matrix and how to calculate this matrix using different methods in PyTorch. We use Jacobian matrix in various machine learning applications. Jacobian Matrix We use Jacobian matrix to calculate relation between the input and output variable. Jacobian matrix has all the partial derivatives of vector valued function. We can use this matrix in various applications machine learning applications. Here are some of its usages − For analyzing the gradients and derivatives of functions in multivariable calculus. Solving differential equations of systems. Calculating inverse of vector-values functions. Analyzing stability of dynamic ... Read More

In this article we will learn about pandas, DataFrame and how we can iterate over rows and columns in the pandas DataFrame using various methods. Iteration is a basic operation we do when we have tabular data containing rows and columns. To install the pandas in the system execute the command in cmd. pip install pandas Method 1. Using Iterrows() Method. Example import pandas as pd df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'], 'Age': [21, 20, 23, 23], 'Roll': [12, 13, 14, 15], 'Game': ['Cricket', 'Lodu', 'Chess', ... Read More

When an object at very high temperature is suddenly dropped in a cooler liquid and if it is assumed that the conductive resistance of the solid is very small in comparison to the surrounding convective resistance then the heat transfer analysis is called as lumped capacitance analysis (as shown in the figure given below). Here, we treat the system as a lump. In that case, we can assume that the rate of change of internal energy of lump will be equal to the heat interaction with the surrounding fluid. Mathematically, this can be written as − $$\mathrm{pcV\frac{\partial T}{\partial t} ... Read More

It is the most straightforward iterative strategy for tackling systems of linear equations shown below. $$\mathrm{a_{1, 1}\: x_{1} \: + \: a_{1, 2} \: x_{2} \: + \: \dotso\dotso \: + \: a_{1, n} \: x_{n} \: = \: b_{1}}$$ $$\mathrm{a_{2, 1} \: x_{1} \: + \: a_{2, 2} \: x_{2} \: + \: \dotso\dotso \: + \: a_{2, n} \: x_{n} \: = \: b_{2}}$$ $$\mathrm{\vdots}$$ $$\mathrm{a_{n, 1} \: x_{1} \: + \: a_{n, 2} \: x_{2} \: + \: \dotso\dotso \: + \: a_{n, n} \: x_{n} \: = \: b_{n}}$$ The fundamental concept is: each linear ... Read More

Introduction In this tutorial, we implement an approach to find the longest subsequence with different adjacent characters. Here, the longest subsequence is a subsequence that contains the maximum number of string characters with different adjacent characters. To implement the approach to finding the longest subsequence, consider a string s, and iterate We use two approaches to resolve the problem statement of finding the longest subsequence having different adjacent characters. Greedy ApproachIt is one of the most used algorithms to solve the data structure problem. This approach tries all possible cases and chooses the most suitable. Dynamic programmingIt ... Read More

Introduction In this article, we check validate image file extension using regular expressions. Image file extension in this article, is the valid extension for an image file which consists of file name and file extension . The valid image file extension follows some rules which we define in this article. Regular expression or regex is used for pattern matching in strings or string searching algorithms. Its functionalities are defined in the header file. It is used with a regex keyword followed by a variable. In this C++ tutorial, we check whether the input string is a valid image file ... Read More

Introduction In this C++ tutorial, we check whether the input Aadhar number is valid using Regular Expression. Regular expression or regex is used for pattern matching in strings or string searching algorithms. Its functionalities are defined in the header file. It is used with a regex keyword followed by a variable. Indian citizens are issued an Aadhaar number, which is a unique identification number. It is a 12-digit unique number and no two people have a similar AADHAAR number. Syntax regex regular_expression_patternname return regex_match(value, regular_expression_patternname); In this tutorial to check the validity of an Aadhar number we ... Read More