Priyanshu Singh

We know that the definition of mean is the sum of all values divided by the number of values. Similarly, the mean() method in Pandas is also used to calculate the mean or the average of the values in a DataFrame. It can be applied to the entire DataFrame or along a specific axis (rows […]

The read_excel() function is a part of the Pandas library of Python which is specifically designed for reading data from Excel files. Excel files are commonly used for storing tabular data, and the read_excel() function provides a convenient and flexible way to import this tabular data for analysis and manipulation within a Python environment. In

The to_excel() function in Pandas is used to export a DataFrame to an Excel file. In just a few lines of code, we can take our DataFrame and export it to an Excel File. We have to only specify the file name, and Pandas will do the rest for us. It’s like telling our computer,

The pandas.melt() function in the Pandas library is used for reshaping or transforming DataFrames. It is particularly useful for converting wide-format DataFrames into long-format ones. The pandas.melt() function essentially “unpivots” a DataFrame, making it more suitable for certain types of analyses and visualizations In this article, we will first go over the syntax and parameters

In Pandas, the pandas.crosstab() function is used to compute a cross-tabulation of two (or more) factors. It is a convenient way to analyze the relationship between two or more categorical variables in a DataFrame. In this article, we will first understand the syntax and the parameters of pandas.crosstab() function, then we will look at some examples to

The numpy.full_like() function in NumPy is designed to create a new array with the same shape and data type as a given input array with all elements set to a specified fill value. This function is beneficial when we want to initialize an array with a constant value and want the new array to have

The numpy.zeros_like() function in NumPy creates an array of zeros with the same shape and data type as a specified existing array. This function is handy when we want to initialize an array with zeros but maintain an already existing array’s structure (shape and data type). Syntax of numpy.zeros_like() Function When we provide the original

The groupby() method in the Pandas library is a powerful and flexible tool for grouping data based on one or more criteria and then for applying a method to each group independently. It is also a fundamental tool for data analysis and manipulation in Pandas, enabling efficient and flexible operations on grouped data. It plays

The numpy.full() function in the NumPy library is designed to create a new array with a specified shape, filling all its elements with a constant value. It provides a convenient and efficient way to generate arrays with uniform values, such as creating matrices initialized with a specific constant. This function is particularly useful in scenarios

The numpy.mod() function in Python is a part of the NumPy library used for various numerical and mathematical operations. It is used to compute the element-wise remainder of the division between two arrays. In this article, we will understand Python numpy.mod() function, its syntax, and demonstrate it with various examples. Let’s get started. Also Read: numpy.multiply() in Python