Andrea RidolfiThis article presents different types of widgets that can be embedded within a matplotlib figure, in order to create and personalize highly interactive plots. Exploiting the matplotlib package .widget(), it is hence possible to create personalized buttons that allows controlling different properties of the graphs that are plotted in the main window. This represents a … Read more
This article describes how to generate interactive plots by using the .widgets package from the matplotlib library. As can be inferred from the name, the .widgets package allows creating different types of interactive buttons, which can be used for modifying what is displayed in a matplotlib graph. In particular, this article will focus on the … Read more
This article deals with the analysis and processing of signals, more specifically on how to identify and calculate the peaks contained in a given signal. Motivation Being able to identify and hence work with the peaks of a signal is of fundamental importance in lots of different fields, from electronics to data science and economics. … Read more
Heatmaps are a specific type of plot which exploits the combination of color schemes and numerical values for representing complex and articulated datasets. They are largely used in data science application that involves large numbers, like biology, economics and medicine. In this video we will see how to create a heatmap for representing the total … Read more
This article explores the use of the functions .UnivariateSpline() and .LSQUnivariateSpline(), from the Scipy package. What Are Splines? Splines are mathematical functions that describe an ensemble of polynomials which are interconnected with each other in specific points called the knots of the spline. They’re used to interpolate a set of data points with a function … Read more
In this article we will explore how to perform interpolations in Python, using the Scipy library. Scipy provides a lot of useful functions which allows for mathematical processing and optimization of the data analysis. More specifically, speaking about interpolating data, it provides some useful functions for obtaining a rapid and accurate interpolation, starting from a … Read more
In this article, you’ll explore how to generate exponential fits by exploiting the curve_fit() function from the Scipy library. SciPy’s curve_fit() allows building custom fit functions with which we can describe data points that follow an exponential trend. In the first part of the article, the curve_fit() function is used to fit the exponential trend … Read more
The np.polyfit() function, accepts three different input values: x, y and the polynomial degree. Arguments x and y correspond to the values of the data points that we want to fit, on the x and y axes, respectively. The third parameter specifies the degree of our polynomial function. For example, to obtain a linear fit, … Read more