How to do exponential and logarithmic curve fitting in Python

Curve fitting is the process of comparing a set of data to a continuous set of points. Exponential and logarithmic graphs are common for curve fitting and finding a line of best fit.

Solution for How to do exponential and logarithmic curve fitting in Python : You can use numpy.array(), numpy.log(), and numpy.polyfit() to do exponential and logarithmic curve fitting Call numpy.array(list) with list as a list of x and y values to create an ndarray from list. Call numpy.log(array) with array as the x and y value ndarray to take the log of the values in array. To approximate a logarithmic curve with a polynomial and determine the coefficients of the curve that best fits the data, call numpy.polyfit(x, y, degree) with x as the logged ndarray of x values, y as the ndarray of y values, and degree as an integer specifying the degree of the fitting polynomial.

To approximate an exponential curve with a polynomial and determine the coefficients of the curve that best fits the data, call numpy.polyfit(x, y, degree) with x as the ndarray of x values, y as the logged ndarray of y values, and degree as an integer specifying the degree of the fitting polynomial.

x_data = np.array([10, 20, 30, 40, 50])


how-to-do-exponential-and-logarithmic-curve-fitting-in-python