I am aware of many probabilistic functions builted-in Python, with the random module.
I’d like to know if, given a list of floats, it would be possible to find the distribution equation that best fits the list?
I don’t know if numpy does it, but this function could be compared (not equal, but similar) with the Excel’s “Trend” function.
How would I do that?
Advertisement
Answer
Look at numpy.polyfit
numpy.polyfit(x, y, deg, rcond=None, full=False)
Least squares polynomial fit.
Fit a polynomial p(x) = p[0] * x**deg + … + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.