PYTHON FOR DATA SCIENTIST | HOW TO FIT GAUSSIAN ON HISTOGRAM PLOT #coding | SCIPY CUTVE FIT

In this tutorial, we'll explore how to fit a Gaussian (normal) distribution to a histogram using Python and the scipy library. Gaussian fitting is a common technique in data analysis to model and understand the underlying distribution of a dataset.

🔍 Topics Covered:

Generating example data
Creating a histogram
Defining a Gaussian function
Visualizing the histogram and the fitted Gaussian curve

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PYTHON FOR DATA SCIENTIST| HOW TO FIT GAUSSIAN ON HISTOGRAM PLOT #coding | SCIPY CUTVE FIT

Fitting Gaussian on Histogram with Python | Data Analysis Tutorial
-------------------------------------CODE-----------------------------------------------------
#import library
import random
import numpy as np # array
# data
# plot histogram
y=n
# for fitting histogram
def gauss(x,amp,mu,sigma): # deinition of function
# fit the curve/histogram
popt,pcov=curve_fit(gauss,x,y,p0=[120,0,2]) # popt= optimize parameter
# plot the fitted gaussian curve
print('amp',popt[0]) # amp
print('meam',popt[1])# mean mu
print('sigma',popt[2]) # sigma