Code Types Demonstration

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import pandas as pd

def sample_normal_dist(mu: float, sigma: float, n: int, plot=True):
    """
    Samples from a normal distribution with given parameters with 
    the option to plot

    Args:
        mu (float): mean.
        sigma (float): standard deviation.
        n (int): number of samples.
    Returns:
        np.array: array of samples.
    """
    # Generate random samples from a normal distribution
    samples = np.random.normal(loc=mu, scale=sigma, size=n)
    if plot:
        # Visualize the distribution of the samples using a histogram
        plt.hist(samples, bins=30, density=True, alpha=0.6, color='g')
        plt.title('Histogram of Samples from Normal Distribution')
        plt.xlabel('Value')
        plt.ylabel('Probability Density')
        plt.grid(True)
        plt.show()
    # Display the mu and sigma
    df = pd.DataFrame({"True": [mu, sigma], 
                       "Sample": [np.mean(samples), np.std(samples)]}, 
                      index=["Mu", "Sigma"])
    display(df.round(3))
    return samples

mu = 0
sigma = 1
n = 1000
sample_normal_dist(mu, sigma, n)

# show using local import
from example_python_script import sample_normal_dist_module

mu = 0
sigma = 1
n = 1000
sample_normal_dist_module(mu, sigma, n)