I am trying to write Pandas code that would allow me to sample DataFrame using a normal distribution. The most convinient way is to use random_state parameter of the sample method to draw random samples, but somehow employ numpy.random.Generator.normal to draw random samples using a normal (Gaussian) distribution. import pandas as pd import numpy as np import random # Generate a list of unique random numbers temp = random.sample(range(1, 101), 100) df = pd.DataFrame({'temperature': temp}) # Sample normal rng = np.random.default_rng() triangle_df.sample(n=10, random_state=rng.normal())  This obviously doesn't work. There is an issue with random_state=rng.normal().
Passing a Generator to sample just changes the way the generator is initialized, it won't change the distribution that is used. Random sampling is uniform (choice is used internally [source]) and you can't change that directly with the random_state parameter. Also note that normal sampling doesn't really make sense for discrete values (like the rows of a DataFrame). Now let's assume that you want to sample the rows of your DataFrame in a non-uniform way (for example with weights that follow a normal distribution) you could use the weights parameter to pass custom weights for each row. Here is an example with normal weights (although I'm not sure if this makes much sense): rng = np.random.default_rng() weights = abs(rng.normal(size=len(df))) sampled = df.sample(n=10000, replace=True, weights=weights)  Another example based on this Q/A. Here we'll give higher probabilities to the rows from the middle of the DataFrame: from scipy.stats import norm N = len(df) weights = norm.pdf(np.arange(N)-N//2, scale=5) df.sample(n=10, weights=weights).sort_index()  Output (mostly rows around 50):  temperature 43 94 44 50 47 80 48 99 50 63 51 52 52 1 53 20 54 41 63 3  Probabilities of sampling with a bias for the center (and sampled points): 