How to Sample DataFrame Rows Using a Gaussian Distribution?

The issue is that the random_state parameter isn’t meant to accept a value from a distribution. It expects either an integer seed or a numpy Generator instance to reproduce randomness. In your code, calling rng.normal() returns a random float from the normal (Gaussian) distribution, which isn’t a valid seed or Generator. This is why you get an error.

If your goal is to sample rows from your DataFrame with weights based on a normal (Gaussian) distribution, you should do the following:

1. Compute weights that follow a Gaussian shape.
2. Pass these weights to the sample method via the weights parameter.
3. (Optionally) set random_state to an integer to ensure reproducibility.

For example, suppose you want to weight rows so that those near the center of the DataFrame have higher probability (like a bell-shaped curve). One way is to treat the row indices as positions and compute Gaussian probabilities based on their distance from a chosen center:

PYTHONCopied
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})

# Create positions for each row (here using the row index)
positions = np.arange(len(df))

# Define the center and standard deviation for the normal distribution
center = len(df) / 2
std_dev = 10  # adjust as needed

# Calculate weights using the Gaussian (normal) probability density function
weights = np.exp(-0.5 * ((positions - center) / std_dev) ** 2)
# Normalize weights so that they sum to 1
weights /= weights.sum()

# Sample 10 rows using the computed weights; random_state can be an integer for reproducibility
sampled_df = df.sample(n=10, weights=weights, random_state=42)

print(sampled_df)

Key Points

• random_state:
  Use this parameter to control reproducibility. Do not pass a float from a distribution (like rng.normal()).

• weights:
  Use the weights parameter if you want to sample according to a probability distribution. Here, we calculated the weights using the Gaussian formula. Adjust the center and standard deviation (std_dev) as needed for your specific use case.

This approach uses the Gaussian distribution to determine each row’s chance of being sampled, which seems to meet your goal of sampling your DataFrame “using a normal distribution.”