使用以下方法解决了该问题：

mean = df['Mean']
std_dev = df['SD']
dist = np.random.normal(mean, std_dev)

for i in range(1000):
    col_name = 'col_' + str(i)
    df[col_name] = np.random.normal(dist)'
    
    col_name_q = 'col_q_' + str(i)
    df[col_name_q] = pd.qcut(df[col_name], 5, labels = False)

你可以使用numpy在一次调用中创建所有的随机数。一旦将这些列添加到DataFrame中，就可以在每个新列上使用qcut并添加这些列。这不需要任何（显式）for循环。

import pandas as pd
import numpy as np

data = {"Mean":[60, 62, 65, 59],
        "STD":[0.81, 0.91, 0.87, 0.86]}

df = pd.DataFrame(data)

N = 10
n_col_names = [f"n+{i+1}" for i in range(N)]
q_col_names = [f"q+{i+1}" for i in range(N)]

rng = np.random.default_rng(42)
df[n_col_names] = rng.normal(df["Mean"], df["STD"], size=(N, df.shape[0])).T
df[q_col_names] = df[n_col_names].apply(lambda x: pd.qcut(x, 5, labels=False))