Creating pandas dataframe in normal distribution

A want to create a sample dataframe -- based on a json template -- that looks as real as possible. Hence normal distribution.

This is what I have tried

import json, random
import pandas as pd

sample_data = """{"product1":[
    {"category":"Fruits",
    "productlist":["Bell Peppers","Red Chillies", "Onions", "Tomatoes"]}
],
"product2":[
    {"category":"Vegetables",
    "productlist":["Apple","Mango","Banana"]}
]}"""

products = json.loads(sample_data)

colHeaders = []

for k,v in products.items():
    colHeaders.append(v[0]['category'])

df = pd.DataFrame(columns= colHeaders)

for i in range (1000):
    itemlist = []
    for k,v in products.items():
        itemlist.append(random.choice(v[0]['productlist']))
    #print(itemlist)
    df.loc[len(df)] = itemlist

print(df)

I am not sure I am doing it correctly. If not, please help me with

• How to check if the data frame rows represent a normal distribution?
• How to try other distributions in this case?

Other related Stack Overflow questions I have referred are:

• Select one element from a list using python following the normal distribution
• Create distribution in Pandas