python - Creating pandas dataframe in normal distribution

Question

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

question from:https://stackoverflow.com/questions/65672116/creating-pandas-dataframe-in-normal-distribution

Answer 1

I think what you should do is generate integers in normal distribution and make them the indices of the list. Also graphing the numbers you generated is in my opinion the best way to check whether they are a normal distribution, it should resemble the normal distribution bell shape. However since 20 is such a small number, it may not exactly be the desired shape which is something to keep in mind. The following link I think has all the information you need.

How to generate a random normal distribution of integers