如何使用 Python 使用一个数据集来模拟另一个数据集？

Question

I have the following example df:

import pandas as pd

cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000]
        }

df = pd.DataFrame(cars, columns = ['Brand','Price'], index=['Car_1','Car_2','Car_3','Car_4'])

Is there a way to generate a new dataset with parameters from the first one? I want the 'Brand' values to be the same, but 'Price' values to be randomly generated by using the min.,max, and mean of the original values in 'Price'.

I can code this manually, but wondering if there is a way to have python detect the parameters of a dataframe and create a new one, with those parameters.

Note: the example dataframe I provided is tiny, but these concepts would apply to bigger data.

Answer 1

You can use np.random.randint. The first 2 parameters set the bounds, and the third gives the number of values.

import pandas as pd
import numpy as np

df2 = pd.DataFrame({'Brand': df['Brand'],
                    'Price': np.random.randint(df['Price'].min(),
                                               df['Price'].max(), df.shape[0])})

print(df2)

       Brand            Price
Car_1  Honda Civic      29797
Car_2  Toyota Corolla   33306
Car_3  Ford Focus       26237
Car_4  Audi A4          23580