How can I deep copy a pandas DataFrame where some values are lists, make changes in the copied dataframe, and not change the list in the original?

If a Pandas DataFrame has a column where each row contains a list, and that DataFrame is (deep) copied, the changes in the new DataFrame on that column effect the original DataFrame. This happens regardless of how I instantiate the column. How can this be avoided? Is this an inherent flaw to using lists inside a DataFrame?

import pandas as pd
import numpy as np

df = pd.DataFrame({'Person': ['Jack', 'Bob', 'Alice'], 'Age': [20, 30, 40]})
df['Flavors'] = np.empty((len(df), 0)).tolist()
df['Colors'] = [[] for _ in range(len(df))]
df['Friends'] = [[],[],[]]
print(df)
df2 = df.copy(deep=True)
df2.loc[0, 'Flavors'].append('Apple')
df2.loc[0, 'Colors'].append('Red')
df2.loc[0, 'Friends'].append('Rick')
print(df)
print(df2)

Gives output:

  Person  Age Flavors Colors Friends
0   Jack   20      []     []      []
1    Bob   30      []     []      []
2  Alice   40      []     []      []
  Person  Age  Flavors Colors Friends
0   Jack   20  [Apple]  [Red]  [Rick]
1    Bob   30       []     []      []
2  Alice   40       []     []      []
  Person  Age  Flavors Colors Friends
0   Jack   20  [Apple]  [Red]  [Rick]
1    Bob   30       []     []      []
2  Alice   40       []     []      []

I would expect the output to be this:

  Person  Age Flavors Colors Friends
0   Jack   20      []     []      []
1    Bob   30      []     []      []
2  Alice   40      []     []      []
  Person  Age  Flavors Colors Friends
0   Jack   20  [Apple]     []      []
1    Bob   30       []     []      []
2  Alice   40       []     []      []
  Person  Age  Flavors Colors Friends
0   Jack   20  [Apple]  [Red]  [Rick]
1    Bob   30       []     []      []
2  Alice   40       []     []      []

It's not the best solution but an idea is to store your dataframe on disk (or memory) then reload it from buffer:

df = pd.DataFrame({'Person': ['Jack', 'Bob', 'Alice'], 'Age': [20, 30, 40]})
df['Flavors'] = np.empty((len(df), 0)).tolist()
df['Colors'] = [[] for _ in range(len(df))]
df['Friends'] = [[],[],[]]
print(df)

with io.BytesIO() as buf:
    df.to_pickle(buf)
    buf.seek(0)
    df2 = pd.read_pickle(buf)

df2.loc[0, 'Flavors'].append('Apple')
df2.loc[0, 'Colors'].append('Red')
df2.loc[0, 'Friends'].append('Rick')
print(df)
print(df2)

Output:

  Person  Age Flavors Colors Friends
0   Jack   20      []     []      []
1    Bob   30      []     []      []
2  Alice   40      []     []      []
  Person  Age Flavors Colors Friends
0   Jack   20      []     []      []
1    Bob   30      []     []      []
2  Alice   40      []     []      []
  Person  Age  Flavors Colors Friends
0   Jack   20  [Apple]  [Red]  [Rick]
1    Bob   30       []     []      []
2  Alice   40       []     []      []