Fill in a specific column of Python Pandas data frames with two given series

import numpy as np
import pandas as pd

df1 = pd.DataFrame(
    {
        "Fruit name": ["Apple", "Tomato", "Banana", "Grape"],
        "Fruitid": [0, 1, 2, 3],
        "Amount": [600, 700, 300, 400],
        "Country of origin": [Seoul", "Seoul", "Jeju", "Daejeon",
        "Average amount of origin": [800, 800, 200, 500],
    }
)
df2 = pd.DataFrame(
    {
        "Fruit name": ["Apple", "Banana", "Geobong"],
        "Fruitid": [0, 2, 4],
        "Country of origin": [Seoul", "Jeju", "Daejeon",
        "Amount": [np.NaN, np.NaN, np.NaN],
        "Required Value": [600, 300, 500],
    }
)
s1 = pd.Series ([600, 700, 300, 400], index=["Apple", "Tomato", "Banana", "Grape"])
s2 = pd.Series ([800, 200, 500], index=["Seoul", "Jeju", "Daejeon"])

colums_org = df2.columns

df2 = df2.set_index ("fruit name")
df2["amount"] = s1
df2 = df2.reset_index()

print(df2.to_markdown())
"""
|    | fruit name | fruit id | country of origin | amount | desired value |
|---:|:---------|---------:|:---------|-------:|-----------:|
|  0 | Apple | 0 | Seoul | 600 | 600 |
|  1 | Banana | 2 | Jeju | 300 | 300 |
|  2 | Geobong | 4 | Daejeon | nan | 500 |
"""

df2 = df2.set_index ("origin")
df2.loc[df2["amount"].isna(), "amount"] = s2
df2 = df2.reset_index()

print(df2.to_markdown())
"""
|    | country of origin | fruit name | fruit id | amount | desired value |
|---:|:---------|:---------|---------:|-------:|-----------:|
|  0 | Seoul | Apple | 0 | 600 | 600 |
|  1 | Jeju | Banana | 2 | 300 | 300 |
|  2 | Daejeon | Geobong | 4 | 500 | 500 |
"""

df2 = df2[colums_org]
print(df2.to_markdown())
"""
|    | fruit name | fruit id | country of origin | amount | desired value |
|---:|:---------|---------:|:---------|-------:|-----------:|
|  0 | Apple | 0 | Seoul | 600 | 600 |
|  1 | Banana | 2 | Jeju | 300 | 300 |
|  2 | Geobong | 4 | Daejeon | 500 | 500 |
"""

select
  total.item_name,
  total.area_name,
  ifnull(some.price, total.default_price) AS posible_price -- look this part up in magic.some and write total if not
from (
  select
    items.id AS item_id,
    items.name As item_name,
    areas.id AS area_id,
    areas.name AS area_name,
    areas.default_value AS default_price
  From items, areas -- combine the numbers of all possible cases
) as total -- add 'already known fruit+origin information' to each possible case
left join item_area_pricings some -- this table corresponds to df1
  ON some.item_id = total.item_id AND some.area_id = total.area_id -- Both conditions must be met
order by total.item_id, total.area_id;