如何创建一个 pandas 系列（列），基于与另一个 Dataframe 中的值的匹配？

Question

my question is the following: I do not know very well all the pandas methods and I think that there is surely a more efficient way to do this: I have to load two tables from.csv files to a postgres database; These tables are related to each other with an id, which serves as a foreign key, and comes from the source data, however I must relate them to a different id controlled by my logic.

I explain graphically in the following image:

Im trying to create a new Series based on the "another_id" that i have and apply a function that loop through a dataframe Series to compare if have the another code and get their id

def check_foreign_key(id, df_ppal):
  if id:
    for i in df_ppal.index:
      if id == df_ppal.iloc[i]['another_id']:
        return df_ppal.iloc[i]['id']

dfs['id_fk'] = dfs['another_id'].apply(lambda id : check_foreign_key(id, df_ppal))

In this point i think that it is not efficient because I have to loop in all column to match the another_id and get and get its the correct id that I need is in yellow in the picture.

So I should think about search algorithms to make the task more efficient, but I wonder if pandas does not have a method that allows me to do this faster, in case there are many records.

I need a dataframe like a this table that have a new column "ID Principal" based on matching Another_code, with another dataframe column.

ID	ID Principal	Another_code
1	12	54
2	12	54
3	13	55
4	14	56
5	14	56
6	14	56

Answer 1

Well indeed, I was not understanding very well all the pandas functions, I could solve my problem using merge, I did not know that pandas had a good implementation of the typical Join in SQL.

This documentation helped me a lot:

1. https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging

2. Pandas Merging 101

Finally my answer:

new_df = principal.merge(secondary, on='another_id')

I thank you all!