如何根据 Pandas 中的另一个 DataFrame 更改 DataFrame 特定列中的值

Question

I have a dataframe called 'data':

USER   FIELD1
Jack        1
Jill        2
Kane        3

and a separate dataframe called 'ids' that serves to be used as a conversion table:

ID            ID_NEW
Jack        Jack_NEW
Jill        Jill_NEW
Tyler      TYLER_NEW

How can I iterate through 'data' to replace the USER value if it is found in the 'ids' dataframe while keeping values that are not found? To end up with something like:

USER           FIELD1
Jack_NEW            1
Jill_NEW            2
Kane                3

Answer 1

You can do this in a couple steps. First, you join the two DataFrames together on "USER = ID" using pd.merge . This will leave nulls in "ID_NEW" where the dfs don't join, so then you bring in the "USER" values for those using Series.combine_first . Finally, you reassign column names and filter down to just the columns you want to keep.

merged = pd.merge(data, ids, how="left", left_on="USER", right_on="ID")
merged["USER"] = merged["ID_NEW"].combine_first(merged["USER"])
data = merged[["USER", "FIELD1"]]

If you have a lot of columns in data , you could do data = merged[data.columns] at the end.