Is there a better way to fill missing values between two dataframes?

I'm trying to fill missing values in one dataframe based on the data in other dataframes. My first dataframe looks like this

[df1]:

Key Value
1   15
2   30
23  NaN
1   15
4   60
2   30
5   NaN
2   NaN
4   NaN
25  NaN
62  NaN
32  NaN
21  315
21  NaN

And the dataframe I want to extract the information from looks like this one:

[df2]:
Key   Value
23        "fine"
5         "five"
2         30
4         60
25        "two"
62        "bar"
32        "foo"
21        315

   Key     Value
    2   30.0
   23  "fine"
    1   15.0
    4   60.0
    2   30.0
    5   "five"
    2   30.0
    4   60.0
   25  "two"
   62  "bar"
   32  "foo"
   21  315.0
   21  315.0

I've managed to do this iterating over the rows of both dataframes, using

for i, value in df1.iterrows():
    for j, value2 in df2.iterrows():
        if df1.loc[i,"Key"]==df2.loc[j,"Key"]:
            df1.loc[i,"Value"]=df2.loc[j,"Value"]
        else:
            pass

but using this with my actual information (Dataframes of around 100000 rows and 20+ columns each) takes too long and I believe there's a better way to do it with a pandas built-in function. Thanks in advance!

Solution 1:

You can achieve that with only one line of code:

df1 = df1.fillna(df2)