How to add data to NaN rows from another data-CodePudding

How can i add datas from another data, but without removing NaN values? I have three data similar to this

df_main = df_main = pd.DataFrame({'ID': ['10', '11', '12', '13', '14', '15', '16'], 'Name': [ np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})

   ID  Name
0  10   NaN
1  11   NaN
2  12   NaN
3  13   NaN
4  14   NaN
5  15   NaN
6  16   NaN

df2 = pd.DataFrame({'ID': ['10', '11', '12'], 'Name': [ 'Peter', 'Bruce', 'Tony']})

   ID   Name
0  10  Peter
1  11  Bruce
2  12   Tony

df3 = pd.DataFrame({'ID': ['15', '16'], 'Name': ['Wanda', 'Natasha']})

   ID     Name
0  15    Wanda
1  16  Natasha

What I want to have is data like this:

   ID     Name
0  10     Peter
1  11     Bruce
2  12     Tony
3  13      NaN
4  14      NaN
5  15    Wanda
6  16  Natasha

I tried this code but it did not work

for id in df2['ID'].unique():
    if id in df_main['ID'].unique():
        df_main.loc[df_main['ID'] == id, 'Name'] = df2.loc[df2['ID'] == id, 'Name']

for id in df3['ID'].unique():
    if id in df_main['ID'].unique():
        df_main.loc[df_main['ID'] == id, 'Name'] = df3.loc[df3['ID'] == id, 'Name']

CodePudding user response：

IIUC, you can use concat with GroupBy.first :

out = pd.concat([df2, df_main, df3]).groupby("ID", as_index=False).first()

Output :

print(out)

   ID     Name
0  10    Peter
1  11    Bruce
2  12     Tony
3  13     None
4  14     None
5  15    Wanda
6  16  Natasha

CodePudding user response：

concat df2/df3 and map the values:

df_main['Name'] = df_main['ID'].map(pd.concat([df2, df3]).set_index('ID')['Name'])

Output:

   ID     Name
0  10    Peter
1  11    Bruce
2  12     Tony
3  13      NaN
4  14      NaN
5  15    Wanda
6  16  Natasha