Merge inside the merge only if the first doesn’t return a match

I have 3 dataframes (df1, df2 & df3), the main one (df1) and two additional ones which contain 1 column amongst others that I want to bring over to the main dataframe.

Sample dfs:

df1 = {'Col_f': ['Georgia', 'Nevada', 'New York', 'Texas', 'Arizona'],
          'Col_g': ['SUV', 'Coupe', 'Wagon', 'Crossover', 'Sedan']}
df1 = pd.DataFrame(df1)

df2 = {'Col_g': ['SUV', '4x4', 'Wagon', 'Truck', 'Sedan'],
 'Objective': ['15%', '13%', '55%', '40.4%', '2.48%']}
df2 = pd.DataFrame(df2)

df3 = {'Col_f': ['Georgia', 'California', 'Pennsylvania', 'Texas', 'Arizona'],
 'Objective': ['15%', '13%', '55%', '40.4%', '2.48%']}
df3 = pd.DataFrame(df3)

df1 df2

and df3

I am using the following code:

df1_new = pd.merge(df1, df2, on = 'Col_g', how = 'left')

Which returns the following df:

df_new = {'Col_f': ['Georgia', 'California', 'Pennsylvania', 'Texas', 'Arizona'],
          'Col_g': ['SUV', 'Coupe', 'Wagon', 'Crossover', 'Sedan'],
         'Objective': ['15%', ' ', '55%', ' ', '2.48%']}

Then for the two empty strings for the “Objective” column I want to continue with a second merge (or an excel vlookup which I guess are the same), to fill in those empty ones.

Code that I thought would be used for the second merge:

df1_newer = pd.merge(df_new, df3, on = 'Col_f', how = 'left')

Desired final output.

df_newer = {'Col_f': ['Georgia', 'California', 'Pennsylvania', 'Texas', 'Arizona'],
          'Col_g': ['SUV', '4x4', 'Wagon', 'Truck', 'Sedan'],
         'Objective': ['15%', '13%', '55%', '40.4%', '2.48%']}

Any suggestions would be more than appreciated!

Answer

After merging the three dataframes, you can use mask or np.where to conditionally assign value

df1_new = pd.merge(df1, df2, on='Col_g', how='left')['Objective']
df1_newer = pd.merge(df1, df3, on='Col_f', how='left')['Objective']

print(df1_new)

0      NaN
1      NaN
2      55%
3      NaN
4    2.48%
Name: Objective, dtype: object

print(df1_newer)

0      15%
1      NaN
2      NaN
3    40.4%
4    2.48%
Name: Objective, dtype: object

df1['Objective'] = df1_new.mask(df1_new.isna(), df1_newer)

#or

import numpy as np

df1['Objective'] = np.where(df1_new.isna(), df1_newer, df1_new)

print(df1)

      Col_f      Col_g Objective
0   Georgia    Minivan       15%
1    Nevada      Coupe       NaN
2  New York      Wagon       55%
3     Texas  Crossover     40.4%
4   Arizona      Sedan     2.48%