How merge or join two dataframes, but keeping certain columns of both

Question

How merge or join two dataframes, but keeping certain columns of both?

I need to merge this two dataframes into one. dataframe 2 has all the columns dataframe 1 just need the column "leads"

Dataframe1

campaignid leads 
35119190 391 
31664745 365 
4899110 211 
325772660 195 
64002140 131 
143679198 58 
283494007 45 

Dataframe2

campaignid cost time reach 
35119190 391 391 391 
31664745 365 391 391 
4899110 211 391 391 
325772660 195 391 391 
64002140 131 391 391 
143679198 58 391 391 
283494007 45 391 391

Desired result:

Dataframe2

campaignid cost time reach leads 
35119190 391 391 391 391 
31664745 365 391 391 365 
4899110 211 391 391 211 
325772660 195 391 391 195 
64002140 131 391 391 131 
143679198 58 391 391 58 
283494007 45 391 391 45 

g_spend.to_dict()

{'id': {0: 35119190,
  1: 64002140,
  2: 272351300,
  3: 4899110,},
 'Campaign_ID_name': {0: 'brand',
  1: '-',
  2: '-',
  3: 'science',
,
 'Month': {0: '2019|08',
  1: '2019|08',
  2: '2019|08',
  3: '2019|08',
},
 'Account': {0: 'a',
  1: 'a',
  2: 'b',
  3: 'c',
},
 'campaignid': {0: 35119190,
  1: 64002140,
  2: 272351300,
  3: 4899110,
 },
 'campaign_name': {0: 'All_Brand',
  1: 'All',
  2: 'All_GBHS',
  3: 'All_Science',
},
 'cost': {0: '$59,399.37 ',
  1: '$12,660.37 ',
  2: '$5,631.96 ',
}}

grouped_cw.to_dict()

{'leads': {'1076533154': 40.0,
  '143679198': 58.0,
  '169278078': 13.0,
  '1729099155': 8.0,
}}

Answer 1

Let's use map:

df2['leads'] = df2['campaignid'].map(df1.set_index('campaignid')['leads'])
df2

Output:

   campaignid  cost  time  reach  leads
0    35119190   391   391    391    391
1    31664745   365   391    391    365
2     4899110   211   391    391    211
3   325772660   195   391    391    195
4    64002140   131   391    391    131
5   143679198    58   391    391     58
6   283494007    45   391    391     45

Try

df2['leads'] = df2['campaignid'].map(grouped_cw)

Answer 2

pd.merge(Dataframe1, Dataframe2, on='campaignid')