python - 如何将系列与数据框合并并保留双方数据

Question

Python。如何将系列与数据框合并并保留双方数据

使用 concat，它为我提供了“g_spend”中的所有值，但不是“grouped_cw”中的所有值，leads= NaN

t = pd.concat([pd.DataFrame(grouped_cw), g_spend], ignore_index=False)

t.head()

output:

 id Campaign_ID_name  Month  Account  campaignid  campaign_name cost  leads
0  35119190 brand    2019|08  SU    35119190  Brand     $59 NaN

使用 join，它给我所有来自“grouped_cw”的值，但不是来自“g_spend”的值，leads= NaN 相反...

t = pd.concat([pd.DataFrame(grouped_cw), g_spend], ignore_index=False)

t.head()

output:

    Account Campaign_ID_nameMonthcampaign_namecampaignidcostid  leads
1076533154  NaN NaN NaN NaN NaN NaN NaN 40.0
143679198   NaN NaN NaN NaN NaN NaN NaN 58.0
169278078   NaN NaN NaN NaN NaN NaN NaN 13.0
1729099155  NaN NaN NaN NaN NaN NaN NaN 8.0
2016404066  NaN NaN NaN NaN NaN NaN NaN 6.0

期望的输出:

可以创建一个 for 循环来计算“lead”并放入 g_spend 数据帧中的新列中，不带(join、merge、concat 等)

无论什么有效! :)

   Campaign_ID_name  Month  Account  campaignid  campaign_name  costh     leads
0    35119190   35119190 brand    2019|08  SU   35119190  Brand     $59    391

这是数据，目标是加入然后通过campaignid

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()

1076533154 引用campaignid40.0 是潜在客户数量

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

Answer 1

从to_dict()命令中可以看出，问题在于g_spend.campaignid是数字，而grouped_cw.index是字符串。例如，您可以将 g_spend.campaignid 转换为字符串并合并:

g_spend.campaignid = g_spend.campaignid.astype(str)

g_spend.merge(grouped_cw, left_on='campaignid', right_index=True)