的Python /大熊猫：如何做一个匹配连接的多个列

Question

问题：

我想加入两个表一起使用更复杂的连接标准。一张桌子有三种不同的电话号码，另一张桌子有两种不同的电话号码。我不知道每行的主要电话号码是哪一个。因此，我想根据以下标准加入：第一个表格中的number,Phone1,Phone2列的电话号码可以在第二个表中的ANI或DNIS列中找到。

样本数据：

一个数据帧看起来像这样...

      application_uuid  number  Phone1  Phone2 
0  b7754a2e-84be-4aec-a04e-0eba93dca5d8 5196942368   NaN   NaN 
1  6ca3f0c3-0c83-4ebd-afe3-23977f1c6608 6475219092   NaN   NaN 
2  3b5a083e-7765-4f27-941d-d2b4cbd6f26a 6476256563   NaN   NaN 
3  229fee54-437f-4812-abec-7034fcb9a655  None   NaN   NaN 
4  866a2cd2-5628-4e6b-b649-d92e2f0585ce 7092164418 7096391545 7092164977 
5  8259410d-8d3d-4381-a0b3-6d6ce67b0917 6476387217 6476387217 6475313526 
6  c359b03b-5e5f-4d4e-a5b0-ee37ac90c292  None   NaN   NaN 
7  d70414a9-8fd9-4d1d-a77d-17f06743fd00 7054987969   NaN   NaN 
8  0452edf9-2d58-4ad5-b1e2-0621ac517104 6136219401   NaN   NaN 
9  cb3ab85c-fd42-4aff-a9b8-1743565b31e6  None   NaN   NaN 
10  563e3e4d-e59a-4afc-b804-91aa14de919d 7056582202 7056582202 7056584200 
11  3dd1df61-a36f-490b-ac15-225a83a21551  None 7096899998 7096899998 
12  6bc42df3-e869-4794-a595-e3238ccf5284 5873415009   NaN   NaN 
13  8bf11117-038f-4d2d-b4c6-9b2c6423d626 6473435642   NaN   NaN 
14  0a854fe5-af66-40b0-b202-3e9367dc5a75 6478594204   NaN   NaN 
15  b5884de8-2e0c-4b38-a3fd-7911cf4840b1 7787075288 7787075288 7787075288 
16  f74cf212-cff0-48cc-b210-539dcdcccf72 7802676838 7806678567  None 
17  9bffe5bf-b5d8-4e74-b4c9-9f1b5b238af3  None   NaN   NaN 
18  dce91c00-a1ea-4111-a6ee-5ff5fd0cfb5f 6476093140   NaN   NaN 
19  29cd024e-2c51-4682-b274-809c3cfb2b2b  None   NaN   NaN 
20  ec55317b-fc20-416a-b26d-e95300f89c79  None   NaN   NaN 
21  b3d00cd8-9d8e-415e-99b1-d8944e7b31e1  None   NaN   NaN 
22  b3328787-edb7-4e08-a76c-370a74135fba  None   NaN   NaN 
23  c8baf235-e702-41db-b4f8-8c2bf38109bf  None   NaN   NaN 
24  cd9179bc-0594-4d25-9d7f-ddf6671777e2 7802428155   NaN   NaN 
25  370855c0-b3fa-4d87-8d54-b84d34e7f35f  None   NaN   NaN 
26  82244e78-3802-4890-96f6-e5267172f0e9  None   NaN   NaN 
27  c7b0054c-29ac-4c76-bc5d-8cdbc93f5157 7052093358 7055268791 7052093358 
28  d90e6e87-f7ef-43e1-9c85-35572fae838c 4039696044   NaN   NaN 
29  bdd2474f-f4be-402b-8672-d73da90d7066  None   NaN   NaN 

另据帧看起来像这样...

 CALL ID CALL TYPE   ANI   DNIS TALK TIME 
0  615262 Inbound 6479246923 8.557236e+09 00:00:00 
1  615263 Inbound 5196519186 8.557236e+09 00:00:00 
2  615264 Inbound 7095679350 8.557236e+09 00:00:00 
3  615265 Inbound 7095679350 8.557236e+09 00:00:00 
4  615266 Inbound 7095679350 8.557236e+09 00:00:00 
5  615267 Inbound 7095679350 8.557236e+09 00:00:00 
6  615268 Inbound 7095679350 8.557236e+09 00:00:00 
7  615269 Inbound 7095679350 8.557236e+09 00:00:00 
8  615270 Inbound 7095679350 8.557236e+09 00:00:00 
9  615271 Inbound 7095679350 8.557236e+09 00:00:00 
10  615272 Inbound 4035634231 8.557236e+09 00:00:00 
11  615273 Inbound 7095679350 8.557236e+09 00:00:00 
12  615274 Inbound 7095679350 8.557236e+09 00:00:00 
13  615275 Inbound 7095679350 8.557236e+09 00:00:00 
14  615276 Inbound 7095679350 8.557236e+09 00:00:00 
15  615277 Inbound 7095679350 8.557236e+09 00:00:00 
16  615278 Inbound 7095679350 8.557236e+09 00:00:00 
17  615279 Inbound 9057972416 8.557236e+09 00:00:00 
18  615280 Inbound 9057972416 8.557236e+09 00:00:00 
19  615281 Inbound 9057972416 8.557236e+09 00:00:00 
20  615282 Manual 8557235626 8.005635e+09 00:00:11 
21  615283 Inbound 9057972416 8.557236e+09 00:00:00 
22  615284 Inbound 4169991603 8.557236e+09 00:00:00 
23  615285 Manual 8557235626 4.162977e+09 00:01:05 
24  615286 Manual 8557235626 8.002569e+09 00:00:55 
25  615287 Inbound 4169967207 8.557236e+09 00:07:48 
26  615288 Inbound 4169788047 8.557236e+09 00:01:29 
27  615289 Inbound 9057972416 8.557236e+09 00:01:39 
28  615290 Inbound 8002568964 8.557236e+09 00:04:21 
29  615291 Manual 8557235626 7.059751e+09 00:00:19 

我的方法：

我的方法是将每行内的电话号码作为列表中的单个列添加。然后我创建了一个搜索功能。这种方式不实用，不雅，太慢。

def f(row): 
    phone_numbers_59 = phone_data['Number'].tolist() 
    callid = phone_data['CALL ID'].tolist() 

    get_callid = [] 
    for i in range(0, len(phone_numbers_59)): 
     if any([x in phone_numbers_59[i] for x in row['Numbers']]): 
      get_callid.append(callid[i]) 

    if len(get_callid) > 0: 
     return get_callid 
    else: 
     return "NA" 

s = data.apply(f, axis=1) 

Answer 1

number，Phone1，Phone2可以在发现无论是ANI或DNIS

，如果你需要在一个时间的条件之一（不要用Python语言编写大for环路所以，很简单，正如你看到的那样缓慢）：

for col in ('ANI', 'DNIS'): 
    right = df2.set_index(col, drop=False) 
    df1 = df1.join(right, 'number', rsuffix='_num_'+col) 
    df1 = df1.join(right, 'Phone1', rsuffix='_p1_'+col) 
    df1 = df1.join(right, 'Phone2', rsuffix='_p2_'+col) 

什么意思s的做法是将列添加到df1六次：每个组合一次。 rsuffix用于消除列名的歧义。你可能会得到多个匹配（也许Phone1匹配ANI和Phone2匹配DNIS），在这种情况下，由您决定如何解决或组合它们（可能使用groupby()）。