我有 2 個要進行比較的資料框。請找到下面的資訊，并感謝任何幫助。
df 1 顯示ID之間的關系
df1 =
IDA IDB Relationship
A100 A200 Parent
A200 A500 Spouse
A111 A112 Child
A112 A111 Parent
df2 包含一個 ID 串列，如果 ID 的 Party 1 和 Party 2 之間存在任何形式的關系，我將對照 df1 檢查并映射關系（如果有多個關系，則為第一個實體）
df2 =
Sender Receiver
[A900,A200] [A500,A220]
[A150,A100] [A400]
[A400,A112] [A500]
[A700,A112] [A111,A001]
這是我的預期輸出和解釋
Output =
Sender Receiver Relationship
[A900,A200] [A500,A220] Spouse #A200 and A500
[A150,A100] [A400] NAN #No match
[A400,A112] [A500] NAN #No match
[A700,A112] [A111,A001] Parent #A112 and A111
uj5u.com熱心網友回復：
我無法測驗它，因為您沒有提供資料樣本，但類似的東西應該可以作業：
Output = df2.copy()
detected_relations = []
for transaction in df2.iterrows:
Receiver = transaction.Receiver
Sender = transaction.Sender
df = df1[(df1.IDA.isin(Sender) & df1.IDB.isin(Receiver)) | (df1.IDB.isin(Sender) & df1.IDA.isin(Receiver))]
detected_relations = detected_relations df.Relationship
Outpout["Relationship"] = detected_relations
uj5u.com熱心網友回復：
您可以將資訊提取到本機 python 資料結構中，然后將其與原始資料合并DataFrames-
要做到這一點 - 我首先會在 df2 中的Sender和Receiver列中配對 -
def make_pairs(row):
senders = row['Sender'].replace("[", "").replace("]", "").split(",")
receivers = row['Receiver'].replace("[", "").replace("]", "").split(",")
pairs = [(s, r) for s in senders for r in receivers]
return pairs
send_receive_combinations = df2.apply(make_pairs, axis=1).to_dict()
然后將IDA和IDBfrom的組合映射df1到字典中：
rels = {(ida, idb): rel for ida, idb, rel in df1.values}
然后可以使用 dict 理解（甚至是簡單的 for 回圈）對感興趣的值進行子集化
rel_pairs = {key: rels[pair] for key, combination in send_receive_combinations.items() for pair in combination if pair in rels}
最后，我們可以將其dict與df2-
df2['relationship'] = df2.index
df2['relationship'] = df2['relationship'].map(rel_pairs)
print(df2)
Sender Receiver relationship
#0 [A900,A200] [A500,A220] Spouse
#1 [A150,A100] [A400] NaN
#2 [A400,A112] [A500] NaN
#3 [A700,A112] [A111,A001] Parent
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