如何将列的值转换为行-dataframgroupby

Question

我有一张“借款人个人ID”和“贷款ID”表。BwrPersonld LoanId113225  16330113225  27073113225  68842113253  16341113269  16348113285  16354113289  26768113297  16360113299  16361113319  16369113418  16403113418  26854我想知道哪些贷款属于同一借款人。所以我“groupby”“BwrPersonalId”和“LoanId”，如下所示。现在我就这样期待着。这是我的代码，但它不起作用。grouped = pd.DataFrame()unique = loan['BwrPersonId'].unique()grouped['BwrPersonId'] = ''*len(loan['BwrPersonId'].unique())grouped['Loan1'] = ''grouped['Loan2'] = ''grouped['Loan3'] = ''grouped['Loan4'] = ''grouped['Loan5'] = ''grouped.iloc[:,0] = uniquefor i in grouped.index:    idloan = loan.loc[loan['BwrPersonId'] == unique[i], 'LoanId']    grouped.iloc[i,1:len(idloan)+1] = idloan    print(i)我现在该怎么做呢？还有其他方法可以简化代码吗？非常感谢你的帮助。

Answer 1

基本上，您需要做的是创建一个临时变量，该临时变量将使用要排序的数据，以及负责 Id 的名称，以便根据贷款对 Id 进行排序。

import pandas as pd

import numpy as np

from collections import defaultdict

from itertools import count

dict = defaultdict(count)

id, name = pd.factorize([*zip(grouped.id, grouped.name)])

joined = np.array([next(dict[x]) for x in id])

lenOfr, Max = len(name), joined.max() + 1

temp = np.empty((lenOfr, Max), dtype=np.object)

temp[id, joined] = grouped.LoanId

df1 = pd.DataFrame(name.tolist(), columns=['BwrPersonId'])

df2 = pd.DataFrame(temp, columns=['Loan1', 'Loan2', 'Loan3', 'Loan4', 'Loan5'])

final = df1.join(df2)