如何根据索引转置值？

Question

我有一个问题。

想象一下我有以下df：

**Document type** **Invoicenumber Invoicedate**  description quantity unit price line amount

Invoice             123            28-08-2020
0                   NaN            17-09-2020    test        1,5        5              20
0                   NaN            16-04-2020    test2       1,5        5              20

Invoice             456            02-03-2020
0                   NaN            NaN           test3       21         3              64
0                   0              NaN           test3       21         3              64            
0                   0              NaN           test3       21         3              64            

有0的行属于上一行，是同一个文档的行项。

我的目标是转置行项目，以便它们在每张发票的同一行上：

**Document type** **Invoicenumber Invoicedate**  description#1  description#2 quantity quantity#2 unit price   unit price #2 line amount line amount #2 
    
    Invoice             123            28-08-2020    test           test2          1,5      1,5     5            5              20         20

第二个发票行相同 如何做到这一点？

请帮忙
根据要求：

{'Document Type': {0: 'IngramMicro.AccountsPayable.Invoice',
  1: 0,
  2: 0,
  3: 'IngramMicro.AccountsPayable.Invoice',
  4: 0,
  5: 0,
  6: 0},
 'Factuurnummer': {0: '0.78861803',
  1: 'NaN',
  2: 'NaN',
  3: '202130534',
  4: 'NaN',
  5: 'NaN',
  6: 'NaN'},
 'Factuurdatum': {0: '2021-05-03',
  1: nan,
  2: nan,
  3: '2021-09-03',
  4: nan,
  5: nan,
  6: nan},
 'description': {0: nan,
  1: 'TM 300 incl onderstel 3058C003 84433210 4549292119381',
  2: 'ESP 5Y 36 inch 7950A539 00000000 4960999794266',
  3: nan,
  4: 'Basistarief A3 Office',
  5: 'Toeslag 100 km enkele reis Leveren installeren Xerox VL C7020 05-03-2021',
  6: 'Toeslag 100 km enkele reis Leveren installeren Xerox VL C7020 05-03-2021'},
 'quantity': {0: nan, 1: 1.0, 2: 1.0, 3: nan, 4: 1.0, 5: 1.0, 6: 2.0},
 'unit price': {0: nan,
  1: 1211.63,
  2: 742.79,
  3: nan,
  4: 260.0,
  5: 30.0,
  6: 30.0},
 'line amount': {0: nan, 1: 21.0, 2: 21.0, 3: nan, 4: 260.0, 5: 30.0, 6: 30.0}}

Answer 1

一种不聪明但可行的方法是通过迭代原始数据帧的行来生成具有所需列的新数据帧。

import ast
import sys
import pandas as pd
from io import StringIO

TESTDATA = StringIO("""Document type;Invoicenumber;Invoicedate;description;quantity;unit price;line amount
Invoice;123;28-08-2020
0;NaN;17-09-2020;test;1,5;5;20
0;NaN;16-04-2020;test2;1,5;5;20
Invoice;456;02-03-2020
0;NaN;NaN;test3;21;3;64
0;0;NaN;test3;21;3;64
0;0;NaN;test3;21;3;64
    """)

df = pd.read_csv(TESTDATA, delimiter=";")

df_new = pd.DataFrame(columns=df.columns)

first_type_index = 0
type_count = 0

for index, row in df.iterrows():
    if row['Document type'] == '0':
        type_count += 1

        df_new.loc[first_type_index, f'description#{type_count}'] = row['description']
        df_new.loc[first_type_index, f'quantity#{type_count}'] = row['quantity']
        df_new.loc[first_type_index, f'unit price#{type_count}'] = row['unit price']
        df_new.loc[first_type_index, f'line amount#{type_count}'] = row['line amount']

        row['Document type']
        row['Invoicenumber']
    else:
        first_type_index = index
        type_count = 0
        df_new.loc[first_type_index] = row

df_new.drop(['description', 'quantity', 'unit price', 'line amount'], axis=1, inplace=True)

|   | Document type | Invoicenumber | Invoicedate | description#1 | quantity#1 | unit price#1 | line amount#1 | description#2 | quantity#2 | unit price#2 | line amount#2 | description#3 | quantity#3 | unit price#3 | line amount#3 |
| 0 | Invoice       |         123.0 |  28-08-2020 | test          | "1,5"      |          5.0 |          20.0 | test2         | "1,5"      |          5.0 |          20.0 |               |            |              |               |
| 3 | Invoice       |         456.0 |  02-03-2020 | test3         | 21         |          3.0 |          64.0 | test3         | 21         |          3.0 |          64.0 | test3         |         21 |          3.0 |          64.0 |

要使用列名，您可以使用

column_to_swap = df_new.columns[3:]
len_swap = len(column_to_swap)

column_swap = [None] * len_swap

j = 0

# 4 is the count of duplicated columns
# description, quantity, unit price, line amount
for i in range(4):
    fast_i = i

    while fast_i < len_swap:
        column_swap[j] = column_to_swap[fast_i]
        fast_i += 4
        j += 1

df_new = df_new.reindex(columns=list(df_new.columns.values[0:3])+column_swap)

|   | Document type | Invoicenumber | Invoicedate | description#1 | description#2 | description#3 | quantity#1 | quantity#2 | quantity#3 | unit price#1 | unit price#2 | unit price#3 | line amount#1 | line amount#2 | line amount#3 |
| 0 | Invoice       |         123.0 |  28-08-2020 | test          | test2         |               | "1,5"      | "1,5"      |            |          5.0 |          5.0 |              |          20.0 |          20.0 |               |
| 3 | Invoice       |         456.0 |  02-03-2020 | test3         | test3         | test3         | 21         | 21         |         21 |          3.0 |          3.0 |          3.0 |          64.0 |          64.0 |          64.0 |