Python 每个列的数据透视表小计

我是否可以使用pandas中的pivot_表实现所需的输出（如下所示）或类似的数据集。我正在尝试做一些类似的事情：

pivot_table(df, rows=['region'], cols=['area','distributor','salesrep'], 
            aggfunc=np.sum, margins=True).stack(['area','distributor','salesrep'])

但我只得到每个区域的小计，若我将区域从cols移动到rows，那个么我将只得到每个区域的小计

数据集：

region area distributor salesrep sales invoice_count Central Butterworth HIN MARKETING TLS 500 25 Central Butterworth HIN MARKETING TLS 500 25 Central Butterworth HIN MARKETING OSE 500 25 Central Butterworth HIN MARKETING OSE 500 25 Central Butterworth KWANG HENGG TCS 500 25 Central Butterworth KWANG HENGG TCS 500 25 Central Butterworth KWANG HENG LBH 500 25 Central Butterworth KWANG HENG LBH 500 25 Central Ipoh SGH EDERAN CHAN 500 25 Central Ipoh SGH EDERAN CHAN 500 25 Central Ipoh SGH EDERAN KAMACHI 500 25 Central Ipoh SGH EDERAN KAMACHI 500 25 Central Ipoh CORE SYN LILIAN 500 25 Central Ipoh CORE SYN LILIAN 500 25 Central Ipoh CORE SYN TEOH 500 25 Central Ipoh CORE SYN TEOH 500 25 East JB LEI WAH NF05 500 25 East JB LEI WAH NF05 500 25 East JB LEI WAH NF06 500 25 East JB LEI WAH NF06 500 25 East JB WONDER F&B SEREN 500 25 East JB WONDER F&B SEREN 500 25 East JB WONDER F&B MONC 500 25 East JB WONDER F&B MONC 500 25 East PJ PENGEDAR NORM 500 25 East PJ PENGEDAR NORM 500 25 East PJ PENGEDAR SIMON 500 25 East PJ PENGEDAR SIMON 500 25 East PJ HEBAT OGI 500 25 East PJ HEBAT OGI 500 25 East PJ HEBAT MIGI 500 25 East PJ HEBAT MIGI 500 25 地区分销商销售代表销售发票\u计数 中环巴特沃斯欣营销TLS 500 25 中环巴特沃斯欣营销TLS 500 25 中环巴特沃斯欣市场部OSE 500 25 中环巴特沃斯欣市场部OSE 500 25 中环巴特沃斯光亨TCS 500 25 中环巴特沃斯光亨TCS 500 25 中环巴特沃斯光亨LBH 500 25 中环巴特沃斯光亨LBH 500 25 中环怡保SGH EDERAN CHAN 500 25 中环怡保SGH EDERAN CHAN 500 25 中环怡保SGH EDERAN KAMACHI 500 25 中环怡保SGH EDERAN KAMACHI 500 25 中环怡保核心SYN LILIAN 500 25 中环怡保核心SYN LILIAN 500 25 中环怡保核心系统TEOH 500 25 中环怡保核心系统TEOH 500 25 东JB利华NF05 500 25 东JB利华NF05 500 25 东JB利华NF06 500 25 东JB利华NF06 500 25 东JB WONDER餐饮SEREN 500 25 东JB WONDER餐饮SEREN 500 25 东JB WONDER餐饮MONC 500 25 东JB WONDER餐饮MONC 500 25 东PJ彭格达尔标准500 25 东PJ彭格达尔标准500 25 东PJ PENGEDAR SIMON 500 25 东PJ PENGEDAR SIMON 500 25 东PJ赫巴特奥吉500 25 东PJ赫巴特奥吉500 25 东PJ HEBAT MIGI 500 25 东PJ HEBAT MIGI 500 25 期望输出：

region area distributor salesrep invoice_count sales Grand Total 800 16000 Central Central Total 400 8000 Central Butterworth Butterworth Total 200 4000 Central Butterworth HIN MARKETING HIN MARKETING Total 100 2000 Central Butterworth HIN MARKETING OSE 50 1000 Central Butterworth HIN MARKETING TLS 50 1000 Central Butterworth KWANG HENG KWANG HENG Total 100 2000 Central Butterworth KWANG HENG LBH 50 1000 Central Butterworth KWANG HENG TCS 50 1000 Central Ipoh Ipoh Total 200 4000 Central Ipoh CORE SYN CORE SYN Total 100 2000 Central Ipoh CORE SYN LILIAN 50 1000 Central Ipoh CORE SYN TEOH 50 1000 Central Ipoh SGH EDERAN SGH EDERAN Total 100 2000 Central Ipoh SGH EDERAN CHAN 50 1000 Central Ipoh SGH EDERAN KAMACHI 50 1000 East East Total 400 8000 East JB JB Total 200 4000 East JB LEI WAH LEI WAH Total 100 2000 East JB LEI WAH NF05 50 1000 East JB LEI WAH NF06 50 1000 East JB WONDER F&B WONDER F&B Total 100 2000 East JB WONDER F&B MONC 50 1000 East JB WONDER F&B SEREN 50 1000 East PJ PJ Total 200 4000 East PJ HEBAT HEBAT Total 100 2000 East PJ HEBAT MIGI 50 1000 East PJ HEBAT OGI 50 1000 East PJ PENGEDAR PENDEGAR Total 100 2000 East PJ PENGEDAR NORM 50 1000 East PJ PENGEDAR SIMON 50 1000 地区分销商销售代表发票数量销售 总计800 16000 中环总数400 8000 巴特沃斯市中心巴特沃斯总计200 4000 中环巴特沃斯欣营销欣营销总计100 2000 中环巴特沃斯欣市场部OSE 50 1000 中环巴特沃斯欣营销TLS 50 1000 中环巴特沃斯光亨光亨总计100 2000 中环巴特沃斯光亨LBH 50 1000 中环巴特沃斯光亨TCS 50 1000 中环怡保Ipoh总数200 4000 中环怡保核心同步器核心同步器总计100 2000 中环怡保核心SYN LILIAN 50 1000 中环怡保核心系统TEOH 50 1000 中环怡保SGH EDERAN SGH EDERAN总计100 2000 中环怡保SGH EDERAN CHAN 50 1000 中环怡保SGH EDERAN KAMACHI 50 1000 东区总数400 8000 东区JB总计200 4000 东JB利华利华总数100 2000 东JB利华NF05 50 1000 东JB利华NF06 50 1000 东JB旺德餐饮旺德餐饮总计100 2000 东JB WONDER餐饮MONC 50 1000 东JB WONDER餐饮公司SEREN 50 1000 东PJ PJ总计200 4000 东PJ HEBAT HEBAT总计100 2000 东PJ HEBAT MIGI 50 1000 东PJ HEBAT OGI 50 1000 东PJ PENGEDAR PENDEGAR总计100 2000 东PJ彭格达尔标准50 1000 东PJ PENGEDAR SIMON 50 1000

---

我不知道如何在表中获得小计，但是如果您运行

df.pivot_table(rows=['region','area','distributor','salesrep'],
  aggfunc=np.sum, margins=True)

你会得到

                                            invoice_count  sales
region  area        distributor   salesrep                      
Central Butterworth HIN MARKETING OSE                  50   1000
                                  TLS                  50   1000
                    KWANG HENG    LBH                  50   1000
                    KWANG HENGG   TCS                  50   1000
        Ipoh        CORE SYN      LILIAN               50   1000
                                  TEOH                 50   1000
                    SGH EDERAN    CHAN                 50   1000
                                  KAMACHI              50   1000
East    JB          LEI WAH       NF05                 50   1000
                                  NF06                 50   1000
                    WONDER F&B    MONC                 50   1000
                                  SEREN                50   1000
        PJ          HEBAT         MIGI                 50   1000
                                  OGI                  50   1000
                    PENGEDAR      NORM                 50   1000
                                  SIMON                50   1000
All                                                   800  16000

如果您想要基于例如

区域

和

区域

的总计，您可以运行

df.pivot_table(rows=['region', 'area'], aggfunc=np.sum, margins=True)

导致

                     invoice_count  sales
region  area                             
Central Butterworth            200   4000
        Ipoh                   200   4000
East    JB                     200   4000
        PJ                     200   4000
All                            800  16000

---

我不知道如何在表中获得小计，但是如果您运行

df.pivot_table(rows=['region','area','distributor','salesrep'],
  aggfunc=np.sum, margins=True)

你会得到

                                            invoice_count  sales
region  area        distributor   salesrep                      
Central Butterworth HIN MARKETING OSE                  50   1000
                                  TLS                  50   1000
                    KWANG HENG    LBH                  50   1000
                    KWANG HENGG   TCS                  50   1000
        Ipoh        CORE SYN      LILIAN               50   1000
                                  TEOH                 50   1000
                    SGH EDERAN    CHAN                 50   1000
                                  KAMACHI              50   1000
East    JB          LEI WAH       NF05                 50   1000
                                  NF06                 50   1000
                    WONDER F&B    MONC                 50   1000
                                  SEREN                50   1000
        PJ          HEBAT         MIGI                 50   1000
                                  OGI                  50   1000
                    PENGEDAR      NORM                 50   1000
                                  SIMON                50   1000
All                                                   800  16000

如果您想要基于例如

区域

和

区域

的总计，您可以运行

df.pivot_table(rows=['region', 'area'], aggfunc=np.sum, margins=True)

导致

                     invoice_count  sales
region  area                             
Central Butterworth            200   4000
        Ipoh                   200   4000
East    JB                     200   4000
        PJ                     200   4000
All                            800  16000

---

我们可以使用

groupby

而不是

pivot\u表

：

import numpy as np
import pandas as pd


def label(ser):
    return '{s} Total'.format(s=ser)

filename = 'data.txt'
df = pd.read_table(filename, delimiter='\t')

total = pd.DataFrame({'region': ['Grand Total'],
                      'invoice_count': df['invoice_count'].sum(),
                      'sales': df['sales'].sum()})
total['total_rank'] = 1

region_total = df.groupby(['region'], as_index=False).sum()
region_total['area'] = region_total['region'].apply(label)
region_total['region_rank'] = 1

area_total = df.groupby(['region', 'area'], as_index=False).sum()
area_total['distributor'] = area_total['area'].apply(label)
area_total['area_rank'] = 1

dist_total = df.groupby(
    ['region', 'area', 'distributor'], as_index=False).sum()
dist_total['salesrep'] = dist_total['distributor'].apply(label)

rep_total = df.groupby(
    ['region', 'area', 'distributor', 'salesrep'], as_index=False).sum()

# UNION the DataFrames into one DataFrame
result = pd.concat([total, region_total, area_total, dist_total, rep_total])

# Replace NaNs with empty strings
result.fillna({'region': '', 'area': '', 'distributor': '', 'salesrep':
              ''}, inplace=True)

# Reorder the rows
sorter = np.lexsort((
    result['distributor'].rank(),
    result['area_rank'].rank(),
    result['area'].rank(),
    result['region_rank'].rank(),
    result['region'].rank(),
    result['total_rank'].rank()))
result = result.take(sorter)
result = result.reindex(
    columns=['region', 'area', 'distributor', 'salesrep', 'invoice_count', 'sales'])
print(result.to_string(index=False))

屈服

      region           area        distributor             salesrep  invoice_count  sales
 Grand Total                                                                   800  16000
     Central  Central Total                                                    400   8000
     Central    Butterworth  Butterworth Total                                 200   4000
     Central    Butterworth      HIN MARKETING  HIN MARKETING Total            100   2000
     Central    Butterworth      HIN MARKETING                  OSE             50   1000
     Central    Butterworth      HIN MARKETING                  TLS             50   1000
     Central    Butterworth         KWANG HENG     KWANG HENG Total            100   2000
     Central    Butterworth         KWANG HENG                  LBH             50   1000
     Central    Butterworth         KWANG HENG                  TCS             50   1000
     Central           Ipoh         Ipoh Total                                 200   4000
     Central           Ipoh           CORE SYN       CORE SYN Total            100   2000
     Central           Ipoh           CORE SYN               LILIAN             50   1000
     Central           Ipoh           CORE SYN                 TEOH             50   1000
     Central           Ipoh         SGH EDERAN     SGH EDERAN Total            100   2000
     Central           Ipoh         SGH EDERAN                 CHAN             50   1000
     Central           Ipoh         SGH EDERAN              KAMACHI             50   1000
        East     East Total                                                    400   8000
        East             JB           JB Total                                 200   4000
        East             JB            LEI WAH        LEI WAH Total            100   2000
        East             JB            LEI WAH                 NF05             50   1000
        East             JB            LEI WAH                 NF06             50   1000
        East             JB         WONDER F&B     WONDER F&B Total            100   2000
        East             JB         WONDER F&B                 MONC             50   1000
        East             JB         WONDER F&B                SEREN             50   1000
        East             PJ           PJ Total                                 200   4000
        East             PJ              HEBAT          HEBAT Total            100   2000
        East             PJ              HEBAT                 MIGI             50   1000
        East             PJ              HEBAT                  OGI             50   1000
        East             PJ           PENGEDAR       PENGEDAR Total            100   2000
        East             PJ           PENGEDAR                 NORM             50   1000
        East             PJ           PENGEDAR                SIMON             50   1000

---

我们可以使用

groupby

而不是

pivot\u表

：

import numpy as np
import pandas as pd


def label(ser):
    return '{s} Total'.format(s=ser)

filename = 'data.txt'
df = pd.read_table(filename, delimiter='\t')

total = pd.DataFrame({'region': ['Grand Total'],
                      'invoice_count': df['invoice_count'].sum(),
                      'sales': df['sales'].sum()})
total['total_rank'] = 1

region_total = df.groupby(['region'], as_index=False).sum()
region_total['area'] = region_total['region'].apply(label)
region_total['region_rank'] = 1

area_total = df.groupby(['region', 'area'], as_index=False).sum()
area_total['distributor'] = area_total['area'].apply(label)
area_total['area_rank'] = 1

dist_total = df.groupby(
    ['region', 'area', 'distributor'], as_index=False).sum()
dist_total['salesrep'] = dist_total['distributor'].apply(label)

rep_total = df.groupby(
    ['region', 'area', 'distributor', 'salesrep'], as_index=False).sum()

# UNION the DataFrames into one DataFrame
result = pd.concat([total, region_total, area_total, dist_total, rep_total])

# Replace NaNs with empty strings
result.fillna({'region': '', 'area': '', 'distributor': '', 'salesrep':
              ''}, inplace=True)

# Reorder the rows
sorter = np.lexsort((
    result['distributor'].rank(),
    result['area_rank'].rank(),
    result['area'].rank(),
    result['region_rank'].rank(),
    result['region'].rank(),
    result['total_rank'].rank()))
result = result.take(sorter)
result = result.reindex(
    columns=['region', 'area', 'distributor', 'salesrep', 'invoice_count', 'sales'])
print(result.to_string(index=False))

屈服

      region           area        distributor             salesrep  invoice_count  sales
 Grand Total                                                                   800  16000
     Central  Central Total                                                    400   8000
     Central    Butterworth  Butterworth Total                                 200   4000
     Central    Butterworth      HIN MARKETING  HIN MARKETING Total            100   2000
     Central    Butterworth      HIN MARKETING                  OSE             50   1000
     Central    Butterworth      HIN MARKETING                  TLS             50   1000
     Central    Butterworth         KWANG HENG     KWANG HENG Total            100   2000
     Central    Butterworth         KWANG HENG                  LBH             50   1000
     Central    Butterworth         KWANG HENG                  TCS             50   1000
     Central           Ipoh         Ipoh Total                                 200   4000
     Central           Ipoh           CORE SYN       CORE SYN Total            100   2000
     Central           Ipoh           CORE SYN               LILIAN             50   1000
     Central           Ipoh           CORE SYN                 TEOH             50   1000
     Central           Ipoh         SGH EDERAN     SGH EDERAN Total            100   2000
     Central           Ipoh         SGH EDERAN                 CHAN             50   1000
     Central           Ipoh         SGH EDERAN              KAMACHI             50   1000
        East     East Total                                                    400   8000
        East             JB           JB Total                                 200   4000
        East             JB            LEI WAH        LEI WAH Total            100   2000
        East             JB            LEI WAH                 NF05             50   1000
        East             JB            LEI WAH                 NF06             50   1000
        East             JB         WONDER F&B     WONDER F&B Total            100   2000
        East             JB         WONDER F&B                 MONC             50   1000
        East             JB         WONDER F&B                SEREN             50   1000
        East             PJ           PJ Total                                 200   4000
        East             PJ              HEBAT          HEBAT Total            100   2000
        East             PJ              HEBAT                 MIGI             50   1000
        East             PJ              HEBAT                  OGI             50   1000
        East             PJ           PENGEDAR       PENGEDAR Total            100   2000
        East             PJ           PENGEDAR                 NORM             50   1000
        East             PJ           PENGEDAR                SIMON             50   1000

谢谢我顾