每个cols的Pandas Pivot表小计
[英]Pandas Pivot table subtotals per each cols
问题描述
Can I achieve my Desired Output(given below) or something similar with the following Datasets using pivot_table in pandas. 
我可以在Pandas中使用ivot_table实现所需的输出（如下所示）或与以下数据集类似的东西吗？ I am trying to do something like: 我正在尝试做类似的事情：
pivot_table(df, rows=['region'], cols=['area','distributor','salesrep'],
aggfunc=np.sum, margins=True).stack(['area','distributor','salesrep'])
but I am only getting subtotals per region, if I move area from cols to rows then I will only get subtotals per area. 但是我只得到每个区域的小计，如果我将区域从列移动到行，那么我只会得到每个区域的小计。
Datasets: 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
Desired Output: 所需输出：
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
2 个解决方案
解决方案1
1 2013-04-14 18:41:16
We could use groupby instead of pivot_table : 我们可以使用groupby而不是pivot_table ：
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))
yields 产量
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
解决方案2
0 2013-04-14 11:15:58
I do not know how to get subtotals inside the table, but if you run 我不知道如何在表格中获取小计，但是如果您运行
df.pivot_table(rows=['region','area','distributor','salesrep'],
aggfunc=np.sum, margins=True)
you will get 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
If you want totals based on say region and area , you may run 如果您想基于region和area ，则可以运行
df.pivot_table(rows=['region', 'area'], aggfunc=np.sum, margins=True)
which results in 导致
invoice_count sales
region area
Central Butterworth 200 4000
Ipoh 200 4000
East JB 200 4000
PJ 200 4000
All 800 16000
声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.
相关问题
Pandas中的数据透视表小计 - Pivot table subtotals in Pandas
Pandas 数据透视表 - 向多索引表添加小计 - Pandas Pivot Table - Adding Subtotals to Multiindex Table
使用 MultiIndex 计算 pandas pivot_table 中的小计 - Calculating subtotals in pandas pivot_table with MultiIndex
Pandas数据透视表索引和列的小计 - Subtotals for Pandas pivot table index and column
熊猫数据透视表：列顺序和小计 - Pandas pivot table: columns order and subtotals
Pandas pivot 多索引表小计 - Pandas pivot table subtotals with multi-index
Pandas 数据透视和小计 - Pandas pivot and subtotals
数据透视表小计 - pivot table subtotals
Pandas Pivot Table-agfunc可以获取多索引的小计？ - Pandas Pivot Table- aggfunc to get subtotals on a multi-Index?
将小计添加到多层熊猫数据透视表 - adding subtotals to multiple layers of pandas pivot-table
相关标签