Python Pandas 数据透视表行小计
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Pandas Pivot tables row subtotals
提问by balsagoth
I'm using Pandas 0.10.1
我正在使用 Pandas 0.10.1
Considering this Dataframe:
考虑这个数据框：
Date State City SalesToday SalesMTD SalesYTD
20130320 stA ctA 20 400 1000
20130320 stA ctB 30 500 1100
20130320 stB ctC 10 500 900
20130320 stB ctD 40 200 1300
20130320 stC ctF 30 300 800
How can i group subtotals per state?
我如何对每个州的小计进行分组？
State City SalesToday SalesMTD SalesYTD
stA ALL 50 900 2100
stA ctA 20 400 1000
stA ctB 30 500 1100
I tried with a pivot table but i only can have subtotals in columns
我尝试使用数据透视表，但我只能在列中有小计
table = pivot_table(df, values=['SalesToday', 'SalesMTD','SalesYTD'],\
rows=['State','City'], aggfunc=np.sum, margins=True)
I can achieve this on excel, with a pivot table.
我可以使用数据透视表在 excel 上实现这一点。
采纳答案by Wes McKinney
If you put State and City not both in the rows, you'll get separate margins. Reshape and you get the table you're after:
如果您不将 State 和 City 都放在行中，您将获得单独的边距。重塑，你会得到你想要的桌子：
In [10]: table = pivot_table(df, values=['SalesToday', 'SalesMTD','SalesYTD'],\
rows=['State'], cols=['City'], aggfunc=np.sum, margins=True)
In [11]: table.stack('City')
Out[11]:
SalesMTD SalesToday SalesYTD
State City
stA All 900 50 2100
ctA 400 20 1000
ctB 500 30 1100
stB All 700 50 2200
ctC 500 10 900
ctD 200 40 1300
stC All 300 30 800
ctF 300 30 800
All All 1900 130 5100
ctA 400 20 1000
ctB 500 30 1100
ctC 500 10 900
ctD 200 40 1300
ctF 300 30 800
I admit this isn't totally obvious.
我承认这并不完全显而易见。
回答by Rutger Kassies
You can get the summarized values by using groupby() on the State column.
您可以通过在 State 列上使用 groupby() 来获取汇总值。
Lets make some sample data first:
让我们先制作一些示例数据：
import pandas as pd
import StringIO
incsv = StringIO.StringIO("""Date,State,City,SalesToday,SalesMTD,SalesYTD
20130320,stA,ctA,20,400,1000
20130320,stA,ctB,30,500,1100
20130320,stB,ctC,10,500,900
20130320,stB,ctD,40,200,1300
20130320,stC,ctF,30,300,800""")
df = pd.read_csv(incsv, index_col=['Date'], parse_dates=True)
Then apply the groupby function and add a column City:
然后应用 groupby 函数并添加列 City：
dfsum = df.groupby('State', as_index=False).sum()
dfsum['City'] = 'All'
print dfsum
State SalesToday SalesMTD SalesYTD City
0 stA 50 900 2100 All
1 stB 50 700 2200 All
2 stC 30 300 800 All
We can append the original data to the summed df by using append:
我们可以使用 append 将原始数据附加到求和的 df 中：
dfsum.append(df).set_index(['State','City']).sort_index()
print dfsum
SalesMTD SalesToday SalesYTD
State City
stA All 900 50 2100
ctA 400 20 1000
ctB 500 30 1100
stB All 700 50 2200
ctC 500 10 900
ctD 200 40 1300
stC All 300 30 800
ctF 300 30 800
I added the set_index and sort_index to make it look more like your example output, its not strictly necessary to get the results.
我添加了 set_index 和 sort_index 以使其看起来更像您的示例输出，它不是获得结果所必需的。
回答by Richard Mao
How about this one ?
这个怎么样 ？
table = pd.pivot_table(data, index=['State'],columns = ['City'],values=['SalesToday', 'SalesMTD','SalesYTD'],\
aggfunc=np.sum, margins=True)
回答by hs moon
I Think this subtotal example code is what you want(similar to excel subtotal)
我认为这个小计示例代码是你想要的（类似于 excel 小计）
I assume that you want group by columns A, B, C, D, than count column value of E
我假设您希望按 A、B、C、D 列分组，而不是计算 E 的列值
main_df.groupby(['A', 'B', 'C']).apply(lambda sub_df: sub_df\
.pivot_table(index=['D'], values=['E'], aggfunc='count', margins=True)
output:
输出：
A B C D E
a 1
a a a b 2
c 2
all 5
a 3
b b a b 2
c 2
all 7
a 3
b b b b 6
c 2
d 3
all 14
