我想显示行和列的总计。我使用margin = True，但输出未显示行总数，如下代码和输出：
import pandas as pd
df2 = pd.read_csv("https://www.dropbox.com/s/90y07129zn351z9/test_data.csv?dl=1",encoding="latin-1")
df2['received'] = pd.to_datetime(df2['received'])
df2['sent'] = pd.to_datetime(df2['sent'])
pvt_all = df2.dropna(axis=0, how='all', subset=['received', 'sent'])\
.pivot_table(index=['site'], values=['received','sent'],\
aggfunc='count', margins=True, dropna=False)
pvt_all['to_send']= pvt_all['received']-pvt_all['sent']
pvt_all=pvt_all[['received','sent','to_send']]
pvt_all
received sent to_send
site
2 32.0 27.0 5.0
3 20.0 17.0 3.0
4 33.0 31.0 2.0
5 40.0 31.0 9.0
All 125.0 106.0 19.0
下面的数据样本虽然很长，但仍可以为您提供便利。您还可以在上面的df矢量中提供的url中找到。 DataFrame由四个变量组成：日期，地点，接收和发送。
date site received sent
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答案 0 :(得分：1)
尝试使用eval：
df2.dropna(axis=0, how='all', subset=['received', 'sent'])\
.pivot_table(index='site', values=['received','sent'],\
aggfunc='count', margins=True, dropna=False).eval('Total = received + sent')
输出：
received sent Total
site
2 32 27 59
3 20 17 37
4 33 31 64
5 40 31 71
All 125 106 231
答案 1 :(得分：0)
df2 = pd.read_csv("https://www.dropbox.com/s/90y07129zn351z9/test_data.csv?dl=1",encoding="latin-1")
df2['received'] = pd.to_datetime(df2['received'])
df2['sent'] = pd.to_datetime(df2['sent'])
import pandas as pd
pvt_all = df2.dropna(axis=0, how='all', subset=['received', 'sent'])\
.pivot_table(index=['site'], values=['received','sent'],\
aggfunc='count', margins=True, dropna=False)
pvt_all['to_send']= pvt_all['received']-pvt_all['sent']
#pvt_all['Total'] = pvt_all.received + pvt_all.sent + pvt_all.to_send
pvt_all['Total'] = pvt_all.sum(axis=1) #Add Total column for row subtotal \
#and Total
pvt_all=pvt_all[['received','sent','to_send','Total']]
pvt_all
received sent to_send Total
site
2 32.0 27.0 5.0 64.0
3 20.0 17.0 3.0 40.0
4 33.0 31.0 2.0 66.0
5 40.0 31.0 9.0 80.0
All 125.0 106.0 19.0 250.0