I want to display row and column total. I am using margin=True but the output does not show row total as below code and output:
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
Sample data below to allow easiness for you besides it is a long one. You can also find in url provided in df vector above. The DataFrame consists of four variables: date, site, received and sent.
date site received sent
7/10/2018 2
7/10/2018 2
7/11/2018 2
7/11/2018 2
7/11/2018 2
7/12/2018 2
7/12/2018 2
7/12/2018 2
7/13/2018 2 7/13/2018 12:50 7/18/2018 14:44
7/13/2018 2
7/18/2018 2
7/19/2018 2
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7/24/2018 2
7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/24/2018 2
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7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/24/2018 2
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7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/23/2018 2
7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/24/2018 2
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7/9/2018 2 7/9/2018 15:19 7/11/2018 10:25
7/10/2018 2 7/10/2018 12:26 7/11/2018 10:25
7/10/2018 2
7/19/2018 2
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7/19/2018 2 7/19/2018 14:22 7/25/2018 10:35
7/23/2018 2
7/23/2018 2 7/23/2018 15:53 7/25/2018 10:35
7/23/2018 2
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7/10/2018 2 7/10/2018 12:26 7/11/2018 10:25
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7/11/2018 2 7/11/2018 14:54 7/18/2018 14:44
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7/26/2018 2 7/26/2018 15:35
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7/24/2018 2 7/24/2018 15:31 7/25/2018 10:35
7/25/2018 2
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7/5/2018 2 7/5/2018 15:15 7/11/2018 10:25
7/6/2018 2
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7/9/2018 2
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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