Я хочу отобразить общее количество строк и столбцов. Я использую margin = True, но на выходе не отображается общее количество строк, как показано ниже, и вывод:
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
Пример данных ниже, чтобы облегчить вам, кроме того, это длинный. Вы также можете найти в URL-адресе, указанном выше в векторе df. DataFrame состоит из четырех переменных: дата, сайт, получено и отправлено.
date site received sent
7/10/2018 2
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7/11/2018 2
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7/12/2018 2
7/12/2018 2
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7/13/2018 2 7/13/2018 12:50 7/18/2018 14:44
7/13/2018 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
7/24/2018 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
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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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7/9/2018 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
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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
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7/10/2018 2 7/10/2018 12:26 7/11/2018 10:25
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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
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7/5/2018 2 7/5/2018 15:15 7/11/2018 10:25
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7/2/2018 2 7/2/2018 15:38 7/4/2018 11:35
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6/20/2018 5
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6/19/2018 5 6/19/2018 14:20 6/28/2018 14:20
6/19/2018 5 6/19/2018 14:20 6/28/2018 14:20
6/27/2018 5
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6/28/2018 5 6/28/2018 11:30 7/4/2018 15:30
6/28/2018 5
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7/24/2018 5
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6/28/2018 5
7/2/2018 5
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6/20/2018 5
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6/27/2018 5 6/27/2018 14:36 6/28/2018 14:20
6/27/2018 5
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6/20/2018 5 6/19/2018 12:40 6/28/2018 14:20
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6/26/2018 5
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6/26/2018 5 6/26/2018 2:36 6/28/2018 14:20
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6/27/2018 5 6/27/2018 14:36 6/28/2018 14:20
6/27/2018 5
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7/4/2018 4 7/4/2018 13:48 7/10/2018 13:30
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7/3/2018 4 7/3/2018 14:30 7/4/2018 15:45
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