Python Pandas sum unique groupby and total all at the bottom

timlaw71 0
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I'm trying to group a dataframe and sum each group and then sum the entire dataframe at the bottom. I have this working but I'm stumped with the grand total. I would also like to figure out how to have only one value in the host_count for each group.

import pandas as pd
# import numpy as np

data = {'host': ['1.1.1.1', '192.168.1.1', '192.168.1.1', '192.168.1.1', '172.16.1.2', '192.168.1.1', '172.16.1.2', '10.2.3.4', '10.0.0.1', '10.0.0.1'],
        'url': ['capps.test.com', 'attilab-admin.test.com', 'emea-solutions-admin.test.com', 'ilab-admin.test.com', 'learning.test.com', 'mktextfw.test.com', 'sandbox.learning.test.com', 'mynetwork.test.com', 'www.letsdoit.com', 'www.mysite.com']}

df = pd.DataFrame(data)

df['host_count'] = df.groupby('host')['url'].transform('count')
pivot1 = pd.pivot_table(df, index=['host', 'url'], columns=None, fill_value=0, dropna=True).sort_values(by='host_count', ascending=False)
print(pivot1)

eww image of code

dataframe pandas python
2 Answers

Use .loc to add a new row, which goes to the bottom of the frame by default. I think your problem can better be solved with groupby rather than pivot:

def summarize(group):
    tmp = group.groupby("url").sum()
    tmp.loc["All"] = tmp.sum()
    return tmp

result = df.groupby("host").apply(summarize)
result.loc[("All", "All"), :] = result[result.index.get_level_values("url") != "All"].sum()
Code Different0

Is this what you're looking for?

df.pivot_table(index='host', values='url', aggfunc='nunique', margins=True)

Output:

             url
host
1.1.1.1        1
10.0.0.1       2
10.2.3.4       1
172.16.1.2     2
192.168.1.1    4
All           10
BeRT2me0
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