Multiple filter in columns and rows in python pandas

Question

I have this code after filtering a CSV file:

import pandas as pd

data1=pd.read_csv('Report1.csv',encoding = "ISO-8859-1")

df = data1[pd.notnull(data1['Assignee'])]

group=(df['type'].value_counts())

print(group)

But I'm stuck here when the csv looks like:

Type     Assignee        status

request  madan          assigned

ris      madan          resolved

request  kumar          resolved

incide   kumar          assigned

incide   madan          assigned

I need output as:

madan has 2 assigned and 1 resolved (1  request, 1 ris , 1 inciden)

kumar has 1 assigned and 1 resolved (1 request , 1 incident )

Please Help. Thanks in advance.

gyoza · Accepted Answer · 2018-08-22 03:24:27Z

1

You can use crosstab in pandas

pd.concat([pd.crosstab(df.Assignee, df.status), pd.crosstab(df.Assignee, df.Type)], axis=1)

Output:

          assigned  resolved  incide  request  ris
Assignee
kumar            1         1       1        1    0
madan            2         1       1        1    1

answered Aug 22, 2018 at 3:24

gyoza

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3 Comments

Madan Raj Over a year ago

pd.concat([pd.crosstab(df['Assignee+'], df['Status*']), pd.crosstab(df['Assignee+'], df['Case Type*']], axis=1))

gyoza Over a year ago

Yes, you are right, df['colname'] is a formal way and df.colname is more of a syntax sugar with limitations in column namings. It is generally not a good idea to include spaces, + or - signs in column names since as you experienced the syntax sugar does not work anymore.

gyoza Over a year ago

Your first comment is the way :)

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