从一个数据框中获取唯一计数作为熊猫中另一个数据框中的值

Question

I have two pandas dataframes and I want to get some unique row counts from one dataframe ( responses ) as column values in the other dataframe ( contacts )

import pandas as pd

contacts = pd.read_csv('contacts.csv', encoding='ISO-8859-1')
responses = pd.read_csv('campaign_responses.csv', encoding='ISO-8859-1')

contacts.head()

    contact_id          job_title  country  Email    Webinar
0   0031B00002cPLuFQAW  manager    US       0        0
1   0031B00002Z2zMYQAZ  admin      UK       0        0
2   003a000001nHioCAAS  manager    DE       0        0

Note: Email and Webinar will be 0 for all rows. They're placeholder values for the moment.

responses.head()

    campaign_type      contact_id
0   Email              0031B00002cPLuFQAW
1   Webinar            0031B00002Z2zMYQAZ
2   Webinar            0031B00002cPLuFQAW
3   Webinar            0031B00002cPLuFQAW
4   Email              003a000001nHioCAAS
5   Email              003a000001nHioCAAS

I'd like to get a count of how many times each contact has responded to each campaign type as an attribute in the contacts data frame.

The final contacts data frame should look like this (based on the data above)

    contact_id          job_title  country  Email    Webinar
0   0031B00002cPLuFQAW  manager    US       1        2
1   0031B00002Z2zMYQAZ  admin      UK       0        1
2   003a000001nHioCAAS  manager    DE       2        0

Answer 1

Seems like you need

pd.crosstab(df.contact_id,df.campaign_type)
Out[37]: 
campaign_type       Email  Webinar
contact_id                        
0031B00002Z2zMYQAZ      0        1
0031B00002cPLuFQAW      1        2
003a000001nHioCAAS      2        0

Answer 2

Short and simple:

df.groupby(['contact_id', 'campaign_type']).size().unstack('type', fill_value=0)

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Edit: neither short nor simple, see other answer.