import pandas as pd
data = [['a', 'TRUE'], ['a', 'FALSE'], ['a', 'TRUE'], ['b', 'TRUE'], ['b', 'TRUE'], ['b', 'TRUE'],
['b', 'FALSE'], ['c', 'TRUE'], ['c', 'TRUE']]
df = pd.DataFrame(data, columns=['ID', 'PASS'])
df['value'] = 1
result = df.pivot_table(values='value', index='ID', columns='PASS', aggfunc='sum', fill_value=0)
result['Total'] = result.agg(sum, axis=1)
result
PASS FALSE TRUE Total
ID
a 1 2 3
b 1 3 4
c 0 2 2
Another way to do it is by groupby and unstack , such that:
df = df.groupby(["ID","PASS"])['PASS'].count().unstack(fill_value=0)
df['total'] = df['FALSE']+df['TRUE']
desired result:
PASS FALSE TRUE Total
ID
a 1 2 3
b 1 3 4
c 0 2 2
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.