filter/iterate to each item in a column using python pandas

Question

I've a datafram

    inputfile           UserId UserGroup   status
0  samlefile1  user1@gmail.com       xyz    pass
1    testfile  user2@gmail.com       abc    pass 
2     example  user3@gmail.com       xyz    pass
3     example  user3@gmail.com       xyz    fail

so I'm trying to select each item in 'inputfile' column and check how many times it is passed in 'status' column and convert to json 'numberOfTimesSuccess':1

{inputfile:samplefile1, UserId:user1@gmail.com, UserGroup:xyz, status:pass, numberofTimesuccess:1}
{inputfile:testfile, UserId:user2@gmail.com, UserGroup:xyz, status:pass, numberofTimesuccess:1}
{inputfile:example, UserId:user3@gmail.com, UserGroup:xyz, status:pass, numberofTimesuccess:1}

{inputfile:example2, UserId:user3@gmail.com, UserGroup:xyz, status:"", numberofTimesuccess:1}

Here is my python code:

filt = df['inputfile'] = 'samplefile1'
success = df[filt]['status'].str.contains('pass').sum
newdict={}
newdict['numberOfTimesSuccess'] = success

output: 
{numberOfTimesSuccess : 1 }

I'm only getting output for one item in a column but I want for all the items in a column and convert to json.

Answer 1

First you need to Count # of success per each InputFile then merge it with base dataframe. Then use df.to_json with orient='records' to convert each row to JSON.

import json
df = pd.DataFrame([
    ['samlefile1','user1@gmail.com', 'xyz' ,'pass'],
    ['samlefile1','user5@gmail.com', 'xyz' ,'pass'],
    ['samlefile1','user6@gmail.com', 'xyz' ,'pass'],
    ['testfile','user2@gmail.com', 'abc' ,'pass'],
    ['example','user3@gmail.com', 'xyz' ,'pass'],
    ['example','user3@gmail.com', 'xyz' ,'fail']],columns = ['InputFile','UserId', 'UserGroup' ,'status']
)

#Count no of status per filename
Input_status_count = df.groupby(['InputFile']).agg(success_count=('status', lambda x: x[x == 'pass'].count()),
                                                   fail_count=('status', lambda x: x[x == 'fail'].count()))

#Merge the 2 files on 'InputFile' column
FinalDF = pd.merge(df, Input_status_count,on = "InputFile" )
JSON_String = FinalDF.to_json(orient='records')
JSON_String

Output:

[
    {"InputFile":"samlefile1","UserId":"user1@gmail.com","UserGroup":"xyz","status":"pass","success_count":3,"fail_count":0},
    {"InputFile":"samlefile1","UserId":"user5@gmail.com","UserGroup":"xyz","status":"pass","success_count":3,"fail_count":0},
    {"InputFile":"samlefile1","UserId":"user6@gmail.com","UserGroup":"xyz","status":"pass","success_count":3,"fail_count":0},
    {"InputFile":"testfile","UserId":"user2@gmail.com","UserGroup":"abc","status":"pass","success_count":1,"fail_count":0},
    {"InputFile":"example","UserId":"user3@gmail.com","UserGroup":"xyz","status":"pass","success_count":1,"fail_count":1},
    {"InputFile":"example","UserId":"user3@gmail.com","UserGroup":"xyz","status":"fail","success_count":1,"fail_count":1}
]