My code below groups by values and creates a list of values that were once the length of arrays. But how can I return the id that has the largest sum of each number in the elements:
Original Json read into df (not same data as printed because it was too long)
{
"kind":"admin#reports#activities",
"etag":"\"5g8\"",
"nextPageToken":"A:1651795128914034:-4002873813067783265:151219070090:C02f6wppb",
"items":[
{
"kind":"admin#reports#activity",
"id":{
"time":"2022-05-05T23:59:39.421Z",
"uniqueQualifier":"5526793068617678141",
"applicationName":"token",
"customerId":"cds"
},
"etag":"\"jkYcURYoi8\"",
"actor":{
"email":"blah@blah.net",
"profileId":"1323"
},
"ipAddress":"107.178.193.87",
"events":[
{
"type":"auth",
"name":"activity",
"parameters":[
{
"name":"api_name",
"value":"admin"
},
{
"name":"method_name",
"value":"directory.users.list"
},
{
"name":"client_id",
"value":"722230783769-dsta4bi9fkom72qcu0t34aj3qpcoqloq.apps.googleusercontent.com"
},
{
"name":"num_response_bytes",
"intValue":"7158"
},
{
"name":"product_bucket",
"value":"GSUITE_ADMIN"
},
{
"name":"app_name",
"value":"Untitled project"
},
{
"name":"client_type",
"value":"WEB"
}
]
}
]
},
{
"kind":"admin#reports#activity",
"id":{
"time":"2022-05-05T23:58:48.914Z",
"uniqueQualifier":"-4002873813067783265",
"applicationName":"token",
"customerId":"df"
},
"etag":"\"5T53xK7dpLei95RNoKZd9uz5Xb8LJpBJb72fi2HaNYM/9DTdB8t7uixvUbjo4LUEg53_gf0\"",
"actor":{
"email":"blah.blah@bebe.net",
"profileId":"1324"
},
"ipAddress":"54.80.168.30",
"events":[
{
"type":"auth",
"name":"activity",
"parameters":[
{
"name":"api_name",
"value":"gmail"
},
{
"name":"method_name",
"value":"gmail.users.messages.list"
},
{
"name":"client_id",
"value":"927538837578.apps.googleusercontent.com"
},
{
"name":"num_response_bytes",
"intValue":"2"
},
{
"name":"product_bucket",
"value":"GMAIL"
},
{
"name":"client_type",
"value":"WEB"
}
]
}
]
}
]
}
current code:
df = pd.json_normalize(response['items'])
df['test'] = df.groupby('actor.profileId')['events'].apply(lambda x: [len(x.iloc[i][0]['parameters']) for i in range(len(x))])
output:
ID
1002306 [7, 7, 7, 5]
1234444 [3,5,6]
1222222 [1,3,4,5]
desired output
id total
1002306 26
Sorry had to fill up more space, as there was so much code
There’s no need to construct the intermediate df and do groupby on it. You can use pass the record and meta paths to json_normalize to directly flatten the json data. Then your job seems to be about counting the number of rows per actor.profileId and finding the maximum.
df = pd.json_normalize(response['items'], ['events','parameters'], ['actor'])
df['actor.profileId'] = df['actor'].str['profileId']
out = df.value_counts('actor.profileId').pipe(lambda x: x.iloc[[0]])
Output:
actor.profileId
1323 7
dtype: int64