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Appending multiple values of a json into a Single Pandas column using python

SrikanthB
June 19, 2023
194 views
0 votes
2 Answers

I have trouble in getting values from a Json and storing in a Dataframe.
My Json looks like

{
  "issues": [
    {
      "expand": "operations",
      "id": "1",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": "false"
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "2",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1002",
            "value": "Desktop",
            "id": "1002",
            "disabled": false
          },
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": false
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "3",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1003",
            "value": "ios",
            "id": "1002",
            "disabled": false
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "4",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1002",
            "value": "Desktop",
            "id": "1002",
            "disabled": false
          },
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": false
          },
          {
            "self": "https://url1003",
            "value": "ios",
            "id": "1003",
            "disabled": false
          }
        ]
      }
    }
  ]
}

Below is my part of the code

        df2=pd.dataframe()
        d=pd.json_normalize(json.loads(df1['customfield_100'].to_json(orient='record')))
        filtered_component=[]
        for index in range(len(issues.id)):
             if((pd.json_normalize(df1['customfield_100'][index])).size>0):
                 filtered_component.append(d[0][index]['value']
             else:
                 filtered_component.append('No Component')
          df2['Component']=filterd_component

When i list df2[‘Component’], i am getting the below output

'Mobile'
'Desktop'
'ios'
'Desktop'

I would like my output to be like(when i list df2[components]) i.e if more than single value for customfield_100 then i would like those to be separated by ;. I am not sure how the loop/code should be written for it

'Mobile'
'Desktop';'Mobile'
'ios'
'Desktop';'Mobile';'ios'

Answers

If data contains your parsed Json data then you can do:

all_data = []
for i in data['issues']:
    for k, v in i['fields'].items():
        for vv in v:
            all_data.append({'main_id': i['id'], 'field_id': k, **vv})

df = pd.DataFrame(all_data)
print(df)

This prints:

  main_id         field_id             self    value    id disabled
0       1  customfield_100  https://url1001   Mobile  1001    false
1       2  customfield_100  https://url1002  Desktop  1002    False
2       2  customfield_100  https://url1001   Mobile  1001    False
3       3  customfield_100  https://url1003      ios  1002    False
4       4  customfield_100  https://url1002  Desktop  1002    False
5       4  customfield_100  https://url1001   Mobile  1001    False
6       4  customfield_100  https://url1003      ios  1003    False

Then you can group by main_id, e.g.:

df = df.groupby('main_id')['value'].agg(';'.join)
print(df)

This prints:

main_id
1                Mobile
2        Desktop;Mobile
3                   ios
4    Desktop;Mobile;ios
Name: value, dtype: object

Another possible solution:

df = pd.json_normalize(
    data, 
    record_path=['issues', 'fields', 'customfield_100'], 
    meta=[['issues', 'id']])

df.groupby('issues.id')['value'].agg(';'.join)

where

data = {
  "issues": [
    {
      "expand": "operations",
      "id": "1",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": False
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "2",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1002",
            "value": "Desktop",
            "id": "1002",
            "disabled": False
          },
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": False
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "3",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1003",
            "value": "ios",
            "id": "1002",
            "disabled": False
          }
        ]
      }
    },
    {
      "expand": "operations",
      "id": "4",
      "fields": {
        "customfield_100": [
          {
            "self": "https://url1002",
            "value": "Desktop",
            "id": "1002",
            "disabled": False
          },
          {
            "self": "https://url1001",
            "value": "Mobile",
            "id": "1001",
            "disabled": False
          },
          {
            "self": "https://url1003",
            "value": "ios",
            "id": "1003",
            "disabled": False
          }
        ]
      }
    }
  ]
}

Output:

issues.id
1                Mobile
2        Desktop;Mobile
3                   ios
4    Desktop;Mobile;ios
Name: value, dtype: object

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