Faster way to convert Pandas dataframe into nested json

Question

I have data that looks like this:

player, goals, matches
ronaldo, 10, 5
messi, 7, 9

I want to convert this dataframe into a nested json, such as this one:

{
    "content":[
        {
            "player": "ronaldo",
            "events": {
                "goals": 10,
                "matches": 5
            }
        },
        {
            "player": "messi",
            "events": {
                "goals": 7,
                "matches": 9
            }
        }
    ]
}

This is my code, using list comprehension:

df = pd.DataFrame([['ronaldo', 10, 5], ['messi', 7, 9]], columns=['player', 'goals', 'matches'])
d = [{'events': df.loc[ix, ['goals', 'matches']].to_dict(), 'player': df.loc[ix, 'player']} for ix in range(df.shape[0])]
j = {}
j['content'] = d

This works, but the performance is really slow when I have a lot of data. Is there a faster way to do it?

Answer 1

Use pandas.to_json . Fast and easy https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html

df.T.to_json()

Answer 2

try :

df.to_json(orient = "records")

The problem is it doesn't stack goals and matches on the event column , I'm not sure though if you can do it without looping