DataFrame by especific columns in Python pandas to a JSON response?

Question

I can't figure out how to generate this output:

       "name"    "hp"    "hpperlevel"
"1"    Annie     524     88
"2"    Olaf      597.24  93

With this:

{
    "type": "champion",
    "version": "7.24.1",
    "data": {
        "1": {
            "title": "the Dark Child",
            "stats": {
                "armorperlevel": 4,
                "attackdamage": 50.41,
                "mpperlevel": 50,
                "attackspeedoffset": 0.08,
                "mp": 334,
                "armor": 19.22,
                "hp": 524,
                "hpregenperlevel": 0.55,
                "attackspeedperlevel": 1.36,
                "attackrange": 575,
                "movespeed": 335,
                "attackdamageperlevel": 2.625,
                "mpregenperlevel": 0.8,
                "critperlevel": 0,
                "spellblockperlevel": 0.5,
                "crit": 0,
                "mpregen": 6,
                "spellblock": 30,
                "hpregen": 5.424,
                "hpperlevel": 88
            },
            "id": 1,
            "key": "Annie",
            "name": "Annie"
        },
        "2": {
            "title": "the Berserker",
            "stats": {
                "armorperlevel": 3,
                "attackdamage": 68,
                "mpperlevel": 42,
                "attackspeedoffset": -0.1,
                "mp": 315.6,
                "armor": 35,
                "hp": 597.24,
                "hpregenperlevel": 0.9,
                "attackspeedperlevel": 2.7,
                "attackrange": 125,
                "movespeed": 350,
                "attackdamageperlevel": 3.5,
                "mpregenperlevel": 0.575,
                "critperlevel": 0,
                "spellblockperlevel": 1.25,
                "crit": 0,
                "mpregen": 7.466,
                "spellblock": 32.1,
                "hpregen": 8.512,
                "hpperlevel": 93
            },
            "id": 2,
            "key": "Olaf",
            "name": "Olaf"
        },

The goal is DataFrame the first table with the JSON response (the second code) in Python pandas, or if I need to do something with the JSON response first i will be happy to know a hint.

The only method I know is pandas.DataFrame(Data["data"]) but the output is not the desired, yet i don't want to make a pandas.DataFrame(Data["data"]["1"]["stats"], index = [0], columns = ["hp", "hpperlevel"]) more than 100 times (the total numbers from the API) multiplied by the groups I want to make and merge them all.

The final output of my program is save the data as a excel file but the only big problem is make the groups first.

Any response will be greatly appreciated!

Answer 1

You can create a single dataframe with a couple of list comprehensions passed to the dataframe constructor:

import pandas

def get_stats(data):
    return (data['id'], data['name'], data['stats']['hp'], data['stats']['hpperlevel'])

response = {
    "data": {
        "1": {
            "stats": {"hp": 524, "hpperlevel": 88},
            "id": 1,
            "name": "Annie"
        },
        "2": {
            "stats": {"hp": 597.24, "hpperlevel": 93},
            "id": 2,
            "name": "Olaf"
        }
    }
}

df = pandas.DataFrame(
    data=[get_stats(response['data'][key]) for key in response['data']],
    index=[int(key) for key in response['data']],
    columns=['id', 'name', 'hp', 'hpperlevel']
).set_index('id')

And that gives me:

     name      hp  hpperlevel
id                           
1   Annie  524.00          88
2    Olaf  597.24          93