将100+个结构相同的json文件合并成一个dataframe再保存到json

Question

我有 100+ json 文件保存在本地文件夹中，所有这些文件都具有相同的结构。 每个 json 的结构包含 3 个字典，但是我只对 3 个字典中的一个感兴趣。我想将它们全部连接成一个大 dataframe，然后将它们再次保存为单独的 Z466DEEC76ECDF25FCA6D38571F6 我已经尝试了很多堆栈上建议的解决方案，但似乎无法使其正常工作。 任何帮助将不胜感激。

import json
import os
import pandas
import io


path_to_json =  r"C:\Users\stuar\Documents\Kilmarnock FC\Match Data Raw\Season Events\Championship VAEP"

list_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]


resultdict = {}

for file in list_files:
    #the with avoids the extra step of closing the file
    with open(os.path.join(path_to_json, file), "r") as inputjson:
        resultdict[file] = json.load(inputjson)

[![具有 69 个字典元素的数据框][1]][1]

[![69 个元素的内部][2]][2]

这是我到目前为止的代码尝试以及我得到的 dataframe。 我可以看到我想要的数据（每个 json 文件中的“事件”[屏幕截图 2]）。 I just need to know how to get all the 'events' data from each json file into one big dataframe [1]: https://i.stack.imgur.com/P4U8z.png [2]: https://i .stack.imgur.com/y7R8M.png

这是我的 json 文件的摘录..

 "events": [
        {
            "id": 1115549940,
            "playerId": 62288,
            "teamId": 8516,
            "matchId": 5237886,
            "matchPeriod": "1H",
            "eventSec": 4.029,
            "eventId": 8,
            "eventName": "Pass",
            "subEventId": 85,
            "subEventName": "Simple pass",
            "positions": [
                {
                    "x": 49,
                    "y": 50
                },
                {
                    "x": 38,
                    "y": 50
                }
            ],
            "tags": [
                {
                    "id": 1801,
                    "tag": {
                        "label": "accurate"
                    }
                }
            ]
        },
        {
            "id": 1115549942,
            "playerId": 8900,
            "teamId": 8516,
            "matchId": 5237886,
            "matchPeriod": "1H",
            "eventSec": 5.122,
            "eventId": 8,
            "eventName": "Pass",
            "subEventId": 83,
            "subEventName": "High pass",
            "positions": [
                {
                    "x": 38,
                    "y": 50
                },
                {
                    "x": 75,
                    "y": 5
                }
            ],
            "tags": [
                {
                    "id": 1802,
                    "tag": {
                        "label": "not accurate"
                    }
                }
            ]
        },
        {
            "id": 1115548583,
            "playerId": 446252,
            "teamId": 8531,
            "matchId": 5237886,
            "matchPeriod": "1H",
            "eventSec": 8.18,
            "eventId": 8,
            "eventName": "Pass",
            "subEventId": 82,
            "subEventName": "Head pass",
            "positions": [
                {
                    "x": 25,
                    "y": 95
                },
                {
                    "x": 45,
                    "y": 92
                }
            ],
            "tags": [
                {
                    "id": 1802,
                    "tag": {
                        "label": "not accurate"
                    }
                }
            ]
        },
        {
            "id": 1115549944,
            "playerId": 232042,
            "teamId": 8516,
            "matchId": 5237886,
            "matchPeriod": "1H",
            "eventSec": 10.09,
            "eventId": 8,
            "eventName": "Pass",
            "subEventId": 83,
            "subEventName": "High pass",
            "positions": [
                {
                    "x": 55,
                    "y": 8
                },
                {
                    "x": 82,
                    "y": 21
                }
            ],

Answer 1

这是我的解决方案，似乎有效......

import json
import os
import pandas as pd
import io


path_to_json =  r"C:\Users\stuar\Documents\Kilmarnock FC\Match Data Raw\Season Events\Championship VAEP"

list_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]

#set up an empty dictionary
resultdict = {}

for file in list_files:
    #the with avoids the extra step of closing the file
    with open(os.path.join(path_to_json, file), "r") as inputjson:
        #the dictionary key is set to filename here, but you could also use e.g. a counter
        resultdict[file] = json.load(inputjson)
        
new_df = pd.concat({
        k: pd.DataFrame.from_dict(v, 'index') for k, v in resultdict.items()
    }, 
    axis=0)

new_df = new_df.reset_index()
new_df = new_df[new_df.level_1 != 'formations']
new_df = new_df = new_df[new_df.level_1 != 'substitutions']

new_df_t = new_df.T
new_df_melt = new_df_t.melt()

new_df_melt = new_df_melt.drop(new_df_melt.index[0:2])

new_df_melt = new_df_melt.drop('variable', 1)

new_df_melt = pd.concat([new_df_melt.drop(['value'], axis=1), new_df_melt['value'].apply(pd.Series)], axis=1)

new_df_melt = new_df_melt.drop(0, 1)

new_df_melt['id'] = new_df_melt['id'].astype(int)

new_df_melt.to_json (r"C:\Users\stuar\Documents\Kilmarnock FC\Match Data Raw\Season Events\Championship VAEP\events_Championship.json")