在python中从多个JSON文件提取信息到单个CSV文件 [英] Extracting infromation from multiple JSON files to single CSV file in python

问题描述

我有一个包含多个字典的JSON文件：

I have a JSON file with multiple dictionaries:

{"team1participants": 
[ {
        "stats": {
            "item1": 3153, 
            "totalScore": 0, 
            ...
        }
   },
   {
        "stats": {
            "item1": 2123, 
            "totalScore": 5, 
            ...
        }
   },
   {
        "stats": {
            "item1": 1253, 
            "totalScore": 1, 
            ...
        }
   }
],
"team2participants": 
[ {
        "stats": {
            "item1": 1853, 
            "totalScore": 2, 
            ...
        }
   },
   {
        "stats": {
            "item1": 21523, 
            "totalScore": 5, 
            ...
        }
   },
   {
        "stats": {
            "item1": 12503, 
            "totalScore": 1, 
            ...
        }
   }
]
}

b $ b

换句话说，JSON有多个键。每个键都有一个列表，其中包含各个参与者的统计信息。

In other words, the JSON has multiple keys. Each key has a list containing statistics of individual participants.

我有很多这样的JSON文件，我想将其解压缩为单个CSV文件。我当然可以手动这样做，但这是非常乏味。我知道DictWriter，但它似乎只适用于单个词典。我也知道字典可以连接，但它会有问题，因为所有的字典有相同的键。

I have many such JSON files, and I want to extract it to a single CSV file. I can of course do this manually, but this is very tedious. I know of DictWriter, but it seems to work only for single dictionaries. I also know that dictionaries can be concatenated, but it will be problematic because all dictionaries have the same keys.

如何有效地提取这个CSV文件？ p>

How can I efficiently extract this to a CSV file?

推荐答案

您可以使数据整齐，以便每一行都是唯一的观察结果。

You can make your data tidy so that each row is a unique observation.

teams = []
items = []
scores = []
for team in d:
    for item in d[team]:
        teams.append(team)
        items.append(item['stats']['item1'])
        scores.append(item['stats']['totalScore'])


# Using Pandas.
import pandas as pd

df = pd.DataFrame({'team': teams, 'item': items, 'score': scores})
>>> df
    item   score               team
0   1853       2  team2participants
1  21523       5  team2participants
2  12503       1  team2participants
3   3153       0  team1participants
4   2123       5  team1participants
5   1253       1  team1participants

您也可以使用列表解析而不是循环。

You could also use a list comprehension instead of a loop.

results = [[team, item['stats']['item1'], item['stats']['totalScore']] 
           for team in d for item in d[team]]
df = pd.DataFrame(results, columns=['team', 'item', 'score'])

然后，您可以执行数据透视表，例如：

You can then do a pivot table, for example:

>>> df.pivot_table(values='score ', index='team ', columns='item', aggfunc='sum').fillna(0)
item               1253   1853   2123   3153   12503  21523
team                                                       
team1participants      1      0      5      0      0      0
team2participants      0      2      0      0      1      5

此外，现在它是一个数据框架，很容易将它保存为CSV。

Also, now that it is a dataframe, it is easy to save it as a CSV.

df.to_csv(my_file_name.csv)

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