代码之家 › 专栏 › 技术社区 › Shankar Panda

如何使用Python将列表中的嵌套json存储到文本文件中?

python-jsonschema python-3.x json python

Shankar Panda · 技术社区 · 6 年前

我正在创建一个嵌套的json,并将其存储在一个列表对象中。下面是我的代码,它按照预期获得了正确的层次化json。

数据源,数据源,类别,类别,子类别,子类别劳工统计局,44,就业和工资,44,就业和工资,44

import pandas as pd
df=pd.read_csv('queryhive16273.csv')
def split_df(df):
   for (vendor, count), df_vendor in df.groupby(["datasource", "datasource_cnt"]):
       yield {
           "vendor_name": vendor,
           "count": count,
           "categories": list(split_category(df_vendor))
       }

def split_category(df_vendor):
   for (category, count), df_category in df_vendor.groupby(
       ["category", "category_cnt"]
   ):
       yield {
           "name": category,
           "count": count,
           "subCategories": list(split_subcategory(df_category)),
       }

def split_subcategory(df_category):
   for (subcategory, count), df_subcategory in df_category.groupby(
       ["subcategory", "subcategory_cnt"]
   ):
       yield {
           "count": count,
           "name": subcategory,
             }


abc=list(split_df(df))

abc包含如下所示的数据。这是预期的结果。

[{
    'count': 44,
    'vendor_name': 'Bureau of Labor Statistics',
    'categories': [{
        'count': 44,
        'name': 'Employment and wages',
        'subCategories': [{
            'count': 44,
            'name': 'Employment and wages'
        }]
    }]
}]

现在我尝试将其存储到json文件中。

with open('your_file2.json', 'w') as f:
    for item in abc:
       f.write("%s\n" % item)
        #f.write(abc)

你能帮帮我吗。

{
    'count': 44,
    'vendor_name': 'Bureau of Labor Statistics',
    'categories': [{
        'count': 44,
        'name': 'Employment and wages',
        'subCategories': [{
            'count': 44,
            'name': 'Employment and wages'
        }]
    }]
}

[{
    "count": 44,
    "vendor_name": "Bureau of Labor Statistics",
    "categories": [{
        "count": 44,
        "name": "Employment and wages",
        "subCategories": [{
            "count": 44,
            "name": "Employment and wages"
        }]
    }]
}]

2 回复 | 直到 6 年前

jlandercy 6 年前

使用您的数据和PSL json 给我:

TypeError: Object of type 'int64' is not JSON serializable

这只意味着一些numpy对象存在于嵌套结构中,并且没有 encode 方法将其转换为JSON序列化。

当对象本身缺少字符串转换时,强制encode使用字符串转换足以使代码正常工作:

import io
d = io.StringIO("datasource,datasource_cnt,category,category_cnt,subcategory,subcategory_cnt\nBureau of Labor Statistics,44,Employment and wages,44,Employment and wages,44")
df=pd.read_csv(d)

abc=list(split_df(df))

import json
json.dumps(abc, default=str)

它返回一个有效的JSON(但带有 int 转化为 str ):

'[{"vendor_name": "Bureau of Labor Statistics", "count": "44", "categories": [{"name": "Employment and wages", "count": "44", "subCategories": [{"count": "44", "name": "Employment and wages"}]}]}]'

如果不适合您的需要,请使用专用的 Encoder

import numpy as np
class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.int64):
            return int(obj)
        return json.JSONEncoder.default(self, obj)

json.dumps(abc, cls=MyEncoder)

这将返回请求的JSON:

'[{"vendor_name": "Bureau of Labor Statistics", "count": 44, "categories": [{"name": "Employment and wages", "count": 44, "subCategories": [{"count": 44, "name": "Employment and wages"}]}]}]'

另一种选择是在编码之前直接转换数据:

def split_category(df_vendor):
   for (category, count), df_category in df_vendor.groupby(
       ["category", "category_cnt"]
   ):
       yield {
           "name": category,
           "count": int(count), # Cast here before encoding
           "subCategories": list(split_subcategory(df_category)),
       }

buran 6 年前

import json

data = [{
    'count': 44,
    'vendor_name': 'Bureau of Labor Statistics',
    'categories': [{
        'count': 44,
        'name': 'Employment and wages',
        'subCategories': [{
            'count': 44,
            'name': 'Employment and wages'
        }]
    }]
}]

with open('your_file2.json', 'w') as f:
    json.dump(data, f, indent=2)

[
  {
    "count": 44,
    "vendor_name": "Bureau of Labor Statistics",
    "categories": [
      {
        "count": 44,
        "name": "Employment and wages",
        "subCategories": [
          {
            "count": 44,
            "name": "Employment and wages"
          }
        ]
      }
    ]
  }
]