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python - great_expectations：expect_column_values_to_match_json_schema 不将 json 模式作为输入

问题描述

我正在尝试调用

expect_column_values_to_match_json_schema

按照

https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/dataset/dataset/index.html#great_expectations.dataset.dataset.Dataset.expect_column_values_to_match_json_schema

jschema = {
        "type" : "object",
     "properties" : {
         "xyz" : {"type" : "string"}
     }
        }
df_ge.expect_column_values_to_match_json_schema(column='abc',json_schema=jschema,row_condition="a=='1' and b=='2' and c=='3'",condition_parser='pandas')

但是我收到了这个错误

File "/some/path/lib/python3.7/site-packages/great_expectations/dataset/pandas_dataset.py", line 1554, in matches_json_schema
    val_json = json.loads(val)
  File "/usr/lib/python3.7/json/__init__.py", line 341, in loads
    raise TypeError(f'the JSON object must be str, bytes or bytearray, '
TypeError: the JSON object must be str, bytes or bytearray, not dict

所以我尝试了

df_ge.expect_column_values_to_match_json_schema(column='abc',json_schema=json.loads(str(jschema)),row_condition="a=='1' and b=='2' and c=='3'",condition_parser='pandas')

但后来我明白了

df_ge.expect_column_values_to_match_json_schema(column='pg',json_schema=json.loads(str(pgschema)),row_condition="a=='1' and b=='2' and c=='3'",condition_parser='pandas')
  File "/usr/lib/python3.7/json/__init__.py", line 348, in loads
    return _default_decoder.decode(s)
  File "/usr/lib/python3.7/json/decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/usr/lib/python3.7/json/decoder.py", line 353, in raw_decode
    obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)

我怎样才能创建一个合适的 json 对象来提供给这个方法？

标签： pythonjsonpandasjsonschemagreat-expectations

解决方案

没有“JSON 对象”之类的东西。JSON 是一种序列化格式，所以唯一可以是 JSON 的就是一串字节。这样的字符串可以用 的相反来创建json.loads，即json.dumps：

df_ge.expect_column_values_to_match_json_schema(column='pg',json_schema=json.dumps(pgschema),row_condition="a=='1' and b=='2' and c=='3'",condition_parser='pandas')

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