使用python同时替换json多个指定key的value

1.如何同时替换json多个指定key的value

import json
from jsonpath_ng import  parse

def join_paths(regx_path,new_value,dict_replace):
    """
    eg: join_paths(regx_path='$..host..namespace', new_value="9999999999", dict_replace=pydict)
    :param regx_path: the path of replaced key
    :param new_value: the new value of key to be replaced
    :param dict_replace:  the initial_dict that to be replaced
    :return: dict
    """
    data = dict_replace
    jsonpath_expr = parse(regx_path)
    str_path_list=[str(match.full_path) for match in jsonpath_expr.find(dict_replace)]
    def cast_dict_path(path_list):
        cast_list = []
        for str_path in  path_list:
            path_split_list=str_path.split('.')
            path = ''
            for i in path_split_list:
                if i.count('[')==1 and i.count(']')==1:
                    path=path+'[%s]'%eval(i)[0]
                else:
                    path=path+"['%s']"%i
            cast_list.append(path)
        #[ "['role_parameters']['guest']['args']['data']['train_data'][0]['namespace']" ]
        return cast_list
    cast_paths=cast_dict_path(str_path_list)
    for i in cast_paths:
        if isinstance(new_value,str):
            fullpath="data"+i+"='%s'"%new_value
            abs_path=fullpath
            exec(abs_path)
        if isinstance(new_value,(int,list,float)):
            fullpath = "data" + i + "={}".format(new_value)
            abs_path=fullpath
            exec(abs_path)
    return data

def muti_replace(rep_list,initial_dict:dict):
    """
    format of rep_list:
    [
        (regx_path1 ,new_value1) ],
        (regx_path2 ,new_value2 )
    ]
     for example:
    >> final_dict=muti_replace([('$..hetero_lr_0..eps',0.7777),('$..host..namespace',8888888)],initial_dict=pydict)

     initial_dict :the key  need to replaced dict ,type dict
    """
    dict_list=[]
    for i in rep_list:
            regx_path ,new_value=i[0],i[1]
            dict_next=join_paths(regx_path,new_value,dict_replace=initial_dict)
            dict_list.append(dict_next)
    for k in dict_list:
        initial_dict.update(k)
    print(json.dumps(initial_dict,indent=5))
    return initial_dict

if __name__ == '__main__':

    final_dict=muti_replace([('$..hetero_lr_0..eps',0.7777),('$..host..namespace',8888888)],initial_dict=pydict)

　测试数据：

{
  "initiator": {
    "role": "guest",
    "party_id":
  },
  "job_parameters": {
    "work_mode":
  },
  "role": {
    "guest": [

    ],
    "host": [

    ],
    "arbiter": [

    ]
  },
  "role_parameters": {
    "guest": {
      "args": {
        "data": {
          "train_data": [
            {
              "name": "breast_guest",
              "namespace": "breast_guest"
            }
          ]
        }
      },
      "dataio_0": {
        "with_label": [true],
        "label_name": ["y"],
        "label_type": ["int"],
        "output_format": ["dense"],
        "missing_fill": [true],
        "outlier_replace": [true]
      },
      "feature_scale_0": {
        "method": ["min_max_scale"]
      },
      "hetero_feature_binning_0": {
        "method": ["quantile"],
        "compress_thres": [],
        "head_size": [],
        "error": [0.001],
        "bin_num": [],
        "cols": [-],
        "adjustment_factor": [0.5],
        "local_only": [false],
        "transform_param": {
          "transform_cols": [-],
          "transform_type": ["bin_num"]
        }
      },
      "hetero_feature_selection_0": {
        "select_cols": [-],
        "filter_methods": [[
          "unique_value",
          "iv_value_thres",
          "coefficient_of_variation_value_thres",
          "iv_percentile",
          "outlier_cols"
        ]],
        "local_only": [false],
        "unique_param": {
          "eps": [1e-]
        },
        "iv_value_param": {
          "value_threshold": [1.0]
        },
        "iv_percentile_param": {
          "percentile_threshold": [0.9]
        },
        "variance_coe_param": {
          "value_threshold": [0.3]
        },
        "outlier_param": {
          "percentile": [0.95],
          "upper_threshold": []
        }
      },
      "evaluation_0": {
        "eval_type": ["binary"],
        "pos_label": []
      }
    },
    "host": {
      "args": {
        "data": {
          "train_data": [
            {
              "name": "breast_host",
              "namespace": "breast_host"
            }
          ]
        }
      },
      "dataio_0": {
        "with_label": [false],
        "output_format": ["dense"],
        "outlier_replace": [true]
      },
      "feature_scale_0": {
        "method": ["standard_scale"],
        "need_run": [false]
      },
      "hetero_feature_binning_0": {
        "method": ["quantile"],
        "compress_thres": [],
        "head_size": [],
        "error": [0.001],
        "bin_num": [],
        "cols": [-],
        "adjustment_factor": [0.5],
        "local_only": [false],
        "transform_param": {
          "transform_cols": [-],
          "transform_type": ["bin_num"]
        }
      },
      "hetero_feature_selection_0": {
        "select_cols": [-],
        "filter_methods": [[
          "unique_value",
          "iv_value_thres",
          "coefficient_of_variation_value_thres",
          "iv_percentile",
          "outlier_cols"
        ]],
        "local_only": [false],
        "unique_param": {
          "eps": [1e-]
        },
        "iv_value_param": {
          "value_threshold": [1.0]
        },
        "iv_percentile_param": {
          "percentile_threshold": [0.9]
        },
        "variance_coe_param": {
          "value_threshold": [0.3]
        },
        "outlier_param": {
          "percentile": [0.95],
          "upper_threshold": []
        }
      },
      "evaluation_0": {
        "need_run": [true]
      }
    }
  },
  "algorithm_parameters": {
    "feature_scale_0": {
        "need_run": true
    },
    "hetero_feature_binning_0": {
      "need_run": true
    },
    "hetero_feature_selection_0": {
      "need_run": true
    },
    "hetero_lr_0": {
      "penalty": "L2",
      "optimizer": "rmsprop",
      "eps": 1e-,
      "alpha": 0.01,
      "max_iter": ,
      "converge_func": "diff",
      "batch_size": -,
      "learning_rate": 0.15,
      "init_param": {
        "init_method": "random_uniform"
      },
      "cv_param": {
        "n_splits": ,
        "shuffle": false,
        "random_seed": ,
        "need_cv": false
      }
    }
  }
}