Python 解析库json及jsonpath pickle的实现

1. 数据抽取的概念

2. 数据的分类

3. JSON数据概述及解析

3.1 JSON数据格式

3.2 解析库json

json模块是Python内置标准库，主要可以完成两个功能：序列化和反序列化。JSON对象和Python对象映射图如下：

3.2.1 json序列化

对象(字典/列表) 通过 json.dump()/json.dumps() ==> json字符串。示例代码如下：

import json
class Phone(object):
 def __init__(self, name, price):
  self.name = name
  self.price = price

class Default(json.JSONEncoder):
 def default(self, o):
  print(o) # o: <__main__.Phone object at 0x10aa52c90>
  return [o.name, o.price]

def parse(obj):
 print(obj)
 return {"name": obj.name, "price": obj.price}

person_info_dict = {
 "name": "Amo",
 "age": 18,
 "is_boy": True,
 # "n": float("nan"), # float("nan"):NaN float("inf")=>Infinity float("-inf")=>-Infinity
 "phone": Phone("苹果8plus", 6458),
 "hobby": ("sing", "dance"),
 "dog": {
  "name": "藏獒",
  "age": 5,
  "color": "棕色",
  "isVIP": True,
  "child": None
 },
}

"""
obj:需要序列化的对象 字典/列表 这里指的是person_info_dict
indent: 缩进 单位: 字符
sort_keys: 是否按key排序 默认是False不排序
cls: json.JSONEncoder子类 处理不能序列化的对象
ensure_ascii: 是否确保ascii编码 默认是True确保 "苹果8plus"==>"\u82f9\u679c8plus" 所以改为False
default: 对象不能被序列化时，调用对应的函数解析
"""

# 将结果返回给一个变量
result = json.dumps(person_info_dict,
     indent=2,
     sort_keys=True,
     ensure_ascii=False,
     # cls=Default,
     default=parse,
     # allow_nan=False 是否处理特殊常量值
     # 默认为True 但是JSON标准规范不支持NaN, Infinity和-Infinity
     )
print(result)
with open("dump.json", "w", encoding="utf8") as file:
 # json.dump是将序列化后的内容存储到文件中 其他参数用法和dumps一致
 json.dump(person_info_dict, file, indent=4, ensure_ascii=False, default=parse)

3.2.2 json反序列化

json字符串通过json.load()/json.loads()==> 对象(字典/列表)，示例代码如下：

import json
class Phone(object):
 def __init__(self, name, price):
  self.name = name
  self.price = price

def pi(num):
 return int(num) + 1

def oh(dic):
 if "price" in dic.keys():
  return Phone(dic["name"], dic["price"])
 return dic

def oph(*args, **kwargs):
 print(*args, **kwargs)

# 我自己本地有一个dump.json文件
with open("dump.json", "r", encoding="utf8") as file:
 # content = file.read()
 # parse_int/float: 整数/浮点数钩子函数
 # object_hook: 对象解析钩子函数 将字典转为特定对象 传递给函数的是字典对象
 # object_pairs_hook: 转化为特定对象 传递的是元组列表
 # parse_constant: 常量钩子函数 NaN/Infinity/-Infinity
 # result = json.loads(content, object_hook=oh, parse_int=pi, object_pairs_hook=oph)
 result = json.load(file, parse_int=pi, object_hook=oh) # 直接将文件对象传入
 print(type(result)) # <class 'dict'>
 print(result)

4. jsonpath

jsonpath三方库，点击这里这里进入官网，通过路径表达式，来快速获取字典当中的指定数据，灵感来自xpath表达式。命令安装：

pip install --user -i http://pypi.douban.com/simple --trusted-host pypi.douban.com jsonpath

或者：

4.1 使用

语法格式如下：

from jsonpath import jsonpath
dic = {....} # 要找数据的字典
jsonpath(dic, 表达式)

常用的表达式语法如下：

4.2 使用示例

案例一用到的字典如下：

dic = {
 "person": {
  "name": "Amo",
  "age": 18,
  "dog": [{
   "name": "小花",
   "color": "red",
   "age": 6,
   "isVIP": True
  },
   {
    "name": "小黑",
    "color": "black",
    "age": 2
   }]
 }
}

将上述抽象成一个树形结构如图所示：

需求及结果如下：

代码如下：

from jsonpath import jsonpath

dic = {
 "person": {
  "name": "Amo",
  "age": 18,
  "dog": [{
   "name": "小花",
   "color": "red",
   "age": 6,
   "isVIP": True
  },
   {
    "name": "小黑",
    "color": "black",
    "age": 2
   }]
 }
}

# 1.获取人的年龄
print(jsonpath(dic, "$.person.age")) # 获取到数据返回一个列表 否则返回False
# 2.获取第2个小狗的年龄
print(jsonpath(dic, "$..dog[1].age"))
# 3.获取所有小狗的年龄
print(jsonpath(dic, "$..dog[0,1].age"))
print(jsonpath(dic, "$..dog[*].age"))
# 4.获取是VIP的小狗
print(jsonpath(dic, "$..dog[?(@.isVIP)]"))
# 5.获取年龄大于2的小狗
print(jsonpath(dic, "$..dog[?(@.age>2)]"))
# 6.获取最后一个小狗
print(jsonpath(dic, "$..dog[-1:]"))
print(jsonpath(dic, "$..dog[(@.length-1)]"))

上述代码执行结果如下：

案例二用到的字典如下：

book_dict = {
 "store": {
  "book": [
   {"category": "reference",
    "author": "Nigel Rees",
    "title": "Sayings of the Century",
    "price": 8.95
    },
   {"category": "fiction",
    "author": "Evelyn Waugh",
    "title": "Sword of Honour",
    "price": 12.99
    },
   {"category": "fiction",
    "author": "Herman Melville",
    "title": "Moby Dick",
    "isbn": "0-553-21311-3",
    "price": 8.99
    },
   {"category": "fiction",
    "author": "J. R. R. Tolkien",
    "title": "The Lord of the Rings",
    "isbn": "0-395-19395-8",
    "price": 22.99
    }
  ],
  "bicycle": {
   "color": "red",
   "price": 19.95
  }
 }
}

将上述抽象成一个树形结构如图所示：

需求及结果如下：

代码如下：

from jsonpath import jsonpath

book_dict = {
 "store": {
  "book": [
   {"category": "reference",
    "author": "Nigel Rees",
    "title": "Sayings of the Century",
    "price": 8.95
    },
   {"category": "fiction",
    "author": "Evelyn Waugh",
    "title": "Sword of Honour",
    "price": 12.99
    },
   {"category": "fiction",
    "author": "Herman Melville",
    "title": "Moby Dick",
    "isbn": "0-553-21311-3",
    "price": 8.99
    },
   {"category": "fiction",
    "author": "J. R. R. Tolkien",
    "title": "The Lord of the Rings",
    "isbn": "0-395-19395-8",
    "price": 22.99
    }
  ],
  "bicycle": {
   "color": "red",
   "price": 19.95
  }
 }
}
# 1.store中的所有的book的作者
print(jsonpath(book_dict, "$.store.book[*].author"))
print(jsonpath(book_dict, "$..author"))
# 2.store下的所有的元素
print(jsonpath(book_dict, "$.store[*]"))
print(jsonpath(book_dict, "$.store.*"))
# 3.store中的所有的内容的价格
print(jsonpath(book_dict, "$..price"))
# 4.第三本书
print(jsonpath(book_dict, "$..book[2]"))
# 5.最后一本书
print(jsonpath(book_dict, "$..book[-1:]"))
print(jsonpath(book_dict, "$..book[(@.length-1)]"))
# 6.前两本书
print(jsonpath(book_dict, "$..book[0:2]"))
# 7.获取有isbn的所有书
print(jsonpath(book_dict, "$.store.book[?(@.isbn)]"))
# 8.获取价格大于10的所有的书
print(jsonpath(book_dict, "$.store.book[?(@.price>10)]"))
# 9.获取所有的数据
print(jsonpath(book_dict, "$..*"))

5. Python专用JSON解析库pickle

pickle处理的json对象不通用，可以额外的把函数给序列化。示例代码如下：

import pickle

def eat():
 print("Amo在努力地写博客~")

person_info_dict = {
 "name": "Amo",
 "age": 18,
 "eat": eat
}

# print(pickle.dumps(person_info_dict))
with open("pickle_json", "wb") as file:
 pickle.dump(person_info_dict, file)

with open("pickle_json", "rb") as file:
 result = pickle.load(file)
 result["eat"]()

JsonPath与XPath语法对比：

Json结构清晰，可读性高，复杂度低，非常容易匹配，下表中对应了XPath的用法。

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