Python 处理 JSON 数据

JSON 文件以人类可读的格式将数据存储为文本。JSON 代表 JavaScript 对象表示法。Pandas 可以使用 read_json 函数读取 JSON 文件。

输入数据

通过将以下数据复制到文本编辑器（如记事本）中来创建 JSON 文件。使用 .json 扩展名保存文件，并选择文件类型为 所有文件 (*.*)。

{ 
   "ID":["1","2","3","4","5","6","7","8" ],
   "Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ]
   "Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
   
   "StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
      "7/30/2013","6/17/2014"],
   "Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}

读取 JSON 文件

pandas 库的 read_json 函数可用于将 JSON 文件读入 pandas DataFrame。

import pandas as pd

data = pd.read_json('path/input.json')
print (data)

当我们执行上述代码时，它会产生以下结果。

         Dept  ID    Name  Salary   StartDate
0          IT   1    Rick  623.30    1/1/2012
1  Operations   2     Dan  515.20   9/23/2013
2          IT   3   Tusar  611.00  11/15/2014
3          HR   4    Ryan  729.00   5/11/2014
4     Finance   5    Gary  843.25   3/27/2015
5          IT   6   Rasmi  578.00   5/21/2013
6  Operations   7  Pranab  632.80   7/30/2013
7     Finance   8    Guru  722.50   6/17/2014

读取特定列和行

与上一章中我们已经看到的读取 CSV 文件的方法类似，在将 JSON 文件读取到 DataFrame 后，pandas 库的 read_json 函数也可用于读取某些特定列和特定行。 为此，我们使用名为 .loc() 的多轴索引方法。我们选择显示某些行的 Salary 和 Name 列。

import pandas as pd
data = pd.read_json('path/input.xlsx')

# Use the multi-axes indexing funtion
print (data.loc[[1,3,5],['salary','name']])

当我们执行上述代码时，它会产生以下结果。

   salary   name
1   515.2    Dan
3   729.0   Ryan
5   578.0  Rasmi

将 JSON 文件读取为记录

我们还可以应用 to_json 函数以及参数将 JSON 文件内容读入单个记录。

import pandas as pd
data = pd.read_json('path/input.xlsx')

print(data.to_json(orient='records', lines=True))

当我们执行上述代码时，它会产生以下结果。

{"Dept":"IT","ID":1,"Name":"Rick","Salary":623.3,"StartDate":"1/1/2012"}
{"Dept":"Operations","ID":2,"Name":"Dan","Salary":515.2,"StartDate":"9/23/2013"}
{"Dept":"IT","ID":3,"Name":"Tusar","Salary":611.0,"StartDate":"11/15/2014"}
{"Dept":"HR","ID":4,"Name":"Ryan","Salary":729.0,"StartDate":"5/11/2014"}
{"Dept":"Finance","ID":5,"Name":"Gary","Salary":843.25,"StartDate":"3/27/2015"}
{"Dept":"IT","ID":6,"Name":"Rasmi","Salary":578.0,"StartDate":"5/21/2013"}
{"Dept":"Operations","ID":7,"Name":"Pranab","Salary":632.8,"StartDate":"7/30/2013"}
{"Dept":"Finance","ID":8,"Name":"Guru","Salary":722.5,"StartDate":"6/17/2014"}