Python 数据科学教程：Pandas 读取和处理 JSON 文件数据

terry 2年前 (2023-09-25) 阅读数 55 #后端开发

JSON 文件以可读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记事本等文本编辑器中来创建 JSON 文件。选择所有文件 (.) 作为文件类型，并使用扩展名 .json 保存文件，假设保存的文件名为：input.json。

{ 
   "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"]
}

JSON

读取 JSON 文件

Pandas 库中的 read_json 函数可用于将 JSON 文件读入 pandas DataFrame 数据结构类型。

import pandas as pd

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

Python

当我们运行上面的代码时，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

Shell

读取特定的列和行

与上一章中看到的读取CSV文件类似，在读取DataFrame中的JSON文件后，read_json从Pandas库函数还可以用来读取一些特定的列和特定的行。使用 .loc() 的多轴索引方法。选择其中显示 salary 和 name 列的某些行。

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']])

Python 当我们运行上面的代码时，它会产生以下结果。 
   salary   name
1   515.2    Dan
3   729.0   Ryan
5   578.0  Rasmi
Shell 将 JSON 文件读取为记录 
 函数 to_json 还可以与参数一起应用，以将 JSON 文件的内容读取到单独的记录中。 
import pandas as pd
data = pd.read_json('path/input.xlsx')

print(data.to_json(orient='records', lines=True))
Python运行上面的示例代码，得到以下结果 -
{"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"}