Python Data Science 简明教程
Python - Processing JSON Data
JSON 文件将数据存储为人类可读文本格式。JSON 代表 JavaScript 对象表示法。Pandas 可以使用 read_json 函数读取 JSON 文件。
Input Data
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。使用 .json 扩展名保存文件,并将文件类型选择为 all files( . ) 。
{
"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"]
}
Read the JSON File
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
Reading Specific Columns and Rows
类似于我们在上一章中已经了解到的用于读取 CSV 文件的内容,Pandas 库的 read_json 函数还可用于在 JSON 文件读入 DataFrame 后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。我们选择为某些行显示 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
Reading JSON file as Records
我们还可以应用 to_json 函数以及参数将 JSON 文件内容读入各个记录。
import pandas as pd
data = pd.read_json('path/input.xlsx')
print(data.to_json(orient='records', lines=True))
当我们执行上面的代码时,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"}