📅  最后修改于: 2020-12-13 14:13:13             🧑  作者: Mango
JSON文件以人类可读格式将数据存储为文本。 JSON代表JavaScript对象符号。熊猫可以使用read_json函数读取JSON文件。
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。保存扩展名为.json的文件,然后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"]
}
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
与我们在上一章中看到的读取CSV文件的方式类似,在将JSON文件读取到DataFrame之后,pandas库的read_json函数也可以用于读取某些特定的列和特定的行。为此,我们使用称为.loc()的多轴索引方法。我们选择为某些行显示“工资和姓名”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,它产生以下结果。
salary name
1 515.2 Dan
3 729.0 Ryan
5 578.0 Rasmi
我们还可以将to_json函数与参数一起应用,以将JSON文件内容读取到单个记录中。
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"}