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JSON file stores data as text in human-readable format. JSON stands for JavaScript Object Notation. Pandas can read JSON files using the read_json function.
Input Data
Create a JSON file by copying the below data into a text editor like notepad. Save the file with .json extension and choosing the file type as 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
The read_json function of the pandas library can be used to read the JSON file into a pandas DataFrame.
import pandas as pd
data = pd.read_json(''path/input.json'')
print (data)
When we execute the above code, it produces the following result.
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
Similar to what we have already seen in the previous chapter to read the CSV file, the read_json function of the pandas library can also be used to read some specific columns and specific rows after the JSON file is read to a DataFrame.
We use the multi-axes indexing method called .loc() for this purpose. We choose to display the Salary and Name column for some of the rows.
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'']])
When we execute the above code, it produces the following result.
salary name 1 515.2 Dan 3 729.0 Ryan 5 578.0 Rasmi
Reading JSON file as Records
We can also apply the to_json function along with parameters to read the JSON file content into individual records.
import pandas as pd
data = pd.read_json(''path/input.xlsx'')
print(data.to_json(orient=''records'', lines=True))
When we execute the above code, it produces the following result.
{"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"}
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