Python - Processing JSON Data - Adglob Infosystem Pvt Ltd

JSON file stores data as text in a 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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