XML_JSON_Parse

Dynamic XML_JSON parser to transform XML and JSON files to CSV using multi threading.

Prerequisites

The module is developed in python3 with usage of xml, json, pandas, threading and queue modules.

Usage / Start Process

For XML parsing

python3 main.py -i  -o  -e Resident

For JSON parsing

python3 main.py -i  -o  -e fruit

Command Line Arguments:

usage: main.py [-h] -i INPUT_FILE -o OUTPUT_FILE -e ELEMENT

Convert XML or JSON file to csv

-h, --help show this help message and exit
-i INPUT_FILE, --input_file INPUT_FILE Source XML or JSON file (mandatory)
-o OUTPUT_FILE, --output_file OUTPUT_FILE Destination csv file (mandatory)
-e ELEMENT, --element ELEMENT element to parse (mandatory)

Example For XML parsing

Input xml file : resident.xml with Resident as element to parse

<State>
<Resident>
<Name>Sample Name</Name>
	<PhoneNumber>1234567891</PhoneNumber>
	<EmailAddress>sample_name@example.com</EmailAddress>
	<Address>
		<StreetLine1>Street Line1</StreetLine1>
		<City>City Name</City>
		<StateCode>AE</StateCode>
		<PostalCode>12345</PostalCode>
	</Address>
</Resident>
<Resident>
	<Name>Sample Name1</Name>
	<PhoneNumber>1234567891</PhoneNumber>
	<EmailAddress>sample_name1@example.com</EmailAddress>
	<Address>
		<StreetLine1>Current Address</StreetLine1>
		<City>Los Angeles</City>
		<StateCode>CA</StateCode>
		<PostalCode>56666</PostalCode>
	</Address>
</Resident>
</State>

python3 main.py -i resident.xml -o resident_output.csv -e Resident

output CSV file : resident_output.csv

Resident##Address##City	Resident##Address##PostalCode	Resident##Address##StateCode	Resident##Address##StreetLine1	Resident##EmailAddress	Resident##Name	Resident##PhoneNumber
Los Angeles	56666	CA	Current Address	sample_name1@example.com	Sample Name1	1234567891
City Name	12345	AE	Street Line1	sample_name@example.com	Sample Name	1234567891

Example For JSON parsing

Input json file : sample_2.json with fruit as element to parse

{
    "fruit":[
        {
            "name":"Apple",
            "binomial name":"Malus domestica",
            "major_producers":[
                "China", 
                "United States", 
                "Turkey"
            ],
            "nutrition":{
                "carbohydrates":"13.81g",
                "fat":"0.17g",
                "protein":"0.26g"
            }
        },
        {
            "name":"Orange",
            "binomial name":"Citrus x sinensis",
            "major_producers":[
                "Brazil", 
                "United States", 
                "India"
            ],
            "nutrition":{
                "carbohydrates":"11.75g",
                "fat":"0.12g",
                "protein":"0.94g"
            }
        },
        {
            "name":"Mango",
            "binomial name":"Mangifera indica",
            "major_producers":[
                "India", 
                "China", 
                "Thailand"
            ],
            "nutrition":{
                "carbohydrates":"15g",
                "fat":"0.38g",
                "protein":"0.82g"
            }
        }
    ]
}

python3 main.py -i sample_2.json -o sample_2_output.csv -e fruit

output CSV file : sample_2_output.csv

fruit##binomial name	fruit##major_producers	fruit##name	fruit##nutrition##carbohydrates	fruit##nutrition##fat	fruit##nutrition##protein
Mangifera indica	India\|\|China\|\|Thailand	Mango	15g	0.38g	0.82g
Citrus x sinensis	Brazil\|\|United States\|\|India	Orange	11.75g	0.12g	0.94g
Malus domestica	China\|\|United States\|\|Turkey	Apple	13.81g	0.17g	0.26g

prabinc/parser