nirojshrestha019/XML_JSON_Parse

Dynamic XML_JSON parser to transform XML and JSON files to CSV in a instant by the use of multi threading.

XML_JSON_Parse

Dynamic XML_JSON parser to transform XML and JSON files to CSV in a instant by the use of multi threading.

Prerequisities

The module was developed in python3 with usage of xml, json, pandas, threading, queue module.

Usage / Start Proces

For XML parsing

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

For JSON parsing

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

Command Line Arguments:

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

Convert XML or JSON file to csv

optional arguments:
• -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