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mxjvsjmespath

MIT 2 2 598
58.1 thousand (month) Feb 09 2017 per(7 months ago)
2,032 2 55 MIT
1.0.1(1 year, 8 months ago) Feb 09 2022 134.1 million (month)

mxj is a Go library for working with JSON and XML data. It allows you to convert between JSON and XML, merge JSON and XML documents, and extract values from JSON and XML using a simple and intuitive API.

One of the main features of mxj is its ability to work with JSON and XML data in a struct-like manner, allowing you to access values using dot notation.

JMESPath (pronounced “james path”) allows you to declaratively specify how to extract elements from a JSON document.

In web scraping, jmespath is a powerful tool for parsing and reshaping large JSON datasets. Jmespath is fast and easily extendible following it's own powerful query language.

For more see the Json parsing introduction section.

Example Use


package main

import (
  "fmt"

  "github.com/clbanning/mxj"
)

func main() {
  // Parse the JSON string
  jsonData := []byte(`
    {
      "name": "John Doe",
      "age": 30,
      "address": {
        "street": "Main St",
        "city": "Anytown",
        "state": "CA",
        "zip": "12345"
      },
      "phones": [
        "555-555-5555",
        "555-555-5556"
      ]
    }
  `)
  mv, err := mxj.NewMapJson(jsonData)
  if err != nil {
    fmt.Println("Error:", err)
    return
  }

  // Extract the name
  name, _ := mv.ValueForPath("name")
  fmt.Println("name:", name)  // "John Doe"

  // Extract the city
  city, _ := mv.ValueForPath("address.city")
  fmt.Println("city:", city)  // "Anytown"

  // Extract all phone numbers
  phones, _ := mv.ValuesForPath("phones")
  for _, phone := range phones {
    fmt.Println("phone:", phone)
  }
  // "555-555-5555"
  // "555-555-5556"
}
import jmespath

data = {
    "data": {
        "info": {
            "products": [
                {"price": {"usd": 1}, "_type": "product", "id": "123"}, 
                {"price": {"usd": 2}, "_type": "product", "id": "345"}
            ]
        }
    }
}

# easily reshape nested dataset to flat structure:
jmespath.search("data.info.products[*].{id:id, price:price.usd}", data)
[{'id': '123', 'price': 1}, {'id': '345', 'price': 2}]

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