nested-lookup is a convenient way to parse multi-depth JSON documents which are often encountered in web scraping.
Using nested-lookup we can easily extract deeply nested data-field just by providing key value.
The library provides a number of functions for searching and extracting data from nested dictionaries, including:
nested_lookup: search for a key within a nested dictionary and returns the associated value.nested_update: update a key-value pair within a nested dictionary.nested_has: check if a key exists within a nested dictionary.nested_values: returns all the values within a nested dictionary, including values within nested dictionaries.
The library is designed to be flexible and can work with dictionaries of any size and structure, making it a useful tool for working with complex and nested data structures.
jsonquery is a Go library that allows you to parse and extract data from JSON documents using JSONPath expressions.
JSONPath is similar to XPath, but it is designed specifically for working with JSON documents.
The jsonquery library allows you to traverse the JSON tree structure and
extract values using JSONPath expressions. It provides a simple and intuitive API
for querying the JSON data, and it is built on top of the popular jsoniter library.
```python
from nested_lookup import nested_lookup
my_document = {
"name" : "Rocko Ballestrini",
"email_address" : "test1@example.com",
"other" : {
"secondary_email" : "test2@example.com",
"EMAIL_RECOVERY" : "test3@example.com",
"email_address" : "test4@example.com",
},
}
# retrieving all keys can be useful in dataset overview
from nested_lookup import get_all_keys
get_all_keys(my_document)
['name', 'email_address', 'other', 'secondary_email', 'EMAIL_RECOVERY', 'email_address']
# key/value stats can also be useful for data overview:
from nested_lookup import get_occurrence_of_key, get_occurrence_of_value, get_occurrences_and_values
data = {"products": [{"category": "t-shirt"},{"category": "underwear"},{"category": "t-shirt"}]}
get_occurrence_of_key(data, key='category')
3
get_occurrence_of_value(data, value='t-shirt')
2
get_occurrences_and_values([data], "t-shirt") # count t-shirt products
{
't-shirt': {
'occurrences': 2,
'values': [{'category': 't-shirt'}, {'category': 't-shirt'}]
}
}
# it can also be used to delete/alter values:
from nested_lookup import nested_alter
data = {"products": [{"price": 10}, {"price": 14}]}
nested_alter(data, "price", lambda price: price * 1.4)
{'products': [{'price': 14.0}, {'price': 19.599999999999998}]}
nested_delete(data, "price")
{'products': [{}, {}]}
```
```go
package main
import (
"fmt"
"log"
"github.com/antchfx/jsonquery"
)
func main() {
// Parse the JSON string
doc, err := jsonquery.Parse([]byte(`
{
"name": "John Doe",
"age": 30,
"address": {
"street": "Main St",
"city": "Anytown",
"state": "CA",
"zip": "12345"
},
"phones": [
"555-555-5555",
"555-555-5556"
]
}
`))
if err != nil {
log.Fatal(err)
}
// Extract the name
name := jsonquery.FindOne(doc, "name")
fmt.Println(name.InnerText()) // "John Doe"
// Extract the city
city := jsonquery.FindOne(doc, "address.city")
fmt.Println(city.InnerText()) // "Anytown"
// Extract all phone numbers
phones := jsonquery.Find(doc, "phones[*]")
for _, phone := range phones {
fmt.Println(phone.InnerText())
}
// "555-555-5555"
// "555-555-5556"
}
```