[Blog](https://scrapfly.io/blog) / [data-parsing](https://scrapfly.io/blog/tag/data-parsing) / [Introduction to Parsing JSON with Python JSONPath](https://scrapfly.io/blog/posts/parse-json-jsonpath-python) # Introduction to Parsing JSON with Python JSONPath by [Bernardas Alisauskas](https://scrapfly.io/blog/author/bernardas) Apr 10, 2026 10 min read [\#data-parsing](https://scrapfly.io/blog/tag/data-parsing) [\#python](https://scrapfly.io/blog/tag/python) [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fscrapfly.io%2Fblog%2Fposts%2Fparse-json-jsonpath-python "Share on LinkedIn") JSONPath is a path expression language to parse JSON data. It's used to query data from JSON objects using a similar syntax to the XPath query language used to parse XML documents. As the name implies, JSONPath syntax is heavily inspired by XPath and offers similar atomic expressions and querying capabilities: - Array slicing - Filtering and wildcard matching - Function calls and custom function extensions - Recursive data lookup, such as finding specific data points across the entire dataset nodes In this tutorial, we'll explore how to use the JSONPath expressions for web scraping. For this, we'll be using the Python client, but the same concepts can be applied to other JSONPath implementations. Let's get started! ## Key Takeaways Master jsonpath python for extracting structured data from JSON APIs using advanced query expressions with array slicing, filtering, and recursive data lookup techniques. - Implement JSONPath expressions with array slicing and wildcard matching for complex JSON data extraction - Use recursive data lookup techniques to find specific data points across nested JSON structures - Configure custom function extensions and filtering expressions for advanced JSON querying - Parse JSON responses from web APIs using JSONPath for structured data extraction workflows - Debug JSONPath queries by testing expressions against sample JSON data structures - Handle complex JSON structures with nested arrays and objects using advanced JSONPath syntax **Get web scraping tips in your inbox**Trusted by 100K+ developers and 30K+ enterprises. Unsubscribe anytime. ## JSONPath Setup JSONPath's query specification lacks a centralized standard, resulting in slight differences for each language implementation across different projects. Below are common clients for different languages: | Language | Implementation | |---|---| | Python | - \[jsonpath-ng\](https://github.com/h2non/jsonpath-ng) - \[jsonpath2\](https://github.com/pacifica/python-jsonpath2/) - \[jsonpath-rw\](https://github.com/kennknowles/python-jsonpath-rw) | | JavaScript | - \[jsonpath-plus\](https://www.npmjs.com/package/jsonpath-plus) | | Ruby | - \[jsonpath\](https://github.com/joshbuddy/jsonpath) | | R | - \[rjsonpath\](https://github.com/blmoore/rjsonpath) | | Go | - \[ojg\](https://github.com/ohler55/ojg) - \[jsonpath\](https://github.com/kubernetes/client-go/tree/master/util/jsonpath) | For this guide, we'll be using the JSONPath Python implementation, specifically [jsonpath-rw](https://github.com/kennknowles/python-jsonpath-rw). Install it using the following `pip` command: shell```shell pip install jsonpath-rw ``` ## Introduction to Python JSONPath Let's start out with simple JSONPath Python parser. We'll parse the below JSON object using simple string operations: python```python import jsonpath_ng.ext as jp data = { "products": [ {"name": "Apple", "price": 12.88, "tags": ["fruit", "red"]}, {"name": "Peach", "price": 27.25, "tags": ["fruit", "yellow"]}, {"name": "Cake", "tags": ["pastry", "sweet"]}, ] } # find all product names: query = jp.parse("products[*].name") for match in query.find(data): print(match.value) # find all products with price > 20 query = jp.parse("products[?price>20].name") for match in query.find(data): print(match.value) ``` The above JSONPath finder uses the `products[*].name` expression to iterate over the `products` key as the root object where its elements are first-class objects using the `products[*].name` expressions. Then, we return the name property of each element. Since JSONPath supports filtering expressions, we use the `[?price>20]` parse method to return all elements with a price greater than 20. ### JSONPath Expressions The below table illustrates some of the common binary comparison operators used with a JSONPath finder: | operator | function | |---|---| | `$` | object root selector | | `@` or `this` | current object selector | | `..` | recursive descendant selector | | `*` | wildcard, selects any key of an object or index of an array | | `[]` | subscript operator | | `[start:end:step]` | array slice operator | | `[?]` or `(?)` | filter operator where predicate is some evaluation rule like `[?price>20]`, more examples: | | | `[?price > 20 & price < 10]` multiple | | | `[?address.city = "Boston"]` for exact matches | | | `[?description.text =~ "house"]` for containing values | The above expression examples open the door for powerful JSON parsing options. Let's explore using them in the context of web scraping! Scrapfly #### Extract structured data automatically? Scrapfly's Extraction API uses AI to turn any webpage into structured data — no selectors needed. [Try Free →](https://scrapfly.io/register)## Web Scraper Example Let's JSONPath in a real example scraper by taking a look at how it would be used in web scraping with Python. We'll be scraping real estate property data from [realtor.com](https://www.realtor.com/) which is a popular portal for renting and selling real estate properties. This website like many modern websites uses Javascript to render its pages which means we can't just scrape the HTML code. Instead, we'll find the JSON variable data that is used by the frontend to render the page. This is called [hidden web data](https://scrapfly.io/blog/posts/how-to-scrape-hidden-web-data#what-is-hidden-web-data). Hidden web data can often be found in the HTML code and extracted using HTML parser however this data is often filled with keywords, ids and other non-data fields that's why we'll be using JSONPath to extract only the useful data. Let's take a look at any random example property like [this one](https://www.realtor.com/realestateandhomes-detail/335-30th-Ave_San-Francisco_CA_94121_M17833-49194) If we take a look at the page source we can see the JSON data set hidden in a `