How to Parse JSON with ChatGPT in Web Scraping?

JSON (JavaScript Object Notation) has become one of the most popular data formats on the web. Many websites serve data in JSON format, which provides a simple and lightweight way to exchange information between web servers and clients.

However, raw JSON data retrieved through web scraping can be messy and contain a lot of unnecessary metadata. Before analyzing scraped JSON datasets, we need to parse, filter, and transform the data into a clean structure. This process is known as JSON parsing.

In this post, we'll explore how ChatGPT can make JSON parsing incredibly easy by automatically generating parsing code for us.

Why JSON Parsing is Important in Web Scraping

When scraping data in JSON format, the raw extracted JSON objects contain the entire response from the server. This includes a lot of extra noise that needs to be cleaned up before analysis. Here are some common challenges when working with raw scraped JSON data:

  • The data is nested inside complex objects instead of a flat structure. This makes accessing specific fields difficult.
  • There are redundant metadata fields describing the structure instead of just the data.
  • Important data fields are buried deep inside nested objects.
  • Array elements contain inconsistent structures.
  • Unnecessary data types like nulls and booleans clutter the dataset.

Cleaning up scraped JSON into a tidy dataframe requires filtering, flattening, reformatting, and transforming the noisy data fields. Doing this manually with traditional JSON parsing libraries like Python's json module is time-consuming and complex for large datasets. This is where AI assistance can make an enormous difference.

Generating Parsing Code with ChatGPT

ChatGPT is a conversational AI assistant created by OpenAI. It is trained on massive datasets to generate human-like conversations. We can take advantage of ChatGPT's natural language capabilities to automatically generate JSON parsing code for us. The steps are simple:

  1. Get a sample of the raw scraped JSON data
  2. Paste the sample data into ChatGPT
  3. Ask it to generate clean parsing code in Python
  4. ChatGPT will analyze the structure and return cleaned up Python code for parsing
  5. Take the code and integrate it into your scraper

Let's walk through an example to see it in action.

ChatGPT JSON Parsing Example

For this demo, we'll be scraping a dataset of posts from the social news site Hacker News. I've already built a basic scraper using the Python Requests library to extract raw JSON data from the Hacker News API.

Here are the steps we'll follow:

1. Import Libraries

import json
import requests

2. Send Request to API

This retrieves a JSON array of posts in raw format.

url = ''
response = requests.get(url)
data = response.json()

3. Copy Raw JSON Sample

We'll take a slice of the JSON to copy a sample into ChatGPT:

data_sample = data[:2]
print(json.dumps(data_sample, indent=2))

Which prints:

    "by" : "dhouston",
    "descendants" : 71,
    "id" : 8863,
    "kids" : [ 8952, 9224, 8917, 8884, 8887, 8943, 8869, 8958, 9005, 9671, 8940, 9067, 8908, 9055, 8865, 8881, 8872, 8873, 8955, 10403, 8903, 8928, 9125, 8998, 8901, 8902, 8907, 8894, 8878, 8870, 8980, 8934, 8876 ],
    "score" : 111,
    "time" : 1175714200,
    "title" : "My YC app: Dropbox - Throw away your USB drive",
    "type" : "story",
    "url" : ""
    "by" : "thorax",
    "descendants" : 38,
    "id" : 8927,
    "kids" : [ 8972, 8942, 8967, 8962, 8940, 8934, 8959, 9001, 8975, 8870, 8976, 10403, 8928, 8943, 8964, 8998, 8950, 8997, 8951, 10454, 9005, 8965, 8963, 8960 ],
    "score" : 26,
    "time" : 1175707620,
    "title" : "Git ready: 5 days until Github launch",
    "type" : "story",
    "url" : ""

4. Pass Sample to ChatGPT

Now we'll send this sample to ChatGPT along with instructions to generate parsing code:

ChatGPT, here is a sample of messy JSON data I extracted from a web API. Can you please generate Python code to parse this into a clean pandas dataframe with columns for every field? The parsed data should be flattened to remove nesting. Please focus on extracting the key fields like title, score, url, etc. into a tidy structure and filter out unnecessary metadata.

5. ChatGPT Returns Clean Parsing Code

And here is what ChatGPT generated:

import json
import pandas as pd

# Sample JSON data
data_sample = [PASTE JSON SAMPLE HERE] 

# Parse JSON into DataFrame
records = []
for item in data_sample:
    record = {
        'title': item['title'],
        'score': item['score'],
        'url': item.get('url',''),
        'by': item['by'],
        'time': item['time']       
df = pd.DataFrame(records)


This code flattens the nested JSON into a clean Pandas dataframe with only the fields we need, ignoring all the unnecessary metadata.

6. Integrate Parsing Code into Scraper

Finally, we can take this autogenerated code and integrate it into our scraper:

import json 
import requests
import pandas as pd

url = ''

response = requests.get(url)
data = response.json() 

# Pass full JSON response to parsing code
records = []

for item in data:
   record = {
       'title': item['title'],
       'score': item['score'],
       'url': item.get('url',''),
       'by': item['by'],
       'time': item['time']       
df = pd.DataFrame(records)

And that's it! With just a few lines of autogenerated code from ChatGPT, we were able to parse a complex JSON dataset extracted through web scraping cleanly. The benefits are:

  • No need to manually analyze the JSON structure
  • Flattened data without nesting
  • Focus on the valuable fields and filter out metadata
  • Clean pandas dataframe ready for analysis

As you can see, letting ChatGPT handle the messy work of parsing scraped JSON can save an enormous amount of time and effort.

Advanced JSON Parsing with ChatGPT

In addition to flattening JSON data, we can use ChatGPT for more advanced transformations:

  • Parsing timestamps into datetime formats
  • Extracting elements from nested arrays
  • Parsing strings into structured data
  • Concatenating/joining related fields
  • Filtering records by conditionals
  • Applying custom cleaning functions

ChatGPT is remarkably good at understanding specifics about the structure of JSON data. It can analyze nested objects and irregular formats to generate robust parsing code. Some examples of advanced instructions:

  • Can you parse the nested ‘kids' array into a pipe-delimited string column called ‘kids_ids'?
  • Please parse the ‘time' epoch integer into a Pandas datetime column called ‘created_at'
  • Keep only records where ‘url' is not empty
  • Convert the ‘score' to a numeric float type
  • Concatenate ‘title' and ‘url' into a column called ‘post'

ChatGPT will return Python code implementing these transformations seamlessly.

The key is providing a representative sample of your real scraped data and descriptive instructions. ChatGPT will analyze the structure and generate clean code customized for your data.

ChatGPT JSON Parsing Limitations

ChatGPT is not perfect, and there are some limitations to be aware of:

  • Sample size – ChatGPT has a limited context size, so you can only pass a small snippet of the JSON. For larger datasets, it may fail to understand the full structure.
  • No custom logic – While excellent at parsing, ChatGPT cannot add complex custom Python processing logic tailored to your use case. Some manual coding is still required.
  • Brittle code – The autogenerated parsing code can break easily if the JSON structure changes in the future. Expect to refine the code over time as the API changes.
  • No validation – ChatGPT won't validate that the parsing code handles all edge cases properly. You should still review the parsed DataFrames.

So ChatGPT is not a complete solution but rather an aid to accelerate development. Human oversight for correctness and maintenance is still essential.

ChatGPT JSON Parsing Best Practices

To get the most out of ChatGPT for JSON parsing, here are some tips:

  • Use a representative sample – Include varied examples covering different types of records and nested fields.
  • Use descriptive instructions – Explain the desired structure and important fields to extract.
  • Focus on flattening data – Nested JSON is difficult to work with, so parse to a flat tidy structure.
  • Review the code – Don't blindly execute ChatGPT's code without verifying it first.
  • Expect iterations – Refine your instructions and samples over multiple tries to improve results.
  • Maintain the code – Periodically regenerate the parsing code as the API JSON changes.
  • Handle errors – Wrap parsing code in try/except blocks to catch issues gracefully.
  • Structure code well – Break parsing steps into logical functions for easier understanding.
  • Comment thoroughly – Add comments explaining the parsing logic for future reference.

JSON Parsing Options Beyond ChatGPT

While ChatGPT is incredibly useful, there are also many Python libraries for processing JSON data:

  • json – Python's built-in module for parsing JSON strings.
  • pandas – Powerful DataFrame-based JSON parsing using pandas.json_normalize().
  • jsonpath-ng – Query JSON documents with succinct jsonpath syntax.
  • jq – Feature-rich command-line JSON processor for filtering, map/reduce, etc.
  • Apache Drill – High performance distributed JSON querying via SQL.

For production-grade JSON processing at scale, these libraries provide optimization, validation, and more control compared to ChatGPT's basic parsing. However, ChatGPT can greatly accelerate early-stage parsing code when iterating on an API scraper. It saves the effort of manually analyzing JSON structures across samples.

So consider combining ChatGPT's automated parsing with robust JSON libraries like Pandas for a comprehensive solution.


Parsing scraped JSON data is a crucial yet painful step in the web scraping workflow. ChatGPT provides an incredible new tool to eliminate much of this drudgery.

By automatically generating parsing logic tailored to your scraped JSON, ChatGPT enables rapid iteration when developing scrapers. It frees us to focus on high-value analysis and visualization of parsed data rather than constantly wrestling with messy JSON.

Of course, ChatGPT should not be used blindly without human oversight. Production systems require rigorous testing and validation beyond ChatGPT's capabilities. But for parsing exploratory scraped data, nothing can beat ChatGPT's sheer speed at turning messy JSON into clean, usable datasets.

Give ChatGPT a try on your next web scraping project – you'll be amazed at what it can parse!

John Rooney

John Rooney

John Watson Rooney, a self-taught Python developer and content creator with a focus on web scraping, APIs, and automation. I love sharing my knowledge and expertise through my YouTube channel, My channel caters to all levels of developers, from beginners looking to get started in web scraping to experienced programmers seeking to advance their skills with modern techniques. I have worked in the e-commerce sector for many years, gaining extensive real-world experience in data handling, API integrations, and project management. I am passionate about teaching others and simplifying complex concepts to make them more accessible to a wider audience. In addition to my YouTube channel, I also maintain a personal website where I share my coding projects and other related content.

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