Parse JSON Python For Fast And High-Quality Web Scraping

JSON is a common syntax for structuring, storing, and exchanging data. Most people use it to exchange data between a server and a web application. However, you can also use it with Python for fast and high-quality web scraping.

Table of Contents

Read this guide to learn how to parse JSON Python and web scrape. We will also explain and demonstrate how parsing JSON data with Python works.

What Does JSON Stand For?

Before we dive into parse JSON Python, Python read JSON file, and Python load JSON from file, let’s define JSON.

JSON stands for JavaScript Objective Notation. A lightweight format for moving and storing data, JSON is syntactically identical to JavaScript object creation code. Due to this similarity, a JavaScript program can easily convert JSON data into native JavaScript equivalents. However, other languages — such as Python — can also read JSON, since JSON format is text only. That’s why Python read JSON file works.

JSON data can be one of the following data types:

• Number: Numbers or numeric data types can be exact or approximate. Exact numeric data types include integer and decimal data types, while approximate types include floating point data types.
• String: In computer programming, a string is a sequence of digits, letters, punctuation, and other valid characters.
• List: Also known as a sequence, a list is an abstract data type that can store ordered non-null elements. Each element is enclosed within square brackets and separated by a comma. An example would be list1=[3,4,5,6].
• Boolean: A boolean or bool is a data type with two possible data values: false or true.

Here’s what a JSON string looks like:

{

“name”: “Canada”,

“population”: 38530969,

“capital”: “Ottawa”,

“languages”: [

“English”,

“French”

]

}

As you can see, JSON data resembles Python dictionaries or associative arrays — implementations of data structures that feature a collection of key-value pairs, each of which maps the key to its associated value.

Before JSON and parse JSON Python became popular, most companies used XML to represent data objects as text. Here’s what the information above looks like in XML:

<?xml version=”1.0″ encoding=”UTF-8″?>

<country>

<name>Canada</name>

<population>38530969</population>

<capital>Ottawa</capital>

<languages>

<language>English</language>

<language>French</language>

</languages>

</country>

Clearly, JSON is much more lightweight than XML. This is why so many developers, scrapers, marketers, and small business owners prefer JSON over XML.

How Does Python JSON Work?

Now that you have the answer to “What does JSON stand for,” let’s look at how Python JSON works.

Python supports JSON natively, thanks to its JSON modules. You can use the JSON module to convert JSON data from JSON to Python object equivalents such as list and dictionary and vice versa.

Here’re the JSON equivalents of Python objects:

Python	JSON Equivalent
str	string
True	true
False	false
dict	object
None	null
dict	object

 

The Python JSON module also has four Python JSON parse and Python read JSON file key functions: load(), loads(), read(), and reads(). The s at the end of loads() and read(s) stand for string.

What Is Web Scraping and How Does It Work With JSON Loads?

Web scraping is the process of extracting data from a site and exporting it into a useful format, such as a spreadsheet. Many marketers, small business owners, and developers use it to:

• Monitor competitors’ products and services,
• Analyze industry and market trends,
• Research marketing campaigns and commercial offers, and
• Pick the best advertising channels.

Traditionally, web scraping was done manually, with scrapers copying and pasting the website information they wanted into spreadsheets. However, this can take a lot of time and energy, especially when you’re a large business with thousands of competitors to analyze. Manual scraping may also be impossible if you want to scrape real-time or big data.

Accordingly, many developers, marketers, and small business owners use programming languages like Python to scrape. Although scraping through programming languages takes time and effort, once you master it, you’ll be able to scrape multiple websites quickly and efficiently.

This is especially true if you use an application programming interface (API) web scraper. As data extraction tools designed for specific programs, databases, and websites, scraping APIs provide structured and valuable data to companies and eliminate the need for data scraping and individual research. They also provide the following advantages:

• Structured data: Regular web scraping results in a ton of unorganized data that must be thoroughly processed. A scraping API simplifies the process by filtering out unnecessary data and only providing relevant data in a legible and structured format.
• Time savings: Scraping APIs automate the scraping process and narrow thousands of requests into one.
• Target website(s) won’t be overwhelmed by traffic: Finally, a scraping API benefits the target website(s) and their owners by discouraging manual scraping. In other words, the website(s) don’t have to face website crashes since the users don’t have to send out a flurry of requests for scraping.

A prime example of a web scraping API is Scraping Robot’s API, which exposes a single API endpoint and works with JSON loads and other modules. To get the data you need, just send an HTTP request to https://api.scrapingrobot.com with your API key.

How Does Python JSON Parse Work?

Parse JSON Python requires programming knowledge. However, you can quickly master Python JSON parsing with practice. Just follow these steps to get started:

1. Parse a JSON string in Python

To work with files containing JSON objects or JSON strings, fetch parse JSON Python modules. Import the module by typing the following:

import json

You can now use the JSON module in Python to:

• Python load JSON from file and
• Parse JSON strings and files with JSON objects

For example, here’s how you would Python load JSON via json.loads().

import json

 

# assigns a JSON string to a variable called coder

coder = ‘{“name”: “Joe”, “coding languages”: [“C++”, “Ruby on Rails”]}’

 

# parses the data and assign it to a variable called coder_dict

coder_dict = json.loads(coder)

 

# The output will be {‘name’: ‘Joe’, ‘coding languages’: [‘C++’, ‘Ruby on Rails’]}

print(coder_dict)

 

# The output will be [‘C++’, ‘Ruby on Rails’]

print(coder_dict[‘coding languages’])

In this example, coder is a JSON string, while coder_dict is a dictionary.

Note, however, that json.load() takes either an input/output or string and converts it to a Ruby Hash or Array. If you want to just convert a JSON string into a Ruby Hash, you can use json.parse.

2. Get Python to read JSON files

You can use json.load() to read files with JSON objects.

To illustrate, let’s say you have a file called coder.json with a JSON object.

{“name”: “Joe”,

“coding languages”: [“C++”, “Ruby on Rails”]

}

You can parse the file by running a command to fetch json:

import json

 

with open(‘path_to_file/coder.json’, ‘r’) as f:

data = json.load(f)

 

# The output will be {‘name’: ‘Joe’, ‘coding languages’: [‘C++’, ‘Ruby on Rails’]}

print(data)

The open() function reads the JSON file, while json.load() parses the file to give us the dictionary called data.

3. Use Python create JSON object

The next step involves Python create JSON object. To convert a dictionary or Python object to JSON string, you can fetch JSON and use the json.dumps() method.

 

import json

 

person_dict = {‘name’: ‘John’,

‘age’: 39,

‘children’: 2

}

person_json = json.dumps(person_dict)

 

# The output will be {“name”: “John”, “age”: 39, “children”: 2}

print(person_json)

4. Use Python write JSON to file

Then, we involve Python write JSON to file by using the json.dump() method as follows:

import json

 

person_dict = {“name”: “Aubrey”,

“languages”: [“English”, “Japanese”],

“married”: False,

“age”: 28

}

 

with open(‘person.txt’, ‘w’) as json_file:

json.dump(person_dict, json_file)

5. Print JSON

The last parse Json Python step is printing JSON data in a more readable format to analyze and debug the information. You can do this by passing additional parameters sort_keys and indent to json.dump() and json.dumps().

import json

 

person_string = ‘{“name”: “Joseph”, “languages”: “German”, “numbers”: [3, 1.7, null]}’

 

# Fetches the dictionary

person_dict = json.loads(person_string)

 

# Prints this JSON data

print(json.dumps(person_dict, indent = 4, sort_keys=True))

You will get this once you run the program:

 

{

“languages”: “German”,

“name”: “Joseph”,

“numbers”: [

3,

1.7,

null

]

}

As you can see, there are four spaces for indentation. The program also sorted the keys in ascending order because we set sort_keys as “true.”

Accelerate Your Scraping Through Scraping Robot

Python JSON parsing and json.parse are great ways to scrape websites. However, it’s not ideal, especially for larger projects with more complex data types. While it’s faster and more reliable than manual scraping, the parse JSON Python process still requires a lot of manual effort compared to automated scraping tools like Scraping Robot.

Powerful and speedy, Scraping Robot can extract data from hundreds or thousands of websites in seconds or minutes, allowing companies to cut down on hiring costs and increase efficiency. Our tool also comes with the following cutting-edge features:

• The ability to avoid and bypass CAPTCHAs before scraping a website
• Browser scalability
• The ability to look for new anti-scraping updates from target websites
• JavaScript rendering
• Automatic metadata parsing
• Beautiful graphs of how many scrapes you’ve done in the last day, week, and month
• Records of your most recently used projects and modules, keeping previous results always accessible

What’s more, Scraping Robot is extremely affordable. After signing up for a free account, you will receive 5,000 scrapes per month and access to all of our features, including 24/7/365 customer support, new modules added monthly, frequent improvement updates, and seven-day storage. If you want more scrapes and features, you can join our paid tiers:

• Business tier: Scraping Robot’s Business tier offers a maximum of 500,000 scrapes per month at only $0.0018 per scrape.
• Enterprise tier: The Enterprise tier offers over 500,000 scrapes per month, with each scrape costing as low as $0.00045. You will also gain the ability to make custom API requests.

Interested in scraping quickly without having to parse JSON Python? Create a free Scraping Robot account today.

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