How To Use JSON Dumps In Python To Store Scraped Web Data

Navigating the complexities of web scraping involves more than just data extraction. It also calls for an efficient means of organizing and preserving the acquired data for future use. This guide aims to demystify how to use json dumps in Python to structure and store your scraped web data in a streamlined manner. Utilizing the versatility of JSON (JavaScript Object Notation), you’ll learn how to transform intricate datasets, such as arrays and objects, into a simplified, standardized string format. Learning how to use json dumps as efficiently and effectively as possible paves the way for easy data storage, seamless transmission, and practical downstream analysis, optimizing the overall efficacy of your web scraping projects.

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What Is JSON.dumps in Python?

learn json.dumps in python

In Python, the ‘json.dumps’ function is part of the standard library’s ‘json’ module. The acronym JSON stands for JavaScript Object Notation, which is a lightweight data-interchange text format that’s easy for humans to read and write and easy for machines to parse and generate. The human-friendly text format also helps make learning how to use json dumps in Python easier.

What does JSON.dumps do in Python?

The function takes a Python object and converts it to a string representation of a JSON-formatted object. This is particularly useful for data interchange between a Python application and a web service or any other application that communicates using JSON.


json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)


import json

# Python dictionary

person = {

“name”: “John”,

“age”: 30,

“city”: “New York”,

“children”: [“Alice”, “Bob”]


# Convert Python object to JSON string

person_str = json.dumps(person)



{“name”: “John”, “age”: 30, “city”: “New York”, “children”: [“Alice”, “Bob”]}

Best practices and considerations

  • Not all Python objects can be serialized to JSON: Basic data types like dictionaries, lists, strings, numbers, booleans, and None can be easily serialized, but more complex types like custom objects, file handles, or database connections cannot be directly converted to JSON using ‘json.dumps’. For such types, you’d typically convert them to a serializable representation first before using ‘json.dumps’.
  • Serializing custom Python classes: If you’re working with custom Python classes, these won’t serialize directly. You would need to implement methods like ‘__dict__()’ to return a dictionary representation of the class or manually handle the serialization using the ‘default’ parameter.


def serialize_complex(obj):

if isinstance(obj, complex):

return {“real”: obj.real, “imag”: obj.imag}

json_str = json.dumps(2 + 3j, default=serialize_complex)

  • Handling circular references: Python dictionaries can have circular references, which will cause ‘json.dump’ to enter an infinite loop. If you suspect your data may have such structures, pre-process them to remove or flag circular references.
  • Encoding enums and tuples: Note that ‘json.dump’ does not serialize complex Python types like enums or tuples by default. If you have these types in your data, you would need to create custom serialization logic using the ‘default’ parameter.
  • Dealing with ‘NaN’ and infinity: The ‘allow_nan’ parameter can control how ‘json.dump’ handles ‘NaN’, ‘Infinity’, and ‘-Infinity’. By default, it’s set to ‘True’, which means they will be encoded into the JSON output. Some JSON parsers might have issues with these values, so be cautious.
  • Optimizations for large datasets: For large datasets, both in terms of depth and breadth, consider using alternative libraries like ‘ujson’ or ‘simplejson’ that are designed for performance, albeit sometimes at the cost of strict compliance to the JSON specification.
  • Memory limits: For exceptionally large dictionaries or lists, you may run into memory limitations. In such cases, consider using generators or other strategies to handle data chunking efficiently while learning how to use json dumps in Python.
  • Data integrity: While ‘json.dump’ and ‘json.loads’ are reliable, remember that floating-point numbers might not maintain their exact value when round-tripped due to the way floating-point arithmetic works.
  • Multiple object serialization: ‘json.dump’ is designed to serialize a single Python object at a time. If you need to serialize multiple Python objects into one JSON stream, you’ll have to manage that manually or consider using JSON arrays.
  • Type mismatches: If you’re serializing Python data types that do not have a direct JSON equivalent (e.g., sets or byte arrays), be cautious. These types will need to be transformed into a compatible format manually.

Whether you’re just learning how to use json dumps in Python or you’re an expert, it’s always a good idea to check out Python’s ‘json’ documentation if you run into issues or have questions.

JSON.Dump vs. JSON.Dumps

to know json.dump vs json.dumps

Both ‘json.dump’ and json.dumps are used to serialize Python objects into JSON format. However, the key difference is where they output the serialized data:

  • ‘json.dump’: Writes a Python object to a file-like object in JSON format. Use ‘json.dump’ when you want to write JSON data directly to a file.
  • ‘json.dumps’: Serializes a Python object to a JSON formatted string. Use ‘json.dumps’ when you need the JSON data as a string, typically for sending it over the network.

Syntax Comparison

– ‘json.dump’: ‘json.dump(obj, fp, *, skipkeys=False, ensure_ascii=True, …)’

– ‘json.dumps’: ‘json.dumps(obj, *, skipkeys=False, ensure_ascii=True, …)’

Why serialize Python objects to a JSON string for Web scraping?

JSON is a widely accepted standard for data transmission, making it ideal for web scraping tasks. Serializing Python objects to JSON strings allows for easier data interchange between the Python web scraping code and web services or APIs.

Why convert Python objects into JSON strings for Web scraping?

  • Interoperability: By knowing how to use json dumps in Python to convert objects into JSON strings, you can save or send the scraped data in a format that can be easily understood and processed by various systems, languages, and libraries.
  • Versatility: The ‘json.dumps’ function can be used with other Python standard library modules, like ‘collections’, for more advanced data serialization tasks as you learn more about how to use json dumps in Python.
  • Human-readable: JSON is a text-based, human-readable format.
  • Data persistence: JSON strings can be written to files, making them useful for data storage.
  • Network transmission: Transmitting data between a server and a client often involves sending JSON strings via HTTP.

Best practices and considerations

  • Boost performance efficiency: ‘json.dump’ can be more memory-efficient when dealing with large data structures as it writes directly to a file, whereas ‘json.dumps’ operates in memory, meaning the entire serialized object must fit into memory. Ensure that enough memory is available before using ‘json.dumps’.
  • Consider using separate threads or asynchronous paradigms: Both ‘json.dump’ and ‘json.dumps’ are synchronous operations that will block the thread. For I/O-bound or high-latency environments, you may need to run these operations in a separate thread or use asynchronous programming paradigms. If your application runs on asynchronous programming, you can use asynchronous file-writing methods with ‘json.dump’ to fit into an ‘async’ workflow while learning how to use json dumps in Python.
  • Watch out for bottlenecks: Both serialization and deserialization can cause performance bottlenecks, especially for large objects. Performance profiling tools can help identify slow parts of your JSON handling code while learning how to use json dumps in Python.
  • Tweak data size and structure to reduce execution times:  If you’re operating in performance-critical environments, consider measuring the execution time of ‘json.dump’ and ‘json.dumps’ with various data sizes and structures. This will help you identify performance implications while learning how to use json dumps in Python.
  • Security consideration: While learning how to use json dumps in Python, it’s crucial to validate the incoming data to ensure it does not contain malicious payloads. Never execute JSON data directly or use it to populate data structures without proper validation and sanitation.
  • Atomic file writes: When writing to a file using ‘json.dump’, if an error occurs during the operation, you may end up with a partially written file. Therefore, it’s often good to first serialize to a string with ‘json.dump’ and then write to the file, ensuring atomicity.
  • File locking: When using ‘json.dump’ to write directly into a file, consider using file-locking mechanisms to avoid data corruption when the file is accessed by multiple processes or threads.
  • File encoding: While learning how to use json dumps in Python, be sure to specify the file’s encoding (usually ”utf-8”) to ensure it’s consistent with the data being written.
  • Partial writes: As ‘json.dump’ writes directly to a file, there is a chance of partial writes in case of an error. Implementing robust error handling is crucial to learning how to use json dumps in Python effectively.
  • Thread safety: Both ‘json.dump’ and ‘json.dump’ are thread-safe by default, but if you are extending their functionality or using them in highly concurrent systems, you may need to ensure that the objects being serialized are also thread-safe.
  • Exception handling: Both ‘json.dump’ and ‘json.dump’ can throw exceptions like ‘TypeError’ for unserializable objects. Advanced exception handling logic can be added to gracefully manage such situations without crashing the application.

How To Dump a Dictionary to JSON Using Python

dump a dictionary to json

You can use ‘json.dumps’ to serialize a Python dictionary to a JSON string. To save a Python dictionary to a JSON file, use ‘json.dump’.

Example for serializing a Python dictionary to a JSON string

import json

my_dict = {“name”: “Alice”, “age”: 29}

json_string = json.dumps(my_dict)

Example for saving a Python dictionary to a JSON file

import json

with open(“data.json”, “w”) as f:

json.dump(my_dict, f)

Best practices and considerations

  • Skipping invalid keys: Setting ‘skipkeys=True’ will ignore keys that are not of a basic type (str, int, float, bool, None).
  • Character encodings: The ‘ensure_ascii’ parameter is by default set to ‘True’, which escapes all non-ASCII characters. If you want to preserve Unicode characters while learning how to use json dumps in Python, set this parameter to ‘False’.
  • Dictionary-like objects: In Python, other types like ‘collections.OrderedDict’ or custom objects implementing the ‘Mapping’ interface can also be passed to ‘json.dump’, as long as they conform to the dictionary-like contract (i.e., they can be passed to ‘dict()’ to create a dictionary).
  • Keys must be strings: Remember that in a JSON object, the keys must be strings. If your dictionary uses non-string keys, you’ll need to convert them first or specify the ‘skipkeys=True’ option to ignore them when you want to dump dict to JSON in Python.
  • Recursion limit: Deeply nested dictionaries might hit Python’s recursion limit, leading to a ‘RecursionError’. You might need to increase the recursion limit using ‘sys.setrecursionlimit()’ in extreme cases, although this could come with its own set of problems, so keep the recursion limit in mind as you learn how to use json dumps in Python.
  • Immutable keys: In Python, dictionary keys can be of any hashable type. However, in JSON, keys must be strings. This often necessitates conversion or another form of data transformation before using ‘json.dump’.
  • Metadata inclusion: Sometimes, when you’re using json dumps in Python, you may need to include metadata in your JSON object that isn’t part of the Python dictionary. In those cases, you’ll have to manually insert this information into the dictionary before calling ‘json.dump’.
  • Custom serialization: In some advanced use cases, you might need to implement custom serialization logic by subclassing ‘json.JSONEncoder’. This allows for fine-grained control over how objects are serialized to JSON.

JSON.Dumps Pretty Printing in Python

pretty-print the JSON string

The ‘indent’ parameter can be used to pretty-print the JSON string, making it easier to read.


json.dumps(my_dict, indent=4)

Best practices and considerations

  • Custom separators: While learning how to use json dumps in Python, you can specify custom separators for the serialized JSON using the ‘separators’ parameter. The default is ‘(‘,’, ‘: ‘)’.


json_str = json.dumps(dictionary, indent=4, separators=(“. “, ” = “))

  • Multi-line strings: If your Python object includes string data that itself includes line breaks, the ‘json.dump’ method will escape these as ‘\n’ within the output string, preserving the multi-line nature of the string.
  • Compression: Pretty-printing outputs can significantly increase the size of the resulting JSON string due to additional white spaces and indentation, leading to higher storage and transmission costs. Use it wisely while learning how to use json dumps in Python, especially in resource-constrained or high-latency environments.
  • Resource trade-off: Pretty printing consumes additional system resources. If you’re dealing with a huge dataset while learning how to use json dumps in Python, make sure you have enough system memory to prevent bottlenecks or crashes.
  • Streamlining output: Pretty printing can be useful for human readability but can be a hindrance when the output is meant to be parsed by machines. While learning how to use json dumps in Python, evaluate the needs of both the producers and consumers of the JSON data before pretty printing.
  • Version-specific features: Different versions of Python’s ‘json’ module may offer different pretty-printing features. Always refer to the version-specific documentation to understand the full range of capabilities while learning how to use json dumps in Python.
  • Integration with logging systems: Pretty-printed JSON is often more difficult to parse with automated log management solutions. If your JSON data is destined for a logging system while learning how to use json dumps in Python, pretty printing may not be advisable.
  • Line breaks and indentation: Different platforms and text editors may interpret line breaks and indentation differently. Be cautious when pretty-printing JSON if the output will be consumed on different platforms.

JSON.Dumps for Formatted Outputs

how to separate elements in the JSON string

For more control over formatting while learning how to use json dumps in Python, you can use the ‘separators’ parameter to specify how to separate elements in the JSON string.


json.dumps(my_dict, indent=4, separators=(“. “, ” = “))


The ‘json.loads’ function takes a JSON-formatted string and deserializes it into a Python object. In other words, it reverses what ‘json.dump’ does.


json.loads(s, *, cls=None, object_hook=None, parse_float=None, …)

Best Practices and Considerations

  • Custom decoding with ‘object_hook’: The ‘object_hook’ parameter lets you specify a function to further process the decoded object.


def deserialize_complex(dct):

if “real” in dct and “imag” in dct:

return complex(dct[“real”], dct[“imag”])

return dct

obj = json.loads(json_str, object_hook=deserialize_complex)

  • Handling of duplicate keys in JSON Objects: While the JSON standard does not specify how duplicate keys in objects should be handled, ‘json.loads’ uses the last occurrence of a key to populate the corresponding Python dict object.
  • The ‘parse_float’ and ‘parse_int’ parameters: The ‘parse_float’ and ‘parse_int’ parameters can be used to specify custom functions to process floating-point numbers and integers, respectively. This allows you to have fine-grained control over the parsing process as you’re just learning how to use json dumps.
  • Unexpected data types: Be cautious of the data types when deserializing; ‘json.loads’ will convert all integer and floating-point numbers to Python’s ‘int’ and ‘float’ types. This might lead to type inconsistencies if you’re expecting a different numeric type, so keep that in mind as you’re learning how to use json dumps in Python.
  • Backwards compatibility: While learning how to use json dumps in Python, always check the Python documentation corresponding to your version becuase backward-incompatible changes may sometimes be introduced in newer versions.
  • Data validation: Beyond just loading JSON data, there are specialized libraries like JSONSchema that can validate JSON data against predefined schemas, offering another layer of data integrity and security.
  • Security risks: Malformed or malicious JSON strings can be a security risk. While learning how to use json dumps in Python, always validate the source of any JSON data you are loading using ‘json.loads’ .
  • Custom deserialization: Just as you can customize serialization, ‘json.JSONDecoder’ can be subclassed to customize how JSON data is deserialized into Python objects.

JSON.Load vs. JSON.Loads

Both 'json.load' and 'json.loads'

Both ‘json.load’ and ‘json.loads’ read JSON objects and return a Python object. However, what they read is different:

  • ‘json.load’: Reads a JSON object from a file-like object and returns a Python object.
  • ‘json.loads’: Reads a JSON object from a string and returns a Python object.

Syntax Comparison

– ‘json.load’: ‘json.load(fp, *, cls=None, object_hook=None, …)’

– ‘json.loads’: ‘json.loads(s, *, cls=None, object_hook=None, …)’

Best practices and considerations

  • Streaming large JSON data: If you’re working with streaming data, ‘json.load’ might not be suitable as it expects to read from a file-like object with an end. Specialized libraries or custom logic may be needed for stream handling.
  • Reading from network streams: For reading JSON data from network streams, file-like objects that support ‘.read()’ can be passed to ‘json.load’ for more efficient memory usage, especially for large JSON payloads.
  • Error handling and recovery: Both ‘json.load’ and ‘json.loads’ do not provide native support for recovering from errors in the middle of a data stream and will raise a ‘JSONDecodeError’ if the input is not valid JSON. Proper error handling should be implemented when reading from files or strings to ensure robustness. If resilience to partial data corruption is a requirement, additional error-recovery logic may also be necessary.
  • Buffering: ‘json.load’ can sometimes be less memory-efficient if the underlying file-like object has a large buffer. If you want to learn how to use json dumps in Python in the most efficient way, be sure to set an appropriate buffer size if memory usage is a concern.
  • Large files: When dealing with large JSON files, it might be more efficient to read the file in chunks or to stream it when using ‘json.load’. This will help avoid running into memory issues.
  • Garbage collection: If you’re reading a very large JSON file using ‘json.load’, keep an eye on Python’s garbage collector. In some cases, you may benefit from manually controlling garbage collection to optimize memory use.
  • I/O blocking: When using ‘json.load’, be aware that the function can block the I/O, affecting the performance of concurrent systems. Asynchronous I/O or multi-threading may be necessary in such cases.
  • Progressive parsing: For real-time or streaming applications, neither ‘json.load’ nor ‘json.loads’ offer out-of-the-box support for progressive parsing. Specialized libraries or custom logic may be necessary for such scenarios. 

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Wrapping Up

conclusion on json dump in python

Learning how to use json dumps in Python will give you invaluable tools for your next web scraping project. It offers a straightforward way to serialize Python objects into JSON strings, which are universally accepted for data interchange over the web. Whether you are dumping dictionaries or pretty-printing JSON outputs, Python’s ‘json’ module has got you covered.

Rayobyte’s JSON-compatible web scrapers extend this capability by making it easier to scrape, extract, and process web data. They also mesh well with Rayobyte’s proxies and Python’s JSON functionalities, providing a holistic solution for data collection and analysis needs. Harness the full potential of Python’s JSON handling by getting started with Rayobyte’s advanced web scraping solutions today.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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