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It is working perfectly with complex responses. The two-steps comparison method is an effective way of ensuring that your APIs are working as expected.
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bug(str(()) + " Differences : " + str(ddiff)) Raise Exception("cannot continue data comparison with unequal lengths of responses")ĭdiff = DeepDiff(expected_response, response_content, exclude_paths=modification_path) Len(expected_response)) + " length of actual response is " + str(len(response_content))) bug(str(()) + " length of expected response is " + str( If len(expected_response) != len(response_content):
JSON COMPARE IN PYTHON CODE
# validate lengths upfront to avoid index exceptions in data comparison code bug(str(()) + " Allowed modifications : " + str(modification_path)) bug(str(()) + " Received response : " + str(response_content)) bug(str(()) + " Expected response : " + str(expected_response)) Here is an example of the function that allows us to compare separate values in JSON, where expected_responseis our expected response, response_contentis the body of the received response and modification_pathis excluded part from comparison: def full_comparison(expected_response, response_content, modification_path):
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This library has advanced functionality, please, find more information here: deepdiff It allows you to compare not only zero-level JSONs but also more complex JSON. The next step is dedicated to comparing separate values of the response using deepdiff library. Validate(instance=json_data, schema=schema)Įxcept as err: Here is my example of code to validate JSON Schema (where json_data - received response and schema - our constructed schema): def validateJsonSchema(json_data, schema): For more information: Schema Validation - jsonschema 3.2.0 documentation () Since I use Python Framework (unittest) for API testing, the simplest way to validate an instance under a given schema is to use the validate() function.
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There are many tools and libraries available to test API responses against a JSON Schema. Imagine that your API's endpoint POST /api/user/login returns the following response: , In other words, JSON Schema is a contract for your JSON document that defines the expected data types and format of each field in the response.
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Describes your existing data format(s).I would suggest the following sequencing:įirstly, let's bring a better understanding of what does JSON Schema mean ( JSON Schema | The home of JSON Schema (): JSON Schema is a vocabulary that allows you to annotate and validate JSON documents. All findings are adjusted to the initial framework idea. *Initially I use a Python framework based on unittest library. This problem I faced recently at work encouraged me to consider a two-steps comparison method. However, managing large and diverse responses might be quite challenging. The following example shows arrays under JSON with Python.Testing and validating JSON APIs is a crucial part of implementing high-quality web and mobile services. Python encode() function encodes the Python object into a JSON string representation. For this tutorial we have downloaded and installed Demjson as follows −Įncodes the Python object into a JSON string representation.ĭecodes a JSON-encoded string into a Python object.
JSON COMPARE IN PYTHON INSTALL
Environmentīefore you start with encoding and decoding JSON using Python, you need to install any of the JSON modules available. Let's start with preparing the environment to start our programming with Python for JSON.
JSON COMPARE IN PYTHON HOW TO
This chapter covers how to encode and decode JSON objects using Python programming language.
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