Line 26: Our dictionary is now finally converted and stored in JSONData! We can finally print it to see the result using print(JSONData).Ĭongratulations, our code is now ready to run! The following image depicts the output of the star dictionary we converted to JSON. Line 25: Lastly, we pass ensure_ascii = False to json.dumps to ensure that non-ASCII characters in the input data are preserved without escaping. Summary: By default, iTest tries to identify any json data when parsing responses, but there are some situations when the parser could identify some json. Line 24: We pass separators = (",", ": ") to json.dumps to specify that a comma and space should be used as the element separator while a colon and space should be used as the key-value separator. Line 23: We pass sort_keys = True to json.dumps to ensure that the keys in the resulting JSON string are sorted in alphabetical order. Line 22: We pass indent = 4 to json.dumps to specify that the resulting JSON string should be indented with 4 spaces for better readability. Line 21: We pass the star dictionary to this method. We call the json.dumps method to achieve this conversion. For example, we can specify to convert floating-point values to decimal.Decimal instances instead of using the native Python float: > import decimal > json.loads ('123.4', parsefloatdecimal.Decimal) Decimal ('123.4') or use floats for every value, even if they could be converted to integer. Line 20: A variable JSONData is created so that it can be assigned the JSON object after conversion. The 'parse' hooks are fairly self-explanatory. Lines 3–18: We define a dictionary named star with the keys name, spectral_type, temperature, distance_ly, aliases, and details. Additionally, we showcased a method that leverages json.loads() with an object hook to convert JSON objects to custom Python classes.Line 1: We import the json module in Python. We started by using the json module's loads() function and then discussed the alternative approach of using ast.literal_eval() for basic JSON parsing. In this blog, we explored several methods to convert a JSON-formatted string to a Python data structure. Print(result.name, result.age, result.city) Result = convert_to_json_method3(json_string) ![]() Let's test the function with a sample JSON string and examine the output: json_string = '' ![]() The try-except block helps us handle cases where the input string is not in valid JSON format. Inside the function, we use the loads() function to parse the JSON string and convert it into a Python dictionary. In this method, we import the json module and define a function called convert_to_json_method1. ![]() import jsonĭef convert_to_json_method1(json_string): The loads() function allows us to convert a JSON-formatted string to a Python dictionary. ![]() The json module is part of the Python standard library and provides handy functions to work with JSON data. Method 1: Using the json module's loads() function It consists of key-value pairs, and array data structures, and supports nested objects, making it an ideal choice for data representation in modern applications. JSON is a lightweight data-interchange format that is easy for both humans and machines to read and write. In this blog, we will delve into various methods of converting a string to JSON in Python.īefore we begin, let's clarify what JSON is. Python, being a versatile programming language, provides robust support for JSON parsing and serialization.
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