The orient argument is useful to specify how we want our JSON string to be formatted, and there are various options for both Series and DataFrame input. If no path is provided, the method returns the JSON string as output and returns nothing if the path is provided. The Dataframe.to_json(path, orient) method of the Pandas module, takes DataFrame and path as input and converts it into a JSON string, and saves it at the provided path. With open( 'myfile.json', 'w') as file_json:Ĭonvert CSV File to JSON File in Python Using the Dataframe.to_json() Method in Python If the JSON file has a different structure, you may need to adjust the code accordingly.Reader = csv. In the example I provided, the JSON file is an array of objects, with each object having the same keys. It is essential to mention that the structure of the JSON file should match the structure that is expected by the code. The file can be generated from different sources, such as an API or data scraping, or it could be a file that you have created on your own. It could be any file that has a valid JSON format, such as a file containing information about products, customers, or weather data. The data.json file in the previous example is a file that contains data in JSON format. If the JSON file has a different structure, you may need to adjust the code accordingly.
Please note that this code assumes that the JSON file is an array of objects, each having the same keys. The fieldnames for the writer are set to the keys of the first element of the data.
Then, it creates a new CSV file and uses the CSV module’s DictWriter() class to write the data to the file. In this example, the code first opens the JSON file using the open() function and the JSON module’s load() function, which reads the file’s contents and converts it to a Python object. # Create a new CSV file and write the data with open( 'data.csv', 'w', newline = '') as csv_file: Import csv import json # Open the JSON file with open( 'data.json') as json_file:
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