In many cases the data will be a small static dataset that is unlikely to change regularly. TSV) will be required to generate some output. Oftentimes in programming, data in a tabular source such as an Excel spreadsheet, Google Sheet, CSV file, or some other form of delimited file (e.g.
The strength of JSON is its flexibility, allowing for a given dataset to be stored in a variety of different ways, ensuring that the minimum reworking of the data is required given an expected use case for that data. This format of data representation is used primarily in programming, and is a default datatype in most programming languages (although they can be called different things - i.e. The below example code demonstrates how to convert a CSV file to a JSON file in Python using the Dataframe.to_json() method.JSON, or JavaScript Object Notation, is a format of data storage that utilizes key-value (or attribute-value) pairs to represent data. Since the Dataframe.to_json() method takes a DataFrame as input, we will use the pandas.readcsv() method to first read the CSV file as DataFrame.
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
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