![]() ![]() It may be because you did something that went against our community guidelines. If you think we shouldn't have suspended your account, get in touch with us. If you're having trouble logging into Pinterest with Facebook, Google or Apple, you may need to adjust your settings in your Facebook app, Google account or Apple account. JSON (JavaScript Object Notation) is a popular data storing and transferring tool used in many server-side programming. Python supports JSON using its in-built package called json. We can convert a JSON structure into a comma-delimited textual structure. In this article, you will learn about the different methods to convert JSON to CSV structure. What are CSV files?ĬSV (Comma Separated Values) is a file format utilized for storing data in a tabular fashion. The structure of a CSV file can be interpreted in a spreadsheet or database format. It can store data in plain text ( usually, string & number data type). The record will contain one or more fields that are comma-separated values. JSON data usually contains data in key-value pairs. These keys will be the headers for the CSV file and the values as descriptive data that remain indented in json. It is a built-in Python module that implements classes for reading & writing tabular data in CSV structure. Using this, programmers can write this data in the format approved by Excel or read data from excel or CSV files. Programmers and developers can also represent the CSV formats recognized by other apps or define their special-purpose CSV formats. Since it is a built-in module, you do not need to install it separately. # Opening a CSV file for writing in write modeįirst, we will import the json and csv modules. Next, we will open the JSON file & load its data in the 'data' object. Next, we have to open a CSV file for writing in write mode. ![]() Then, use the for loop to fetch the data from the stud_data. Now, assign the cnt.keys() in the header. Provide the csv_writer.writerow() method and pass the header as the argument. Once you count the values, you should close the data_file.close(). It is a fast, flexible, powerful, & easy to implement open-source data analysis tool developed on top of Python language. It is a data manipulation & analysis library that worked well with conversations and various files like CSV, JSON, etc. ![]()
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