Furthermore, we can convert almost any standard data type to any other data type using the Pandas library.I have this json file which contains a list of objects In cases like this, the Pandas library can make for an efficient way to explore and analyze the data. Working with large JSON datasets can deteriorate, mainly when they are too large to fit into memory. In the final step, we need to use the Pandas to_csv() function to convert the Pandas object to CSV data or export it into a file. We used the read_json() function in the second step to convert it into a Pandas object. In the first step, we prepared the JSON file. The exported file’s name is the streaming.csv file, in the same directory as the export.json file. So that is why the returning value here is None. In this case, we don’t need to return any data because we are exporting the file. PdObj.to_csv('streaming.csv', index=False) We are exporting in the same directory as the export.json file. We need to provide the export path to create a CSV file. We have disabled the index because we don’t need an index in CSV data. PdObj = pd.read_json('export.json', orient='index') Let’s convert Pandas object to CSV data and print it in the console. The JSON text is done through quoted-string, which contains the value in key-value mapping within įrom the output, we can say that we have transformed the json string into a Pandas object.
To use json in Python, we must import the json package in Python script. The first step to convert json to csv is to read json data using the Pandas read_json() function and then convert it to csv using the to_csv() function.
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