It check for the key-value pairs in the dict object.

We normalize the dict object using the normalize_json() function.

It offers a lot of functionalities and operations that can be performed on the dataframe. It performs operations by converting the data into a pandas.DataFrame format.

Pandas is a free source python library used for data manipulation and analysis. The same can be achieved through the use of Pandas Python library. 'title': 'IT stocks to see a jump this month',

In the final step, we write the CSV data generated in the earlier step to a preferred location provided through the filepath parameter.

This function concatenates each record using a comma (,) and then all these individual records are appended with a new line (‘ ’ in python).

The desired CSV data is created using the generate_csv_data() function.

If the value is again a dict then it concatenates the key string with the key string of the nested dict.

It checks for the key-value pairs in the dict object. The read_json() function is used for the task, which taken the file path along with the extension as a parameter and returns the contents of the JSON file as a python dict object. This will help us to make use of python dict methods to perform some operations.

The first step is to read the JSON file as a python dict object.

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Convert csv file to json python