Step 2: Open the CSV file using a file handlerĪ file handler can easily be initiated, there are many ways to do this but we will stick to the safest one i.e. Input function can also be used to display some string while asking for input Input function by default takes input in the form of a string and this is exactly what we need. This can be achieved with the help of the input function. Example: Suppose the JSON file looks like this: We want to convert the above JSON to CSV file with key as headers. Step 1: Take input the CSV file and the JSON file paths For simple JSON data consisting of key and value pairs, keys will be headers for the CSV file and values the descriptive data. We will approach his problem in various small steps so that we understand the problem thoroughly and easily. To convert JSON to CSV in Python, use the pandas tocsv() function. It is commonly used for transmitting data in web applications, so whenever there’s a need to send some data from the server to the client, the data is first converted to JSON and then sent to the client so it can be displayed on a web page, or vice versa. As you can see, my solution is not memory efficient since I'm reading all the file into memory.
I won't describe the functionality in too much detail since I have reasonable docstrings for that. JSON is a standard text-based format for representing structured data based on JavaScript object syntax. Simply take a JSON file as input and convert the data in it into a CSV file.
Why would you want to convert CSV to JSON? We’ll learn how to use the JSON (JavaScript Object Notation) library of Python and will try to understand the logic behind this conversion. new_df = new_df.apply(lambda x: ', '.In this article, we will convert CSV to JSON using a simple Python script. This line will convert list value to the string. New_df.drop(columns='Details', inplace=True) # To drop the 'Details' json columnĪdditionally, here is the step to handle the list value of the json row. details_df = pd.DataFrame(original_df.tolist())įinally, we can concat the details_df to the original_df. Then, we'll create new details dataframe by converting the each row json to the list of json. You can covert the json data inside the rows to the dataframe and concat it to the original dataframe.įirst, create the original dataframe as you mention.
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