If your file is too big or your data is a bit more complex, you can try our online JSON to CSV converter, which can handle huge files and more use cases. Here’s the final code to convert JSON to CSV: import jsonĭf.to_csv('output.csv', index=False, encoding='utf-8')
If you want to configure the output, check out the documentation page for the to_csv function here Buy ConvertCSV a Coffee at Step 1: Select your input Step 2: Choose output. Pandas has a convenient function for this: df.to_csv('input.csv', index=False, encoding='utf-8')Īnd here is the resulting CSV file: id,name,address.city Use this tool to convert JSON into CSV (Comma Separated Values) or Excel. Now that our data is normalized to a tabular format, let’s write our CSV file! In our case, if we print the resulting dataframe, it will look something like this: print(df) id name address.cityĪs you can see the address which was an object, was flattened out to a column. With open('input.json', encoding='utf-8') as file:īecause the JSON format can hold structured data like nested objects and arrays, we have to normalize this data so that it can be respresented in the CSV format.Ī quick way to do this is to use the pandas.normalize_json function, which will take JSON data and normalize it into a tabular format, you can read more about this function here. importing the required libraries import csv import json defining the function to convert CSV file to JSON file def convjson(csvFilename, jsonFilename). If you are importing a CSV file, you may specify fields to import and the types of those fields under. To sort, add the -sort-keys flag to the end. Let’s load the JSON file using the json Python module: import json Under Select Input File Type, select either JSON or CSV. Note that as pnd pointed out in the comments below, in Python 3.5+ the JSON object is no longer sorted by default. You can install pandas using pip running this command: $ pip install pandas
You could technically use the standard json and csv modules from Python to read the JSON file and write a CSV file, but depending on how your JSON file is structured, it can get complicated, and pandas has some great functions to handle these cases, without mentioning it’s really fast, so that’s what we will use. Let’s say we have a JSON file that looks like this: [ Convert JSON to CSV using this online tool Upload your JSON file by clicking the blue button (or paste your JSON text / URL into the textbox) (Press the cog. # Read the JSON file as python dictionaryĭata = read_json(filename=r"article.json")ĭataframe = pandas.In this tutorial we’ll be converting a JSON file to CSV using Python. Looking for a all column data in a tabular format file encountered an error") But looking for a generic function which would be able to convert any nested JSON file to CSV.īut json_normalize and flaten modules only provide a single row at the end with all the column data in it. Tried using json_normalize(), flatten module as well.
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