So, we use JSON to store and exchange data? Have no fear, fellow Pythoneers and Pythonistas. Luckily, this is a pretty common task, and-as with most common tasks-Python makes it almost disgustingly easy. One way or another, you’re up to your neck in JSON, and you’ve got to Python your way out. Perhaps you’re gathering information through an API or storing your data in a document database. Chances are you’re here because you need to transport some data from here to there. Since its inception, JSON has quickly become the de facto standard for information exchange. Watch it together with the written tutorial to deepen your understanding: Working With JSON Data in Python

# Function converts the json string to a dataframe and appends it directly to the CSV fileĭef add_json_string_to_csv(api_result_string):ĭf_with_new_data.to_csv('output.Watch Now This tutorial has a related video course created by the Real Python team. The advantage is that all data is already in the CSV if the program crashes or if you terminate it. Instead of building a huge dataframe over time, the code below appends the fetched data directly into the CSV file. The output of above code: item amount weight price bestbeforeendeateĮDIT: I had another look at the problem and thought I share another solution, which might be better for you. # The dataframe you want to store everythingĭf = add_new_entry_to_dataframe(df, newly_fetched_result) Input_parsed = json.loads(api_result_string)ĭf_with_new_data = pd.json_normalize(input_parsed)

# Function converts the api result to the dataframe and appends it to dfĭef add_new_entry_to_dataframe(df, api_result_string): In order to convert a JSON string into a dict you can simply use the json library. Pandas has a function called json_normalize, which can directly convert a dict into a dataframe.

Python json csv