Doing that manually is both error prone & tedious task. Json_response = expansions.flatten(json_response)ĭf = pd.Frequently, we get raw data in the form of excel or csv format and for writing test cases or for testing APIs, we need to convert that data to JSON format. Json_response = connect_to_endpoint(search_url, headers, query_params) Raise Exception(response.status_code, response.text) Response = requests.request("GET", url, headers=headers, params=params) I manage to do so with the following code: query_params = ĭef connect_to_endpoint(url, headers, params): I mainly need the content of the tweet, the name of the user and the location of the tweet. I am using the academic API to get tweets between september and november 2019 in Sweden. Hi all, I am new here and might be asking the same question as the one that has been answered 20 days ago, but if so I didn’t really get the answer…! It would be referenced_tweets and ntions.Īlso, I have passed on the feedback that data nested in this way can be difficult to work with to the product team, for us to consider as we make future improvements. You may also want to drop any remaining columns with the data we parsed out. Now you can view your main data frame, and you should see the new columns you created. ent_df = df.apply(pd.Series)ĭf = newĭf = new_b Your file may have more mentions per Tweet. new_df = df.apply(pd.Series)ĭf = normįor the ntions object, this was a bit more complex because the file I had up to 2 mentions per Tweet. I also renamed the new df columns to it add easier to add the columns to the primary data frame. As indicated in the medium article, you can do the merge right away, but I personally prefer to do it step by step to have more control. This is based on the “not so good solution” from this article. I started with reference_tweets and separated this further. I used pd.json_normalize to create the data frame.
I also parsed out the data object in the payload to make it easier for this example. data = json.load(open('json_example.json')) I did this slightly differently from you, but I think the outcome is the same. I downloaded the data from postman as you did and saved it to a JSON file. I first imported JSON and pandas in my notebook. Hi wanted to follow up here since I figured out how to get this parse out and get this to work.
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