I need to flatten JSON file so that I can get output in table format.Ihavetried but not getting the output that I want This is my JSON file :- { "records": [ { "name": "A", "last_name": "B", "special_values": [ {…
I have 100 url and when I click it, it will show json file. But the json file is a little bit complicated, it looks like this: { "release": [ { "id":"1234", "version":"1.0", "releaseDate":"2023-07-31", "xxx": "ssss", "yyy": "uuuu" } {…
I have html file. I make list with dataframes with this comand: html_df = pd.read_html(folder + '/' + file) I delete values from few dfs in list and now I need save it as new html file with original structure.…
I have a SQL table and have made it a dataframe with Python. Then I calculated new values for a new column. As you know, it's easy to add new columns with values to a dataframe as long as the…
I have a list that contains nested json data and I would like to convert everything that is in the 'data:' tag into a data frame. The list contains multiple records inside the emptylist = [{'data': {'id': 7478290440, 'version': 0,…
I have 2 columns in the table, Column A and Column B User inputs: A, B, C User can give any of the above inputs. If user input is A then column B needs to be selected. If user input…
I have a Pandas DataFrame with this column: This is an extraction from a database in Mongo but I don't know how to handle a column containing both [] and {}: How can split this column into two columns? Desired…
enter image description hereI had this problem a year ago and once again need help with Pandas. Embarrassingly having the same problem as last year as an api has been updated. Original Question Here - I have a nested json…
I tried to convert HTML to Dataframe using pd.read_html but it automatically removes the multiple spaces in between the text. For Example: <table> <tr> <td>35</td> <td>Juan Cruz</td> </tr> <tr> <td>23</td> <td>Philip Philips</td> </tr> Expected Output: [ 0 1 35 Juan…
I have this nested json data: { "result": [ { "deviceid": 33, "devicename": "server101", "objectName": "CPU", "data": [ { "value":0.59, "rvalue":null }, { "value":90, "rvalue":null }, { "value": 85, "rvalue":null } ] }, { "deviceid": 30, "devicename": "server10", "objectName": "CPU",…