How to Normalize JSON data into a pandas dataframe with json_normalize Question: I’m trying to transform this complex object into a pandas dataframe data={ "customers": [ { "a": ‘true’, "addresses": [{"city": "Park"}], "c": { "address1": "200"}, "d": "[email protected]", "e": {"f": "sub"}, "h": 100, } ] } I’ve tried several ways but none of them work. …
JSON Normalize with Value as Column Question: I have the following JSON; { "data": [ { "gid": "1203715497540179", "completed": false, "custom_fields": [ { "gid": "1203887422469746", "enabled": true, "name": "Inputs", "description": "", "display_value": null, "resource_subtype": "text", "resource_type": "custom_field", "text_value": null, "type": "text" }, { "gid": "1126427465960522", "enabled": false, "name": "T-Minus", "description": "", "display_value": "54", "resource_subtype": "text", …
Normalize a nested json file in Python Question: Suppose a list of objects: one, two, three,… Every object is composed of name, foo1, and foo2 fields. [{ ‘name’:’one’, ‘foo2’:{ ‘id’:’1.1′, ‘id’:’1.2′ }, ‘foo1’:[ { ‘foo2’:{ ‘id’:’1.1′, ‘name’:’one.one’ } }, { ‘foo2’:{ ‘id’:’1.2′, ‘name’:’one.two’ } } ] }] If the object is normalize using: obj_row_normalize = …
Python Converting Nested array of objects in JSON to flattened excel Question: Python Converting Nested array of objects in JSON to flattened excel Current Code def read_json(filename): jsonData = {} try: with open(filename, "r", encoding="utf-8") as f: jsonData = json.loads(f.read()) except: raise Exception(f"Reading {filename} file encountered an error") return jsonData json_data = read_json(filename=".json") df = …
How to use pd.json_normalize to retrieve the data I need Question: I have this JSON list in Python: [{‘id’: ‘TC2-FFA’, ‘shortCode’: ‘TC2-FFA’, ‘dataSet’: {‘datumPrecision’: 2, ‘id’: ‘TC2_37’, ‘shortCode’: ‘TC2_37’, ‘shortDescription’: ‘Clean Continent to US Atlantic coast’, ‘displayGroup’: ‘BCTI’, ‘datumUnit’: ‘Worldscale’, ‘data’: [{‘value’: 156.11, ‘date’: ‘2023-03-06’}], ‘apiIdentifier’: ‘RDSX9KRCHQI9TVGID5O7XQGBP1KKBZ0F’}, ‘datumUnit’: ‘WS’, ‘datumPrecision’: 3, ‘projectionStartOn’: ‘2005-01-04T00:00:00’, ‘projectionEndOn’: ‘2023-03-06T00:00:00’, …
Using json_normalize function to create a relational data model? Question: I have a nested Python dictionary that I want to convert into a relational model. I am struggling to parse the dictionary into two related tables: a "workspace" table and a "datasets" table – joined by the key workspace_id simplified_dict ={ "workspaces":[ { "workspace_id":"d507422c", "workspace_name":"Workspace …
(Pandas/Dataframe) pandas.json_normalize on nested JSON data without uniform record_path Question: I’m attempting to convert a large JSON file to a CSV, but the field that I need to be able to sort data on in the Spreadsheet is all in one cell whenever I convert it to CSV/Normalize the JSON. The main thing I need …
Converting nested JSON into a flattened DataFrame Question: I have a data load in nested json format and I want to get a nested dataframe { "page_count": 21, "page_number": 1, "page_size": 300, "total_records": 6128, "registrants": [ { "id": "23lnTNqyQ3qkthfghjgkk", "first_name": "HUGO", "last_name": "MACHA ILLEN", "email": "[email protected]", "address": "", "city": "", "country": "", "zip": "", "state": …
Flatten JSON with columns having values as a list in dataframe Question: I have the following data frame: | Category | idlist | | ——– | —————————————————————–| | new | {100: [‘1A’, ‘2B’], 200: [‘2A’, ‘3B’], 300: [‘3A’, ‘3B’, ‘4B’]} | | old | [’99A’, ’99B’] | I want it to be converted into this: …
Flattened and Convert list of Nested Dictionary to Pandas Dataframe Question: I have list of nested dictionary,with slightly different structure. I need to convert it into dataframe. Nested dictionary- dct = [{‘2022-03-31’: {‘A’: 12323, ‘B’: 123123},{‘2021-03-31’: {‘A’: 12, ‘B’: 123}}] I tried pd.json_normalize(dict) but ,it didn’t work properly because of date. df = pd.json_normalize(dct) df …
