I have a large script that saves data to a postgres database. Tell me how can I get rid of [' and ']. I need the data in the storage to be saved without these characters total.to_csv("C:/Users/.../total_type19.csv", index = False,…
I have a big script, the result is that the data is stored in a dataframe and then in csv. Then csv is opened and written to PostgreSQL. But there is a problem that the data type of one column…
I have the following List/Dataframe: [['title1','title1_chapter1','title1_chapter1_section1','title1_chapter1_href1'], ['title1','title1_chapter1','title1_chapter1_section2','title1_chapter1_href2'], ['title1','title1_chapter2','title1_chapter2_section1','title1_chapter2_href1'], ['title1','title1_chapter2','title1_chapter2_section2','title1_chapter2_href2'], ['title2','title2_chapter1','title2_chapter1_section1','title2_chapter1_href1']] I want to transform it to the following nested JSON such that it can be used in bootstrap treeview. [ { "nodes": [ { "nodes": [ { "text": "title1_chapter1_section1", "href":…
I have a python pandas dataframe, which should be converted to a nested JSON (based on the entry found in the column 'laterality'). My dataframe looks like this: import pandas as pd df1 = pd.DataFrame({'id': [1, 1, 1, 2], 'laterality':…
I need some help on using json_normalizer to create multiple subcolumns from main columns ? The below code is able to generate an output file but its puts everything in one column however i need something seperate columns with heading…
UPDATE: Json Files: LINK I have a multitude of Json files that have the same individual structure: 0: Title Location Price Net area Gross area ... 1: Title Location ... 2: ... One Json file may run from index 0:…
import io import pandas as pd csv_data = '''App_name,pre-prod,prod,stage matching-image,nginx,nginx,nginx mismatching-image,nginx,nginx,nginx:1.23.3-alpine''' df = pd.read_csv(io.StringIO(csv_data), sep=",") html_table = df.tohtml() Is there a way to compare the values of columns in dataframe and use it in conditional formatting ? I want compare…
I want to parse and process my dataframe data. I tried using join, assign ... etc. I succeed parsing 'allowed' column with below code: allowed_expanded = df1.allowed.apply(lambda x:pd.Series(x)) allowed_expanded.columns = ['{}.{}'.format('allowed',i) for i in allowed_expanded] and the result: # allowed_expanded…
I have DataFrame like below: data type: COL1 - float COL2 - int COL3 - int COL4 - float COL5 - float COL6 - object COL7 - object Source code: a = pd.DataFrame() a["COL1"] = [0.0, 800.0] a["COL2"] = [2,…
I have a json output which looks like this. {'pagination': {'limit': 100, 'offset': 0, 'count': 38, 'total': 38}, 'data': [{'name': 'Ceco Environmental Corp', 'symbol': 'CECE', 'has_intraday': False, 'has_eod': True, 'country': None, 'stock_exchange': {'name': 'NASDAQ Stock Exchange', 'acronym': 'NASDAQ', 'mic': 'XNAS',…