I am trying to change color of text in the rows of a table created using df.to_html() Condition is if text "Critical" exists in a column then whole row's text should be Red colored. Sample Table - Severity Name Description…
I am trying to fetch as rows the different values inside each href element from the following website: https://www.bmv.com.mx/es/mercados/capitales There should be 1 row that matches each field on the provided headers for each different href element on the html…
Hi I have json string contain 3 nested json arrays and i need to split those 3 arrays into json records in python I try below code to : the three nested array is : 'questionGroups' which contain another nested…
I have a data frame that contains a column called identifiers and i need to change this to a json list. list_identifiers=df['identifier'].tolist() listjson=json.dumps(list_identifiers) It looks like some Nan values are invalid and i get this error when i am running…
Given the following Pandas DataFrame (the original DataFrame has 200+ rows): import pandas as pd df = pd.DataFrame({ 'child': ['Europe', 'France', 'Paris','North America', 'US', 'Canada'], 'parent': ["", 'Europe', 'France',"", 'North America', 'North America'], 'value': [746.4, 67.75, 2.16, 579,331.9, 38.25] })…
I have this data in Excel File When read with this logic, df = pd.read_excel(xls_file_path) # Convert DataFrame to JSON json_data = df.to_json(orient='records') print(json_data) [{"Name":"Deepak Kalindi","Employee ID":101,"Joining Date":1704153600000,"Leaving Date":null,"Monthly Salary":12000,"Number of full days":27,"Number of half days":2.5,"Number of leaves":1.5,"OT (full Day)":3,"OT…
I used to be able to view data variables in Visual Code. Recently I keep getting this error "Python package 'pandas' version 0.20.0 (or above) is required for viewing data." despite the error message, the installed pandas version IS above…
I encountered the following problem scraping the https://nuforc.org/subndx/?id=all URL, which contains a long list of pages (1497) with a 100-line table for each page. I used the following Python code to scrap the web table: import pandas as pd insight_ufo…
I have json with nested structure and each json has different keys. json_field 1 {"1": {"id": 1, "value": "value1"}, "2": {"id": 2, "value": "value2"}} 2 {"1": {"id": 1, "value": "value1"}, "3": {"id": 3, "value": "value3"}} 3 {"3": {"id": 3, "value":…
I have a Json file with the following (file name is double.JSON) {"OUT":"074531@@@;1-1=29=0;1-2=193=0;1-3=199=0;1-4=403=0;1-5=334=0;1-6=295=0;1-7=999=0;1-8=57=0;1-9=77=0;1-10=166=0;1-11=116=0;1-12=999=0;2-1=9.3=1;2-2=89=0;2-3=203=0;2-4=346=0;2-5=150=0;2-6=54=0;2-7=466=0;2-8=12=0;2-9=34=0;2-10=115=0;2-11=88=0;2-12=629=0;3-1=87=0;3-2=510=0;3-3=845=0;3-4=873=0;3-5=773=0;3-6=563=0;3-7=999=0;3-8=197=0;3-9=260=0;3-10=601=0;3-11=933=0;3-12=999=0;4-1=60=0;4-2=458=0;4-3=644=0;4-4=540=0;4-5=243=0;4-6=351=0;4-7=999=0;4-8=117=0;4-9=201=0;4-10=256=0;4-11=693=0;4-12=999=0;5-1=16=0;5-2=138=0;5-3=174=0;5-4=674=0;5-5=337=0;5-6=152=0;5-7=731=0;5-8=43=0;5-9=53=0;5-10=181=0;5-11=141=0;5-12=999=0;6-1=80=0;6-2=466=0;6-3=552=0;6-4=999=0;6-5=436=0;6-6=416=0;6-7=999=0;6-8=116=0;6-9=257=0;6-10=676=0;6-11=356=0;6-12=873=0;7-1=35=0;7-2=219=0;7-3=257=0;7-4=483=0;7-5=287=0;7-6=220=0;7-7=474=0;7-8=74=0;7-9=77=0;7-10=186=0;7-11=121=0;7-12=999=0;8-1=52=0;8-2=360=0;8-3=410=0;8-4=820=0;8-5=474=0;8-6=410=0;8-7=999=0;8-8=94=0;8-9=145=0;8-10=326=0;8-11=169=0;8-12=999=0;9-1=34=0;9-2=231=0;9-3=325=0;9-4=492=0;9-5=218=0;9-6=318=0;9-7=999=0;9-8=51=0;9-9=59=0;9-10=187=0;9-11=141=0;9-12=999=0;10-1=59=0;10-2=304=0;10-3=575=0;10-4=902=0;10-5=365=0;10-6=492=0;10-7=999=0;10-8=122=0;10-9=208=0;10-10=246=0;10-11=307=0;10-12=999=0;11-1=78=0;11-2=520=0;11-3=902=0;11-4=999=0;11-5=873=0;11-6=712=0;11-7=999=0;11-8=154=0;11-9=273=0;11-10=458=0;11-11=239=0;11-12=999=0;12-1=55=0;12-2=307=0;12-3=314=0;12-4=751=0;12-5=458=0;12-6=436=0;12-7=999=0;12-8=101=0;12-9=157=0;12-10=300=0;12-11=221=0;12-12=483=0;13-1=11=0;13-2=138=0;13-3=181=0;13-4=410=0;13-5=178=0;13-6=133=0;13-7=721=0;13-8=25=0;13-9=28=0;13-10=94=0;13-11=73=0;13-12=999=0;14-1=15=0;14-2=161=0;14-3=208=0;14-4=257=0;14-5=199=0;14-6=165=0;14-7=552=0;14-8=27=0;14-9=42=0;14-10=131=0;14-11=51=0;14-12=999=0"} and want to pull the table values and turn it into the following table in python. I have tried using the following Pandas codes but it is…