I have a bunch of data saved as json strings in Pandas DataFrame. I'd like to aggregate the DataFrame based on json data. Here's some sample data: data = { 'id': [1, 2, 3], 'name': ['geo1', 'geo2', 'geo3'], 'json_data': […
I have a Pandas DataFrame with JSON data. I'd like to extract the most recent year and the corresponding val and add them in as new column. Sample DataFrame: data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'],…
I have created a table using the flask db migrate method. Below is how my model is class MutualFundsDataTest(db.Model): id = db.Column(db.Integer, primary_key=True, autoincrement=True) Name = db.Column(db.Text, nullable=False) MF_Code = db.Column(db.Text, nullable=False) Scheme_Code_Direct = db.Column(db.Text, nullable=True) Scheme_Code_Regular = db.Column(db.Text, nullable=True)…
I have a data frame which has 2 columns Id and Loan_Info. Loan_Info column stores data into JSON format which I need to convert it into data frame where every key would be new column and corresponding key value would…
I have a below JSON object that is retrieved by a query r = {'metaData': [{'columnAliases': ['ltp', 'mcap', 'wsma30uptrend','15 day contraction'], 'availableLimit': 477, 'maxRows': 1, 'isTrend': True, 'limit': 2, 'groups': ['symbol'], 'tradeTimes': [1692210600000], 'lastUpdateTime': 1692266340000}], 'groups': ['BALRAMCHIN', 'GHCL'], 'time': 138,…
I am trying to extract regex patterns from links in a Pandas table generated from the page. The code generating the Pandas data frame is given below: import pandas as pd import re url = 'https://www.espncricinfo.com/records/year/team-match-results/2005-2005/twenty20-internationals-3' base_url = 'https://www.espncricinfo.com' table…
I'm triyng do bulk insert data into two tables with a many to many relationship, no issue if I insert on a single table but I'm unable to insert in both at once. CREATE TABLE IF NOT EXISTS `mydbv3`.`PRODOTTI` (…
I have the following JSON structure, { 'total_numbers':1, 'data':[ { 'col3':'2', 'col4':[ { 'col5':'P', 'col6':'H' } ], 'col7':'2023-06-19T09:29:28.786Z', 'col9':{ 'col10':'TEST', 'col11':'[email protected]', 'col12':'True', 'col13':'999', 'col14':'9999' }, 'col15':'2023-07-10T04:46:43.003Z', 'col16':False, 'col17':[ { 'col18':'S', 'col19':'H' } ], 'col20':True, 'col21':{ 'col22':'sss', 'col23':'0.0.0.0', 'col24':'lll' }, 'col25':0,…
I am using an API pull to extract data from the AESO API in python. My code is as follows: API_KEY = 'api_key_here' merit_order_url = 'https://api.aeso.ca/report/v1/meteredvolume/details?startDate=2022-01-01' url = merit_order_url headers = {'accept': 'application/json', 'X-API-Key': API_KEY} response = requests.get(url, headers=headers) The…
The data that is being acquired by this code, is not being formatted into proper csv format. import requests import csv def Download_data(): s = requests.Session() headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66…