rajvermacas

import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._

object ScalaListEquivalents {
  def main(args: Array[String]): Unit = {
    // Initialize Spark session
    val spark = SparkSession.builder
      .appName("CSV Loader")
      .config("spark.master", "local")

rajvermacas

import pandas as pd
import numpy as np

def calculate_combined_score(df):
    """
    Calculate a combined score based on normalized 6M return and sum of ranks.
    Returns the DataFrame with additional columns for analysis.
    
    Parameters:
    df (pandas.DataFrame): DataFrame with '6M Return' and 'Sum of Ranks' columns

rajvermacas

import java.time.{LocalDate, DayOfWeek}
import java.time.temporal.ChronoUnit

object WeekdayCounter {

  def countWeekdays(start: LocalDate, end: LocalDate): Long = {
    var weekdayCount = 0L
    var currentDate = start

    while (!currentDate.isAfter(end)) {

rajvermacas

import logging

# Create a custom logger for your project
my_logger = logging.getLogger('my_project')
my_logger.setLevel(logging.DEBUG)

# Create a handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)

rajvermacas

//@version=5
indicator("Var Understanding", overlay=true)

myFunction() =>
    var int counter = 0  // Initialized once per bar
    counter := counter + 1
    counter

// Scenario 1
// On the same bar, multiple calls will reset the var

rajvermacas

You are an AI model specialized in software programming, data engineering, and Apache Spark. Your role is to assist users in solving technical questions related to programming in languages such as Java, Scala, Python, JavaScript, and Spark, and to address general coding issues, dependency challenges, and data processing with Spark. You provide precise explanations, troubleshooting guidance, and solutions to errors or debugging requests.

Your responsibilities include:
1. **Answering Programming Questions**: Deliver detailed solutions and explanations for coding problems in Java, Scala, Python, JavaScript, and Spark.
2. **Resolving Data Engineering and Spark Queries**: Address issues regarding data pipelines, ETL processes, Spark configurations, transformations, and optimizations within distributed data systems.
3. **Debugging Errors**: Analyze and diagnose code errors or performance issues, especially in Spark-related code, providing corrective steps, recommendations, and insights to ensure optimal performanc

rajvermacas

const scrapeData = () => {
  // 1. Find the table
  const sections = document.querySelectorAll("#screener-table > table");
  /* 
  - document.querySelectorAll finds all elements matching the CSS selector
  - "#screener-table > table" means:
     - Find element with ID "screener-table"
     - ">" means direct child
     - "table" means find table elements
  - Returns a NodeList of matching tables

rajvermacas

import yfinance as yf

def get_stock_metrics(ticker_symbol):
    # Create a Ticker object
    stock = yf.Ticker(ticker_symbol)
    
    # Fetch the data
    info = stock.info
    
    # Extract the requested metrics

rajvermacas

import threading
import time
import gc
import logging as logger
import traceback
import sys
import os

# Set up logger
logger.basicConfig(level=logger.INFO, format='%(asctime)s - %(message)s', datefmt='%H:%M:%S')

rajvermacas

1. 
I have a list of objects. The objects are of type Document. This Document is a user defined class. The list has 100000 documents. After doing the business logic when the for loop tries to find out the next element from the list. it is almost taking 100 ms which is unexpected. 
I need to find out what is making the iteration of elements so time consuming. How can i go deep into debuging the logic of for loop which can show some insight as to what is taking time. my for loop looks like below:

for document in documents:
    logger.info("Start processing of document")
    # I have business logic here
    logger.info("Processing of document completed")

After the log