I’m interested in using Ruby for data science tasks, particularly for working with large datasets. While Ruby may not be as commonly associated with data science as languages like Python or R, I believe it has potential for certain data processing tasks. However, I’m facing challenges when dealing with large datasets, and I’d like some guidance on optimizing my code.
Here’s a simplified example of my Ruby code:
require 'csv'
# Reading a large CSV file into memory
data = []
CSV.foreach('large_dataset.csv', headers: true) do |row|
data << row.to_h
end
# Performing data analysis on the loaded dataset
total_sales = data.map { |row| row['Sales'].to_f }.sum
average_price = data.map { |row| row['Price'].to_f }.reduce(:+) / data.length
puts "Total Sales: #{total_sales}"
puts "Average Price: #{average_price}"
The problem is that when dealing with large CSV files, this code becomes slow and memory-intensive. I’ve heard about streaming and batching techniques in Python for handling such cases, but I’m not sure how to implement similar strategies in Ruby.
Could someone with experience in data science with Ruby provide guidance on how to efficiently handle and process large datasets, while avoiding memory issues and slow execution times? Are there specific Ruby gems or techniques that are well-suited for this purpose? Any insights or code optimizations would be greatly appreciated. Thank you!
