In Python, you can use the Pandas library to reduce tuples by converting them into a DataFrame and then performing reduction operations such as aggregation or summation. This is particularly useful when working with collections of data that are grouped into tuples.
import pandas as pd
# Sample data
data = [(1, 'A', 100), (1, 'B', 200), (2, 'A', 300), (2, 'B', 400)]
# Create DataFrame
df = pd.DataFrame(data, columns=['Group', 'Category', 'Value'])
# Reduce tuples by grouping and summing the 'Value'
reduced_df = df.groupby(['Group', 'Category']).sum().reset_index()
print(reduced_df)
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?