hosseinmoein/DataFrame

What it solves

It provides a high-performance, in-memory C++ container for tabular data analysis, eliminating the overhead associated with Python-based tools like Pandas or Polars. It allows developers to perform complex data exploration, transformation, and statistical analysis directly in C++ with a focus on memory efficiency and execution speed.

How it works

DataFrame implements a templatized, heterogeneous container where columns are treated as first-class citizens. Unlike traditional spreadsheets, it supports multidimensional data by allowing columns to contain other containers or DataFrames. To maximize performance, it ensures column data is stored in contiguous memory, avoids unnecessary data copying, and utilizes extensive multithreading across its API for large datasets.

Who it’s for

It is designed for data scientists, quantitative traders, and C++ developers who require efficient tabular data processing for statistical, machine-learning, or financial applications.

Highlights

• Advanced Data Manipulation: Supports slicing, joining, merging, grouping, cross-tabulation, and pivoting.
• Built-in Analytical Algorithms: Includes a wide range of "visitors" for basic stats (Mean, STDEV), advanced analysis (PCA, FFT, Polynomial Fit), and trading indicators.
• Type Flexibility: Supports any built-in or user-defined type without requiring additional code.
• High Efficiency: Optimized for large datasets with a focus on contiguous memory layout and minimal pointer chasing.