# hosseinmoein/DataFrame: a C++ DataFrame for statistical, financial and ML analysis > A header-centric C++23 container for tabular and multidimensional data, with analytical visitors and multithreaded APIs. It is aimed at C++ developers who want Pandas-like operations without leaving the language. **hosseinmoein/DataFrame** — C++ DataFrame for statistical, financial, and ML analysis in modern C++ - Repository: https://github.com/hosseinmoein/DataFrame - Website: https://hosseinmoein.github.io/DataFrame/ - Stars: 2,986 · Forks: 360 - Language: C++ - License: BSD-3-Clause - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/hosseinmoein-dataframe ## What hosseinmoein/DataFrame solves, and for whom The README describes DataFrame as a templatized and heterogeneous C++ container for data analysis in statistical, machine-learning or financial applications. The stated motivation is to offer data manipulation comparable to Python's Pandas or R's data.frame without the Python overhead, which places it squarely in native C++ codebases: pricing engines, backtesting systems, numerical services and any process where marshalling a table across a language boundary costs more than the analysis itself. The design premise is that columns are first-class citizens. Operations and access to columns are described as more efficient and easier than dealing with data row by row. That is a deliberate contrast with row-oriented containers such as a vector of structs, and it shapes everything downstream: filtering, grouping and analytical visitors all act on columns. A second premise is that data need not be two-dimensional. Columns can be vectors of any type, including other DataFrames, so a DataFrame can be of any dimension. This is where the library diverges from the spreadsheet metaphor. It is a container that happens to look tabular, not a table that was extended to hold arrays. ## Columnar storage, templated columns and visitor algorithms The architecture visible from the repository is a header-centric library: include/ holds the implementation, src/ and examples/ provide build targets, and benchmarks/ and test/ sit alongside them. The README states the library follows principles including supporting any type, built-in or user defined, without needing new code, and never chasing pointers the way linked lists, std::any or pointer-to-base designs do. Combined with the description of native types and contiguous memory storage in package.json, the intent is that a column of doubles is a contiguous buffer, not a chain of heap allocations. Analysis is organized around visitors. The README lists basic statistics such as Mean, Stdev and Moving Averages, and more involved work such as PCA, Polynomial Fit, FFT and Eigens, plus a collection of trading indicators. The API is described as open-ended, meaning custom algorithms can be added. Many of these algorithms are stated to work with both scalar and multidimensional datasets. Multithreading is applied across almost all APIs for large datasets, according to the README. That is a claim about internal implementation, and it carries an operational consequence: the library is not a single-threaded kernel you can drop into a thread pool without thought. Where the README is silent is on thread-safety guarantees for concurrent mutation of the same DataFrame, and on how the threading threshold is configured. If you plan to call these APIs from multiple threads, that is the first thing to check in the headers rather than assume. ## Building hosseinmoein/DataFrame and running a first analysis The README points to examples/hello_world.cc as the starting point and to the published documentation for full code samples. Package registries are referenced through badges: a Conan Center recipe and a vcpkg port both exist, and the repository carries a CMakeLists.txt at the top level. The README does not spell out a from-source build command, so the honest path is to read CMakeLists.txt and examples/CMakeLists.txt in the repository and follow the options they define. The README gives no install line to copy, so the only commands that can be quoted with confidence are the ones the repository itself contains. The examples directory has its own CMakeLists.txt, and the top-level CMakeLists.txt defines the library target: ```bash cmake -S . -B build cmake --build build ``` That sequence assumes the standard CMake layout the repository uses; confirm the option names in CMakeLists.txt rather than copying flags from elsewhere. The registry badges point at Conan Center and vcpkg, but the README does not print an install command for either, so check the recipe page for the current package name and version before pinning anything. Once linked, the mental model to hold is column-first. You construct a DataFrame, append columns by name, then run visitors over them. The repository's hello_world example is the reference for the exact call sequence, and the CheatSheet.pdf linked from the README is the fastest way to find the visitor name for a given statistic before writing code. ## Where hosseinmoein/DataFrame is the wrong tool The clearest boundary is the language itself. If your analysis lives in Python, this library does not help you, and the search traffic around it confirms the confusion: people arrive looking for pandas, dataframe loc, dataframe groupby or dataframe to csv, and find a C++ project instead. There is no Python binding documented in the README, so treating it as a faster pandas is a category error. The second boundary is the toolchain. The project advertises C++23. That is a recent standard, and it constrains which compilers and which production environments you can target. A codebase pinned to an older standard, or a platform whose default toolchain lags, will need to upgrade before this library is even an option. Because the implementation is header-centric, that requirement propagates into every translation unit that includes it. The third is scale and persistence. DataFrame is described as an in-memory library for exploration, transformation and statistical analysis. It is not a query engine over files on disk, and the README makes no claims about out-of-core execution. If your dataset does not fit in memory, the multithreading described in the README will not save you; you need a different execution model, not a faster container. ## How it differs from pandas, data.frame and Polars The README makes a direct comparison: it says the depth and breadth of functionality offered by C++ DataFrame alone are greater than the functionality offered by packages such as Pandas, data.frame and Polars combined. That is a strong claim from the project itself, and it should be read as a statement about API surface, not about ecosystem or ergonomics. The real difference is where the work happens. Pandas, R's data.frame and Polars each ship a runtime, a package manager story and, in Polars' case, a query engine with lazy evaluation. DataFrame ships as a C++ library you compile into your binary. There is no interpreter, no serialization boundary and no garbage collector between your data and the analysis. For a latency-sensitive trading or pricing path, that is the whole point. The cost is on the other side of the ledger. You lose the interactive workflow, the notebook ecosystem and the ability to hand a script to a colleague. You also take on compile times and template error messages, which is the normal price of a templated C++ container that supports arbitrary column types without new code. The README's comparison is about what you can express; it says nothing about how quickly you can express it. ## Maintenance, releases and the BSD-3-Clause licence The repository is not archived, and the last push was on 2026-09-05. Releases are frequent by the standard of a single-maintainer C++ library: 4.0.1 in April 2026, 4.0.2 in June 2026, and 4.1.0 in August 2026. A version jump of that size between 4.0.x and 4.1.0 suggests API additions rather than a rewrite, but the release notes are the place to confirm that, and they are not reproduced here. Upgrade cost is dominated by the header-centric design. Because the implementation lives in include/ and is compiled into your translation units, a version bump means recompiling everything that includes it. Any change to a template signature or a visitor interface surfaces at compile time across your codebase rather than at a link boundary. Budget for that, and pin a version in whichever registry you use rather than tracking master. The licence is BSD-3-Clause, stated in the repository and in package.json. It permits redistribution and use in source and binary forms provided the copyright notice, the list of conditions and the disclaimer are retained, and it forbids using the author's name or contributor names to endorse derived products. That is a permissive licence with an attribution requirement and no copyleft clause, but the header text in the README is the authoritative version and a lawyer should read it, not this paragraph. ## Conclusion Adopt it if your pipeline is already C++ and you want columnar storage, group-by, joins and analytical visitors without a Python boundary. Do not adopt it if your team works in Python and expects pandas semantics, or if you need a stable ABI across compiler versions. Before committing, verify that your toolchain supports C++23, check the CMakeLists.txt build options against your platform, and confirm that the visitors you need exist in the documentation rather than assuming pandas parity. ## FAQ ### What is hosseinmoein/DataFrame? It is a templatized, heterogeneous C++ container for in-memory data exploration, transformation and statistical analysis, aimed at statistical, machine-learning and financial applications. The README compares its role to Pandas or R's data.frame, but implemented natively in C++ with columns as first-class citizens. ### What are the differences between hosseinmoein/DataFrame and pandas? DataFrame is a compiled C++ library with no Python runtime, while pandas is a Python package. The README states that the depth and breadth of functionality in C++ DataFrame alone exceeds Pandas, data.frame and Polars combined, which is a claim about API surface rather than ecosystem. ### How do I install hosseinmoein/DataFrame? The README shows Conan Center and vcpkg badges for the library, and the repository has a top-level CMakeLists.txt for building from source. It does not document a Python installation path, because the project is a C++ library rather than a Python package. ## Sources - [hosseinmoein/DataFrame on GitHub](https://github.com/hosseinmoein/DataFrame) - [License: BSD-3-Clause](https://github.com/hosseinmoein/DataFrame/blob/master/LICENSE) - [Project website](https://hosseinmoein.github.io/DataFrame/) - [README](https://github.com/hosseinmoein/DataFrame/blob/master/README.md) - [Releases](https://github.com/hosseinmoein/DataFrame/releases) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/hosseinmoein-dataframe