Dataframe
C++ DataFrame for statistical, financial, and ML analysis in modern C++
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What is Dataframe?
DataFrame is a high-performance C++ library for in-memory data exploration, transformation, and statistical analysis, designed for data scientists, quant traders, and C++ developers who need efficient tabular data processing without Python overhead. This library provides functionalities similar to Python's Pandas or R's data.frame, but with greater depth and breadth of features than Pandas, data.frame, and Polars combined.
The library supports slicing data in many different ways, joining, merging, and group-by operations. It includes a large collection of analytical algorithms in the form of visitors, ranging from basic statistics like Mean, STDEV, and Moving Averages to advanced analysis like PCA, Polynomial Fit, FFT, and Eigens, along with trading indicators. Users can easily add their own custom algorithms.
DataFrame employs extensive multithreading in almost all its APIs for large datasets, making it especially suitable for analyzing massive datasets. The library follows key principles including contiguous memory storage for all column data, avoiding unnecessary data copying, supporting any built-in or user-defined types without new code, and remaining self-contained with only C++ language and standard library dependencies.
The library is ideal for C++ developers who need fast, efficient tabular data processing, quant traders requiring financial and trading indicators, and data scientists working with large datasets who want to avoid Python overhead while maintaining Pandas-like functionality.
Performance benchmarks show C++ DataFrame significantly outperforms Polars and Pandas, with calculation times of 1.26 seconds versus 4.88 seconds for Polars and 40.33 seconds for Pandas on 300m row datasets.
Dataframe pricing
Pricing model: Freemium
Free and open source under BSD License. No paid plans or tiers. The project requests sponsorship from users who use it in production capacity as a form of appreciation, but there are no mandatory fees or subscription plans.
Dataframe pros
- Extremely fast performance with multithreading support
- Handles datasets up to 10 billion rows per column
- Contiguous memory storage for all column data
- Supports any built-in or user-defined types without new code
- Larger feature set than Pandas, data.frame, and Polars combined
- Extensive collection of built-in statistical algorithms
- Includes financial and trading indicators out of the box
- Advanced algorithms like PCA, FFT, Polynomial Fit, and Eigens
- Easy to add custom algorithms
- No Python overhead for C++ applications
- Consistent performance across multiple runs
- Self-contained library with only C++ standard library dependencies
- Multi-column sort functionality
- Custom pick and delete operations
- Join, merge, and group-by operations
- Works seamlessly with scalar and multidimensional datasets
- Memory efficient - never uses more space than needed
Dataframe cons
- Requires C++ knowledge and compilation
- No built-in garbage-in-garbage-out protection for users
- Requires index column unlike Polars
- Steep learning curve for non-C++ developers
- No Python or other language bindings included
- Limited documentation compared to mature libraries
- Open source with no official paid support
- May require C++20 or C++23 compiler for best results
Frequently asked questions about Dataframe
What is DataFrame?
DataFrame is a high-performance C++ library for in-memory data exploration, transformation, and statistical analysis. It provides similar functionalities to Python's Pandas or R's data.frame but with greater performance and a larger feature set, designed for data scientists, quant traders, and C++ developers.
How does DataFrame compare to Pandas?
DataFrame offers greater depth and breadth of functionalities than Pandas, data.frame, and Polars combined. In benchmarks, DataFrame calculated statistics in 1.26 seconds versus Pandas' 40.33 seconds on 300m row datasets, and handled 10 billion rows while Pandas cannot handle similarly large datasets efficiently.
How does DataFrame compare to Polars?
DataFrame outperforms Polars significantly in benchmarks. On 300m rows, DataFrame completed calculations in 1.26 seconds versus Polars' 4.88 seconds. DataFrame can handle datasets up to 10 billion rows per column, while Polars crashed at 300m rows due to memory issues. DataFrame also shows more consistent performance across runs.
What statistical algorithms are included?
DataFrame includes basic statistics like Mean, Standard Deviation, Return, and Moving Averages, as well as advanced algorithms like PCA (Principal Component Analysis), Polynomial Fit, Fast Fourier Transform of arbitrary length, and Eigens calculations.
Does DataFrame support multithreading?
Yes, DataFrame employs extensive multithreading in almost all its APIs for large datasets, which makes it especially suitable for analyzing large datasets and contributes to its high performance.
Can I add custom algorithms?
Yes, users can easily add their own custom algorithms to DataFrame. The library is designed to allow easy extension with user-defined algorithms that work with the existing data structure.
What types of data does DataFrame support?
DataFrame supports any type, either built-in or user-defined, without needing new code. All column data is stored in contiguous memory space for maximum efficiency.
Is DataFrame free to use?
Yes, DataFrame is free and open source under BSD License. There are no paid plans or tiers. The author requests sponsorship from users who use it in production capacity as appreciation, but there are no mandatory fees.
What data operations can I perform?
You can slice data in many different ways, join, merge, and group-by the data, multi-column sort, custom pick and delete data, run statistical, summarization, financial, and ML algorithms, and add custom algorithms.
What are the design principles of DataFrame?
The library follows 8 principles: 1) Support any built-in or user-defined type without new code, 2) Never chase pointers, 3) Contiguous memory for all column data, 4) Never use more space than needed, 5) Avoid copying data, 6) Use multithreading only when it makes sense, 7) No garbage-in-garbage-out protection, 8) Self-contained with only C++ standard library dependencies.