This is a C++ statistical library that provides an interface similar to Pandas package in Python.
A DataFrame can have one index column and many data columns of any built-in or user-defined type.
You could do almost everything you could do with Pandas within the C++ syntax and type checking framework. You can add/delete any data column type, slice, run summarization functors, transpose, etc. like Pandas.
You can slice the data frame and instead of getting another data frame you can opt to get a view. A view is a data frame that is a reference to a slice of the original data frame. So if you change the data in the view the corresponding data in the original data frame will also be changed.
Instances of DataFrame are not multithreaded safe. In other words, instances of DataFrame must not be used in multiple threads without protection. But DataFrame utilizes multithreading in two different ways:
- There are asynchronous versions of some methods. For example, you have both sort() and sort_async(). The latter returns a std::future which could execute in parallel.
- [Not fully implemented yet] DataFrame uses multiple threads, internally and unbeknown to the user, in some of its algorithms when appropriate. User can control (or turn off) the multithreading by calling set_thread_level() which sets the max number of threads to be used. The default is 0. The optimal number of threads is a function of users hardware/software environment and usually obtained by trail and error. set_thread_level() and threading level in general is a static property and once set, it applies to all instances.
Installing using CMake
mkdir build cd build cmake .. make install
cd build make uninstall