DataFrame

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.

Views
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.

Multithreading
Instances of DataFrame are not multithreaded safe. In other words, a single instance of DataFrame must not be used in multiple threads without protection. But DataFrame utilizes multithreading in two different ways:

1. 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.
2. 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.

Example Code

// Define a DataFrame with unsigned long index type that uses std::vector
typedef DataFrame<unsigned long, std::vector>	MyDataFrame;

MyDataFrame                df;
std::vector<int>           intvec = { 1, 2, 3, 4, 5 };
std::vector<double>        dblvec = { 1.2345, 2.2345, 3.2345, 4.2345, 5.2345 };
std::vector<double>        dblvec2 = { 0.998, 0.3456, 0.056, 0.15678, 0.00345, 0.923, 0.06743, 0.1 };
std::vector<std::string>   strvec = { "Insight", "John Dow", "Alakazam", "Persian Prince", "Bugs Bunny" };
std::vector<unsigned long> ulgvec = { 1UL, 2UL, 3UL, 4UL, 5UL, 8UL, 7UL, 6UL }
std::vector<unsigned long> xulgvec = ulgvec;

// This is only one way of loading data into a DataFrame instance. There are
// many different ways of doing it. Please see the documentation, or dataframe_tester.cc
int rc = df.load_data(std::move(ulgvec),  // Index column
                      std::make_pair("int_col", intvec),
                      std::make_pair("dbl_col", dblvec),
                      std::make_pair("dbl_col_2", dblvec2),
                      std::make_pair("str_col", strvec),
                      std::make_pair("ul_col", xulgvec));

// This is another way of loading a DataFrame
MyDataFrame       df2;
std::future<bool> fut = df2.read_async("sample_data.csv");

fut.get();
        
// Sort the Frame by index
df.sort<MyDataFrame::TimeStamp, int, double, std::string>();
// Sort the Frame by column “dbl_col_2”
df.sort<double, int, double, std::string>("dbl_col_2");

// A functor to calculate mean, variance, skew, kurtosis, defined in DataFrameVisitors.h file
// You can implement your own algorithms and extend the DataFrame easily 
StatsVisitor<double>  stats_visitor;
// Calculate the stats on column “dbl_col”
df.visit<double>("dbl_col", stats_visitor);

Date Time
DateTime class included in this library is a very cool and handy object to manipulate date/time with nanosecond precision.

DataFrame Documentation

DateTime Documentation

DataFrame Test File

Heterogeneous Vectors Test File
Date/Time Test File

Contributions

License

Installing using CMake

mkdir build
cd build
cmake ..
make install

Uninstalling

cd build
make uninstall