NOTE: This is the C++ Version. Por the python/cython version see here
Cloth is a high performance C++ implementation of the Pandas API.
In exchange for a fraction of the features of Pandas, Cloth has a fraction of the execution time and a fraction of the resources of Pandas.
To obtain such speeds Cloth is implemented using Eigen to hold the underlying data and Nanobinds for the fast C++ -> Python bindings. Note that because the data is stored within Eigen, it must be homogenous. This is akin to the DataMatrix in very early versions of Pandas.
Syntax within Cloth are implemented and binded in such a way that is (nearly) identical to Pandas. Check out tests/test_cloth.py for an example of how to use Cloth in Python.
The biggest advantage to using Cloth as opposed to Pandas, is the TimeSeries and TimeFrame datatypes. These are specific implementations of the Series and DataFrame meant for Financial Data over time. Specifically these implementations offer a fast method of performing decimal calculations--without the inaccuracies brought on by floating point numbers (a necessity when dealing with money)--via the decimal datatype.
cloth.Seriescloth.DataFrame
cloth.Frame.loc[string]cloth.Frame.loc[slice]cloth.Frame.loc[cloth.slice]
cloth.Frame.iloc[int]cloth.Frame.iloc[slice]cloth.Frame.iloc[cloth.slice]
cloth.Frame.where(boolean array)boolean array -> cloth.Series > value
cloth.DataFrame[string]
cloth.datetime(ISO string)
cloth.timedelta(interval)
cloth.frame.resample(timedelta)