This talk presents a user-friendly python framework of the geometry-oblivious fast multipole methods (GOFMM). We complemented the existing GOFMM data structure by writing matrix multiplication and inverse. Then, we built an interface to integrate the existing C++ programs into Python using the Simplified Wrapper and Interface Generator (SWIG). The wrapper enables users to directly use various GOFMM routines, such as the aforementioned matrix-vector multiplication and matrix inverse, without any complicated compilation. It also allows users to input a numpy array into the existing GOFMM data structure in C++. As a result, this feature opens the door to various powerful data analysis 
tools that we can use to study the efficiency and accuracy of GOFMM operations. We wrote a library for error analysis to calculate the error of GOFMM matrix-vector multiplication and inverse. To expand usability of GOFMM, we constructed a Python library that contains major GOFMM operations and error analysis. This library can be installed directly from the Python Package Index (PyPI).