3.0 Scientific Python
3.0 Scientific Python#
Estimated time to complete this notebook: 5 minutes
Why is Python so popular for research work?
Historically, FORTRAN was the most popular “language of technical computing”. Later, MATLAB was created with strong built-in support for efficient numerical analysis with matrices (the mat in MATLAB is for Matrix, not Maths), and plotting.
Early Python users developed three critical libraries, to match the power of MATLAB for scientific work:
Matplotlib, the plotting library created by John D. Hunter
NumPy, a fast matrix maths library created by Travis Oliphant
IPython, the precursor of the notebook, created by Fernando Perez
By combining a plotting library, a matrix maths library, and an easy-to-use interface allowing live plotting commands in a persistent environment, the powerful capabilities of MATLAB were matched by a free and open toolchain.
Further tools such as
scipy are built on, extend, or utilise these libraries.
In this module we will use these libraries to deal with data of the type that might be used in a research project.