Simple tools for data handling and analysis in Python.
This repository contains examples of how to solve various data analysis problems in research using Python libraries.
The examples are presented as Jupyter notebooks, partly also accessible via Binder without local Python installation.
We recommend installing Python and Jupyter using the conda package manager. The miniconda distribution provides a minimal Python and conda installation. A more comprehensive, but also larger distribution is Anaconda, which includes many well-known Python libraries for Data Science.
Additional installation instructions for Python libraries required by different tools can be found in the corresponding Jupyter Notebooks.
Kindly brought to you by University Library Bern. For hints and questions refer to Digital Scholarship Services.
This Digital Toolbox is a joint project of the Digital Scholarship Services and Subject Specialists of the Sciences Libraries.
Digital Scholarship (DS) - this term is used to describe the increasing use of new digital, data-driven methods in various branches of arts and humanities, social sciences and sciences. Researchers work on the basis of, with and on specific data sets and data structures, use data analysis, machine learning and big data technologies, and publish research results in new digital forms. With Digital Scholarship Services the University Library Bern actively supports these digital and data-driven methods in research and teaching.
