  Online resources today contain an abundant amount of code snippets for
documentation, collaboration, learning, and problem-solving purposes. Their
executability in a "plug and play" manner enables us to confirm their quality
and use them directly in projects. But, in practice that is often not the case
due to several requirements violations or incompleteness. However, it is a
difficult task to investigate the executability on a large scale due to
different possible errors during the execution. We have developed a scalable
framework to investigate this for SOTorrent Python snippets. We found that with
minor adjustments, 27.92% of snippets are executable. The executability has not
changed significantly over time. The code snippets referenced in GitHub are
more likely to be directly executable. But executability does not affect the
chances of the answer to be selected as the accepted answer significantly.
These properties help us understand and improve the interaction of users with
online resources that include code snippets.
