Use `math.log10` instead of `math.log` for logarithmic plots

[giordano]

Project velocity can use logarithmic axis, which is quite useful for spreading out projects, but it uses math.log

8Knot/8Knot/pages/chaoss/visualizations/project_velocity.py

Lines 358 to 361 in abf4cc3

	df_consolidated["log_num_commits"] = df_consolidated["Commit"].apply(lambda x: math.log(x) if x != 0 else 0)
	df_consolidated["log_num_contrib"] = df_consolidated["num_unique_contributors"].apply(
	lambda x: math.log(x) if x != 0 else 0
	)

8Knot/8Knot/pages/chaoss/visualizations/project_velocity.py

Line 376 in abf4cc3

df_consolidated["log_prs_issues_actions_weighted"] = df_consolidated["prs_issues_actions_weighted"].apply(math.log)

However I think that for humans it's easier to reason in terms of decades than it is of...powers of the Euler constant. If I a repo has a number of log10(commit) between 3 and 4 I can immediately tell "oh, that repo has ~thousands of commits", if it's between 1 and 2 I can easily guess "that one instead has only a few dozens", etc...while I have much more difficulties making the same quick guesses with the natural logarithm. A variation is to plot in log10 scale but use the linear values on the axes ticks: 1, 10, 100, 1000, etc..., which makes things even easier to read.

[cdolfi]

@Sukuna0007Abhi also good option

[Sukuna0007Abhi]

Thanks ma'am @cdolfi

[Sukuna0007Abhi]

Hi ma'am @cdolfi , I used both approach on this pls check on your free time. PR #1060