Jupyter Notebook Analysis of Salary Data Spreadsheet

It&#x27;s interesting - the company I work for (non-US, consulting) did analysis of gender pay gaps internally and noted that on average, females get 70% of male pay. However, <i>within the same role</i>, the difference was just 3%. Their focus is now on flexible working and figuring out why women are excluded&#x2F;not interested in particular roles.

I&#x27;m trying to collect a similar dataset for freelancer hourly rates on Reddit:<p><a href="https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;freelance&#x2F;comments&#x2F;4bsk14&#x2F;freelance_hourly_rates_a_crowdsourced_spreadsheet&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;freelance&#x2F;comments&#x2F;4bsk14&#x2F;freelance...</a><p>Tried it on Hacker News as well, but it didn&#x27;t get any attention:<p><a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=11337833" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=11337833</a>

As best I can tell, that last graph would&#x27;ve been clearer as a bar graph and not a scatter plot (or at least better labels). The x axis merely indicates the location. So, &quot;mean annual salaries by location (in kUSD)&quot; might be a good title for the last one.

I appreciate this if only as a great example of using jupyter notebook and pandas. Thanks!

github repo:<p><a href="https:&#x2F;&#x2F;github.com&#x2F;jrenner&#x2F;hacker-news-salaries-data" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jrenner&#x2F;hacker-news-salaries-data</a>

What strikes me that salary~experience (both company and overall) correlation is basically nonexistent in this particular dataset, at least looking visually.

I may have missed it but it looks like the plot from cell &#x27;In [64]&#x27; hasn&#x27;t converted from Euros and GBP to Dollars.

I&#x27;ve got 99 problems, but a boss ain&#x27;t one!

Awesome! Think you should convert the currencies at current spot rates instead of just removing the currency sign though :)

It looks like all the analysis is just on base salary. It would be much more interesting if it included bonus and stock.

Interesting there seems to be a shift in the 0-2 year job length position. I wonder if this is because the common trend is to jump ship at 2 yrs, so folks are going out job hunting, and getting a competitive raise at their current position with the job offer in hand?

Most interesting to me was that the gender gap was so much more pronounced in SV than outside<p>non-SV male median: 97000<p>non-SV female median: 90000 (92% of male)<p>SV male median: 137120<p>SV female median: 99187 (72% of male!)<p>Unless there&#x27;s some external factor here, things look pretty damning in SV...

Log-log scales would really help. Otherwise - points in the low range are too dense to be useful (big differences in density make be hard to spot, or even - invisible).