Here are some figures that I like

@MaxGhenis Add to these the Lorenz curve, Gini coefficient, wealth shares, and variance of log wealth. These are common measures of inequality. And I like the way that OG-USA puts these in an Inequality class object.

Gini coefficient. The Gini coefficient for the SCF is calculated in OG-USA in the in wealth.py module, lines 133-137 and in the Inequality class of the utils.py module, lines 469-501. The Wikipedia Gini coefficient page has some nice expressions for the equation for computing the Gini coefficient.
Lorenz curve. The Lorenz curve shows what percent of the wealth is held by what percent of the population. A 45-degree line signifies uniformly distributed wealth across the whole distribution of individuals (i.e., x% of the wealth is held by the first x% of the individuals for all x). A right angle from the lower left origin to the lower right origin to the upper right corner signifies ultimately concentrated wealth (i.e., 0% of the wealth is held by the first x% of individuals for all x except for the wealthiest individual who holds 100% of the wealth). The Gini coefficient above is defined as (area A) / (area A + area B). Zhiya Zuo has a blog post on how to plot the Lorenz curve using Python.
Wealth shares of percentiles. The percent of wealth held by individuals in the yth percentile bin, where y is defined as the x_jth percentile minus the x_ith percentile. The deciles, quintiles, and quartiles are a good examples. OG-USA uses the following quantiles [0.25, 0.25, 0.20, 0.10, 0.10, 0.09, 0.01].
Variance of log wealth. The variance of log wealth is another common measure of inequality. However, it has a problem if wealth can be zero or negative.
Wealth percentile ratios. Another measure of inequality is the ratio of wealth levels from different percentiles. For example, the 90/10 ratio is the wealth level at the 90th percentile divided by the wealth level at the 10th percentile.

PSLmodels / scf

Here are some figures that I like #6