 Minimal polynomial of an affine function of a projection

For a linear projection $P$ on a nonzero finite-dimensional vector space, $V=\ker P\oplus \mathrm{im}P$. The linear map $\alpha I+\beta P$ acts by the scalar $\alpha$ on the first summand and $\alpha +\beta$ on the second. Its minimal polynomial is the product of the distinct corresponding linear polynomials whose summands are nonzero. If $\beta =0$ this is $x-\alpha$; if $P=0$ or $P=I$ only the associated single factor remains. Otherwise, for $\beta \ne 0$, it is $(x-\alpha )(x-\alpha -\beta )$. This follows directly from an adapted basis, so it works in every characteristic.