  Colour-Magnitude Diagrams provide a convenient way of comparing populations
of similar objects. When well populated with precise measurements, they allow
quick inferences to be made about the bulk properties of an astronomic object
simply from its proximity on a diagram to other objects. We present here a
Python toolkit which allows a user to produce colour-magnitude diagrams of
transiting exoplanets, comparing planets to populations of ultra-cool dwarfs,
of directly imaged exoplanets, to theoretical models of planetary atmospheres,
and to other transiting exoplanets. Using a selection of near- and mid-infrared
colour-magnitude diagrams, we show how outliers can be identified for further
investigation, and how emerging sub-populations can be identified.
Additionally, we present evidence that observed differences in the
\textit{Spitzer}'s 4.5\mu m flux, between irradiated Jupiters, and field brown
dwarfs, might be attributed to phosphine, which is susceptible to photolysis.
The presence of phosphine in low irradiation environments may negate the need
for thermal inversions to explain eclipse measurements. We speculate that the
anomalously low 4.5\mu m flux flux of the nightside of HD 189733b and the
daysides of GJ 436b and GJ 3470b might be caused by phosphine absorption.
Finally, we use our toolkit to include \textit{Hubble} WFC3 spectra, creating a
new photometric band called the `Water band' (\textit{W$_{JH}$}-band) in the
process. We show that the colour index [\textit{W$_{JH}$-H}] can be used to
constrain the C/O ratio of exoplanets, showing that future observations with
\textit{JWST} and \textit{Ariel} will be able to distinguish these populations
if they exist, and select members for future follow-up.
