If my distribution looks similar to Poisson but not an actual Poisson
distribution which is verified using a QQ plot,
We use the probability distributions to approximate the distribution of the data. Empirical distribution won't exactly fit the theoretical distribution.
is there any way to
convert such distribution to a proper Poisson distribution using
python?
Poisson distribution is a distribution over non-negative integers, with mean equal to variance. As noticed in comment by @Xi'an, technically you could use inverse transform. But why would you want to do that?
why is it a general practice to convert all the unknown distributions to gaussian.
It isn't. We sometimes transform the data using things such as log transformations, or Box-Cox. There are many reasons for and against transformations in different cases. As noticed by Nick Cox:
The general idea of transformation is that it can be easier to see and
analyze what is happening on a transformed scale, while specifically
there are many techniques for which some approximation to normal
distribution(s) provides, if not conditions that are assumed to be
true, as so often stated, then at least relatively ideal conditions
for summary and inference.
TL;DR we don't "always" transform the variables. We do it sometimes, to make out life easier. We don't expect the data to look exactly like Poisson or normal distribution, we use them only as approximations. You shouldn't be doing things like reshaping the data to fit the desired distribution without good reason, at best this would make your results hard to interpret, if not hard to justify.
