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If the pdf(probability density function) of Y is continuous, it can be obtained by differentiating the cdf(cumulative distribution function). --"Statistical Inference"
My question is: when the pdf of Y is not continuous, can't we obtain the pdf by differentiating the cdf?
Yes! The density of a continuous distribution is the derivative of the CDF.$^{\dagger}$
Example: the uniform distribution, say on $(0,1)$, which has PDF $f(x)= \left\{ \begin{array}{ll} 1 & x\in (0,1)\\ 0 & x\notin (0,1) \\ \end{array} \right.$.
Then the CDF is $F(x)= \left\{ \begin{array}{ll} 0& x\le0\\ x & x\in (0,1)\\ 1 & x\ge0) \\ \end{array} \right.$.
You can see that $\dfrac{dF(x)}{dx} = f(x)$, as you'd expect.
We don't usually talk about the PDF as being continuous, however. Continuous vs discrete concerns the CDF. Fair warning: the details of this quickly get you into heavy real analysis, including measure theory.
$^{\dagger}$In some sense, you always can get the density through a derivative. Measure theory unifies discrete, continuous, and even funkier distributions and gives their densities through something called the Radon-Nikodym derivative.
