Why Entropy to be defined as Joint Probability Distribution Sum?

From Stinson's book, during the demonstration of the following Theorem which says:

$H(X,Y) \leq H(X) + H(Y)$, with equality if and only if $X$ and $Y$ are independent random variables.

The author says to assume $X$ to take the values $x_i$, $i$ in the interval from 1 to m, and $Y$ to take the values $y_j$, $j$ in the interval from 1 to n, he denotes $p_i = \Pr[X=x_i]$, $i$ from 1 to m, and $q_j = \Pr[Y=y_j]$, $j$ from 1 to n. Then, he defines $r_{ij} = \Pr[X = x_i, Y = y_j]$, $i$ from 1 to m and $j$ from 1 to n, my question is:

why is $$p_i = \sum_{j=1}^{n} r_{ij}$$

and $$q_j = \sum_{i=1}^{m} r_{ij}$$

I would like a detailed demonstration. I would also like to comprehend better what $H(X,Y)$ means.

First note that the the comma in the proability is the AND operator; $$ \Pr[X = x , Y = y] = \Pr[X = x \wedge Y = y]$$ This is common notation to simplify the writing.

Now, explicitly write as

$$p_i = \sum_{j=1}^{n} r_{ij} = \Pr[X = x_i \wedge Y = y_0] + \Pr[X = x_i \wedge Y = y_1] + \cdots + \Pr[X = x_i \wedge Y = y_m]$$

Since the random variables $X$ and $Y$ are independent then this is just a partition of the event $x_i$ by the random variable $Y$.

As a solid case, consider two dices; one has the $X$ and the other is the $Y$ as their random variable representing the upper value of the dice. In total there are 36 possible equal values of the two dice roll. Fix the first one, let say $3$ then

\begin{align}\Pr(X=3) = & \Pr(X=3,Y=1)+\\ & \Pr(X=3,Y=2)+\\ & \Pr(X=3,Y=3)+\\ & \Pr(X=3,Y=4)+\\ & \Pr(X=3,Y=5)+\\ & \Pr(X=3,Y=6)\\ = &\frac{1}{36}+ \frac{1}{36}+ \frac{1}{36}+ \frac{1}{36}+ \frac{1}{36} +\frac{1}{36} = \frac{1}{6} \end{align}

$H(X,Y)$ is actually the Joint Entropy and the formula is given by (again the AND);

$$H(X,Y) = -\sum_{x\in\mathcal X} \sum_{y\in\mathcal Y} P(x,y) \log_2[P(x,y)]$$

In our context this is

$$H(X,Y) = -\sum_{x\in X} \sum_{y\in Y} P(X=x,Y=y) \log_2[P(X=x,Y=y)]$$

$H(X,Y)$ is simultaneous evaluation of $X$ and $Y$ and that is equal to first evaluating $X$ then given value of $X$ evaluate the $Y$

$$H(X,Y)= H(X|Y)+H(Y)=H(Y|X)+H(X) $$

Proving this bit long;

\begin{align} H(X,Y) & = − \sum_{i=1}^n \sum_{j=1}^m \Pr(X=x_i,Y =y_j) \log \big( \Pr(X=x_i,Y =y_j) \big)\\ & = − \sum_{i=1}^n \sum_{j=1}^m \Pr(X=x_i,Y =y_j) \log \big( \Pr(X=x_i) \Pr(Y|X = y_j|x_i) \big)\\ & = − \sum_{i=1}^n \sum_{j=1}^m \Pr(X=x_i,Y =y_j) \big[ \log \big( \Pr(X=x_i) \big) + \log \big( \Pr(Y|X = y_j|x_i) \big) \big] \\ & = − \sum_{i=1}^n \left( \sum_{j=1}^m \Pr(X=x_i,Y =y_j) \right) \log \big( \Pr(X=x_i) \big) \\ & - \sum_{i=1}^n \sum_{j=1}^m \Pr(X=x_i,Y =y_j) \log \left( \Pr(Y|X = y_j|x_i) \right)\\ & = H(X) + H(Y|X) \end{align}

Entropy does not depend on what the "labels" or values of the random variable is, it is a property ONLY of the distribution. After all you just use $P(x), P(y), P(x,y)$ etc in the formula not $x,y$.

Once you realize this, the set of probabilities $P(x,y)$ is all you need to use and apply the original definition of entropy for a single random variable. If you like, define a vector random variable $z=(x,y)$ and compute its entropy as $$ -\sum_{z} P(z) \log P(z) $$ which is the same as computing $$ -\sum_{x,y} P(x,y) \log P(x,y) $$ This also means that the joint entropy of a number of random variables $H(x_1,\ldots,x_n)=H(p_1,\ldots,p_n):=H_0$ with $P(x_i)=p_i,$ is the same as the entropy of any reordering (permutation) of the joint distribution so this means

$$ H(p_{\sigma(1))},p_{\sigma(2)},\ldots,p_{\sigma(n)})=H_0 $$ for all permutations $\sigma:\{1,\ldots,n\}\rightarrow \{1,\ldots,n\}.$