Confusion with euclidean distance implementation
First of all, you need to convert your categorical columns to numerical if you like to take them into account (e.g. column $T_m$).
You need to normalize your features as there is variance in scale (e.g. column $G$ vs $BLK$)
You probably need to reduce dimensionality as k-means MIGHT get stuck in Curse of Dimensionality.


The rest is fine. Euclidean distance generalizes for any number of dimensions. 
$$\sqrt{(x_{1} - x_{2})^2 + (y_{1} - y_{2})^2 + (z_{1} - z_{2})^2 + ...}$$

So, when you calculate your k-means, you will get $k$ points each of them in $d$ dimensions ($d$ is the number of columns). Then for each data point you calculate the distance above for data point and the center i.e. 

$$\sqrt{(x_{data} - x_{center})^2 + (y_{data} - y_{center})^2 + (z_{data} - z_{center})^2 + ...}$$

Python does it for you easily.