The given answer by @lydiash is correct. But, as you're new to numpy, I'd like to also give you some insights about what's the point of using numpy :)
Let's simplify your code a bit. I see that a2 contains all data for a plot. But first row, denoting x values, is needed only for plotting, and you're working only with actual y values. So, let's split them!
xs = np.arange(10) # gives you exactly [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]
ys = np.array([ 0 , 1 , 2 , 3 , 2 , 1 , 0 , 1 , 2 , 3 ])
plt.plot(xs, ys, 'r-x')
print(f'y values: {ys}') # prints "y values: [0 1 2 3 2 1 0 1 2 3]"
In the second part you're looking for max y value and change it to zero. You can do it in a for loop, of course. But also you could make use of power of numpy - vector operations!
As you've calculated the max value with y_max = np.max(ys) (which also can be written as y_max = ys.max()), you can directly change ys in one hop:
y_max = ys.max()
print(f'max value found: {y_max}') # prints "max value found: 3"
ys[ys == y_max] = 0 # here the changing of max values to zero happens!
print(f'new y values: {ys}') # prints "new y values: [0 1 2 0 2 1 0 1 2 0]"
Expression ys == y_max is kinda a filter on all indices of ys - only indices of matched values are taken.
Optional clarification: If you want to understand ys == y_max part better, just print what it gives:
print(ys == y_max)
# prints "[False False False True False False False False False True]"
# as you see, `True` values are exactly at positions of maximum value
It gives you a mask on all indices of the array. And you can use it further as some sort of filter - ys[mask] returns only values filtered by that mask.
