Why Exception Handling doesn't print text? - python-3.x

Question

My question is why Python doesn't execute the print statement in the Exception Handling code below. I am trying to calculate the log of volumes for a bunch of stocks. Each stock has 1259 volume values. But Python generates a RunTimeWarning "divide by zero encountered in log". So I try to use Exception Handling to locate where the log input is zero, but Python doesn't execute the print statement under except. The print statement is supposed to print the name of the stock and the index in the array where the volume is zero. Why?
Here is the code:
for i, stock in enumerate(df.columns):
volumes = df[stock].to_numpy()
for r in range(len(volumes)): # len(volumes) = 1259
try:
v = np.log(volumes[r])
except:
print(stock, r)
Here is the Error that follows after the RunTimeWarning.
LinAlgError Traceback (most recent call last)
<ipython-input-6-6aa283671e2c> in <module>
13 closes = df_close[stock].to_numpy()
14 volumes = df_vol[stock].to_numpy()
---> 15 indicator_values_all_stocks[i] = indicator.price_volume_fit(volumes, closes, histLength)
16
17 indicator_values_all_stocks_no_NaN = indicator_values_all_stocks[:, ~np.isnan(indicator_values_all_stocks).any(axis=0)]
~\Desktop\Python Projects Organized\Finance\Indicator Statistics\B.57. Price Volume Fit\indicator.py in price_volume_fit(volumes, closes, histLength)
1259 x = log_volumes[i - histLength:i]
1260 y = log_prices[i - histLength:i]
-> 1261 model = np.polyfit(x, y, 1, full = True)
1262 slope[i] = model[0][0]
1263
<__array_function__ internals> in polyfit(*args, **kwargs)
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\lib\polynomial.py in polyfit(x, y, deg, rcond, full, w, cov)
629 scale = NX.sqrt((lhs*lhs).sum(axis=0))
630 lhs /= scale
--> 631 c, resids, rank, s = lstsq(lhs, rhs, rcond)
632 c = (c.T/scale).T # broadcast scale coefficients
633
<__array_function__ internals> in lstsq(*args, **kwargs)
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\linalg\linalg.py in lstsq(a, b, rcond)
2257 # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
2258 b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2259 x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
2260 if m == 0:
2261 x[...] = 0
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_lstsq(err, flag)
107
108 def _raise_linalgerror_lstsq(err, flag):
--> 109 raise LinAlgError("SVD did not converge in Linear Least Squares")
110
111 def get_linalg_error_extobj(callback):
LinAlgError: SVD did not converge in Linear Least Squares