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We present the open-source Python code pyMCZ that determines oxygen abundanceand its distribution from strong emission lines in the standard metallicitycalibrators, based on the original IDL code of Kewley & Dopita (2002) withupdates from Kewley & Ellison (2008), and expanded to include more recentlydeveloped calibrators. The standard strong-line diagnostics have been used toestimate the oxygen abundance in the interstellar medium through variousemission line ratios in many areas of astrophysics, including galaxy evolutionand supernova host galaxy studies. We introduce a Python implementation ofthese methods that, through Monte Carlo sampling, better characterizes thestatistical oxygen abundance confidence region including the effect due to thepropagation of observational uncertainties. These uncertainties are likely todominate the error budget in the case of distant galaxies, hosts of cosmicexplosions. Given line flux measurements and their uncertainties, our codeproduces synthetic distributions for the oxygen abundance in up to 15metallicity calibrators simultaneously, as well as for E(B-V), and estimatestheir median values and their 68% confidence regions. We test our code onemission line measurements from a sample of nearby supernova host galaxies (z <0.15) and compare our metallicity results with those from previous methods. Ourmetallicity estimates are consistent with previous methods but yield smallerstatistical uncertainties. Systematic uncertainties are not taken into account.We offer visualization tools to assess the spread of the oxygen abundance inthe different calibrators, as well as the shape of the estimated oxygenabundance distribution in each calibrator, and develop robust metrics fordetermining the appropriate Monte Carlo sample size. The code is open accessand open source and can be found at https://github.com/nyusngroup/pyMCZ(Abridged)

Monte Carlo Method for Calculating Oxygen Abundances and Their Uncertainties from Strong-Line Flux Measurements