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lang-py
pd.DataFrame({'Type':list('ABBC')*100000, 'Set':list('ZZXY')*100000})-size),numpy.whereoutpacesmap, but the list comprehension is king (about 50% faster thannumpy.where).df['color'] = ['red' if (x['Set'] == 'Z') & (x['Type'] == 'B') else 'green' for x in df]df['color_type'] = np.where(df['Set']=='Z', 'green', df['Type']).iterrows()is notoriously sluggish and the DataFrame shouldn't be modified while iterating.