error_bad_lines = False is not ignoring ValueError
I have a very long csv file with some bad lines. Here is a sample of a bad line between some good lines:
2016.07.03 09:43:38\t0.01055556\t16.146\t26.9444444444444\n
2016.07.03 09:43:43\t0.01194444\t16.146\t26.9883333333333\n
2016.07.03 09:43:48\t0.01333333\t16.105\t27.0383333333333\n
2016.07.03 09:43:53\t0.01472222\t16.102\t42731,00\n
2016.07.03 09:43:58\t0.01611111\t16.138\t27.1822222222222\n

So basically the sensor has made a mistake when writing the 4th line, and written 42731,00 instead of an actual number. I want to just skip lines like that, so I read this file with the following statement:
a = pd.read_csv(StringIO(bdy), sep = '\t',
                        skiprows = 2,
                        header = None, error_bad_lines = False, warn_bad_lines = True,
                        parse_dates = [0], decimal = '.')

This results in the 4th column in the resulting df to be of type object (a string) instead of float64.
If i try to force the reader to parse by doing:
pd.read_csv(StringIO(bdy), sep = '\t',
                        skiprows = 2,
                        header = None, error_bad_lines = False, warn_bad_lines = True,
                        parse_dates = [0], decimal = '.', dtype={1:np.float64,2:np.float64,3:np.float64})
i get: ValueError: could not convert string to float...but it is not being ignored even though i have set error_bad_lines = False. So how can i actually parse this correctly - preferrebly without having to supply dtype? Is this a bug?
