Reading csv in python pandas and handling bad values
You can pass a custom list of values to be treated as missing using pandas.read_csv . Alternately you can pass functions to the converters argument.
I am using pandas to read a csv file. The data are numbers but stored in the csv file as text. Some of the values are non-numeric when they are bad or missing. How do I filter out these values and convert the remaining data to integers. 
I assume there is a better/faster way than looping over all the values and using isdigit() to test for them being numeric. 
Does pandas or numpy have a way of just recognizing bad values in the reader? If not, what is the easiest way to do it? Do I have to specific the dtypes to make this work?