python - drop rows with errors for pandas data coercion -
i have dataframe, need convert columns floats , ints, has bad rows, ie., values in column should float or integer instead string values. if use df.bad.astype(float) , error, expected. if use df.bad.astype(float, errors='coerce') , or pd.to_numeric(df.bad, errors='coerce') , bad values replaced np.nan , according spec , reasonable. there errors='ignore' , option ignores errors , leaves erroring values alone. but actually, want not ignore errors, drop rows bad values. how can this? i can ignore errors , type checking, that's not ideal solution, , there might more idiomatic this. example test = pd.dataframe(["3", "4", "problem"], columns=["bad"]) test.bad.astype(float) ## valueerror: not convert string float: 'problem' i want this: pd.to_numeric(df.bad, errors='drop') and returns dataframe 2 rows. since bad values replaced np.nan not df.dropna() rid of bad rows now? edi...