Exception Handling in Pandas .apply() function

Question

If I have a DataFrame:

myDF = DataFrame(data=[[11,11],[22,'2A'],[33,33]], columns = ['A','B']) 

Gives the following dataframe (Starting out on stackoverflow and don't have enough reputation for an image of the DataFrame)

   | A  | B  |  0  | 11 | 11 |  1  | 22 | 2A |  2  | 33 | 33 | 

If i want to convert column B to int values and drop values that can't be converted I have to do:

def convertToInt(cell):     try:         return int(cell)     except:         return None myDF['B'] = myDF['B'].apply(convertToInt) 

If I only do:

myDF['B'].apply(int)

the error obviously is:

C:\WinPython-32bit-2.7.5.3\python-2.7.5\lib\site-packages\pandas\lib.pyd in pandas.lib.map_infer (pandas\lib.c:42840)()

ValueError: invalid literal for int() with base 10: '2A'

Is there a way to add exception handling to myDF['B'].apply()

Thank you in advance!

Answer 1

I had the same question, but for a more general case where it was hard to tell if the function would generate an exception (i.e. you couldn't explicitly check this condition with something as straightforward as isdigit).

After thinking about it for a while, I came up with the solution of embedding the try/except syntax in a separate function. I'm posting a toy example in case it helps anyone.

import pandas as pd import numpy as np  x=pd.DataFrame(np.array([['a','a'], [1,2]]))  def augment(x):     try:         return int(x)+1     except:         return 'error:' + str(x)  x[0].apply(lambda x: augment(x))