Create new column into dataframe based on values from other columns using apply function onto multiple columns

I am using apply function to create a new column i.e. ERROR_TV_TIC into dataframe based on existing columns [TV_TIC and ERRORS] values. I am not sure what I am doing wrong. With some conditions it works and with another it doesn't and throw error.

DataFrame:

ERRORS|TV_TIC
|2.02101E+41
['Length of Underlying Symbol for Option Contract is exceeding allowed limits(10 chars)']|nan
['Future Option Indicator is missing']|nan
['Trade Id is missing', 'Future Option Indicator is missing']|nan
['Trade Id is missing', 'Future Option Indicator is missing']|nan

Code when it works:

def validate_tv_tic(trades):
    tv_tiv_errors = list() 
    if pd.isnull(trades['TV_TIC']):
        tv_tiv_errors.append("Initial validations passed still TV_TIC missing")
    if pd.notnull(trades['TV_TIC']) and len(trades['TV_TIC']) != 42:
        tv_tiv_errors.append("Initial validations passed and TV_TIC is also generated but length is != 42 chars")
    return tv_tiv_errors if len(tv_tiv_errors) > 0 else np.nan

trades['ERROR_TV_TIC'] = trades.apply(validate_tv_tic, axis=1)

Code when it doesn't work: Here now condition is on 2 columns of series and I am making sure that I am passing "&" and not "and"

def validate_tv_tic(trades):
    tv_tiv_errors = list()
    if pd.isnull(trades['ERRORS']) & pd.isnull(trades['TV_TIC']):
        tv_tiv_errors.append("Initial validations passed still TV_TIC missing")
    if pd.isnull(trades['ERRORS']) & pd.notnull(trades['TV_TIC']) & len(trades['TV_TIC']) != 42:
        tv_tiv_errors.append("Initial validations passed and TV_TIC is also generated but length is != 42 chars")
    return tv_tiv_errors if len(tv_tiv_errors) > 0 else np.nan

trades['ERROR_TV_TIC'] = trades.apply(validate_tv_tic, axis=1)

Error I am getting: ('The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()', 'occurred at index 3')

Error description with used "and"

Error description when used "&"

My gut feeling is saying that pd.isnull is somewhere causing problem but not sure.