将 numpy 向量化函数中的数据帧作为参数传递

Question

I have a dataframe with 30 columns. I am passing my dataframe to the function and returning values through numpy vectorization. However it`s not working and giving me an error that invalid index to scalar variable. T2 is constant with value 5000

def get_short_incl_MC_rules(df,T2):      
    return 'True' if(df['yield_rank'] < T2 and df['active_events_and_earnings'] == 1 and df['market_cap'] > 500 and df['net_income'] > 0) else False

vectFunc = np.vectorize(get_short_incl_MC_rules)
list(vectFunc(df,T2))

Answer 1

Chain boolean masks together with & for bitwise AND , it is called boolean indexing and it is vectorized operation. Also for improving performance is added values for comparing by numpy arrays:

def get_short_incl_MC_rules(df,T2):      
    return (df['yield_rank'].values < T2) & 
           (df['active_events_and_earnings'].values == 1)  & 
           (df['market_cap'].values > 500)  & 
           (df['net_income'].values > 0)

out = get_short_incl_MC_rules(df,T2)