pandas - under a column, count the total number of a specific value, instead of using value_counts()
You have plenty of ways to do it. You won't see a big difference in performance. My suggestion is to use whatever feels more convenient for you or your team.

import pandas as pd
import numpy as np

#Let's create a dataframe with 10 million integers from 0 to 100
df = pd.DataFrame(np.random.randint(0,100,size=(10000000, 1)), columns=list('A'))

#And now count the value 5 with 4 different ways

%timeit df[df.A == 5].shape[0]
10 loops, best of 3: 25.4 ms per loop

%timeit len(df[df.A == 5])
10 loops, best of 3: 25.4 ms per loop

%timeit len(df[df.A == 5].index)
10 loops, best of 3: 25.6 ms per loop

%timeit df['A'].value_counts()[5]
10 loops, best of 3: 149 ms per loop


As you can see, only the last one takes more time to run.

EDIT: Addition to your comment, you could try this

df = data.groupby('a_1').get_group(a_2)['suffix']
len(df[df.suffix == 'a_3'])