How to find the most frequent value for each id

Let's say, I have number A and they call several people B

A   B

123 987
123 987
123 124
435 567
435 789
653 876
653 876
999 654
999 654
999 654
999 123

I want to find to whom the person in A has called maximum times and also the number of times.

OUTPUT:

A   B           Count
123 987         2
435 567 or 789  1
653 876         2
999 654         3

How one can think of it is,

A      B
123    987 2
       124 1
435    567 1
       789 1
653    876 2
999    654 3
       123 1

Can somebody help me out on how to do this?

We can get the desired output in 2 steps:

1. Count values for each A (value_counts with 2 columns)
2. Filter the rows with the max value for each A (boolean indexing)

# count the unique values in rows
# `sort_index` is optional
df.value_counts(['A','B']).sort_index()

A    B  
123  124    1
     987    2
435  567    1
     789    1
653  876    2
999  123    1
     654    3
dtype: int64

To get the highest values for each unique A:

v = df.value_counts(['A','B'])
agg_df = v[~v.reset_index(level=0).duplicated('A').values]

You can also replace the second part with groupby.agg that gets the max of the count and its corresponding B value. Then the whole thing can be written in one line:

agg_df = (
    df.value_counts(['A','B'])
    .reset_index()
    .groupby('A').agg({'B': 'first', 'count': 'max'})
)

A    B  
999  654    3
123  987    2
653  876    2
435  567    1
dtype: int64