Dataframe count of columns matching value in another column in that row - dataframe

Question

How to find the count of columns with same value as a specified column in the dataframe with large number of rows.
For instance, below df has
df = pd.DataFrame(np.random.randint(0,10,size=(5, 4)), columns=list('ABCD'))
df.index.name = 'id'
A B C D
id
0 7 6 6 2
1 6 5 3 5
2 8 8 0 9
3 0 2 8 9
4 4 3 8 5
bc_cols = ['B', 'C']
df['max'] = df[bc_cols].max(axis=1)
A B C D BC_max
id
0 7 6 6 2 6
1 6 5 3 5 5
2 8 8 0 9 8
3 0 2 8 9 8
4 4 3 8 5 8
For each row, we want to get the number of columns with the value matching the max. I was able to get to by doing this.
df["freq"] = df[bc_cols].stack().groupby(by='id').apply(lambda g: g[g==g.max()].count())
A B C D BC_max BC_freq
id
0 7 6 6 2 6 2
1 6 5 3 5 5 1
2 8 8 0 9 8 1
3 0 2 8 9 8 1
4 4 3 8 5 8 1
But this is turning out to be very inefficient and slow. We need to do this on a fairly large dataframe with several hundred thousand rows so I am looking for an efficient way to do this. Any ideas?

Answer 1

Once you have BC_max why not re-use it:
def get_bc_freq(row):
if (row.B == row.BC_max) and (row.C == row.BC_max):
return 2
elif (row.B == row.BC_max) or (row.C == row.BC_max):
return 1
return 0
df['freq'] = df.apply(lambda row: get_bc_freq(row), axis=1)
Or the prettier one-liner:
df['freq'] = df.apply(lambda row: [row.B, row.C].count(row.BC_max), axis=1)
UPDATE - to make the columns you use more dynamic you could use list comprehension (not sure how much this helps with performance but...):
cols_to_use = ['B', 'C']
df['freq'] = df.apply(lambda row: [row[x] for x in cols_to_use].count(row.BC_max), axis=1)