Finding the most frequent combination in DataFrame

if the order does matter:

df['FROM_TO'] = df['FROM'] + df['TO']

df['COUNT'] = 1

df.groupby(['FROM_TO'])['COUNT'].sum()

gives you all the occurrences in one go. Simply take the max to find the largest occurrence.

If the order does matter first sort the values before:

df.loc[:,:] = np.sort(df.values,axis=1) # if the df only consists of the FROM adn TO columns.

You can group by the two columns together and count the number of occurrences of each pair, then sort the pairs by this count.

The following code does the job:

df.groupby(["From", "To"]).size().sort_values(ascending=False)

and, for the example of the question, it returns:

From        To
-----------------------
Home        Office    3
Restaurant  Office    1
Airport     Home      1

IIUC, SeriesGroupBy.value_counts and Series.idxmax

df.groupby('From')['To'].value_counts().idxmax()

Output

('Home', 'Office')

in general groupby.value_counts is faster than groupby.size

Another way:

df.apply(tuple, axis=1).value_counts().idxmax()

Or

df.apply(tuple, axis=1).mode()

Output

0    (Home, Office)
dtype: object