How to get the most frequent row in table

Question

How to get the most frequent row in a DataFrame? For example, if I have the following table:

   col_1  col_2 col_3
0      1      1     A
1      1      0     A
2      0      1     A
3      1      1     A
4      1      0     B
5      1      0     C

Expected result:

   col_1  col_2 col_3
0      1      1     A

EDIT: I need the most frequent row (as one unit) and not the most frequent column value that can be calculated with the mode() method.

Answer 1

Check groupby

df.groupby(df.columns.tolist()).size().sort_values().tail(1).reset_index().drop(0,1)
   col_1  col_2 col_3  
0      1      1     A  

Answer 2

With NumPy's np.unique -

In [92]: u,idx,c = np.unique(df.values.astype(str), axis=0, return_index=True, return_counts=True)

In [99]: df.iloc[[idx[c.argmax()]]]
Out[99]: 
   col_1  col_2 col_3
0      1      1     A

If you are looking for performance, convert the string column to numeric and then use np.unique -

a = np.c_[df.col_1, df.col_2, pd.factorize(df.col_3)[0]]
u,idx,c = np.unique(a, axis=0, return_index=True, return_counts=True)

Answer 3

You can do this with groupby and size:

df = df.groupby(df.columns.tolist(),as_index=False).size()
result = df.iloc[[df["size"].idxmax()]].drop(["size"], axis=1)
result.reset_index(drop=True) #this is just to reset the index

Answer 4

npi_indexed library helps to perform some actions on 'groupby' type of problems with less script and similar performance as numpy. So this is alternative and pretty similar way to @Divakar's np.unique() based solution:

arr = df.values.astype(str)
idx = npi.multiplicity(arr)
output = df.iloc[[idx[c.argmax()]]]