How to get the most frequent row in table

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

python pandas numpy frequency mode

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_30      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)

python pandas numpy frequency mode

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

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How to get the most frequent row in table

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