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DataFrame.nlargest(n, columns, keep='first')[source]

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.


n : int

Number of rows to return.

columns : label or list of labels

Column label(s) to order by.

keep : {‘first’, ‘last’}, default ‘first’

Where there are duplicate values:

  • first : prioritize the first occurrence(s)
  • last : prioritize the last occurrence(s)


The first n rows ordered by the given columns in descending order.

See also

Return the first n rows ordered by columns in ascending order.
Sort DataFrame by the values
Return the first n rows without re-ordering.


This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.


>>> df = pd.DataFrame({'a': [1, 10, 8, 10, -1],
...                    'b': list('abdce'),
...                    'c': [1.0, 2.0, np.nan, 3.0, 4.0]})
>>> df
    a  b    c
0   1  a  1.0
1  10  b  2.0
2   8  d  NaN
3  10  c  3.0
4  -1  e  4.0

In the following example, we will use nlargest to select the three rows having the largest values in column “a”.

>>> df.nlargest(3, 'a')
    a  b    c
1  10  b  2.0
3  10  c  3.0
2   8  d  NaN

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'a', keep='last')
    a  b    c
3  10  c  3.0
1  10  b  2.0
2   8  d  NaN

To order by the largest values in column “a” and then “c”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['a', 'c'])
    a  b    c
3  10  c  3.0
1  10  b  2.0
2   8  d  NaN

Attempting to use nlargest on non-numeric dtypes will raise a TypeError:

>>> df.nlargest(3, 'b')
Traceback (most recent call last):
TypeError: Column 'b' has dtype object, cannot use method 'nlargest'
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