pandas.DataFrame.nlargest¶
-
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.Parameters: 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)
Returns: DataFrame
The first n rows ordered by the given columns in descending order.
See also
DataFrame.nsmallest
- Return the first n rows ordered by columns in ascending order.
DataFrame.sort_values
- Sort DataFrame by the values
DataFrame.head
- Return the first n rows without re-ordering.
Notes
This function cannot be used with all column types. For example, when specifying columns with object or category dtypes,
TypeError
is raised.Examples
>>> 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 aTypeError
:>>> df.nlargest(3, 'b') Traceback (most recent call last): TypeError: Column 'b' has dtype object, cannot use method 'nlargest'