Python | Pandas DataFrame.nlargest()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas nlargest() method is used to get n largest values from a data frame or a series.

Syntax:

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

Parameters:

n: int, Number of values to select
columns: Column to check for values or user can select column while calling too. [For example: data[“age”].nsmallest(3) OR data.nsmallest(3, “age”)]



keep: object to set which value to select if duplicates exit. Options are ‘first’ or ‘last’

To download the CSV file used, Click Here.

Code #1: Extracting Largest 5 values
In this example, Largest 5 values are extracted and then compared to the other sorted by the sort_values() function. NaN values are removed before trying this method.

Refer sort_values and dropna() function.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas package
import pandas as pd
  
# making data frame from csv file
data = pd.read_csv("employees.csv")
  
# removing null values
data.dropna(inplace = True)
  
# extracting greatest 5
large5 = data.nlargest(5, "Salary")
  
# display
large5

chevron_right


Output:

 

Code #2: Sorting by sort_values()

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas package
import pandas as pd
  
# making data frame from csv file 
data = pd.read_csv("employees.csv")
  
# removing null values
data.dropna(inplace = True)
  
# sorting in descending order
data.sort_values("Salary", ascending = False, inplace = True)
  
# displaying top 5 values
data.head()

chevron_right


Output:

As shown in the output image, the values returned by both functions is similar.



My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.