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.
# 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 |
Output:
Code #2: Sorting by sort_values()
# 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() |
Output:
As shown in the output image, the values returned by both functions is similar.
