nlargest()

Pandas

Highest n number of elements.
Returns series of n largest values sorted in descending order.

Options

n: Returns n number of values. Default is 5
keep : Default is 'first', other values are 'last' & 'all'. How to handle duplicate values

Examples using options

We will use n=2 and return series based on MATH column.
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'CLASS1':['Four','Three','Three','Four','Five','Three']}
my_data = pd.DataFrame(data=my_dict)
df=my_data.nlargest(columns='MATH',n=2)
print(df)
Output
   NAME  ID  MATH CLASS1
0  Ravi   1    80   Four
2  Alex   3    70  Three

keep

Let us set the value ss keep='first' , you can see there are three equal values in MATH column 2nd in the order.
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'CLASS1':['Four','Three','Three','Four','Five','Three']}
my_data = pd.DataFrame(data=my_dict)
df=my_data.nlargest(columns='MATH',n=2,keep='first')
print(df)
Output ( The first one among three equal records are selected with value=70)
   NAME  ID  MATH CLASS1
0  Ravi   1    80   Four
2  Alex   3    70  Three
Now we will change the value as keep='last'
df=my_data.nlargest(columns='MATH',n=2,keep='last')
Output ( The last one among three equal records are selected with value=70 )
   NAME  ID  MATH CLASS1
0  Ravi   1    80   Four
4  King   5    70   Five
Now we will change the value as keep='all'
df=my_data.nlargest(columns='MATH',n=2,keep='all')
Output ( All the three equal values are selected with value = 70 )
   NAME  ID  MATH CLASS1
0  Ravi   1    80   Four
2  Alex   3    70  Three
3   Ron   4    70   Four
4  King   5    70   Five
Pandas Pandas DataFrame sort_values groupby cut


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