Percentile rank of a column in a Pandas DataFrame
Let us see how to find the percentile rank of a column in a Pandas DataFrame. We will use the rank()
function with the argument pct = True
to find the percentile rank.
Example 1 :
# import the module import pandas as pd # create a DataFrame data = { 'Name' : [ 'Mukul' , 'Rohan' , 'Mayank' , 'Shubham' , 'Aakash' ], 'Location' : [ 'Saharanpur' , 'Meerut' , 'Agra' , 'Saharanpur' , 'Meerut' ], 'Pay' : [ 50000 , 70000 , 62000 , 67000 , 56000 ]} df = pd.DataFrame(data) # create a new column of percentile rank df[ 'Percentile Rank' ] = df.Pay.rank(pct = True ) # displaying the percentile rank display(df) |
chevron_right
filter_none
Output :
Example 2 :
# import the module import pandas as pd # create a DataFrame ODI_runs = { 'name' : [ 'Tendulkar' , 'Sangakkara' , 'Ponting' , 'Jayasurya' , 'Jayawardene' , 'Kohli' , 'Haq' , 'Kallis' , 'Ganguly' , 'Dravid' ], 'runs' : [ 18426 , 14234 , 13704 , 13430 , 12650 , 11867 , 11739 , 11579 , 11363 , 10889 ]} df = pd.DataFrame(ODI_runs) # create a new column of percentile rank df[ 'Percentile Rank' ] = df.runs.rank(pct = True ) # displaying the percentile rank display(df) |
chevron_right
filter_none
Output :
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.