Skip to content
Related Articles

Related Articles

Save Article
Improve Article
Save Article
Like Article

Python | Pandas Series.rank()

  • Last Updated : 11 Feb, 2019

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.rank() function compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values.

 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. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

Syntax: Series.rank(axis=0, method=’average’, numeric_only=None, na_option=’keep’, ascending=True, pct=False)



Parameter :
axis : index to direct ranking
method : {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}
numeric_only : Include only float, int, boolean data. Valid only for DataFrame or Panel objects
na_option : {‘keep’, ‘top’, ‘bottom’}
ascending : False for ranks by high (1) to low (N)
pct : Computes percentage rank of data

Returns : ranks : same type as caller

Example #1: Use Series.rank() function to rank the underlying data of the given Series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([10, 25, 3, 11, 24, 6])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

Output :

Now we will use Series.rank() function to return the rank of the underlying data of the given Series object.




# assign rank
result = sr.rank()
  
# Print the result
print(result)

Output :



As we can see in the output, the Series.rank() function has assigned rank to each element of the given Series object.

Example #2: Use Series.rank() function to rank the underlying data of the given Series object. The given data also contains some equal values.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([10, 25, 3, 11, 24, 6, 25])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp', 'Appy']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

Output :

Now we will use Series.rank() function to return the rank of the underlying data of the given Series object.




# assign rank
result = sr.rank()
  
# Print the result
print(result)

Output :

As we can see in the output, the Series.rank() function has assigned rank to each element of the given Series object. Notice equal values has been assigned a rank which is the average of their ranks.




My Personal Notes arrow_drop_up
Recommended Articles
Page :