Pandas Dataframe rank() | Rank DataFrame Entries (original) (raw)

Last Updated : 11 Jul, 2025

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

Pandas DataFrame rank() method returns a rank of every respective entry (1 through n) along an axis of the DataFrame passed. The rank is returned based on position after sorting.

Example:

Python3 `

import pandas as pd df = pd.DataFrame({ 'A': [1, 2, 2, 3, 4], 'B': [5, 6, 7, 8, 9], 'C': [1, 1, 1, 1, 1] }) df['A_rank'] = df['A'].rank() print(df)

Output:

A B C A_rank 0 1 5 1 1.0 1 2 6 1 2.5 2 2 7 1 2.5 3 3 8 1 4.0 4 4 9 1 5.0

Syntax

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

Parameters:

axis: 0 or ‘index’ for rows and 1 or ‘columns’ for Column. method: Takes a string input(‘average’, ‘min’, ‘max’, ‘first’, ‘dense’) which tells pandas what to do with same values. Default is average which means assign average of ranks to the similar values.

numeric_only: Takes a boolean value and the rank function works on non-numeric value only if it's False.

na_option: Takes 3 string input(‘keep’, ‘top’, ‘bottom’) to set position of Null values if any in the passed Series.

ascending: Boolean value which ranks in ascending order if True. pct: Boolean value which ranks percentage wise if True.

Return type: Series with Rank of every index of caller series.

For link to CSV file Used in Code, click here.

Examples

Let's see some examples of how to check the rank of DataFrame data using dataframe.rank() method of the Pandas library.

Example 1

Ranking Column with Unique values In the following example, a new rank column is created which ranks the Name of every Player. All the values in the Namecolumn are unique and hence there is no need to describe a method.

Python `

importing pandas package

import pandas as pd

making data frame from csv file

data = pd.read_csv("nba.csv")

creating a rank column and passing the returned rank series

data["Rank"] = data["Name"].rank()

display

data

sorting w.r.t name column

data.sort_values("Name", inplace = True)

display after sorting w.r.t Name column

data

Output:

As shown in the image, a column 'rank' was created with the rank of every Name. After the sort_value function sorted the DataFrame for names, it can be seen that the rank was also sorted since those were ranking of Names only.

Before Sorting-

before sorting dataframe

After Sorting-

after sorting dataframe

Example 2:

Sorting Column with some similar values in the following example, DataFrame is first sorted for 'team name' and first the method is the default (i.e. average), and hence the rank of same Team players is average. After that min method is also used to see the output.

Python3 `

importing pandas package

import pandas as pd

making data frame from csv file

data = pd.read_csv("nba.csv")

sorting w.r.t team name

data.sort_values("Team", inplace = True)

creating a rank column and passing the returned rank series

change method to 'min' to rank by minimum

data["Rank"] = data["Team"].rank(method ='average')

display

data

Output:

With method='average'

output with method average

With method='min'

output with method min