Pandas rank()

import pandas as pd

# sample DataFrame
data = {'Score': [78, 85, 96, 86, 90]}

df = pd.DataFrame(data)

# rank the score
print(df.rank())

'''
Output

   Score
0    1.0
1    2.0
2    5.0
3    3.0
4    4.0
'''

rank() Syntax

The syntax of the rank() method in Pandas is:

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

rank() Arguments

The rank() method takes the following arguments:

• axis: specifies whether to rank rows or columns
• method: specifies how to handle equal values
• numeric_only: rank only numeric data if True
• na_option: specifies how to handle NaN
• ascending: specifies whether to rank in ascending order
• pct: specifies whether to display the rank as a percentage.

rank() Return Value

The rank() method returns a DataFrame or Series (depending on the input) with the ranks of the data.

Example 1: Basic Ranking

import pandas as pd

data = {'Score': [78, 85, 96, 85, 90]}
df = pd.DataFrame(data)

df['Rank'] = df['Score'].rank()

print(df)

Output

   Score  Rank
0     78   1.0
1     85   2.5
2     96   5.0
3     85   2.5
4     90   4.0

In this example, we ranked the scores using rank(). The rank() method handles equal values by assigning the average rank of those values.

Example 2: Ranking with Method

import pandas as pd

data = {'Score': [78, 85, 96, 85, 90]}
df = pd.DataFrame(data)

# rank using the 'max' method for ties
df['Rank'] = df['Score'].rank(method='max')

print(df)

Output

   Score  Rank
0     78   1.0
1     85   3.0
2     96   5.0
3     85   3.0
4     90   4.0

In this example, we used method='max' to handle equal values. The max method assigns maximum possible rank to the equal values.

Example 3: Ranking in Descending Order

import pandas as pd

data = {'Score': [78, 85, 96, 85, 90]}
df = pd.DataFrame(data)

# rank in descending order
df['Rank'] = df['Score'].rank(ascending=False)

print(df)

Output

0     78   5.0
1     85   3.5
2     96   1.0
3     85   3.5
4     90   2.0

Here, we ranked the scores in descending order with the highest score receiving the lowest rank.

Example 4: Ranking Numeric Data Only

The numeric_only argument is used to rank only numeric columns when applied to a DataFrame.

import pandas as pd

data = {
    'Score': [78, 85, 96, 85, 90],
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva']
}
df = pd.DataFrame(data)

# rank all columns
print('All columns:')
print(df.rank())
print()

# rank numeric columns only
print('Numeric columns:')
print(df.rank(numeric_only=True))

Output

All columns:
   Score  Name
0    1.0   1.0
1    2.5   2.0
2    5.0   3.0
3    2.5   4.0
4    4.0   5.0

Numeric columns:
   Score
0    1.0
1    2.5
2    5.0
3    2.5
4    4.0

Here, the Name column is not ranked in the second case due to the numeric_only=True argument.

Example 5: Handling NaN

We can use the na_option argument to determine how NaN values in the data are handled.

import pandas as pd

data = {'Score': [78, 85, None, 85, 90]}

df = pd.DataFrame(data)

# rank with NaN placed at the bottom
df['Rank'] = df['Score'].rank(na_option='bottom')

print(df)

Output

   Score  Rank
0   78.0   1.0
1   85.0   2.5
2    NaN   5.0
3   85.0   2.5
4   90.0   4.0

In this case, we assigned the highest rank to the NaN value.

Example 6: Ranking as a Percentage

The pct argument returns rankings as relative percentages.

import pandas as pd

data = {'Score': [78, 85, 96, 85, 90]}

df = pd.DataFrame(data)

# rank the scores as percentages
df['Rank_pct'] = df['Score'].rank(pct=True)

print(df)

Output

   Score  Rank_pct
0     78       0.2
1     85       0.5
2     96       1.0
3     85       0.5
4     90       0.8

Here, the rank is displayed as a relative percentage of the highest rank.