Write a program in Python to find the minimum rank of a particular column in a dataframe

Finding the minimum rank of values in a DataFrame column is useful for data analysis and ranking operations. Pandas provides the rank() method with different ranking strategies including the minimum rank approach.

Understanding Minimum Rank

Minimum rank assigns the lowest possible rank to tied values. For example, if two values tie for 1st place, both get rank 1, and the next rank becomes 3 (skipping rank 2).

Creating the DataFrame

Let's start by creating a sample DataFrame with age data ?

import pandas as pd

data = {'Id': [1, 2, 3, 4, 5],
        'Name': ["Adam", "David", "Michael", "Peter", "William"],
        'Age': [12, 13, 14, 12, 13]}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
   Id     Name  Age
0   1     Adam   12
1   2    David   13
2   3  Michael   14
3   4    Peter   12
4   5  William   13

Calculating Minimum Rank

Use the rank() method with method='min' parameter to calculate minimum ranks ?

import pandas as pd

data = {'Id': [1, 2, 3, 4, 5],
        'Name': ["Adam", "David", "Michael", "Peter", "William"],
        'Age': [12, 13, 14, 12, 13]}

df = pd.DataFrame(data)
df["Rank"] = df["Age"].rank(axis=0, method='min', ascending=True)
print("DataFrame with Minimum Rank:")
print(df)

DataFrame with Minimum Rank:
   Id     Name  Age  Rank
0   1     Adam   12   1.0
1   2    David   13   3.0
2   3  Michael   14   5.0
3   4    Peter   12   1.0
4   5  William   13   3.0

How It Works

In the ranking process:

• Ages 12 (Adam and Peter) both get rank 1.0 (minimum rank for tied values)

• Ages 13 (David and William) both get rank 3.0 (next available rank after skipping 2)

• Age 14 (Michael) gets rank 5.0 (next available rank after skipping 4)

Getting Just the Rank Values

To get only the rank series without adding to DataFrame ?

import pandas as pd

data = {'Id': [1, 2, 3, 4, 5],
        'Name': ["Adam", "David", "Michael", "Peter", "William"],
        'Age': [12, 13, 14, 12, 13]}

df = pd.DataFrame(data)
age_ranks = df["Age"].rank(axis=0, method='min', ascending=True)
print("Age column minimum ranks:")
print(age_ranks)

Age column minimum ranks:
0    1.0
1    3.0
2    5.0
3    1.0
4    3.0
Name: Age, dtype: float64

Parameters Explained

• axis=0: Rank along rows (default behavior)

• method='min': Assign minimum rank to tied groups

• ascending=True: Lower values get lower ranks

Conclusion

Use df["column"].rank(method='min') to calculate minimum ranks for tied values in a DataFrame column. This method ensures tied values receive the lowest possible rank in their group.