How to Replicate SQL's DENSE_RANK() Function in Pandas?

Replicating SQL's DENSE_RANK() in Pandas DataFrames

Pandas offers a straightforward way to mimic SQL's DENSE_RANK() function, which assigns ranks without gaps even when encountering tied values. This is crucial for scenarios requiring consecutive rank assignments regardless of duplicates.

Leveraging Pandas' rank() Method

The core solution lies in Pandas' rank() method. By specifying the method='dense' argument, we achieve the desired dense ranking behavior.

Illustrative Example:

Let's consider a sample DataFrame:

import pandas as pd

data = {'Year': [2012, 2013, 2013, 2014], 'Value': [10, 20, 25, 30]}
df = pd.DataFrame(data)

To generate a 'Rank' column mirroring DENSE_RANK(), use this code:

df['Rank'] = df['Year'].rank(method='dense').astype(int)
print(df)

This produces the following output:

<code>   Year  Value  Rank
0  2012     10     1
1  2013     20     2
2  2013     25     2
3  2014     30     3</code>

Notice how the tied 'Year' values (2013) receive the same rank (2), maintaining the dense ranking sequence. The .astype(int) converts the rank to an integer data type for cleaner presentation.

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