How Does Pandas `rank(method='dense')` Assign Unique Ranks to Tied Values?

Pandas dense ranking: assign unique rankings to tied values ​​

In data analysis, it is often necessary to rank values ​​in a data set based on order. In Pandas, the rank function provides a way to calculate ordinal ranking by default. However, sometimes it is necessary to obtain dense rankings, where tied values ​​receive the same ranking rather than being skipped.

Consider the following Pandas data frame:

<code>Year  Value
2012   10
2013   20
2013   25
2014   30</code>

To generate results equivalent to the DENSE_RANK() function on the Year column, proceed as follows:

• Use Pandas Series.rank with method='dense':

<code>df['Rank'] = df.Year.rank(method='dense').astype(int)

print(df)</code>

This will generate a new column called Rank where each year is assigned a dense ranking:

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

In this example, the tied values ​​for 2013 receive the same rank of 2, ensuring that no values ​​are skipped or ranked incorrectly. The astype(int) conversion is optional, but its use is recommended to ensure that rankings are stored as integers.

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