Translating SQL\'s COUNT(DISTINCT) to Pandas: How to Use nunique() Method?

Translating SQL's COUNT(DISTINCT) to Pandas Equivalents

In the realm of data manipulation, Pandas has emerged as a potent tool for managing tabular data. When dealing with diverse data sources, such as Oracle and SQL Server, users may encounter challenges translating SQL queries into efficient Pandas operations. One common task involves counting the number of distinct values—a task that calls for an "equivalent" to SQL's COUNT(DISTINCT) function.

To achieve this in Pandas, let's embark on an exploration of a table with columns representing YEARMONTH, CLIENTCODE, and various other attributes. In SQL, counting distinct clients per year can be accomplished with the following query:

SELECT count(distinct CLIENTCODE) FROM table GROUP BY YEARMONTH;

This query yields a result that displays the count of distinct clients for each year. How can we replicate this functionality in Pandas?

The solution lies in utilizing the nunique() method:

table.groupby('YEARMONTH').CLIENTCODE.nunique()

This expression groups the data by the YEARMONTH column and applies the nunique() method to the CLIENTCODE series within each group. The result is a DataFrame that lists the YEARMONTH values along with the count of distinct clients for each year.

To illustrate, consider a sample DataFrame named table:

   CLIENTCODE  YEARMONTH
0           1     201301
1           1     201301
2           2     201301
3           1     201302
4           2     201302
5           2     201302
6           3     201302

Applying the nunique() method yields:

YEARMONTH
201301       2
201302       3

Hence, Pandas' nunique() method provides the equivalent functionality to SQL's COUNT(DISTINCT) for efficiently counting distinct values within a specified column.

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