SQL analytic functions are used to summarize the large dataset into a simple report. The Data summary produces by these functions can be easily visualized. These functions help a data analytics professional to analyze complex data with ease.

In this article, we combine pandas aggregate and analytics functions to implement SQL analytic functions.

There are many categories of SQL analytics functions. And we will go through these functions one by one. But first, let’s know about the data we use in this article.

Retail Dataset

We will be using Kaggle dataset. Here is an explanation of each column of the dataset.

This dataset has a sales date from 2010–02–05 to 2012–11–01.

Store — store number

Dept — department number

Date — represent a week

Weekly_Sales — weekly sales of the store for each department

IsHoliday — represent holiday in a week

Here are a few rows of a retail dataset.

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Join Pandas Aggregate and SQL Analytic Functions
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