Since we want to find the most profitable stores based on the given data, we need to group the observations in the `df` dataframe by the `'Store'` column values and calculate the total (sum) of the `'Weekly_Sales'` column for each group. Finally, we need to sort the resulting values in descending order.

This hands-on course provides an opportunity to apply and consolidate your skills across a range of data analysis tasks using real-world dataset. You'll learn how to identify and address data issues, and then explore and visualize data using pandas, matplotlib, and seaborn to uncover insights and draw conclusions.

First, let's get acquainted with the data we will be working with. Throughout this section, we will perform the initial set of actions to prepare the data for further analysis.

In the second section, we will continue with the preparatory tasks started in the first section and ensure the data is ready for further exploration.

After preparing the data, we will move on to some exploratory analysis. Throughout this section, we will answer several questions and uncover interesting insights.

In the last section, we will use visualization tools to either confirm or reject the hypotheses we made in the previous section. We will also create visualizations of key indicators for the entire dataset.

The Most Profitable Store

Ratkaisu

Awesome!

Awesome!

The Most Profitable Store

Ratkaisu

Awesome!