How can I visualize the sales trends across different regions of different product categories?
I am currently engaged in a particular task that is related to analyzing the sales for a retail company that can operate in multiple regions and product categories. There is information about the region, product categories, and sales amount in everyday points in the dataset. I want to visualize the sales trends across different regions while also a comparison of the performance of different product categories within each region.
In the context of data science, you can address this issue by using the seaborne’s hue parameters in plotting functions for the purpose of encoding an additional categorical variable within the visualization. By setting the hue parameters to the variable representing product categories you can create separate color-coded subsets of data for each product category.
Here is how you can use the hue parameters in Seaborn to visualize sales data effectively:-
Import seaborn as sns
Import matplotlib.pyplot as plt
# Assuming sales_data is a DataFrame containing sales data with columns: region, product_category, sales_amount
# Load or generate your sales data here
# Set the style for the plots (optional)
Sns.set_style(“whitegrid”)
# Plot sales trends by region with hue representing product categories
Sns.lineplot(data=sales_data, x=”month”, y=”sales_amount”, hue=”product_category”, marker=’o’)
# Add title and labels
Plt.title(‘Sales Trends by Region and Product Category’)
Plt.xlabel(‘Month’)
Plt.ylabel(‘Sales Amount’)
# Show the legend
Plt.legend(title=’Product Category’)
# Show the plot
Plt.show()