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4.9 KiB
4.9 KiB
Pandas Consumer Sales Lab
Learning Objectives
After this lesson, you will be able to:
- Apply what you've learned in the datetime and joining lessons to a real dataset.
- Apply your charting experience to visualize insights based off of your EDA'd data.
To the notebook!
We actually will commence this lesson directly in the Jupyter Notebook, pandas-consumersales.ipynb, to walkthrough the what, why, and how all at once.
Here we have slides reviewing the key concepts.
Exercise Overview
- First, this exercise can easily take more than 60 minutes.
- Think of this as an opportunity to dive into the topic and apply datetime and joining operations to a real dataset.
- Budget time outside of class to continue work on this if you can:
- Remember, the more comfortable you become with this, the more likely you'll use it in your day-to-day life!
Data Background
- This exercise uses a dataset originally used for qlik.
- If you want, try clicking (pun intended) around in their web-based solution to familiarize yourself with the data.
- This is a global food distribution company (canned goods, produce, meats, etc.).
- The data you have about their sales and inventory is distributed across multiple sheets, and even in different languages!
- This is an exercise very similar to what you'd be doing with relational databases with a larger enterprise company.
What Are We Looking For?
- Your boss, Joanna, has requested a report on the following:
- Product Sales
- Margin analysis, by region, by product group.
- Sales by product group
- Sales, and budget, year over year
- Sales Reps
- Sales and sales quantity, by rep, by customer
- Supply Chain
- Inventory vs. Lead Time for all products
- Product Sales
Additional Resources
- Pandas documentation
- DataSchool 30-video series (by a former GA instructor!)
- Qlik Consumer Sales Dataset