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## 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.
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## 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.
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## 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!
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## Data Background
- This exercise uses a dataset originally used for [qlik](https://sense-demo.qlik.com/sense/app/372cbc85-f7fb-4db6-a620-9a5367845dce).
- 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.
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## 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
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## Additional Resources
- Pandas [documentation](https://pandas.pydata.org/pandas-docs/stable/)
- DataSchool [30-video series](http://www.dataschool.io/easier-data-analysis-with-pandas/) (by a former GA instructor!)
- Qlik [Consumer Sales Dataset](https://sense-demo.qlik.com/sense/app/372cbc85-f7fb-4db6-a620-9a5367845dce)