You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
150 lines
4.9 KiB
150 lines
4.9 KiB
<!--
|
|
---
|
|
title: Pandas Consumer Sales Lab
|
|
type: lab
|
|
duration: "1:00"
|
|
---
|
|
-->
|
|
|
|
##  {.separator}
|
|
|
|
<h1>Pandas Consumer Sales Lab</h1>
|
|
|
|
<!--
|
|
|
|
## Overview
|
|
This lab makes use of the datetime and joining capabilites of pandas. Make sure you review those topics first.
|
|
|
|
## Important Notes or Prerequisites
|
|
|
|
- There are **Class Questions** throughout the notebook. Use as much/little time on these as you see fit relative to how your class is pacing
|
|
- This lesson includes high level slides and a Notebook. To present this content, it is recommended you begin directly with the Jupyter Notebook. The student slides contain the wrap-up of the big ideas covered in the notebook.
|
|
|
|
---
|
|
|
|
## Learning Objectives
|
|
*After this lab, 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
|
|
|
|
## Duration
|
|
60 minutes.
|
|
|
|
---
|
|
|
|
## Suggested Agenda
|
|
|
|
| Time | Activity | Purpose |
|
|
|-------------|----------|---------|
|
|
| 0:00 - 0:03 | Welcome |
|
|
| 0:03 - 0:55 | Exercise |
|
|
| 0:55 - 1:00 | Summary |
|
|
|
|
## Materials and Preparation
|
|
- Send out the link to the presentation slides, and help students download the Notebook.
|
|
|
|
## Differentiation and Extensions
|
|
|
|
- This lab/exercise may be difficult for some struggling students
|
|
- There is really no limit to the amount of work you can do here either - if students are struggling, encourage them to produce tabluar results on a subset of the tables given
|
|
- If students are excelling, push them to create more complex (and even interactive) visualizations
|
|
- A solution notebook is provided to show some examples of what can be done with pandas, .plot extension of pandas
|
|
|
|
-->
|
|
|
|
---
|
|
|
|
## 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.
|
|
|
|
<aside class="notes">
|
|
|
|
**Teaching Tip**:
|
|
|
|
- This is an intro to the notebook - use it as an overview to cover the concepts. Budget time accordingly.
|
|
|
|
</aside>
|
|
|
|
|
|
---
|
|
|
|
## 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!
|
|
|
|
<aside class="notes">
|
|
|
|
**Teaching Tip**:
|
|
|
|
- Refer to the lesson overview about time management here
|
|
|
|
</aside>
|
|
|
|
---
|
|
|
|
## 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.
|
|
|
|
<aside class="notes">
|
|
|
|
**Teaching Tips**:
|
|
- Talk to students telling them that this is effectively a small, star-schema transactional database dump.
|
|
- Pretty much exactly what you'd have as a sales data warehouse for a mid-size enterprise company.
|
|
- The dataset covers about 2 years of sales data.
|
|
- It's not too big, not too small.
|
|
|
|
</aside>
|
|
|
|
---
|
|
|
|
## 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
|
|
|
|
<aside class="notes">
|
|
|
|
**Teaching Tips**:
|
|
|
|
- Remind students that these questions are intentionally open-ended.
|
|
- The notebook is designed to help students step-by-step to make a fully joined dataset.
|
|
- After that, the training wheels come off and they are forced to navigate the joined set to answer these questions.
|
|
- Be sure to frame the exercise as something that likely will take them longer than 60 minutes to complete to their satisfaction - that's OK!
|
|
|
|
</aside>
|
|
|
|
---
|
|
|
|
## 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)
|