PYTHR/unit-6-pandas/05-plotting-with-pandas/05-plotting-with-pandas.md

Plotting with Pandas

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**Talking Points**: This lesson introduces the Pandas library and the beginnings of Exploratory Data Analysis. The majority of the lesson should be spent going through code -- whether that is via Jupyter Slides or a Jupyter Notebook demonstration.

To present this content, begin with 04-plotting-with-pandas.ipynb to introduce Pandas as a library and data integrity. Transition to the Jupyter Notebook to introduce reading in data, column manipulation, filtering and sorting; conclude with exercises.

Teaching Tips:

• There are Class Questions littered throughout the notebook. Use as much/little time on these as you see fit relative to how your class is pacing
• There is no Independent Exercise at the end of this lesson. It is aspirational to have time to let students work entirely independently on this time-wise, so consider doing a guided code-along or paired programming. Use this time to have students set their own challenges.
• Pause after learning objectives and level-set for what students will get out of the lesson

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A Note on Delivery

• This unit's lessons will occur in jupyter notebooks
  • Slides will be an introduction to the lesson (no code, just overview)
  • Then, we'll open a notebook and start coding!

**Teaching Tips**: - We could have made this into a speaker note, but it's helpful to get it out there so everybody's on the same page - No repl.it for this unit as we'll be in notebooks

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Plotting with Pandas

• Pandas .plot() functionality is effectively a wrapper for matplotlib
• Matplotlib is a charting library for python and scientific computing
• It's considered the de-facto standard for charting locally
  • It's best for scientific papers, EDA, and general introspection of data
  • It's not so great for production level charts that are embedded in applications (check out d3.js

**Talking Points**:

• Talk briefly about where charts are interpreted, and why different tools may be advantageous

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So, Pandas and Matplotlib

Whats a wrapper?

• A program that abstracts another program to modify its interface

???

• Pandas .plot() functionality references matplotlib behind the scenes
• Matplotlib has a reputation for being fairly complex
  • Even for fairly simple charts, you will frequently write loops
  • A fairly plain chart can be 20-30 lines of code
• Pandas helps us here and most charts can be produced with 1-2 lines of code
  • Some functionality is reduced, but effort is minimized in most cases

**Talking Points**:

• Encourage students to learn matplotlib on their own time if they wish
• Many data science shops use matplotlib as a standard
  • It's a bit older and a little hokey, but it's well supported, open source, and generally gets the job done
• Take some time to talk about the balance between package complexity and utility overall - sometimes a good answer delivered on time beats a perfect answer delivered late

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Talk Data to Me

We'll be using three data sets for this lesson:

• Football Records: International football results from 1872 to 2018
• Avocado Prices: Historical data on avocado prices and sales volume in multiple US markets
• Chocolate Bar Ratings: Expert ratings of over 1,700 chocolate bars

All datasets have been graciously downloaded from Kaggle.com, and we'll discover that the right visualization can often replace a bit of fancy machine learning, if done properly.

**Talking Points**:

• We'll be walking through the data sets during the lesson, feel free to refer to the links there if you wish.

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Chart Types

We'll be covering the following chart types during this lesson:

• Time series line charts
• Categorical bar charts
• Histograms of single columns
• Histograms of entire data frames
• Scatter plots (continuous vs continuous)
• Scatter matricies (multiple scatter plots in a grid)
• Scatter plots with class colors for data points

**Teaching Tips**:

• This is the tip of the iceberg for plots, that's okay
• Assure students that the above charts have been selected specifically to cover the majority of cases you'll encounter
• Take a minute to talk about the common use cases for each of these, as well as the data types they all prefer

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Let's Go!

• Open up your dataset!

**Teaching Tips**:

• Make sure everyone gets to the notebook successfully.
• Have students assist one another and walk around the room to ensure everyone gets to the notebook successfully
• Make sure all students can open and run their Notebooks. It's only the second time they've done so!
• The presentation is also at the top of the Notebook, so students can later reference in one place. Jump down to Importing Pandas.

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