|
|
<!doctype html>
|
|
|
<html lang="en">
|
|
|
<head>
|
|
|
<meta charset="utf-8">
|
|
|
|
|
|
<title></title>
|
|
|
|
|
|
<meta name="description" content="">
|
|
|
|
|
|
|
|
|
<meta name="apple-mobile-web-app-capable" content="yes" />
|
|
|
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
|
|
|
|
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
|
|
|
|
|
|
<!-- For syntax highlighting -->
|
|
|
<link rel="stylesheet" href="../../../../lib/css/zenburn.css">
|
|
|
<link rel="stylesheet" href="../../../../lib/css/prism.css">
|
|
|
|
|
|
<link rel="stylesheet" href="../../../../css/reveal.css">
|
|
|
<link rel="stylesheet" href="../../../../css/theme/ga-title.css" id="theme">
|
|
|
|
|
|
<!--[if lt IE 9]>
|
|
|
<script src="lib/js/html5shiv.js"></script>
|
|
|
<![endif]-->
|
|
|
|
|
|
<link rel="stylesheet" type="text/css" href="https://s3.amazonaws.com/python-ga/proxima-nova/fonts.css" />
|
|
|
|
|
|
</head>
|
|
|
|
|
|
<body class="language-javascript">
|
|
|
|
|
|
<div class="reveal">
|
|
|
|
|
|
<!-- Any section element inside of this container is displayed as a slide -->
|
|
|
<div class="slides">
|
|
|
|
|
|
|
|
|
<!--
|
|
|
---
|
|
|
title: Plotting with Pandas
|
|
|
type: lesson
|
|
|
duration: "1:00"
|
|
|
---
|
|
|
-->
|
|
|
<section id="section" class="level2 separator">
|
|
|
<h2><img src="http://nagale.com/ga-python/images/GA_Cog_Medium_White_RGB.png" /></h2>
|
|
|
<h1>
|
|
|
Plotting with Pandas
|
|
|
</h1>
|
|
|
<!--
|
|
|
|
|
|
## Overview
|
|
|
This lesson covers plotting with pandas, which serves as a wrapper for matplotlib.
|
|
|
|
|
|
## Learning Objectives
|
|
|
In this lesson, students will:
|
|
|
- Use pandas to plot from three different datasets
|
|
|
|
|
|
## Duration
|
|
|
60 minutes
|
|
|
|
|
|
## Suggested Agenda
|
|
|
|
|
|
| Time | Activity |
|
|
|
| --- | --- |
|
|
|
|
|
|
## Suggested Agenda
|
|
|
|
|
|
| Time | Activity | Purpose |
|
|
|
|-------------|----------|---------|
|
|
|
| 0:00 - 0:03 | Welcome |
|
|
|
| 0:03 - 0:15 | Slides |
|
|
|
| 0:15 - 0:17 | NOTE: Switch to Notebook |
|
|
|
| 0:17 - 0:25 | Line Plots |
|
|
|
| 0:25 - 0:35 | Bar Plots and Histograms |
|
|
|
| 0:35 - 0:44 | Scatter Plots |
|
|
|
| 0:44 - 0:58 | Independent Exercises |
|
|
|
| 0:58 - 1:00 | Summary |
|
|
|
|
|
|
## Materials and Preparation
|
|
|
- Send out the presentation link.
|
|
|
- Students will need the data sets and notebook. Consider having a zip file of all notebooks and data sets for the rest of the unit that you hand out at the beginning of this lesson. Alternatively, link them directly in GitHub - remember that they haven't learned GitHub, so you'll need to help them download the files.
|
|
|
|
|
|
## Differentiation and Extensions
|
|
|
|
|
|
- If students are excelling in the first half, consider deeper discussions surrounding five number summaries, data integrity, off-the-cuff filters and sorts
|
|
|
- If students are struggling, work on the code more heavily than the **Class Questions** portions. Make the Independent Exercises be Collective Exercises (as a class)
|
|
|
|
|
|
## In Class: Materials
|
|
|
- Projector
|
|
|
- Internet connection
|
|
|
- Jupyter Notebooks
|
|
|
- Python3
|
|
|
-->
|
|
|
<hr />
|
|
|
<aside class="notes">
|
|
|
<p><strong>Talking Points</strong>: 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.</p>
|
|
|
<p>To present this content, begin with <code>04-plotting-with-pandas.ipynb</code> 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.</p>
|
|
|
<strong>Teaching Tips</strong>: - There are <strong>Class Questions</strong> 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 <strong>Independent Exercise</strong> 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
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="a-note-on-delivery" class="level2">
|
|
|
<h2>A Note on Delivery</h2>
|
|
|
<ul>
|
|
|
<li>This unit’s lessons will occur in <a href="http://jupyter.org/">jupyter notebooks</a>
|
|
|
<ul>
|
|
|
<li>Slides will be an introduction to the lesson (no code, just overview)</li>
|
|
|
<li>Then, we’ll open a notebook and start coding!</li>
|
|
|
</ul></li>
|
|
|
</ul>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Teaching Tips</strong>: - 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</p>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="plotting-with-pandas" class="level2">
|
|
|
<h2>Plotting with Pandas</h2>
|
|
|
<ul>
|
|
|
<li>Pandas <code>.plot()</code> functionality is effectively a wrapper for <a href="https://matplotlib.org/">matplotlib</a></li>
|
|
|
<li>Matplotlib is a charting library for python and scientific computing</li>
|
|
|
<li>It’s considered the de-facto standard for charting locally
|
|
|
<ul>
|
|
|
<li>It’s best for scientific papers, EDA, and general introspection of data</li>
|
|
|
<li>It’s not so great for production level charts that are embedded in applications (check out <a href="https://d3js.org/">d3.js</a></li>
|
|
|
</ul></li>
|
|
|
</ul>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Talking Points</strong>:</p>
|
|
|
<ul>
|
|
|
<li>Talk briefly about where charts are interpreted, and why different tools may be advantageous</li>
|
|
|
</ul>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="so-pandas-and-matplotlib" class="level2">
|
|
|
<h2>So, Pandas and Matplotlib</h2>
|
|
|
<p>Whats a wrapper?</p>
|
|
|
<ul>
|
|
|
<li>A program that <em>abstracts</em> another program to modify its interface</li>
|
|
|
</ul>
|
|
|
<p>???</p>
|
|
|
<ul>
|
|
|
<li>Pandas <code>.plot()</code> functionality references matplotlib behind the scenes</li>
|
|
|
<li>Matplotlib has a reputation for being fairly complex
|
|
|
<ul>
|
|
|
<li>Even for fairly simple charts, you will frequently write loops</li>
|
|
|
<li>A fairly plain chart can be 20-30 lines of code</li>
|
|
|
</ul></li>
|
|
|
<li>Pandas helps us here and most charts can be produced with 1-2 lines of code
|
|
|
<ul>
|
|
|
<li>Some functionality is reduced, but <em>effort is minimized in most cases</em></li>
|
|
|
</ul></li>
|
|
|
</ul>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Talking Points</strong>:</p>
|
|
|
<ul>
|
|
|
<li>Encourage students to learn matplotlib on their own time if they wish</li>
|
|
|
<li>Many data science shops use matplotlib as a standard
|
|
|
<ul>
|
|
|
<li>It’s a bit older and a little hokey, but it’s well supported, open source, and generally gets the job done</li>
|
|
|
</ul></li>
|
|
|
<li>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</li>
|
|
|
</ul>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="talk-data-to-me" class="level2">
|
|
|
<h2>Talk Data to Me</h2>
|
|
|
<p>We’ll be using three data sets for this lesson:</p>
|
|
|
<ul>
|
|
|
<li>Football Records: International football results from 1872 to 2018</li>
|
|
|
<li>Avocado Prices: Historical data on avocado prices and sales volume in multiple US markets</li>
|
|
|
<li>Chocolate Bar Ratings: Expert ratings of over 1,700 chocolate bars</li>
|
|
|
</ul>
|
|
|
<p>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.</p>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Talking Points</strong>:</p>
|
|
|
<ul>
|
|
|
<li>We’ll be walking through the data sets during the lesson, feel free to refer to the links there if you wish.</li>
|
|
|
</ul>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="chart-types" class="level2">
|
|
|
<h2>Chart Types</h2>
|
|
|
<p>We’ll be covering the following chart types during this lesson:</p>
|
|
|
<ul>
|
|
|
<li>Time series line charts</li>
|
|
|
<li>Categorical bar charts</li>
|
|
|
<li>Histograms of single columns</li>
|
|
|
<li>Histograms of entire data frames</li>
|
|
|
<li>Scatter plots (continuous vs continuous)</li>
|
|
|
<li>Scatter matricies (multiple scatter plots in a grid)</li>
|
|
|
<li>Scatter plots with class colors for data points</li>
|
|
|
</ul>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Teaching Tips</strong>:</p>
|
|
|
<ul>
|
|
|
<li>This is the tip of the iceberg for plots, that’s okay</li>
|
|
|
<li>Assure students that the above charts have been selected specifically to cover the majority of cases you’ll encounter</li>
|
|
|
<li>Take a minute to talk about the common use cases for each of these, as well as the data types they all prefer</li>
|
|
|
</ul>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
<section id="lets-go" class="level2">
|
|
|
<h2>Let’s Go!</h2>
|
|
|
<ul>
|
|
|
<li>Open up your dataset!</li>
|
|
|
</ul>
|
|
|
<aside class="notes">
|
|
|
<p><strong>Teaching Tips</strong>:</p>
|
|
|
<ul>
|
|
|
<li>Make sure everyone gets to the notebook successfully.</li>
|
|
|
<li>Have students assist one another and walk around the room to ensure everyone gets to the notebook successfully</li>
|
|
|
<li>Make sure all students can open and run their Notebooks. It’s only the second time they’ve done so!</li>
|
|
|
<li>The presentation is also at the top of the Notebook, so students can later reference in one place. Jump down to <code>Importing Pandas</code>.</li>
|
|
|
</ul>
|
|
|
</aside>
|
|
|
<hr />
|
|
|
</section>
|
|
|
|
|
|
</div>
|
|
|
<footer><span class='slide-number'></span></footer>
|
|
|
</div>
|
|
|
<script src="../../../../lib/js/head.min.js"></script>
|
|
|
<script src="../../../../js/reveal.js"></script>
|
|
|
|
|
|
<script>
|
|
|
|
|
|
var dependencies = [
|
|
|
{ src: '../../../../lib/js/classList.js', condition: function() { return !document.body.classList; } },
|
|
|
{ src: '../../../../plugin/markdown/showdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
|
|
{ src: '../../../../plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
|
|
{ src: '../../../../plugin/prism/prism.js', async: true, callback: function() { /*hljs.initHighlightingOnLoad();*/ } },
|
|
|
{ src: '../../../../plugin/zoom-js/zoom.js', async: true, condition: function() { return !!document.body.classList; } }
|
|
|
];
|
|
|
|
|
|
if (Reveal.getQueryHash().instructor === 1) {
|
|
|
dependencies.push({ src: '../../../../plugin/notes/notes.js', async: true, condition: function() { return !!document.body.classList; } });
|
|
|
}
|
|
|
// Full list of configuration options available here:
|
|
|
// https://github.com/hakimel/reveal.js#configuration
|
|
|
Reveal.initialize({
|
|
|
controls: true,
|
|
|
progress: true,
|
|
|
history: true,
|
|
|
center: false,
|
|
|
slideNumber: true,
|
|
|
|
|
|
// available themes are in /css/theme
|
|
|
theme: Reveal.getQueryHash().theme || 'default',
|
|
|
|
|
|
// default/cube/page/concave/zoom/linear/fade/none
|
|
|
transition: Reveal.getQueryHash().transition || 'slide',
|
|
|
|
|
|
// Optional libraries used to extend on reveal.js
|
|
|
dependencies: dependencies
|
|
|
});
|
|
|
|
|
|
if (Reveal.getQueryHash().instructor === 1) {
|
|
|
Reveal.configure(dependencies.push({ src: '../../../../plugin/notes/notes.js', async: true, condition: function() { return !!document.body.classList; } }));
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
Reveal.addEventListener('ready', function() {
|
|
|
if (Reveal.getCurrentSlide().classList.contains('separator-subhead')) {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga-subhead.css');
|
|
|
} else if (Reveal.getCurrentSlide().classList.contains('separator')) {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga-title.css')
|
|
|
} else {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga.css');
|
|
|
}
|
|
|
});
|
|
|
|
|
|
Reveal.addEventListener('slidechanged', function(e) {
|
|
|
if (Reveal.getCurrentSlide().classList.contains('separator-subhead')) {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga-subhead.css');
|
|
|
} else if (Reveal.getCurrentSlide().classList.contains('separator')) {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga-title.css')
|
|
|
} else {
|
|
|
document.getElementById('theme').setAttribute('href', '../../../../css/theme/ga.css');
|
|
|
}
|
|
|
});
|
|
|
</script>
|
|
|
|
|
|
</body>
|
|
|
</html>
|
