Plotting Tools Intro
Week 1 | Lesson 5.2
After this lesson, you will be able to:
- Describe plotting tools like Matplotlib, seaborn, Tableau
- Sign up for Tableau
Before this lesson, you should already be able to:
- Watch Getting Started
Before this lesson, instructors will need to:
- Ensure Tableau licenses that you requested earlier are available to use
|10 min||Introduction||Matplotlib, seaborn, Tableau|
|20 min||Demo / Guided Practice||matplotlib|
|20 min||Demo / Guided Practice||seaborn|
|20 min||Demo / Guided Practice||Tableau|
|15 min||Independent Practice||Topic description|
|5 min||Conclusion||Topic description|
Introduction: Matplotlib, seaborn, plotly, Tableau (10 mins)
In the previous lesson we covered why data visualizations are so important and some attributes that we want to consider when making them. In this lesson, we're going to be introduced to a few plotting tools that will assist us in making great visualizations.
Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and Jupyter shell (ala MATLAB®* or Mathematica®†), web application servers, and six graphical user interface toolkits. matplotlib
Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. seaborn
Plotly has a graphical user interface for importing and analyzing data into a grid and using stats tools. Graphs can be embedded or downloaded. Mainly used to make creating graphs faster and more efficient. plotly
Tableau queries relational databases, cubes, cloud databases, and spreadsheets and then generates a number of graph that can be combined into dashboards and shared over a computer network or the internet. tableau
Demo / Guided Practice: matplotlib (15 mins)
Seaborn is actually built on top of matplotlib. Matplotlib is the grandfather of python visualization packages. It is extremely powerful but with that power comes complexity. You can typically do anything you need using matplotlib but it is not always so easy to figure out.
One of the biggest reasons that people choose to use packages like seaborn or plotly is because matplotlib takes a lot of work to get semi-reasonable looking visuals. For this reason, we're not going to spend too much time explaining it. But feel free to dig in here
in iPython notebook type:
import matplotlib import matplotlib.pyplot as plt #import matplotlib libary x = [1,2,3,4] #define some data y = [20, 21, 20.5, 20.8] plt.plot(x, y) #plot data plt.show() #show plot
Check: Can you think of a data scenario when matplotlib would be a good tool to use instead of seaborn or plotly?
Demo / Guided Practice: seaborn (15 mins)
Some of the features that seaborn offers are:
- Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - Functions that visualize matrices of data and use clustering algorithms to discover structure in those matrices - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations
Sounds good. Let's put it to work.
Dependencies: numpy, scipy, matplotlib, pandas, and stats models installed
in iPython notebook type:
import numpy as np #by convention numpy is abbreviated np on import import scipy as sp #by convention scipy is abbreviated sp on import import pandas as pd #by convention pandas is abbreviated pd on import import statsmodels as sm #by convention statsmodels is abbreviated sm on import !pip install seaborn import seaborn as sns #by convention seaborn is abbreviated sns on import sns.set_style('whitegrid')
Now let's use seaborn to take a cursory look at the iris data set.
iris = sns.load_dataset("iris") sns.pairplot(iris, hue="species")
Here we can see different levels of categorical variable by color. iris and seaborn
Check: When do you think using seaborn would be best? When do you think using matplotlib would be best? Why?
Demo / Guided Practice: Tableau (15 mins)
Now, let's give Tableau a whirl. Let's watch the short "Getting Started with Visual Analytics" video Tableau can do a lot of things. Don't be overwhelmed by this video, it's just intended to get you started on a few things that Tableau can do and to acknowledge that it can be a useful tool for data visualizations.
Check: Why would someone choose one of these tools over the others? In what use cases?
Independent Practice: Topic (15 minutes)
- Use the "classic-rock-raw-data.csv" file here and use seaborn, plotly, or Tableau to create a visualization. Justify why you chose one plotting tool over the others.
Independent Practice: Bonus
- Really get to know these plotting tools
- Use the StarWars.csv here and create visualizations using seaborn, tableau, or matplotlib. Your choice, but be able to justify why you chose one plotting tool over the others.