1.0 Introduction
Data Visualization is an important and exciting aspect of data science. It reveals information we otherwise wouldn't have noticed. It allows us to showcase the work we've done through visualizations, which can be stagnant or interactive.
1.1 Python Modules
matplotlib is a 2D python plotting library which allows you to generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.
bokeh is an interactive visualization library for modern web browsers presentation.
seaborn is a library for making statistical graphics in Python. It's built on top of matplotlib and is tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.
ggplot is a plotting system built for making profressional-looking plots quickly with minimal code.
2.0 Matplotlib
matplotlib.pyplot is a collection of functions that make matplotlib work similar to matlab. Each pyplot function makes some change to a figure. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes.
2.3 Basic Plots
In this section, we'll overview the basic plot types: line plots, scatter plots, and histograms.
2.3.1 Line Plots
Line graphs are plots where a line is drawn to indicate a relationship between a particular set of x and y values.
import matplotlib.pyplot as plt
To be able to plot anything, we need to provide the data points, so we declare those as follows:
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
Using matplotlib, we can plot a line between plot x and y.
plt.plot(x, y)
And as always, we use the show() method to have the visualizations pop up.
plt.show()
2.3.2 Scatter Plots
Alternatively, you might want to plot quantities with 2 positions as data points. To do this, you first have to import the needed libraries, as always. We'll be using the same data from before:
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
Next, we plot it with the plt.plot() method. Note that the o denotes the type of graph we're plotting.
plt.plot(x, y, 'o')
As always, let's look at what we made:
plt.show()
2.3.3 Histograms
Histograms are very often used in science applications and it's highly likely that you will need to plot them at some point. They are very useful to plot distributions. As before, we'll use numpy and matplotlib.
import numpy as np
import matplotlib.pyplot as plt
First, we'll make the data to plot. We're going to make a normal distribution with 1000 points.
data = np.random.normal(5.0, 3.0, 1000)
Now, we actually make that histogram of the data array and attach a label:
plt.hist(data)
Lastly, let's look at what we've made:
plt.show()
3.0 Customization
The default customization for matplotlib isn't very appealing or even helpful in data visualization tasks.
3.1 Colors
When there are multiple data points or objects, they have to be able to be differentiated between one another. An easy way to do that is by using different marker styles and colors. You can do this by editing the third parameter to include a letter that indicates the color, such as:
plt.plot(x, y, "r")
This will give you the same line as before, but now it'll be red.
3.2 Styles
You can also customize the style of the your lines and markers. With line graphs, you can change the line to be dotted, dashed, etc, for example the following should give you a dashed line now:
plt.plot(x,y, "--")
You can find other linestyles you can use can be found on the [Matplotlib webpage](http://
matplotlib.sourceforge.net/api/pyplot)
With Scatter Plots, you can customize the dots to be squares, pentagons, etc. This will get you the a scatter plot with star markers:
plt.plot(x,y, "*")
3.3 Labels
We want to always label the axes of plots to tell users what they're looking at. You can do this in matplotlib, very easily:
If we want to attach a label on the x-axis, we can do that with the xlabel() function:
plt.xlabel("X Axis")
With a quick modification, we can also do that for the y-axis:
plt.ylabel("Y Axis")
What good is a visualization without a title to let us know what the visualization is showing? Luckily, matplotlib has a built in function for that:
plt.title("Title Here")
Lastly, we can even customize the range for the x and y axes:
plt.xlim(0, 10)
plt.ylim(0, 10)