Jupyter Snippet PDSH 04.02-Simple-Scatter-Plots
Jupyter Snippet PDSH 04.02-Simple-Scatter-Plots
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
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< Simple Line Plots | Contents | Visualizing Errors >
Simple Scatter Plots
Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape. We’ll start by setting up the notebook for plotting and importing the functions we will use:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
Scatter Plots with plt.plot
In the previous section we looked at plt.plot
/ax.plot
to produce line plots.
It turns out that this same function can produce scatter plots as well:
x = np.linspace(0, 10, 30)
y = np.sin(x)
plt.plot(x, y, 'o', color='black');
The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as '-'
, '--'
to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of plt.plot
, or in Matplotlib’s online documentation. Most of the possibilities are fairly intuitive, and we’ll show a number of the more common ones here:
rng = np.random.RandomState(0)
for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:
plt.plot(rng.rand(5), rng.rand(5), marker,
label="marker='{0}'".format(marker))
plt.legend(numpoints=1)
plt.xlim(0, 1.8);
For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:
plt.plot(x, y, '-ok');
Additional keyword arguments to plt.plot
specify a wide range of properties of the lines and markers:
plt.plot(x, y, '-p', color='gray',
markersize=15, linewidth=4,
markerfacecolor='white',
markeredgecolor='gray',
markeredgewidth=2)
plt.ylim(-1.2, 1.2);
This type of flexibility in the plt.plot
function allows for a wide variety of possible visualization options.
For a full description of the options available, refer to the plt.plot
documentation.
Scatter Plots with plt.scatter
A second, more powerful method of creating scatter plots is the plt.scatter
function, which can be used very similarly to the plt.plot
function:
plt.scatter(x, y, marker='o');
The primary difference of plt.scatter
from plt.plot
is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.
Let’s show this by creating a random scatter plot with points of many colors and sizes.
In order to better see the overlapping results, we’ll also use the alpha
keyword to adjust the transparency level:
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(100)
plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,
cmap='viridis')
plt.colorbar(); # show color scale
Notice that the color argument is automatically mapped to a color scale (shown here by the colorbar()
command), and that the size argument is given in pixels.
In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.
For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:
from sklearn.datasets import load_iris
iris = load_iris()
features = iris.data.T
plt.scatter(features[0], features[1], alpha=0.2,
s=100*features[3], c=iris.target, cmap='viridis')
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1]);
We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data: the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower. Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data.
plot
Versus scatter
: A Note on Efficiency
Aside from the different features available in plt.plot
and plt.scatter
, why might you choose to use one over the other? While it doesn’t matter as much for small amounts of data, as datasets get larger than a few thousand points, plt.plot
can be noticeably more efficient than plt.scatter
.
The reason is that plt.scatter
has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.
In plt.plot
, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.
For large datasets, the difference between these two can lead to vastly different performance, and for this reason, plt.plot
should be preferred over plt.scatter
for large datasets.