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================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib

03 - Simple Scatter Plots

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.

%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');


# possible markers are
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,'-*r');

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)

(-1.2, 1.2)

Scatter Plots with plt.scatter

A second, more powerful method of creating scatter plots is the plt.scatter func‐ tion, which can be used very similarly to the plt.plot function

plt.scatter(x,y,marker='o')

<matplotlib.collections.PathCollection at 0x7fe4dcc12040>

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.

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

/tmp/ipykernel_61416/429789784.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  plt.colorbar(); # show color scale

Notice that the color argument is automatically mapped to a color scale (shown here by the colorbar() command), and 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 illustrate 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 meas‐ ured

from sklearn.datasets import load_iris
iris=load_iris()
features=iris.data.T

plt.scatter(features[0], feaatures[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]);