# Scatter Plots with Matplotlib

Learn to create beautiful and informative scatter plots in Python using Matplotlib.

In this Matplotlib tutorial, you will learn to draw insightful scatter plots using the pyplot class in Matplotlib.

Matplotlib is one of the data visualization libraries in Python. The pyplot, a matplotlib class, is a collection of functions that helps in creating different kinds of plots.

### Difference between Line Plots and Scatter Plots

Line plots help you with analyzing trends. On the other hand, scatter plots allow you to observe the relationship between two variables and how the change in one affects the other.

## Syntax of Scatter function

The syntax of scatter() function is given below.

matplotlib.pyplot.scatter(x_axis_data, y_axis_data, s=None,
c=None, marker=None, cmap=None,
vmin=None, vmax=None, alpha=None,
linewidths=None, edgecolors=None)
• x_axis_data - An array containing x-axis data
• y_axis_data - An array containing y-axis data
• s - marker size (can be scalar or array of size equal to size of x or y)
• c - color of sequence of colors for markers
• marker - marker style
• cmap - cmap name
• linewidths - width of marker border
• edgecolor - marker border color
• alpha - blending value, between 0 (transparent) and 1 (opaque)

Except x_axis_data and y_axis_data, all other parameters are optional. Their default value is None.

## Simple Scatter Plot

Let’s plot a simple sin curve using scatter plot.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 5, 50)
y = np.sin(x)

plt.scatter(x, y, color='black')
plt.show()

## Comparing two scatter plots

Let’s plot both sin and cos curves using scatter plot in the same plot.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 5, 50)
y1 = np.sin(x)
y2 = np.cos(x)

plt.scatter(x, y1, label='Sin curve')
plt.scatter(x, y2, label='Cos curve')
plt.legend()
plt.show()

## Using Colors in your plot

We can set our own colour for each scatter plot by using the c or the color parameter.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 5, 50)
y1 = np.sin(x)
y2 = np.cos(x)

plt.scatter(x, y1, label='Sin curve', c='green')
# You can also mention Hex codes of colors
plt.scatter(x, y2, label='Cos curve', color='#FF0000')
plt.legend()
plt.show()

The above code should produce a similar graph.

We can also pass in a sequence of n numbers to be mapped to colors.

Let’s look at the below example.

import matplotlib.pyplot as plt
import numpy as np

# Fix random state for reproducibility
np.random.seed(12345)

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
plt.scatter(x, y, alpha=0.5, c=colors )
plt.show()

As you can see we are passing np.random.rand(N) array as our colours parameter. When you run this, it produces the following result.

## Colormap

Based on the above image, it would be nice to know what each color represents. For this purpose, you can use a colormap.

The Matplotlib module has a number of available colormaps.

A colormap in Matplotlib is like a list of colors, where each color has a value that ranges from 0 to 100.

Let’s add a colormap to our above plot. We can add it by using plt.colorbar().

import matplotlib.pyplot as plt
import numpy as np

# Fix random state for reproducibility
np.random.seed(12345)

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
plt.scatter(x, y, alpha=0.5, c=colors)
plt.colorbar()
plt.show()

The above code should produce the following plot.

## Using different scatter symbols

Scatter symbols don’t have to be circular. You can use any symbol that fits the requirement of your graph.

Let’s plot a scatter plot using the dagger symbol. We will use the marker parameter to pass in necessary symbol for our plot.

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

x = np.arange(0.0, 50.0, 2.0)
y = x ** 1.3 + np.random.rand(*x.shape) * 30.0
s = np.random.rand(*x.shape) * 800 + 500

plt.scatter(x, y, s, c="g", alpha=0.5, marker=r'$\dagger$',
label="Luck")
plt.xlabel("Leprechauns")
plt.ylabel("Gold")
plt.legend(loc='upper left')
plt.show()

## Plotting the Iris dataset from Scikit Learn

Let’s plot the sepal length vs sepal width from the famous iris data set.

from sklearn.datasets import load_iris

from matplotlib import pyplot as plt

# The indices of the features that we are plotting
x_index = 0
y_index = 1

# this formatter will label the colorbar with the correct target names
formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])

plt.figure(figsize=(10, 8))
plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.xlabel(iris.feature_names[x_index])
plt.ylabel(iris.feature_names[y_index])

plt.tight_layout()
plt.show()

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