In this hands-on tutorial, we will learn how to create multiple types of graphs with Matplotlib stateful approach. We will also learn how to customize graphs with legends, annotations, and so on. Let's begin with importing the necessary libraries.

# import numpy
import numpy as np

# import matplotlib
import matplotlib.pyplot as plt

# set figure size for each figure in this tutorial
plt.rcParams["figure.figsize"] = (10,6)

Plotting charts

Let's learn how to plot some basic charts.

Line plot

In this example, we will create a sine wave with x-range from 0 to 100. We will generate data by using Numpy.

# 200 values from interval <0,100>, equidistantly divided
x = np.linspace(0,100,200)
y = np.sin(x)

# line plot
plt.plot(x,y,'red')
plt.show()

Scatter plot

# 200 random values from interval <0,10>
x = 10*np.random.rand(200,1)

# 200 random values from interval <0,15>
y = 15*np.random.rand(200,1)

# scatter plot 
plt.scatter(x,y)
plt.show()

Histogram

# 200 random values from interval <0,15>
y = 15*np.random.rand(200,1)

# histogram with 20 bins
plt.hist(y,bins=20)
plt.show()

Plot customization

Now that we know how to plot basic charts with Matplotlib, it's time to move a little bit further. We will learn some plot customizations.

Graphs with common axes

In this example, we will plot two mathematical functions (sin(x), sin(x)*cos(x)) on one figure with shared axis.

# 200 values from interval <0,100>, equidistantly divided
x = np.linspace(0,100,200)

# sin(x) values
y1 = np.sin(x)

# sin(x)*cos(x) values
y2 =(np.sin(x))*(np.cos(x))

# line plot of sin(x), red line
plt.plot(x,y1,'red')

# line plot of sin(x)*cos(x), blue line
plt.plot(x,y2,'blue')
plt.show()

Subplots

Let's continue with the example above with a small change. Now we want to have a separate figure with its own axis for each function.

# first figure
plt.subplot(2,1,1)
plt.plot(x,y1,'red')
plt.title('sin(x)')

# second figure
plt.subplot(2,1,2)
plt.plot(x,y2,'blue')
plt.title('sin(x)*(cos(x))')

# automatically adjust subplot parameters to give specified padding
plt.tight_layout()
plt.show()

The first two parameters of plt.subplot() are the shapes of the grid with figures. In our example, we created a grid with 2 rows and one column. If we want to have our figures side by side (1 row 2 columns) we simply swap these parameters. The third parameter is the index of the actual figure we are plotting in. The index starts at 1 in the upper left corner of the grid and increases to the right.

Legends

In the next two examples, we will use the popular Iris dataset. For this purpose, we have to import Pandas and Sklearn libraries.

# import pandas
import pandas as pd

# import sklearn datasets
from sklearn import datasets

Load Iris data to a DataFrame:

# load iris dataset
iris = datasets.load_iris()

# create dataframe
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)

# create target
iris_df['target'] = iris.target

# map target values to target names
iris_df['target_name'] =iris_df.target.map(
    {0: 'setosa', 
     1: 'versicolor',
     2: 'virginica'}
)

Print the top 5 rows of the created data frame:

iris_df.head()

Now, let's plot a scatter plot of sepal length and sepal width for each type of the Iris flower:

# Iris setosa
setosa = iris_df[iris_df.target_name == 'setosa']

# Iris versicolor
versicolor = iris_df[iris_df.target_name == 'versicolor']

# Iris virginica
virginica = iris_df[iris_df.target_name == 'virginica']

# plot setosa
plt.scatter(setosa['sepal length (cm)'], setosa['sepal width (cm)'],
            marker ='o', color = 'red', label = 'setosa')

# plot versicolor
plt.scatter(versicolor['sepal length (cm)'], versicolor['sepal width (cm)'],
            marker ='o', color = 'green', label = 'versicolor')

# plot virginica
plt.scatter(virginica['sepal length (cm)'], virginica['sepal width (cm)'],
            marker ='o', color = 'blue', label = 'virginica')

# legend location
plt.legend(loc='upper right')

# plot title
plt.title('Iris flower')

# x-axis title
plt.xlabel('sepal length (cm)')

# y-axis title
plt.ylabel('sepal width (cm)')
plt.show()

By setting the label parameter in each scatter plot we set the name displayed in the legend.

Annotations

If we want to add annotations to the figure we created in the example above, we can do that by making the following changes to the code:

# same code as before
plt.scatter(setosa['sepal length (cm)'],setosa['sepal width (cm)'],
            marker ='o', color = 'red', label = 'setosa')

plt.scatter(versicolor['sepal length (cm)'],versicolor['sepal width (cm)'],
            marker ='o', color = 'green', label = 'versicolor')

plt.scatter(virginica['sepal length (cm)'],virginica['sepal width (cm)'],
            marker ='o', color = 'blue', label = 'virginica')

# new lines of code
######################
plt.annotate('setosa', xy =(5.0,3.5),
             xytext = (4.25,4.0), arrowprops={'color':'red'})
plt.annotate('versicolor', xy =(7.2,3.6),
             xytext = (6.5,4.0), arrowprops={'color':'red'})
plt.annotate('virginica', xy =(5.05,1.95),
             xytext = (5.5,1.75), arrowprops={'color':'red'})
######################

# same code as before
plt.legend(loc='upper right')
plt.title('Iris flower')
plt.xlabel('sepal length (cm)')
plt.ylabel('sepal width (cm)')
plt.ylim(1.5,4.7)
plt.show()

To add annotations we used the plt.annotate() function. The xy parameter is a tuple containing the position which the arrow is pointing to. The xytext is a tuple containing the position where the text of the annotation is placed.