Machine Learning - Draw Basic Graphs with Matplotlib

 Matplotlib is a comprehensive visualization library in Python. It was originated by John Hunter. As an open source software, it is utilized by thousands of researchers and engineers.

In machine learning domain, Matplotlib is also used as an effective tool to draw a variety of graphs such as bars, line, scatter, pie chart, box plot and etc. Here we use it draw several common graphs. 

1. Line

A circle marker is added on each point and the line color is set 'Blue'.

import matplotlib.pyplot as plt
from numpy import randomx = random.randint(100, size=(30))
x = random.randint(100, size=(30))
x.sort()
y = [i + random.normal(loc=20, scale=10) for i in x]

plt.plot(x, y, label="random data", linestyle='solid', color='blue', marker="o")
plt.legend()
plt.title("Line Graph")
plt.show()

2. Bars

Horizontal bar graph can be plotted using barh() function. Surely you can move the legend around in the visualization.

import matplotlib.pyplot as plt
from numpy import random
x = ('Dim A', 'Dim B', 'Dim C', 'Dim D', 'Dim E')
y = random.random(5)
plt.subplot(121)
plt.bar(x, y, label="random data")
plt.legend(bbox_to_anchor=(0, 1), loc='upper left', borderaxespad=0, fontsize=9)
plt.title("Vertical Bar Graph")

plt.subplot(122)
plt.barh(x, y, label="random data")
plt.title("Horizontal Bar Graph")
plt.show()

3. Pie Chart

Pie chart is useful when you want to display proportions of data.

x = ('Dim A', 'Dim B', 'Dim C', 'Dim D', 'Dim E')
y = random.random(5)
plt.pie(y, labels=x)
plt.legend(bbox_to_anchor=(1.2, 1), loc='upper right', borderaxespad=0, fontsize=9)
plt.title("Pie Chart")
plt.show()

4. Scatter

Use the same data sets created for the line graph as shown above.

plt.scatter(x, y, color="hotpink", label="random data")
plt.legend()
plt.title("Scatter Graph")
plt.show()

5. Histograms

Histogram shows the distribution of data.

x = random.binomial(100, 0.5, 300)
plt.hist(x, label='binomial')
plt.legend()
plt.title("Histograms")
plt.show()

6. Box Plot

Box plot illustrates the distribution of data as well as the skewness based on 5 number summary which are minimum, 1st quartile, median, 3rd quantile and maximum.

data = random.normal(loc=random.randint(20), scale=30, size=(1500, 3))
plt.title('Box Plot')
plt.xlabel('Dimension')
plt.ylabel('Measure')
plt.boxplot(data)
plt.show()

7. Probability Plot

Let's also have a look at a simple probability plot example here. A probability plot compares the probabilities of the sample data against a theoretical distribution specified by "stat=" parameter, which is the normal distribution if not specified. 

The points form a straight line if the 2 sets come from the same distribution.

import matplotlib.pyplot as plt
from scipy import stats

x = stats.norm.rvs(loc=25, scale=2, size=30)
res = stats.probplot(x, plot=plt)
ymin, ymax = 19, 31
plt.ylim(ymin, ymax)
plt.show()