Master Matplotlib: Quick Start Guide to Plotting in Python

Matplotlib is a powerful 2D plotting library for Python that works on various platforms.

Runtime Environment

Python must be installed on your machine. Install Matplotlib via pip: sudo pip3 install matplotlib The examples in this article were tested on:

Apple OS X 10.13

Python 3.6.3

matplotlib 2.1.1

numpy 1.13.3

Introduction

Matplotlib can be used in Python scripts, IPython shells, Jupyter notebooks, web application servers, and GUI toolkits. It can easily generate various professional graphs such as histograms, spectra, bar charts, scatter plots, and more, with extensive customization options.

Getting Started Example

# test.py
import matplotlib.pyplot as plt
import numpy as np

data = np.arange(100, 201)
plt.plot(data)
plt.show()

This three‑line script creates a simple linear plot.

The resulting figure looks like this:

Multiple Figures

You can create several figure windows using plt.figure():

# figure.py
import matplotlib.pyplot as plt
import numpy as np

data = np.arange(100, 201)
plt.plot(data)

data2 = np.arange(200, 301)
plt.figure()
plt.plot(data2)

plt.show()

This code produces two separate windows, each showing a line plot for a different range.

Multiple Subplots

To display several plots in a single window, use plt.subplot():

# subplot.py
import matplotlib.pyplot as plt
import numpy as np

data = np.arange(100, 201)
plt.subplot(2, 1, 1)
plt.plot(data)

data2 = np.arange(200, 301)
plt.subplot(2, 1, 2)
plt.plot(data2)

plt.show()

The first subplot occupies the top half, the second the bottom half of the window.

Common Chart Examples

Line Plot

# plot.py
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 6, 9], '-r')
plt.plot([1, 2, 3], [2, 4, 9], ':g')
plt.show()

Scatter Plot

# scatter.py
import matplotlib.pyplot as plt
import numpy as np

N = 20
plt.scatter(np.random.rand(N) * 100, np.random.rand(N) * 100, c='r', s=100, alpha=0.5)
plt.scatter(np.random.rand(N) * 100, np.random.rand(N) * 100, c='g', s=200, alpha=0.5)
plt.scatter(np.random.rand(N) * 100, np.random.rand(N) * 100, c='b', s=300, alpha=0.5)
plt.show()

Pie Chart

# pie.py
import matplotlib.pyplot as plt
import numpy as np

labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
data = np.random.rand(7) * 100
plt.pie(data, labels=labels, autopct='%1.1f%%')
plt.axis('equal')
plt.legend()
plt.show()

Bar Chart

# bar.py
import matplotlib.pyplot as plt
import numpy as np

N = 7
x = np.arange(N)
data = np.random.randint(0, 100, size=N)
colors = np.random.rand(N * 3).reshape(N, -1)
labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
plt.title("Weekday Data")
plt.bar(x, data, alpha=0.8, color=colors, tick_label=labels)
plt.show()

Histogram

# hist.py
import matplotlib.pyplot as plt
import numpy as np

data = [np.random.randint(0, n, n) for n in [3000, 4000, 5000]]
labels = ['3K', '4K', '5K']
bins = [0, 100, 500, 1000, 2000, 3000, 4000, 5000]
plt.hist(data, bins=bins, label=labels)
plt.legend()
plt.show()

Conclusion

This article covered the basic usage of Matplotlib and demonstrated how to create several common chart types. For more advanced features, refer to the official API documentation linked in each section.