Master Matplotlib: Quick Start Guide to Plotting in Python

Runtime Environment

Matplotlib is a powerful 2D plotting library for Python that works across platforms. Before using it, ensure Python is installed on your machine and install Matplotlib via sudo pip3 install matplotlib. The examples were tested on macOS 10.13 with Python 3.6.3, matplotlib 2.1.1, and numpy 1.13.3.

Introduction

Matplotlib supports many environments such as Python scripts, IPython shells, Jupyter notebooks, web application servers, and GUI toolkits. It can easily generate various chart types like histograms, spectra, bar charts, and scatter plots, and offers extensive customization.

Basic Code 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 of integers from 100 to 200.

Plotting Multiple Figures

You can create several figure windows using plt.figure() and plot different data in each:

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

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

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

The subplot function allows you to split a single window into a grid of subplots. For example, plt.subplot(2, 1, 1) creates the first subplot in a 2‑row, 1‑column layout.

Common Plot 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()

The first array defines x‑values, the second y‑values; the style string sets line style and color.

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.show()

Three parameters control point color ( c), size ( s), and transparency ( alpha).

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()

The chart shows the proportion of each weekday with percentage labels.

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, 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()

Each bar represents a random value for a day of the week, with random colors and 80% opacity.

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()

The histogram visualizes the frequency distribution of three random datasets across defined bins.

Conclusion

This guide introduced the basic usage of Matplotlib and demonstrated how to create several common chart types. For deeper customization, refer to the official API documentation linked in each code block.