Comprehensive Matplotlib Tutorial: Titles, Text, Annotations, Labels, Legends, Colors, Markers, Grids, Axes, Styles and More

This tutorial demonstrates a wide range of Matplotlib features for creating and customizing plots in Python.

1. Adding a title

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = [u'SimHei']
plt.rcParams['axes.unicode_minus'] = False
x = np.arange(0, 10)
plt.title('这是一个示例标题')
plt.plot(x, x*x)
plt.show()

2. Adding text – Use plt.text(x, y, 'your text') to place arbitrary text at a specific coordinate.

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = [u'SimHei']
plt.rcParams['axes.unicode_minus'] = False
x = np.arange(-10, 11, 1)
y = x*x
plt.plot(x, y)
plt.title('这是一个示例标题')
plt.text(-2.5, 30, 'function y=x*x')
plt.show()

3. Adding annotations – The annotate() function adds labeled arrows.

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = [u'SimHei']
plt.rcParams['axes.unicode_minus'] = False
x = np.arange(-10, 11, 1)
y = x*x
plt.plot(x, y)
plt.title('这是一个示例标题')
plt.annotate('这是一个示例注释', xy=(0,1), xytext=(-2,22), arrowprops={'headwidth':10,'facecolor':'r'})
plt.show()

4. Setting axis labels – Use plt.xlabel() and plt.ylabel() to name the axes.

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = [u'SimHei']
plt.rcParams['axes.unicode_minus'] = False
x = np.arange(1, 20)
plt.xlabel('示例x轴')
plt.ylabel('示例y轴')
plt.plot(x, x*x)
plt.show()

5. Adding a legend – Call plt.legend() with a list of labels to differentiate multiple lines.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1, 20)
plt.plot(x, x)
plt.plot(x, x*2)
plt.plot(x, x*3)
plt.plot(x, x*4)
plt.legend(['生活','颜值','工作','金钱'])
plt.show()

6. Adjusting colors – The color argument of plot() accepts named colors, hex codes, grayscale values, or RGB tuples.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1,5)
plt.plot(x, color='g')
plt.plot(x+1, color='0.5')
plt.plot(x+2, color='#FF00FF')
plt.plot(x+3, color=(0.1,0.2,0.3))
plt.show()

7. Changing line markers – Use the marker parameter to select symbols such as 'o', '>', 's', etc.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1,5)
plt.plot(x, marker='o')
plt.plot(x+1, marker='>')
plt.plot(x+2, marker='s')
plt.show()

8. Displaying mathematical formulas – Enclose LaTeX‑style expressions in $...$ and use r'...' strings.

import numpy as np
import matplotlib.pyplot as plt
plt.title('chenqionghe')
plt.xlim([1,8])
plt.ylim([1,5])
plt.text(2,4, r'$ \alpha \beta \pi \lambda \omega $', size=25)
plt.text(4,4, r'$ \sin(0)=\cos(\frac{\pi}{2}) $', size=25)
plt.text(2,2, r'$ \lim_{x \rightarrow y} \frac{1}{x^3} $', size=25)
plt.text(4,2, r'$ \sqrt[4]{x}=\sqrt{y} $', size=25)
plt.show()

9. Adding a grid – Call plt.grid() (optional arguments for color, linewidth, linestyle).

import numpy as np
import matplotlib.pyplot as plt
x = ['a','b','c','d']
y = [15,30,45,10]
plt.grid()
plt.plot(x, y)
plt.show()

10. Adjusting tick density – Use plt.locator_params(nbins=20) to set the number of ticks on both axes, or specify 'x' or 'y' for a single axis.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,30,1)
plt.plot(x, x)
plt.locator_params(nbins=20)
plt.show()

11. Setting axis limits – plt.axis([xmin, xmax, ymin, ymax]), plt.xlim(), and plt.ylim() control view ranges.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,30,1)
plt.plot(x, x*x)
plt.xlim(xmin=10, xmax=25)
plt.show()

12. Auto‑formatting date labels – After plotting dates, call plt.gcf().autofmt_xdate() to rotate them.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
x = pd.date_range('2020/01/01', periods=30)
y = np.arange(0,30)
plt.plot(x, y)
plt.gcf().autofmt_xdate()
plt.show()

13. Adding a secondary y‑axis – plt.twinx() creates a twin axis for overlaying another dataset.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1,20)
y1 = x*x
y2 = np.log(x)
plt.plot(x, y1)
plt.twinx()
plt.plot(x, y2, 'r')
plt.show()

14. Filling areas – plt.fill() fills under a curve; plt.fill_between() fills between two curves.

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,5*np.pi,1000)
y1 = np.sin(x)
y2 = np.sin(2*x)
plt.plot(x, y1)
plt.plot(x, y2)
plt.fill(x, y1, 'g')
plt.fill(x, y2, 'r')
plt.title('这是一个示例标题')
plt.show()

plt.fill_between(x, y1, y2, where=y1>y2, interpolate=True)
plt.show()

15. Drawing patches – Use matplotlib.patches to add circles, rectangles, polygons, ellipses, etc.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mptaches
xy1 = np.array([0.2,0.2])
xy2 = np.array([0.2,0.8])
xy3 = np.array([0.8,0.2])
xy4 = np.array([0.8,0.8])
fig, ax = plt.subplots()
circle = mptaches.Circle(xy1, 0.15)
ax.add_patch(circle)
rect = mptaches.Rectangle(xy2, 0.2, 0.1, color='r')
ax.add_patch(rect)
polygon = mptaches.RegularPolygon(xy3, 6, 0.1, color='g')
ax.add_patch(polygon)
ellipse = mptaches.Ellipse(xy4, 0.4, 0.2, color='c')
ax.add_patch(ellipse)
ax.axis('equal')
plt.show()

16. Changing plot style – Use plt.style.use('ggplot') (or any available style) to alter the overall appearance.

import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
fig, axes = plt.subplots(2,2)
ax1, ax2, ax3, ax4 = axes.ravel()
# Example subplots omitted for brevity
plt.show()

The article concludes with a disclaimer that the content is compiled from the internet and retains the original author's copyright.