Python中的 Matplotlib.pyplot.tight_layout()

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。 Pyplot是Matplotlib模块的基于状态的接口，它提供了一个类似 MATLAB 的接口。在 Pyplot 中可以使用各种图，包括线图、等高线图、直方图、散点图、3D 图等。

示例代码

# sample code
import matplotlib.pyplot as plt 
    
plt.plot([1, 2, 3, 4], [16, 4, 1, 8]) 
plt.show()

输出：

matplotlib.pyplot.tight_layout()函数

matplotlib库的pyplot模块中的tight_layout()函数用于自动调整子图参数以给出指定的填充。

Syntax: matplotlib.pyplot.tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)

Parameters: This method accept the following parameters that are described below:

pad: This parameter is used for padding between the figure edge and the edges of subplots, as a fraction of the font size.
h_pad, w_pad: These parameter are used for padding (height/width) between edges of adjacent subplots, as a fraction of the font size.
rect: This parameter is rectangle in the normalized figure coordinate that the whole subplots area will fit into.

Returns: This method does not return any value.

编程需要懂一点英语

下面的示例说明了 matplotlib.pyplot 中的matplotlib.pyplot.tight_layout()函数：

示例 1：

import numpy as np
import matplotlib.pyplot as plt
  
fig, axs = plt.subplots(1, 2)
  
x = np.arange(0.0, 2.0, 0.02)
y1 = np.sin(2 * np.pi * x)
y2 = np.exp(-x)
l1, = axs[0].plot(x, y1)
l2, = axs[0].plot(x, y2, marker ='o')
  
y3 = np.sin(4 * np.pi * x)
y4 = np.exp(-2 * x)
l3, = axs[1].plot(x, y3, color ='tab:green')
l4, = axs[1].plot(x, y4, color ='tab:red', marker ='o')
  
fig.legend((l1, l2), ('Line 1', 'Line 2'), 'upper left')
fig.legend((l3, l4), ('Line 3', 'Line 4'), 'upper right')
  
  
fig.suptitle('matplotlib.pyplot.tight_layout() Example')
plt.tight_layout()
plt.show()

输出：

示例 2：

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import EngFormatter
  
prng = np.random.RandomState(19680801)
  
xs = np.logspace(1, 9, 100)
ys = (0.8 + 0.4 * prng.uniform(size = 100)) * np.log10(xs)**2
  
plt.xscale('log')
  
formatter0 = EngFormatter(unit ='Hz')
plt.plot(xs, ys)
plt.xlabel('Frequency')
  
plt.title('matplotlib.pyplot.tight_layout() Example')
plt.tight_layout()
plt.show()

输出：