python 网格_Python | 网格到情节

python 网格

Most of the time, we need good accuracy in data visualization and a normal plot can be ambiguous. So, it is better to use a grid that allows us to locate the approximate value near the points in the plot. It helps in reducing the ambiguity and therefore, there is a function plt.grid() which generates a grid through the plot and enables better visualization.

大多数时候，我们需要在数据可视化中具有良好的准确性，并且法线图可能会模棱两可。因此，最好使用允许我们在绘图中的点附近定位近似值的网格。它有助于减少歧义，因此有一个函数plt.grid()可以生成整个图的网格并实现更好的可视化。

The following are examples for understanding the implementation Grid.

以下是用于了解实现Grid的示例。

1)网格线图 (1) Line plot with Grid)

2)带网格的条形图 (2) Bar Graph with Grid)

3)带有网格的散点图 (3) Scatter Plot with Grid)

Python代码演示网格图示例 (Python code to demonstrate example of grid to the plot)

# Data Visualization using Python
# Adding Grid
import numpy as np
import matplotlib.pyplot as plt
# Line Plot
N = 40
x = np.arange(N)
y = np.random.rand(N)*10
yy = np.random.rand(N)*10
plt.figure()
plt.plot(x,y)
plt.plot(x,yy)
plt.xlabel('Numbers')
plt.ylabel('Values')
plt.title('Line Plot with Grid')
plt.grid()
plt.show()
# Bar Graph
N = 8
x = np.array([1,2,3,4,5,6,7,9])
xx = np.array(['a','b','c','d','e','f','g','u'])
y = np.random.rand(N)*10
plt.figure()
plt.bar(np.arange(26), np.random.randint(0,50,26), alpha = 0.6)
plt.xlabel('Numbers')
plt.ylabel('Values')
plt.title('Bar Graph with Grid')
plt.grid()
plt.show()
# Scatter Plot
N = 40
x = np.random.rand(N)
y = np.random.rand(N)*10
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii
plt.figure()
plt.scatter(x, y, s=area, c=colors, alpha=0.8)
plt.xlabel('Numbers')
plt.ylabel('Values')
plt.title('Scatter Plot with Grid')
plt.grid()
plt.show()