This article is a supplement to the python data analysis series-matplotlib from entry to abandonment .
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1. Add a legend
When drawing multiple curves in the same graph, in order to facilitate the distinction and make the image more professional, we usually add a legend to the image.
as follows:
import matplotlib.pyplot as plt
import numpy as np
a = np.arange(10,40,2)
b = np.arange(40,70,2)
# 传入xy轴参数，默认为y轴;label 指定图例名称
plt.plot(a,label="a",color="blue")
plt.plot(b,label="b",color="green")
plt.legend(loc="upper left") # 设置图例位置
# 指定xy轴 名称
plt.ylabel("This is Y")
plt.xlabel("This is X")
# 保存图像 默认png格式，其中dpi指图片质量
plt.savefig("05.png", dpi=600)
plt.show() # 展示图片
The input image is as follows:
by specifying the legend name in plot(label=" "), and then specifying the position of the legend through the parameter of legend(), you can add a legend to the image, where the loc parameter is as follows:
- best
- upper right
- upper left
- lower left
- lower right
- right
- center left
- center right
- lower center
- upper center
- center
2. Set the xy axis scale
We have described how to set the image legend above, but careful friends will find that when we set the image x/y axis coordinate scale, we let it automatically adjust, but sometimes we need to artificially control the scale. At this time we can set plt.xticks()/plt.yticks():
import matplotlib.pyplot as plt
import numpy as np
a = np.arange(10,100)
b = np.arange(40,130)
# 设置x/y轴尺度
plt.xticks(a[::5])
plt.yticks(b[::10])
# 传入xy轴参数，默认为y轴;label 指定图例名称
plt.plot(a,label="a",linestyle="--",color="blue")
plt.plot(b,label="b",color="green")
plt.legend(loc="best") # 设置图例位置
# 指定xy轴 名称
plt.ylabel("This is Y")
plt.xlabel("This is X")
# 保存图像 默认png格式，其中dpi指图片质量
plt.savefig("05.png", dpi=600)
plt.show() # 展示图片
The image is as follows:
