{"id":1073045,"date":"2025-01-08T11:21:42","date_gmt":"2025-01-08T03:21:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1073045.html"},"modified":"2025-01-08T11:21:45","modified_gmt":"2025-01-08T03:21:45","slug":"%e5%a6%82%e4%bd%95%e8%ae%a9python%e6%98%be%e7%a4%ba%e6%9b%b4%e7%bb%86%e8%87%b4%e7%9a%84%e5%88%bb%e5%ba%a6-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1073045.html","title":{"rendered":"\u5982\u4f55\u8ba9python\u663e\u793a\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6"},"content":{"rendered":"

\"\u5982\u4f55\u8ba9python\u663e\u793a\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6\"<\/p>\n

\u4e3a\u4e86\u8ba9Python\u663e\u793a\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\uff0c\u901a\u8fc7\u8bbe\u7f6e\u523b\u5ea6\u523b\u753b\u95f4\u9694\u3001\u523b\u5ea6\u6807\u7b7e\u683c\u5f0f\u5316\u3001\u589e\u52a0\u6b21\u523b\u5ea6\u7b49\u65b9\u5f0f\u6765\u5b9e\u73b0\u3002<\/strong> <\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528set_major_locator<\/code>\u548cset_minor_locator<\/code>\u65b9\u6cd5\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u95f4\u9694\uff1b
\u4e8c\u3001\u901a\u8fc7set_major_formatter<\/code>\u548cset_minor_formatter<\/code>\u65b9\u6cd5\u683c\u5f0f\u5316\u523b\u5ea6\u6807\u7b7e\uff1b
\u4e09\u3001\u4f7f\u7528plt.minorticks_on()<\/code>\u65b9\u6cd5\u542f\u7528\u6b21\u523b\u5ea6\u3002
\u4e0b\u9762\u5c06\u8be6\u7ec6\u5c55\u5f00\u8fd9\u4e9b\u65b9\u6cd5\u7684\u5177\u4f53\u4f7f\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u95f4\u9694<\/h3>\n<\/p>\n

\u5728Matplotlib\u4e2d\uff0cset_major_locator<\/code>\u548cset_minor_locator<\/code>\u65b9\u6cd5\u7528\u4e8e\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u7684\u95f4\u9694\u3002\u4e3b\u523b\u5ea6\u901a\u5e38\u7528\u4e8e\u8868\u793a\u4e3b\u8981\u7684\u5206\u5272\u70b9\uff0c\u800c\u6b21\u523b\u5ea6\u7528\u4e8e\u8868\u793a\u66f4\u7ec6\u81f4\u7684\u5206\u5272\u70b9\u3002\u901a\u8fc7\u7ed3\u5408\u8fd9\u4e24\u79cd\u523b\u5ea6\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u9ad8\u56fe\u8868\u7684\u523b\u5ea6\u7ec6\u81f4\u7a0b\u5ea6\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import matplotlib.ticker as ticker<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y)<\/p>\n
\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u95f4\u9694\u4e3a20<\/strong><\/h2>\n
ax.xaxis.set_major_locator(ticker.MultipleLocator(20))<\/p>\n
ax.yaxis.set_major_locator(ticker.MultipleLocator(2000))<\/p>\n
\u8bbe\u7f6e\u6b21\u523b\u5ea6\u95f4\u9694\u4e3a5<\/strong><\/h2>\n
ax.xaxis.set_minor_locator(ticker.MultipleLocator(5))<\/p>\n
ax.yaxis.set_minor_locator(ticker.MultipleLocator(500))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Matplotlib\u5e93\u548cticker\u6a21\u5757\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u4e8c\u7ef4\u56fe\u8868\u3002\u4f7f\u7528set_major_locator<\/code>\u65b9\u6cd5\u5c06x\u8f74\u548cy\u8f74\u7684\u4e3b\u523b\u5ea6\u95f4\u9694\u8bbe\u7f6e\u4e3a20\u548c2000\uff0c\u4f7f\u7528set_minor_locator<\/code>\u65b9\u6cd5\u5c06\u6b21\u523b\u5ea6\u95f4\u9694\u8bbe\u7f6e\u4e3a5\u548c500\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u683c\u5f0f\u5316\u523b\u5ea6\u6807\u7b7e<\/h3>\n<\/p>\n
\u4e3a\u4e86\u8ba9\u523b\u5ea6\u6807\u7b7e\u663e\u793a\u5f97\u66f4\u52a0\u7f8e\u89c2\u548c\u6709\u610f\u4e49\uff0c\u53ef\u4ee5\u4f7f\u7528set_major_formatter<\/code>\u548cset_minor_formatter<\/code>\u65b9\u6cd5\u5bf9\u523b\u5ea6\u6807\u7b7e\u8fdb\u884c\u683c\u5f0f\u5316\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u523b\u5ea6\u6807\u7b7e\u683c\u5f0f\u5316\u4e3a\u767e\u5206\u6bd4\u5f62\u5f0f\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.ticker as ticker<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y)<\/p>\n
\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u95f4\u9694\u4e3a20<\/strong><\/h2>\n
ax.xaxis.set_major_locator(ticker.MultipleLocator(20))<\/p>\n
ax.yaxis.set_major_locator(ticker.MultipleLocator(2000))<\/p>\n
\u8bbe\u7f6e\u6b21\u523b\u5ea6\u95f4\u9694\u4e3a5<\/strong><\/h2>\n
ax.xaxis.set_minor_locator(ticker.MultipleLocator(5))<\/p>\n
ax.yaxis.set_minor_locator(ticker.MultipleLocator(500))<\/p>\n
\u683c\u5f0f\u5316\u523b\u5ea6\u6807\u7b7e\u4e3a\u767e\u5206\u6bd4\u5f62\u5f0f<\/strong><\/h2>\n
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x\/100:.0%}'))<\/p>\n
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y, pos: f'{y\/10000:.0%}'))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528FuncFormatter<\/code>\u5c06x\u8f74\u548cy\u8f74\u7684\u523b\u5ea6\u6807\u7b7e\u683c\u5f0f\u5316\u4e3a\u767e\u5206\u6bd4\u5f62\u5f0f\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u523b\u5ea6\u6807\u7b7e\u663e\u793a\u5f97\u66f4\u52a0\u76f4\u89c2\u548c\u6613\u8bfb\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u542f\u7528\u6b21\u523b\u5ea6<\/h3>\n<\/p>\n
\u9664\u4e86\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u95f4\u9694\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528plt.minorticks_on()<\/code>\u65b9\u6cd5\u542f\u7528\u6b21\u523b\u5ea6\u3002\u6b21\u523b\u5ea6\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5728\u56fe\u8868\u4e2d\u663e\u793a\u66f4\u52a0\u7ec6\u81f4\u7684\u523b\u5ea6\u4fe1\u606f\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.ticker as ticker<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y)<\/p>\n
\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u95f4\u9694\u4e3a20<\/strong><\/h2>\n
ax.xaxis.set_major_locator(ticker.MultipleLocator(20))<\/p>\n
ax.yaxis.set_major_locator(ticker.MultipleLocator(2000))<\/p>\n
\u8bbe\u7f6e\u6b21\u523b\u5ea6\u95f4\u9694\u4e3a5<\/strong><\/h2>\n
ax.xaxis.set_minor_locator(ticker.MultipleLocator(5))<\/p>\n
ax.yaxis.set_minor_locator(ticker.MultipleLocator(500))<\/p>\n
\u542f\u7528\u6b21\u523b\u5ea6<\/strong><\/h2>\n
plt.minorticks_on()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.minorticks_on()<\/code>\u65b9\u6cd5\u542f\u7528\u4e86\u6b21\u523b\u5ea6\u3002\u8fd9\u6837\uff0c\u56fe\u8868\u4e2d\u4e0d\u4ec5\u663e\u793a\u4e86\u4e3b\u8981\u7684\u523b\u5ea6\u70b9\uff0c\u8fd8\u663e\u793a\u4e86\u66f4\u52a0\u7ec6\u81f4\u7684\u6b21\u523b\u5ea6\u70b9\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u793a\u4f8b<\/h3>\n<\/p>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u793a\u4f8b\uff0c\u7ed3\u5408\u4e86\u4e0a\u8ff0\u65b9\u6cd5\u6765\u521b\u5efa\u4e00\u4e2a\u8be6\u7ec6\u7684\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.ticker as ticker<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y)<\/p>\n
\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u95f4\u9694\u4e3a20<\/strong><\/h2>\n
ax.xaxis.set_major_locator(ticker.MultipleLocator(20))<\/p>\n
ax.yaxis.set_major_locator(ticker.MultipleLocator(2000))<\/p>\n
\u8bbe\u7f6e\u6b21\u523b\u5ea6\u95f4\u9694\u4e3a5<\/strong><\/h2>\n
ax.xaxis.set_minor_locator(ticker.MultipleLocator(5))<\/p>\n
ax.yaxis.set_minor_locator(ticker.MultipleLocator(500))<\/p>\n
\u683c\u5f0f\u5316\u523b\u5ea6\u6807\u7b7e\u4e3a\u767e\u5206\u6bd4\u5f62\u5f0f<\/strong><\/h2>\n
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x\/100:.0%}'))<\/p>\n
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y, pos: f'{y\/10000:.0%}'))<\/p>\n
\u542f\u7528\u6b21\u523b\u5ea6<\/strong><\/h2>\n
plt.minorticks_on()<\/p>\n
\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
ax.set_title('DetAI<\/a>led Tick Marks Example')<\/p>\n
ax.set_xlabel('Percentage (X axis)')<\/p>\n
ax.set_ylabel('Percentage (Y axis)')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u7efc\u5408\u793a\u4f8b\u7ed3\u5408\u4e86\u8bbe\u7f6e\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u95f4\u9694\u3001\u683c\u5f0f\u5316\u523b\u5ea6\u6807\u7b7e\u4ee5\u53ca\u542f\u7528\u6b21\u523b\u5ea6\u7684\u65b9\u6cd5\uff0c\u521b\u5efa\u4e86\u4e00\u4e2a\u8be6\u7ec6\u7684\u56fe\u8868\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba9Python\u663e\u793a\u66f4\u52a0\u7ec6\u81f4\u7684\u523b\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u4e13\u4e1a\u6027\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u63d0\u9ad8\u56fe\u8868\u7ec6\u8282\u7684\u5176\u4ed6\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u4e0a\u8ff0\u65b9\u6cd5\u5916\uff0c\u8fd8\u6709\u4e00\u4e9b\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u56fe\u8868\u7684\u7ec6\u8282\u548c\u53ef\u8bfb\u6027\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
1\u3001\u8c03\u6574\u56fe\u8868\u7684\u5927\u5c0f\u548c\u5206\u8fa8\u7387<\/h4>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u56fe\u8868\u7684\u5927\u5c0f\u548c\u5206\u8fa8\u7387\uff0c\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u663e\u793a\u7ec6\u8282\u4fe1\u606f\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots(figsize=(10, 6), dpi=100)  # \u8c03\u6574\u56fe\u8868\u5927\u5c0f\u548c\u5206\u8fa8\u7387<\/p>\n
ax.plot(x, y)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7figsize<\/code>\u53c2\u6570\u8c03\u6574\u4e86\u56fe\u8868\u7684\u5927\u5c0f\uff0c\u901a\u8fc7dpi<\/code>\u53c2\u6570\u8c03\u6574\u4e86\u56fe\u8868\u7684\u5206\u8fa8\u7387\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528\u989c\u8272\u548c\u6837\u5f0f\u533a\u5206\u4e0d\u540c\u6570\u636e<\/h4>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u548c\u6837\u5f0f\uff0c\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u533a\u5206\u4e0d\u540c\u7684\u6570\u636e\u7cfb\u5217\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e24\u4e2a\u6570\u636e\u7cfb\u5217<\/strong><\/h2>\n
x = range(100)<\/p>\n
y1 = [value2 for value in x]<\/p>\n
y2 = [value1.5 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y1, label='y = x^2', color='blue', linestyle='--')<\/p>\n
ax.plot(x, y2, label='y = x^1.5', color='red', linestyle='-.')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
ax.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u548c\u6837\u5f0f\u6765\u533a\u5206\u4e24\u4e2a\u6570\u636e\u7cfb\u5217\uff0c\u5e76\u6dfb\u52a0\u4e86\u56fe\u4f8b\u4ee5\u4fbf\u4e8e\u8bc6\u522b\u3002<\/p>\n<\/p>\n
3\u3001\u6dfb\u52a0\u7f51\u683c\u7ebf<\/h4>\n<\/p>\n
\u901a\u8fc7\u6dfb\u52a0\u7f51\u683c\u7ebf\uff0c\u53ef\u4ee5\u66f4\u5bb9\u6613\u5730\u8ddf\u8e2a\u6570\u636e\u70b9\u7684\u4f4d\u7f6e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
x = range(100)<\/p>\n
y = [value2 for value in x]<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(x, y)<\/p>\n
\u6dfb\u52a0\u7f51\u683c\u7ebf<\/strong><\/h2>\n
ax.grid(True, which='both', linestyle='--', linewidth=0.5)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7ax.grid<\/code>\u65b9\u6cd5\u6dfb\u52a0\u4e86\u7f51\u683c\u7ebf\uff0c\u5e76\u8bbe\u7f6e\u4e86\u7f51\u683c\u7ebf\u7684\u6837\u5f0f\u548c\u5bbd\u5ea6\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684set_major_locator<\/code>\u3001set_minor_locator<\/code>\u3001set_major_formatter<\/code>\u3001set_minor_formatter<\/code>\u65b9\u6cd5\uff0c\u4ee5\u53ca\u542f\u7528\u6b21\u523b\u5ea6\u3001\u8c03\u6574\u56fe\u8868\u5927\u5c0f\u548c\u5206\u8fa8\u7387\u3001\u4f7f\u7528\u989c\u8272\u548c\u6837\u5f0f\u533a\u5206\u4e0d\u540c\u6570\u636e\u3001\u6dfb\u52a0\u7f51\u683c\u7ebf\u7b49\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8ba9Python\u663e\u793a\u66f4\u52a0\u7ec6\u81f4\u7684\u523b\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u4e13\u4e1a\u6027\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u5e2e\u52a9\u4f60\u5728\u6570\u636e\u53ef\u89c6\u5316\u8fc7\u7a0b\u4e2d\u521b\u5efa\u66f4\u52a0\u8be6\u7ec6\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u523b\u5ea6\u663e\u793a\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u523b\u5ea6\u663e\u793a\u3002\u901a\u8fc7\u4f7f\u7528plt.xticks()<\/code>\u548cplt.yticks()<\/code>\u51fd\u6570\uff0c\u4f60\u53ef\u4ee5\u8bbe\u7f6e\u523b\u5ea6\u7684\u4f4d\u7f6e\u548c\u6807\u7b7e\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6\u663e\u793a\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib.ticker<\/code>\u6a21\u5757\u6765\u8bbe\u7f6e\u523b\u5ea6\u7684\u683c\u5f0f\u548c\u95f4\u9694\uff0c\u8fbe\u5230\u66f4\u52a0\u7cbe\u786e\u7684\u663e\u793a\u6548\u679c\u3002<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u5b9e\u73b0\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6\u663e\u793a\uff1f<\/strong>
\u9664\u4e86Matplotlib\uff0cSeaborn\u4e5f\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u9009\u62e9\uff0c\u5b83\u57fa\u4e8eMatplotlib\u5e76\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002Plotly\u5219\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u80fd\u591f\u52a8\u6001\u8c03\u6574\u523b\u5ea6\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u5e93\uff0c\u90fd\u53ef\u4ee5\u901a\u8fc7\u76f8\u5e94\u7684\u523b\u5ea6\u8bbe\u7f6e\u529f\u80fd\u6765\u5b9e\u73b0\u7ec6\u81f4\u7684\u523b\u5ea6\u663e\u793a\uff0c\u5177\u4f53\u5b9e\u73b0\u65b9\u5f0f\u4f1a\u6709\u6240\u4e0d\u540c\u3002<\/p>\n
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\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528MultipleLocator<\/code>\u6765\u8bbe\u7f6e\u523b\u5ea6\u7684\u95f4\u9694\u3002\u4f8b\u5982\uff0c\u4f7f\u7528from matplotlib.ticker import MultipleLocator<\/code>\u53ef\u4ee5\u8bbe\u7f6ex\u6216y\u8f74\u7684\u523b\u5ea6\u95f4\u9694\u4e3a\u7279\u5b9a\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u4f60\u6839\u636e\u6570\u636e\u8303\u56f4\u7684\u9700\u8981\u6765\u8c03\u6574\u523b\u5ea6\uff0c\u4f7f\u5176\u66f4\u52a0\u7b26\u5408\u5b9e\u9645\u9700\u6c42\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u5728\u56fe\u8868\u4e2d\u6dfb\u52a0\u7f51\u683c\u7ebf\u4ee5\u63d0\u9ad8\u523b\u5ea6\u7684\u53ef\u8bfb\u6027\uff1f<\/strong>
\u7edd\u5bf9\u53ef\u4ee5\u3002\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528plt.grid()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u7f51\u683c\u7ebf\u3002\u901a\u8fc7\u8bbe\u7f6ewhich='both'<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u540c\u65f6\u663e\u793a\u4e3b\u523b\u5ea6\u548c\u6b21\u523b\u5ea6\u7684\u7f51\u683c\u7ebf\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\uff0c\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e3a\u4e86\u8ba9Python\u663e\u793a\u66f4\u7ec6\u81f4\u7684\u523b\u5ea6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\uff0c\u901a\u8fc7\u8bbe\u7f6e\u523b\u5ea6\u523b\u753b\u95f4\u9694\u3001\u523b\u5ea6\u6807\u7b7e\u683c\u5f0f\u5316\u3001\u589e\u52a0 […]","protected":false},"author":3,"featured_media":1073057,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073045"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1073045"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073045\/revisions"}],"predecessor-version":[{"id":1073061,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073045\/revisions\/1073061"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1073057"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1073045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1073045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1073045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}