{"id":1086327,"date":"2025-01-08T13:24:30","date_gmt":"2025-01-08T05:24:30","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1086327.html"},"modified":"2025-01-08T13:24:34","modified_gmt":"2025-01-08T05:24:34","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%b0%86%e7%bb%98%e5%9b%be%e7%95%8c%e9%9d%a2%e6%94%be%e5%a4%a7-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1086327.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5c06\u7ed8\u56fe\u754c\u9762\u653e\u5927"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5c06\u7ed8\u56fe\u754c\u9762\u653e\u5927\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539\u7ed8\u56fe\u754c\u9762\u7684\u5927\u5c0f\u6765\u5c06\u7ed8\u56fe\u754c\u9762\u653e\u5927\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u8c03\u6574\u56fe\u5f62\u7684\u5c3a\u5bf8\u3001\u4fee\u6539DPI\u3001\u8c03\u6574\u5b50\u56fe\u5e03\u5c40\u3002<\/strong>\u5176\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u662f\u901a\u8fc7\u8c03\u6574\u56fe\u5f62\u7684\u5c3a\u5bf8\u6765\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002\u5177\u4f53\u64cd\u4f5c\u662f\u4f7f\u7528matplotlib\u5e93\u4e2d\u7684figure<\/code>\u51fd\u6570\uff0c\u5e76\u8bbe\u7f6e\u53c2\u6570figsize<\/code>\u6765\u6539\u53d8\u56fe\u5f62\u7684\u5c3a\u5bf8\uff0c\u4ece\u800c\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\u6765\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u8c03\u6574\u56fe\u5f62\u7684\u5c3a\u5bf8<\/p>\n<\/p>\n

\u4f7f\u7528matplotlib\u5e93\u4e2d\u7684figure<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u8c03\u6574\u7ed8\u56fe\u754c\u9762\u7684\u5927\u5c0f\u3002figure<\/code>\u51fd\u6570\u7684figsize<\/code>\u53c2\u6570\u63a5\u53d7\u4e00\u4e2a\u5305\u542b\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u7684\u5143\u7ec4\uff0c\u5355\u4f4d\u4e3a\u82f1\u5bf8\u3002\u901a\u8fc7\u8bbe\u7f6e\u5408\u9002\u7684\u5c3a\u5bf8\uff0c\u53ef\u4ee5\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n

\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u7ed8\u56fe\u754c\u9762\u5c3a\u5bf8<\/strong><\/h2>\n
plt.figure(figsize=(12, 8))<\/p>\n
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfigsize<\/code>\u53c2\u6570\u88ab\u8bbe\u7f6e\u4e3a(12, 8)\uff0c\u8fd9\u610f\u5473\u7740\u56fe\u5f62\u7684\u5bbd\u5ea6\u4e3a12\u82f1\u5bf8\uff0c\u9ad8\u5ea6\u4e3a8\u82f1\u5bf8\u3002\u8fd9\u6837\u5c31\u53ef\u4ee5\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4fee\u6539DPI<\/p>\n<\/p>\n
DPI\uff08Dots Per Inch\uff0c\u50cf\u7d20\u6bcf\u82f1\u5bf8\uff09\u662f\u4e00\u4e2a\u5f71\u54cd\u56fe\u5f62\u5206\u8fa8\u7387\u7684\u91cd\u8981\u53c2\u6570\u3002\u901a\u8fc7\u589e\u52a0DPI\uff0c\u53ef\u4ee5\u4f7f\u56fe\u5f62\u66f4\u6e05\u6670\uff0c\u4ece\u800c\u5b9e\u73b0\u653e\u5927\u7ed8\u56fe\u754c\u9762\u7684\u6548\u679c\u3002DPI\u53ef\u4ee5\u901a\u8fc7figure<\/code>\u51fd\u6570\u7684dpi<\/code>\u53c2\u6570\u8fdb\u884c\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u7ed8\u56fe\u754c\u9762\u5c3a\u5bf8\u548cDPI<\/strong><\/h2>\n
plt.figure(figsize=(8, 6), dpi=120)<\/p>\n
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdpi<\/code>\u53c2\u6570\u88ab\u8bbe\u7f6e\u4e3a120\uff0c\u8fd9\u5c06\u63d0\u9ad8\u56fe\u5f62\u7684\u5206\u8fa8\u7387\uff0c\u4f7f\u56fe\u5f62\u770b\u8d77\u6765\u66f4\u5927\u66f4\u6e05\u6670\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8c03\u6574\u5b50\u56fe\u5e03\u5c40<\/p>\n<\/p>\n
\u5f53\u56fe\u5f62\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u5b50\u56fe\u5e03\u5c40\u6765\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002matplotlib\u5e93\u63d0\u4f9b\u4e86subplots<\/code>\u51fd\u6570\uff0c\u7528\u4e8e\u521b\u5efa\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u7684\u56fe\u5f62\u3002\u901a\u8fc7\u8bbe\u7f6esubplots<\/code>\u51fd\u6570\u7684figsize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u8c03\u6574\u6574\u4e2a\u56fe\u5f62\u7684\u5c3a\u5bf8\uff0c\u4ece\u800c\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u7684\u56fe\u5f62\uff0c\u5e76\u8bbe\u7f6e\u56fe\u5f62\u5c3a\u5bf8<\/strong><\/h2>\n
fig, axs = plt.subplots(2, 2, figsize=(12, 8))<\/p>\n
axs[0, 0].plot([1, 2, 3, 4], [10, 20, 25, 30])<\/p>\n
axs[0, 1].plot([1, 2, 3, 4], [30, 25, 20, 10])<\/p>\n
axs[1, 0].plot([1, 2, 3, 4], [15, 15, 15, 15])<\/p>\n
axs[1, 1].plot([1, 2, 3, 4], [10, 30, 20, 10])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfigsize<\/code>\u53c2\u6570\u88ab\u8bbe\u7f6e\u4e3a(12, 8)\uff0c\u8fd9\u5c06\u653e\u5927\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528rcParams\u5168\u5c40\u8bbe\u7f6e<\/p>\n<\/p>\n
\u5982\u679c\u9700\u8981\u5728\u6574\u4e2a\u811a\u672c\u4e2d\u7edf\u4e00\u8bbe\u7f6e\u56fe\u5f62\u5c3a\u5bf8\u548cDPI\uff0c\u53ef\u4ee5\u901a\u8fc7matplotlib\u7684rcParams<\/code>\u5168\u5c40\u8bbe\u7f6e\u6765\u5b9e\u73b0\u3002rcParams<\/code>\u662f\u4e00\u4e2a\u5305\u542b\u6240\u6709\u53ef\u914d\u7f6e\u53c2\u6570\u7684\u5b57\u5178\uff0c\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539\u5176\u4e2d\u7684\u53c2\u6570\u6765\u8bbe\u7f6e\u5168\u5c40\u7684\u56fe\u5f62\u5c5e\u6027\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u5168\u5c40\u56fe\u5f62\u5c5e\u6027<\/strong><\/h2>\n
plt.rcParams['figure.figsize'] = (12, 8)<\/p>\n
plt.rcParams['figure.dpi'] = 120<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u4fee\u6539rcParams<\/code>\u5b57\u5178\u4e2d\u7684figure.figsize<\/code>\u548cfigure.dpi<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u5168\u5c40\u8bbe\u7f6e\u56fe\u5f62\u7684\u5c3a\u5bf8\u548cDPI\uff0c\u4ece\u800c\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7ed3\u5408\u5176\u4ed6\u7ed8\u56fe\u5e93<\/p>\n<\/p>\n
\u9664\u4e86matplotlib\u5e93\uff0cPython\u4e2d\u8fd8\u6709\u5176\u4ed6\u7ed8\u56fe\u5e93\u5982seaborn\u3001plotly\u7b49\uff0c\u4e5f\u53ef\u4ee5\u7528\u6765\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002\u4ee5seaborn\u4e3a\u4f8b\uff0cseaborn\u662f\u57fa\u4e8ematplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684\u7ed8\u56fe\u65b9\u6cd5\u3002\u5728\u4f7f\u7528seaborn\u7ed8\u56fe\u65f6\uff0c\u540c\u6837\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574figsize<\/code>\u53c2\u6570\u6765\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u7ed8\u56fe\u754c\u9762\u5c3a\u5bf8<\/strong><\/h2>\n
plt.figure(figsize=(12, 8))<\/p>\n
sns.lineplot(x=[1, 2, 3, 4], y=[10, 20, 25, 30])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u4f7f\u7528seaborn\u7684lineplot<\/code>\u51fd\u6570\u7ed8\u5236\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7matplotlib\u7684figure<\/code>\u51fd\u6570\u8bbe\u7f6e\u56fe\u5f62\u5c3a\u5bf8\uff0c\u4ece\u800c\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u901a\u8fc7\u8c03\u6574\u56fe\u5f62\u7684\u5c3a\u5bf8\u3001\u4fee\u6539DPI\u3001\u8c03\u6574\u5b50\u56fe\u5e03\u5c40\u3001\u4f7f\u7528rcParams<\/code>\u5168\u5c40\u8bbe\u7f6e\u4ee5\u53ca\u7ed3\u5408\u5176\u4ed6\u7ed8\u56fe\u5e93\u7b49\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002\u8c03\u6574\u56fe\u5f62\u7684\u5c3a\u5bf8<\/strong>\u662f\u6700\u5e38\u7528\u4e14\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u8bbe\u7f6efigure<\/code>\u51fd\u6570\u7684figsize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u6539\u53d8\u56fe\u5f62\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\uff0c\u4ece\u800c\u653e\u5927\u7ed8\u56fe\u754c\u9762\u3002\u4fee\u6539DPI<\/strong>\u53ef\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u5206\u8fa8\u7387\uff0c\u4f7f\u56fe\u5f62\u770b\u8d77\u6765\u66f4\u5927\u66f4\u6e05\u6670\u3002\u8c03\u6574\u5b50\u56fe\u5e03\u5c40<\/strong>\u5219\u9002\u7528\u4e8e\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u7684\u56fe\u5f62\uff0c\u901a\u8fc7\u8bbe\u7f6esubplots<\/code>\u51fd\u6570\u7684figsize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u653e\u5927\u6574\u4e2a\u56fe\u5f62\u3002\u4f7f\u7528rcParams\u5168\u5c40\u8bbe\u7f6e<\/strong>\u53ef\u4ee5\u5728\u6574\u4e2a\u811a\u672c\u4e2d\u7edf\u4e00\u8bbe\u7f6e\u56fe\u5f62\u5c3a\u5bf8\u548cDPI\uff0c\u9002\u7528\u4e8e\u9700\u8981\u7edf\u4e00\u8c03\u6574\u56fe\u5f62\u5c5e\u6027\u7684\u60c5\u51b5\u3002\u7ed3\u5408\u5176\u4ed6\u7ed8\u56fe\u5e93<\/strong>\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7ed8\u56fe\u65b9\u6cd5\u548c\u9009\u9879\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u7075\u6d3b\u8c03\u6574\u56fe\u5f62\u7684\u5927\u5c0f\uff0c\u4ece\u800c\u5b9e\u73b0\u653e\u5927\u7ed8\u56fe\u754c\u9762\u7684\u76ee\u7684\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1\u5728Python\u4e2d\u653e\u5927\u7ed8\u56fe\u754c\u9762\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8c03\u6574\u7ed8\u56fe\u754c\u9762\u7684\u5927\u5c0f\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u7684figure()<\/code>\u51fd\u6570\u6765\u8c03\u6574\u7ed8\u56fe\u754c\u9762\u7684\u5927\u5c0f\u3002\u53ef\u4ee5\u8bbe\u7f6efigsize<\/code>\u53c2\u6570\u4e3a\u4e00\u4e2a\u5305\u542b\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u7684\u5143\u7ec4\uff0c\u4f8b\u5982plt.figure(figsize=(10, 6))<\/code>\uff0c\u8fd9\u4f1a\u521b\u5efa\u4e00\u4e2a\u5bbd10\u82f1\u5bf8\uff0c\u9ad86\u82f1\u5bf8\u7684\u7ed8\u56fe\u754c\u9762\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7a97\u53e3\u7684 DPI\uff08\u6bcf\u82f1\u5bf8\u70b9\u6570\uff09\u6765\u8fdb\u4e00\u6b65\u63a7\u5236\u7ed8\u56fe\u7684\u6e05\u6670\u5ea6\u3002<\/p>\n
\u4f7f\u7528\u54ea\u4e2a\u5e93\u7ed8\u56fe\u65f6\u53ef\u4ee5\u65b9\u4fbf\u5730\u653e\u5927\u754c\u9762\uff1f<\/strong>
Matplotlib\u662f\u6700\u5e38\u7528\u7684Python\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u754c\u9762\u5c3a\u5bf8\u8bbe\u7f6e\u3002\u9664\u4e86Matplotlib\uff0cSeaborn\u548cPlotly\u7b49\u5e93\u4e5f\u652f\u6301\u81ea\u5b9a\u4e49\u56fe\u5f62\u5c3a\u5bf8\u3002\u5728Plotly\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528layout<\/code>\u4e2d\u7684width<\/code>\u548cheight<\/code>\u5c5e\u6027\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u5927\u5c0f\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u6027\u56fe\u8868\u7684\u7528\u6237\u3002<\/p>\n
\u5982\u4f55\u5728Jupyter Notebook\u4e2d\u653e\u5927\u7ed8\u56fe\u754c\u9762\uff1f<\/strong>
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