{"id":1131384,"date":"2025-01-08T20:48:09","date_gmt":"2025-01-08T12:48:09","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1131384.html"},"modified":"2025-01-08T20:48:11","modified_gmt":"2025-01-08T12:48:11","slug":"python%e7%9a%84pie%e5%a6%82%e4%bd%95%e7%94%bb%e4%b8%89%e7%bb%b4%e9%a5%bc%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1131384.html","title":{"rendered":"python\u7684pie\u5982\u4f55\u753b\u4e09\u7ef4\u997c\u56fe"},"content":{"rendered":"

\"python\u7684pie\u5982\u4f55\u753b\u4e09\u7ef4\u997c\u56fe\"<\/p>\n

\u8981\u5728Python\u4e2d\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3002<\/strong>Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u652f\u6301\u591a\u79cd\u56fe\u5f62\u548c\u56fe\u8868\uff0c\u5305\u62ec\u4e09\u7ef4\u997c\u56fe\u3002\u901a\u8fc7\u4f7f\u7528\u5b83\u7684mplot3d\u6a21\u5757\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u81ea\u5b9a\u4e49\u4e09\u7ef4\u997c\u56fe\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u7684Matplotlib\u5e93\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\uff0c\u5e76\u63d0\u4f9b\u5b9e\u9645\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u6df1\u5165\u7684\u89e3\u91ca\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5Matplotlib\u5e93<\/h2>\n<\/p>\n

\u8981\u4f7f\u7528Matplotlib\u5e93\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5b83\u5df2\u7ecf\u5b89\u88c5\u3002\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u786e\u8ba4\u5b89\u88c5\u6210\u529f\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5f00\u59cb\u4f7f\u7528\u5b83\u521b\u5efa\u4e09\u7ef4\u997c\u56fe\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757<\/h2>\n<\/p>\n
\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\u3002\u9664\u4e86Matplotlib\u5916\uff0c\u8fd8\u9700\u8981\u5bfc\u5165Numpy\u5e93\u6765\u751f\u6210\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165\u8fd9\u4e9b\u6a21\u5757\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u521b\u5efa\u6570\u636e<\/h2>\n<\/p>\n
\u4e3a\u4e86\u7ed8\u5236\u997c\u56fe\uff0c\u6211\u4eec\u9700\u8981\u4e00\u4e9b\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u5206\u7c7b\u6570\u636e\uff1a<\/p>\n<\/p>\n
labels = ['Category A', 'Category B', 'Category C', 'Category D']<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e9b\u6570\u636e\u5c06\u88ab\u7528\u4e8e\u521b\u5efa\u997c\u56fe\uff0c\u5176\u4e2dlabels<\/code>\u8868\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u540d\u79f0\uff0csizes<\/code>\u8868\u793a\u6bcf\u4e2a\u7c7b\u522b\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe<\/h2>\n<\/p>\n
1\u3001\u521b\u5efa\u57fa\u672c\u7684\u997c\u56fe<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u57fa\u672c\u7684\u4e8c\u7ef4\u997c\u56fe\uff0c\u786e\u4fdd\u6211\u4eec\u7684\u6570\u636e\u662f\u6b63\u786e\u7684\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(8, 8))<\/p>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%')<\/p>\n
plt.title('2D Pie Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6dfb\u52a0\u4e09\u7ef4\u6548\u679c<\/h3>\n<\/p>\n
\u4e3a\u4e86\u6dfb\u52a0\u4e09\u7ef4\u6548\u679c\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528mplot3d\u6a21\u5757\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
2.1\u3001\u521b\u5efa\u4e09\u7ef4\u8f74<\/h4>\n<\/p>\n
\u4f7f\u7528Axes3D<\/code>\u7c7b\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u8f74\uff1a<\/p>\n<\/p>\n
fig = plt.figure(figsize=(10, 7))<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u8ba1\u7b97\u6bcf\u4e2a\u6954\u5f62\u7684\u89d2\u5ea6<\/h4>\n<\/p>\n
\u4e3a\u4e86\u5728\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u7ed8\u5236\u997c\u56fe\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u6bcf\u4e2a\u6954\u5f62\u7684\u89d2\u5ea6\uff1a<\/p>\n<\/p>\n
angles = np.linspace(0, 2 * np.pi, len(sizes) + 1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3\u3001\u521b\u5efa\u4e09\u7ef4\u6954\u5f62<\/h4>\n<\/p>\n
\u4f7f\u7528\u5faa\u73af\u521b\u5efa\u6bcf\u4e2a\u6954\u5f62\uff0c\u5e76\u6dfb\u52a0\u5230\u4e09\u7ef4\u8f74\u4e2d\uff1a<\/p>\n<\/p>\n
for i in range(len(sizes)):<\/p>\n
    x = [0] + np.cos(angles[i:i+2]).tolist() + [0]<\/p>\n
    y = [0] + np.sin(angles[i:i+2]).tolist() + [0]<\/p>\n
    z = np.zeros(4)<\/p>\n
    ax.plot_trisurf(x, y, z, color=plt.cm.viridis(sizes[i] \/ sum(sizes)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.4\u3001\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u89e3\u91ca\u56fe\u8868\uff1a<\/p>\n<\/p>\n
ax.set_title('3D Pie Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5b8c\u6574\u4ee3\u7801\u793a\u4f8b<\/h2>\n<\/p>\n
\u4ee5\u4e0b\u662f\u5b8c\u6574\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Python\u7684Matplotlib\u5e93\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
labels = ['Category A', 'Category B', 'Category C', 'Category D']<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
\u521b\u5efa\u56fe\u5f62\u548c\u4e09\u7ef4\u8f74<\/strong><\/h2>\n
fig = plt.figure(figsize=(10, 7))<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u6954\u5f62\u7684\u89d2\u5ea6<\/strong><\/h2>\n
angles = np.linspace(0, 2 * np.pi, len(sizes) + 1)<\/p>\n
\u521b\u5efa\u4e09\u7ef4\u6954\u5f62<\/strong><\/h2>\n
for i in range(len(sizes)):<\/p>\n
    x = [0] + np.cos(angles[i:i+2]).tolist() + [0]<\/p>\n
    y = [0] + np.sin(angles[i:i+2]).tolist() + [0]<\/p>\n
    z = np.zeros(4)<\/p>\n
    ax.plot_trisurf(x, y, z, color=plt.cm.viridis(sizes[i] \/ sum(sizes)))<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
ax.set_title('3D Pie Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6df1\u5165\u5b9a\u5236\u4e09\u7ef4\u997c\u56fe<\/h2>\n<\/p>\n
1\u3001\u989c\u8272\u548c\u6837\u5f0f<\/h3>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539plot_trisurf<\/code>\u51fd\u6570\u7684\u53c2\u6570\u6765\u5b9a\u5236\u989c\u8272\u548c\u6837\u5f0f\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
ax.plot_trisurf(x, y, z, color=plt.cm.viridis(sizes[i] \/ sum(sizes)), edgecolor='k', linewidth=0.5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6dfb\u52a0\u56fe\u4f8b<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u89e3\u91ca\u56fe\u8868\uff0c\u53ef\u4ee5\u6dfb\u52a0\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
for i in range(len(sizes)):<\/p>\n
    ax.text(np.cos(angles[i]), np.sin(angles[i]), 0.1, labels[i], color='black', ha='center')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8c03\u6574\u89c6\u89d2<\/h3>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7view_init<\/code>\u51fd\u6570\u8c03\u6574\u89c6\u89d2\uff0c\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u4e09\u7ef4\u6548\u679c\uff1a<\/p>\n<\/p>\n
ax.view_init(elev=30, azim=45)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Python\u7684Matplotlib\u5e93\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\u3001\u521b\u5efa\u6570\u636e\u3001\u7ed8\u5236\u57fa\u672c\u7684\u997c\u56fe\u3001\u6dfb\u52a0\u4e09\u7ef4\u6548\u679c\u4ee5\u53ca\u6df1\u5165\u5b9a\u5236\u4e09\u7ef4\u997c\u56fe\u3002\u4f7f\u7528\u8fd9\u4e9b\u6280\u5de7\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u5b9a\u5236\u4e09\u7ef4\u997c\u56fe\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/strong>\u65e0\u8bba\u662f\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u62a5\u544a\u8fd8\u662f\u6f14\u793a\uff0c\u4e09\u7ef4\u997c\u56fe\u90fd\u662f\u4e00\u79cd\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\uff0c\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\u548c\u5de5\u5177\uff1f<\/strong>
\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\u901a\u5e38\u9700\u8981\u4f7f\u7528Matplotlib\u5e93\uff0c\u5b83\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884cpip install matplotlib<\/code>\u547d\u4ee4\u6765\u5b89\u88c5\u8be5\u5e93\u3002\u5728\u4f7f\u7528Matplotlib\u65f6\uff0c\u60a8\u8fd8\u9700\u8981\u5bfc\u5165mpl_toolkits.mplot3d<\/code>\u6a21\u5757\uff0c\u4ee5\u4fbf\u80fd\u591f\u521b\u5efa\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n
\u4e09\u7ef4\u997c\u56fe\u7684\u57fa\u672c\u7ed8\u5236\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\u7684\u4e00\u822c\u6b65\u9aa4\u5305\u62ec\uff1a\u9996\u5148\uff0c\u5bfc\u5165\u6240\u9700\u7684\u5e93\u548c\u6a21\u5757\u3002\u7136\u540e\uff0c\u51c6\u5907\u6570\u636e\uff0c\u8fd9\u5305\u62ec\u997c\u56fe\u7684\u6bcf\u4e2a\u90e8\u5206\u7684\u6807\u7b7e\u548c\u6570\u503c\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528Axes3D<\/code>\u7c7b\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u5750\u6807\u8f74\uff0c\u4f7f\u7528pie<\/code>\u65b9\u6cd5\u7ed8\u5236\u997c\u56fe\u3002\u6700\u540e\uff0c\u8c03\u7528show<\/code>\u65b9\u6cd5\u6765\u663e\u793a\u56fe\u5f62\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u4e09\u7ef4\u997c\u56fe\u7684\u5916\u89c2\uff1f<\/strong>
\u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u81ea\u5b9a\u4e49\u4e09\u7ef4\u997c\u56fe\u7684\u5916\u89c2\uff0c\u5305\u62ec\u8c03\u6574\u989c\u8272\u3001\u6807\u7b7e\u5b57\u4f53\u3001\u56fe\u4f8b\u4f4d\u7f6e\u7b49\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528colors<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u6bcf\u4e00\u90e8\u5206\u7684\u989c\u8272\uff0c\u901a\u8fc7autopct<\/code>\u53c2\u6570\u6765\u663e\u793a\u6bcf\u4e2a\u90e8\u5206\u7684\u767e\u5206\u6bd4\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528explode<\/code>\u53c2\u6570\u6765\u7a81\u51fa\u663e\u793a\u67d0\u4e2a\u7279\u5b9a\u90e8\u5206\uff0c\u4f7f\u56fe\u5f62\u66f4\u52a0\u5438\u5f15\u4eba\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u7ed8\u5236\u4e09\u7ef4\u997c\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c […]","protected":false},"author":3,"featured_media":1131395,"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\/1131384"}],"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=1131384"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1131384\/revisions"}],"predecessor-version":[{"id":1131397,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1131384\/revisions\/1131397"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1131395"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1131384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1131384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1131384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}