{"id":1042603,"date":"2024-12-31T12:58:47","date_gmt":"2024-12-31T04:58:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1042603.html"},"modified":"2024-12-31T12:58:50","modified_gmt":"2024-12-31T04:58:50","slug":"%e5%b7%b2%e7%9f%a5%e6%95%b0%e6%8d%ae%e9%9b%86%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1042603.html","title":{"rendered":"\u5df2\u77e5\u6570\u636e\u96c6\u5982\u4f55\u7528python\u753b\u56fe"},"content":{"rendered":"

\"\u5df2\u77e5\u6570\u636e\u96c6\u5982\u4f55\u7528python\u753b\u56fe\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u56fe\u5f62\u662f\u4e00\u9879\u975e\u5e38\u5e38\u89c1\u4e14\u6709\u7528\u7684\u4efb\u52a1\uff0c\u7279\u522b\u662f\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u9886\u57df\u3002\u8981\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff0c\u6211\u4eec\u901a\u5e38\u4f7f\u7528\u4e00\u4e9b\u4e13\u95e8\u7684\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u548c\u70ed\u529b\u56fe\u7b49\u3002<\/p>\n<\/p>\n

\u5df2\u77e5\u6570\u636e\u96c6\u5982\u4f55\u7528Python\u753b\u56fe\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u52a0\u8f7d\u6570\u636e\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b\u3001\u914d\u7f6e\u56fe\u8868\u5c5e\u6027\u3001\u5c55\u793a\u56fe\u5f62<\/strong><\/p>\n<\/p>\n

\u5176\u4e2d\uff0c\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/strong>\u662f\u6700\u57fa\u7840\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u7ed8\u56fe\u6240\u9700\u7684\u51fd\u6570\u548c\u5de5\u5177\u3002\u4ee5Matplotlib\u4e3a\u4f8b\uff0c\u8fd9\u4e2a\u5e93\u5728Python\u793e\u533a\u4e2d\u975e\u5e38\u6d41\u884c\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u6765\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h2>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u4e00\u4e9b\u5fc5\u8981\u7684\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Pandas\u548cNumPy\u3002Matplotlib\u7528\u4e8e\u7ed8\u56fe\uff0cPandas\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0cNumPy\u5219\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u5bfc\u5165\u8fd9\u4e9b\u5e93\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u7ed8\u56fe\u5e93\uff0c\u800cPandas\u548cNumPy\u5219\u662f\u6570\u636e\u5904\u7406\u7684\u5229\u5668\u3002\u901a\u8fc7\u8fd9\u4e09\u4e2a\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u6570\u636e\u5e76\u5c06\u5176\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u52a0\u8f7d\u6570\u636e<\/h2>\n<\/p>\n
\u5728\u7ed8\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u52a0\u8f7d\u6570\u636e\u96c6\u3002\u6570\u636e\u96c6\u53ef\u4ee5\u6765\u81ea\u591a\u79cd\u6765\u6e90\uff0c\u5982CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001\u6570\u636e\u5e93\u6216API\u63a5\u53e3\u7b49\u3002\u4e0b\u9762\u662f\u52a0\u8f7dCSV\u6587\u4ef6\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
data = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u7684read_csv<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u3002\u8bfb\u53d6\u7684\u6570\u636e\u5c06\u88ab\u5b58\u50a8\u5728\u4e00\u4e2aDataFrame\u5bf9\u8c61\u4e2d\uff0c\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u65b9\u4fbf\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u8fdb\u884c\u591a\u79cd\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b<\/h2>\n<\/p>\n
\u6839\u636e\u6570\u636e\u7684\u7279\u70b9\u548c\u5206\u6790\u76ee\u6807\uff0c\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u8868\u3002\u5e38\u89c1\u7684\u56fe\u8868\u7c7b\u578b\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u548c\u997c\u56fe\u7b49\u3002\u4e0b\u9762\u5206\u522b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u8fd9\u4e9b\u56fe\u8868\u3002<\/p>\n<\/p>\n
1. \u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
\u6298\u7ebf\u56fe\u9002\u5408\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n
plt.figure(figsize=(10, 5))<\/p>\n
plt.plot(data['x'], data['y'], marker='o', linestyle='-')<\/p>\n
plt.title('\u6298\u7ebf\u56fe\u793a\u4f8b')<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.plot<\/code>\u51fd\u6570\u7ed8\u5236\u6298\u7ebf\u56fe\u3002marker<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u6570\u636e\u70b9\u7684\u6807\u8bb0\uff0clinestyle<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u7ebf\u6761\u6837\u5f0f\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u8bbe\u7f6e\u4e86\u56fe\u8868\u7684\u6807\u9898\u548c\u8f74\u6807\u7b7e\uff0c\u5e76\u542f\u7528\u4e86\u7f51\u683c\u7ebf\u3002<\/p>\n<\/p>\n
2. \u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u5408\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n
plt.figure(figsize=(10, 5))<\/p>\n
plt.bar(data['category'], data['value'])<\/p>\n
plt.title('\u67f1\u72b6\u56fe\u793a\u4f8b')<\/p>\n
plt.xlabel('\u7c7b\u522b')<\/p>\n
plt.ylabel('\u503c')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.bar<\/code>\u51fd\u6570\u7ed8\u5236\u67f1\u72b6\u56fe\u3002data['category']<\/code>\u548cdata['value']<\/code>\u5206\u522b\u8868\u793a\u7c7b\u522b\u548c\u5bf9\u5e94\u7684\u503c\u3002<\/p>\n<\/p>\n
3. \u6563\u70b9\u56fe<\/h3>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u5408\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n
plt.figure(figsize=(10, 5))<\/p>\n
plt.scatter(data['x'], data['y'], c='blue', alpha=0.5)<\/p>\n
plt.title('\u6563\u70b9\u56fe\u793a\u4f8b')<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.scatter<\/code>\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe\u3002c<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u70b9\u7684\u989c\u8272\uff0calpha<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u70b9\u7684\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n
4. \u997c\u56fe<\/h3>\n<\/p>\n
\u997c\u56fe\u9002\u5408\u7528\u4e8e\u663e\u793a\u5404\u90e8\u5206\u5360\u603b\u4f53\u7684\u6bd4\u4f8b\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u997c\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u997c\u56fe<\/p>\n
plt.figure(figsize=(8, 8))<\/p>\n
plt.pie(data['value'], labels=data['category'], autopct='%1.1f%%', startangle=140)<\/p>\n
plt.title('\u997c\u56fe\u793a\u4f8b')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.pie<\/code>\u51fd\u6570\u7ed8\u5236\u997c\u56fe\u3002labels<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u5404\u90e8\u5206\u7684\u6807\u7b7e\uff0cautopct<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u767e\u5206\u6bd4\u683c\u5f0f\uff0cstartangle<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u8d77\u59cb\u89d2\u5ea6\u3002<\/p>\n<\/p>\n
\u56db\u3001\u914d\u7f6e\u56fe\u8868\u5c5e\u6027<\/h2>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\uff0c\u6211\u4eec\u53ef\u4ee5\u914d\u7f6e\u4e00\u4e9b\u56fe\u8868\u5c5e\u6027\uff0c\u5982\u989c\u8272\u3001\u5b57\u4f53\u3001\u7f51\u683c\u7ebf\u548c\u56fe\u4f8b\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u56fe\u8868\u914d\u7f6e\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
1. \u8bbe\u7f6e\u989c\u8272<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7color<\/code>\u53c2\u6570\u8bbe\u7f6e\u56fe\u8868\u5143\u7d20\u7684\u989c\u8272\u3002\u4f8b\u5982\uff0c\u8bbe\u7f6e\u67f1\u72b6\u56fe\u7684\u989c\u8272\uff1a<\/p>\n<\/p>\n
plt.bar(data['category'], data['value'], color='green')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bbe\u7f6e\u5b57\u4f53<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7fontdict<\/code>\u53c2\u6570\u8bbe\u7f6e\u6807\u9898\u548c\u8f74\u6807\u7b7e\u7684\u5b57\u4f53\u3002\u4f8b\u5982\uff0c\u8bbe\u7f6e\u6807\u9898\u5b57\u4f53\uff1a<\/p>\n<\/p>\n
plt.title('\u67f1\u72b6\u56fe\u793a\u4f8b', fontdict={'fontsize': 20, 'fontweight': 'bold'})<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u8bbe\u7f6e\u7f51\u683c\u7ebf<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7plt.grid<\/code>\u51fd\u6570\u542f\u7528\u6216\u7981\u7528\u7f51\u683c\u7ebf\u3002\u4f8b\u5982\uff0c\u542f\u7528\u7f51\u683c\u7ebf\uff1a<\/p>\n<\/p>\n
plt.grid(True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u6dfb\u52a0\u56fe\u4f8b<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7plt.legend<\/code>\u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b\u3002\u4f8b\u5982\uff0c\u6dfb\u52a0\u6298\u7ebf\u56fe\u7684\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
plt.plot(data['x'], data['y'], label='\u6298\u7ebf\u56fe')<\/p>\n
plt.legend()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5c55\u793a\u56fe\u5f62<\/h2>\n<\/p>\n
\u5728\u5b8c\u6210\u56fe\u8868\u7684\u7ed8\u5236\u548c\u914d\u7f6e\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528plt.show<\/code>\u51fd\u6570\u5c55\u793a\u56fe\u5f62\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u51fd\u6570\u5c06\u6253\u5f00\u4e00\u4e2a\u7a97\u53e3\uff0c\u663e\u793a\u6211\u4eec\u521b\u5efa\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u7ed3\u8bba<\/h2>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u5730\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u3002Matplotlib\u4f5c\u4e3a\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7075\u6d3b\u7684\u914d\u7f6e\u9009\u9879\uff0c\u9002\u5408\u5404\u79cd\u6570\u636e\u53ef\u89c6\u5316\u9700\u6c42\u3002\u9664\u4e86Matplotlib\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528Pandas\u548cNumPy\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u52a0\u590d\u6742\u548c\u4e13\u4e1a\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u6570\u636e\u53ef\u89c6\u5316\u7684\u65c5\u7a0b\u4e2d\u53d6\u5f97\u6210\u529f\uff01<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b\u6765\u5c55\u793a\u6211\u7684\u6570\u636e\u96c6\uff1f<\/strong>
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