{"id":1074196,"date":"2025-01-08T11:32:25","date_gmt":"2025-01-08T03:32:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1074196.html"},"modified":"2025-01-08T11:32:27","modified_gmt":"2025-01-08T03:32:27","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e5%b8%95%e7%b4%af%e6%89%98%e5%9b%be-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1074196.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\uff0c\u4e3b\u8981\u4f7f\u7528\u4e24\u4e2a\u5e93\uff1aMatplotlib\u548cPandas\u3002\u5e15\u7d2f\u6258\u56fe\u662f\u4e00\u79cd\u6761\u5f62\u56fe\uff0c\u6309\u503c\u7684\u964d\u5e8f\u6392\u5217\uff0c\u901a\u5e38\u4e0e\u7d2f\u8ba1\u767e\u5206\u6bd4\u7ebf\u7ed3\u5408\u4f7f\u7528\u3002<\/strong>\u3001\u901a\u8fc7\u5e15\u7d2f\u6258\u56fe\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6700\u91cd\u8981\u7684\u56e0\u7d20<\/strong>\u3001\u5b83\u5e7f\u6cdb\u5e94\u7528\u4e8e\u8d28\u91cf\u7ba1\u7406\u548c\u4e1a\u52a1\u5206\u6790<\/strong>\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u6240\u9700\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86\u6240\u9700\u7684\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5bfc\u5165\u5e93\u5e76\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u5e76\u51c6\u5907\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u6570\u636e\u96c6\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'Category': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'Value': [50, 30, 15, 4, 1]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe<\/h3>\n<\/p>\n
1\u3001\u521b\u5efa\u6761\u5f62\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6309\u503c\u964d\u5e8f\u6392\u5217\u7684\u6761\u5f62\u56fe\uff1a<\/p>\n<\/p>\n
df = df.sort_values(by='Value', ascending=False)<\/p>\n
\u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.bar(df['Category'], df['Value'], color='C0')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8ba1\u7b97\u7d2f\u8ba1\u767e\u5206\u6bd4\u5e76\u7ed8\u5236\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u7d2f\u8ba1\u767e\u5206\u6bd4\uff0c\u5e76\u5728\u540c\u4e00\u4e2a\u56fe\u4e0a\u7ed8\u5236\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u7d2f\u8ba1\u767e\u5206\u6bd4<\/p>\n
df['Cumulative Percentage'] = df['Value'].cumsum() \/ df['Value'].sum() * 100<\/p>\n
\u7ed8\u5236\u7d2f\u8ba1\u767e\u5206\u6bd4\u6298\u7ebf\u56fe<\/strong><\/h2>\n
ax2 = ax.twinx()<\/p>\n
ax2.plot(df['Category'], df['Cumulative Percentage'], color='C1', marker='D', ms=7)<\/p>\n
ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: '{:.0f}%'.format(x)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u56fe\u5f62\u7f8e\u5316<\/h4>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u5f62\u66f4\u7f8e\u89c2\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u4e00\u4e9b\u6807\u7b7e\u548c\u6807\u9898\uff1a<\/p>\n<\/p>\n
# \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/p>\n
ax.set_title('Pareto Chart')<\/p>\n
ax.set_xlabel('Category')<\/p>\n
ax.set_ylabel('Value')<\/p>\n
ax2.set_ylabel('Cumulative Percentage')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b8c\u6574\u4ee3\u7801<\/h3>\n<\/p>\n
\u5c06\u4e0a\u8ff0\u6b65\u9aa4\u6574\u5408\u5728\u4e00\u8d77\uff0c\u5f97\u5230\u5b8c\u6574\u7684\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u7684\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'Category': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'Value': [50, 30, 15, 4, 1]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df = df.sort_values(by='Value', ascending=False)<\/p>\n
\u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.bar(df['Category'], df['Value'], color='C0')<\/p>\n
\u8ba1\u7b97\u7d2f\u8ba1\u767e\u5206\u6bd4<\/strong><\/h2>\n
df['Cumulative Percentage'] = df['Value'].cumsum() \/ df['Value'].sum() * 100<\/p>\n
\u7ed8\u5236\u7d2f\u8ba1\u767e\u5206\u6bd4\u6298\u7ebf\u56fe<\/strong><\/h2>\n
ax2 = ax.twinx()<\/p>\n
ax2.plot(df['Category'], df['Cumulative Percentage'], color='C1', marker='D', ms=7)<\/p>\n
ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: '{:.0f}%'.format(x)))<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
ax.set_title('Pareto Chart')<\/p>\n
ax.set_xlabel('Category')<\/p>\n
ax.set_ylabel('Value')<\/p>\n
ax2.set_ylabel('Cumulative Percentage')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6df1\u5165\u89e3\u6790\u5e15\u7d2f\u6258\u56fe\u7684\u5e94\u7528<\/h3>\n<\/p>\n
1\u3001\u8d28\u91cf\u7ba1\u7406\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5e15\u7d2f\u6258\u56fe\u5728\u8d28\u91cf\u7ba1\u7406\u4e2d\u5e7f\u6cdb\u5e94\u7528\uff0c\u901a\u8fc7\u5e15\u7d2f\u6258\u56fe\u53ef\u4ee5\u8bc6\u522b\u51fa\u5bfc\u81f4\u95ee\u9898\u7684\u6700\u4e3b\u8981\u56e0\u7d20\u3002\u4f8b\u5982\uff0c\u5728\u751f\u4ea7\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5e15\u7d2f\u6258\u56fe\u8bc6\u522b\u51fa\u5bfc\u81f4\u4ea7\u54c1\u7f3a\u9677\u7684\u6700\u4e3b\u8981\u539f\u56e0\uff0c\u4ece\u800c\u96c6\u4e2d\u8d44\u6e90\u8fdb\u884c\u6539\u8fdb\u3002<\/p>\n<\/p>\n
2\u3001\u4e1a\u52a1\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u4e1a\u52a1\u5206\u6790\u4e2d\uff0c\u5e15\u7d2f\u6258\u56fe\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u51fa\u5bf9\u4e1a\u52a1\u5f71\u54cd\u6700\u5927\u7684\u56e0\u7d20\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u5206\u6790\u5ba2\u6237\u6295\u8bc9\u7684\u539f\u56e0\uff0c\u53ef\u4ee5\u8bc6\u522b\u51fa\u6700\u4e3b\u8981\u7684\u6295\u8bc9\u7c7b\u578b\uff0c\u4ece\u800c\u6539\u8fdb\u5ba2\u6237\u670d\u52a1\u3002<\/p>\n<\/p>\n
\u516d\u3001\u5b9a\u5236\u5316\u5e15\u7d2f\u6258\u56fe<\/h3>\n<\/p>\n
1\u3001\u8c03\u6574\u989c\u8272\u548c\u6837\u5f0f<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u989c\u8272\u548c\u6837\u5f0f\uff0c\u4f7f\u5e15\u7d2f\u6258\u56fe\u66f4\u7b26\u5408\u4e2a\u4eba\u6216\u4f01\u4e1a\u7684\u98ce\u683c\uff1a<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6837\u5f0f<\/p>\n
ax.bar(df['Category'], df['Value'], color='#1f77b4')<\/p>\n
ax2.plot(df['Category'], df['Cumulative Percentage'], color='#ff7f0e', marker='o', linestyle='--')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6dfb\u52a0\u6570\u636e\u6807\u7b7e<\/h4>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u5f62\u66f4\u52a0\u76f4\u89c2\uff0c\u53ef\u4ee5\u5728\u6761\u5f62\u56fe\u548c\u6298\u7ebf\u56fe\u4e0a\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\uff1a<\/p>\n<\/p>\n
# \u6dfb\u52a0\u6761\u5f62\u56fe\u6570\u636e\u6807\u7b7e<\/p>\n
for i in range(len(df)):<\/p>\n
    ax.text(i, df['Value'].iloc[i] + 1, df['Value'].iloc[i], ha='center')<\/p>\n
\u6dfb\u52a0\u6298\u7ebf\u56fe\u6570\u636e\u6807\u7b7e<\/strong><\/h2>\n
for i in range(len(df)):<\/p>\n
    ax2.text(i, df['Cumulative Percentage'].iloc[i] + 1, f"{df['Cumulative Percentage'].iloc[i]:.1f}%", ha='center')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684Matplotlib\u548cPandas\u5e93\u8f7b\u677e\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u3002\u5e15\u7d2f\u6258\u56fe\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u51fa\u6700\u91cd\u8981\u7684\u56e0\u7d20\uff0c\u800c\u4e14\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u96c6\u4e2d\u8d44\u6e90\u8fdb\u884c\u6539\u8fdb\uff0c\u4ece\u800c\u63d0\u9ad8\u6548\u7387\u548c\u6548\u679c\u3002<\/strong>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u5bf9\u5e15\u7d2f\u6258\u56fe\u8fdb\u884c\u5b9a\u5236\u5316\uff0c\u4f7f\u5176\u66f4\u52a0\u7b26\u5408\u4e2a\u4eba\u6216\u4f01\u4e1a\u7684\u98ce\u683c\u548c\u9700\u6c42\u3002\u65e0\u8bba\u662f\u5728\u8d28\u91cf\u7ba1\u7406\u8fd8\u662f\u4e1a\u52a1\u5206\u6790\u4e2d\uff0c\u5e15\u7d2f\u6258\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
 \u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cPandas\u5e93\u6765\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86\u8fd9\u4e24\u4e2a\u5e93\u3002\u7136\u540e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u7684\u9891\u7387\uff0c\u5e76\u5c06\u5176\u6309\u964d\u5e8f\u6392\u5217\uff0c\u6765\u521b\u5efa\u5e15\u7d2f\u6258\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u6b65\u9aa4\uff1a\u5bfc\u5165\u6570\u636e\uff0c\u8ba1\u7b97\u9891\u7387\uff0c\u7ed8\u5236\u6761\u5f62\u56fe\u5e76\u53e0\u52a0\u7d2f\u8ba1\u767e\u5206\u6bd4\u7ebf\u3002<\/p>\n
\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u6570\u636e\u51c6\u5907\u6b65\u9aa4\uff1f<\/strong>
\u5728\u51c6\u5907\u6570\u636e\u65f6\uff0c\u786e\u4fdd\u60a8\u7684\u6570\u636e\u662f\u5206\u7c7b\u7684\uff0c\u5e76\u4e14\u80fd\u591f\u53cd\u6620\u51fa\u95ee\u9898\u7684\u4e3b\u8981\u6765\u6e90\u3002\u6570\u636e\u9700\u8981\u8fdb\u884c\u6574\u7406\u548c\u6e05\u6d17\uff0c\u53bb\u9664\u65e0\u6548\u503c\u548c\u91cd\u590d\u9879\u3002\u63a5\u7740\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u51fa\u73b0\u9891\u7387\uff0c\u5e76\u5bf9\u5176\u8fdb\u884c\u6392\u5e8f\uff0c\u4ee5\u4fbf\u5728\u56fe\u4e2d\u5c55\u793a\u3002<\/p>\n
\u4f7f\u7528\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u5e2e\u52a9\u7ed8\u5236\u5e15\u7d2f\u6258\u56fe\uff1f<\/strong>
\u9664\u4e86Matplotlib\u548cPandas\uff0cSeaborn\u548cPlotly\u4e5f\u662f\u5f88\u597d\u7684\u9009\u62e9\u3002Seaborn\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u56fe\u5f62\uff0c\u800cPlotly\u5141\u8bb8\u60a8\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u6839\u636e\u60a8\u7684\u9700\u6c42\u548c\u6570\u636e\u7684\u590d\u6742\u7a0b\u5ea6\uff0c\u9009\u62e9\u9002\u5408\u7684\u5e93\u53ef\u4ee5\u63d0\u9ad8\u7ed8\u56fe\u7684\u6548\u7387\u548c\u6548\u679c\u3002<\/p>\n
\u5982\u4f55\u89e3\u91ca\u5e15\u7d2f\u6258\u56fe\u4e2d\u7684\u6570\u636e\u548c\u8d8b\u52bf\uff1f<\/strong>
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