![\"python\u5982\u4f55\u753b\u597d\u770b\u7684\u7edf\u8ba1\u56fe\"](\"https:\/\/cdn-docs.pingcode.com\/wp-content\/uploads\/2024\/12\/a5ab31d7-51d7-4b7a-ab65-3e5c8d65e485.webp?x-oss-process=image\/auto-orient,1\/format,webp\")
<\/p>\n<\/p>\n
Python\u5982\u4f55\u753b\u597d\u770b\u7684\u7edf\u8ba1\u56fe\uff0c\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u3001\u8c03\u6574\u56fe\u5f62\u7684\u7f8e\u89c2\u6027\u3001\u589e\u52a0\u4ea4\u4e92\u6027\u3001\u7ed3\u5408pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u6ce8\u91cd\u8272\u5f69\u548c\u6837\u5f0f\u7684\u642d\u914d<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Matplotlib\u548cSeaborn\u53ef\u4ee5\u6709\u6548\u63d0\u5347\u7edf\u8ba1\u56fe\u7684\u7f8e\u89c2\u6027\u3002Matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u5f62\u7ed8\u5236\u529f\u80fd\uff0c\u800cSeaborn\u5219\u662f\u5728Matplotlib\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u901a\u8fc7\u5408\u7406\u4f7f\u7528\u8fd9\u4e24\u79cd\u5de5\u5177\uff0c\u53ef\u4ee5\u7ed8\u5236\u51fa\u4e13\u4e1a\u4e14\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u62e5\u6709\u5f3a\u5927\u7684\u529f\u80fd\u548c\u7075\u6d3b\u7684\u5b9a\u5236\u9009\u9879\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f7f\u7528Matplotlib\u7ed8\u5236\u7edf\u8ba1\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n Matplotlib\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7 x = [1, 2, 3, 4, 5]<\/p>\n y = [10, 20, 25, 30, 40]<\/p>\n plt.plot(x, y)<\/p>\n plt.title('Basic Line Plot')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4e3a\u4e86\u8ba9\u56fe\u5f62\u66f4\u52a0\u7f8e\u89c2\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u63d0\u4f9b\u7684\u5404\u79cd\u53c2\u6570\u548c\u65b9\u6cd5\u6765\u8c03\u6574\u56fe\u5f62\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u56fe\u5f62\u7684\u5927\u5c0f\u3001\u989c\u8272\u3001\u7ebf\u578b\u3001\u5b57\u4f53\u7b49\u3002<\/p>\n<\/p>\n plt.plot(x, y, color='red', linestyle='--', marker='o')<\/p>\n plt.title('Styled Line Plot', fontsize=16)<\/p>\n plt.xlabel('X-axis', fontsize=14)<\/p>\n plt.ylabel('Y-axis', fontsize=14)<\/p>\n plt.grid(True)<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u4f7f\u7528Seaborn\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u51fa\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u7ed8\u56fe\u51fd\u6570\uff0c\u53ef\u4ee5\u76f4\u63a5\u7ed8\u5236\u51fa\u5404\u79cd\u5e38\u89c1\u7684\u7edf\u8ba1\u56fe\u5f62\uff0c\u4f8b\u5982\u7bb1\u7ebf\u56fe\u3001\u6761\u5f62\u56fe\u548c\u70ed\u529b\u56fe\u7b49\u3002<\/p>\n<\/p>\n import numpy as np<\/p>\n data = np.random.randn(100)<\/p>\n sns.boxplot(data=data)<\/p>\n plt.title('Box Plot')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u53c2\u6570\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8c03\u6574\u56fe\u5f62\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u989c\u8272\u8c03\u8272\u677f\u3001\u4e3b\u9898\u6837\u5f0f\u7b49\u3002<\/p>\n<\/p>\n sns.set_style('whitegrid')<\/p>\n tips = sns.load_dataset('tips')<\/p>\n sns.scatterplot(x='total_bill', y='tip', data=tips, hue='day', palette='coolwarm')<\/p>\n plt.title('Scatter Plot with Palette')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Plotly\u662f\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u591a\u79cd\u56fe\u5f62\u7c7b\u578b\u548c\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\u3002\u4f7f\u7528Plotly\u53ef\u4ee5\u521b\u5efa\u51fa\u5177\u6709\u4ea4\u4e92\u6027\u7684\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n Plotly\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\u76f8\u5bf9\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7 df = px.data.iris()<\/p>\n fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Plotly\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u548c\u65b9\u6cd5\u6765\u589e\u52a0\u56fe\u5f62\u7684\u4ea4\u4e92\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u6dfb\u52a0\u60ac\u505c\u63d0\u793a\u3001\u7f29\u653e\u529f\u80fd\u7b49\u3002<\/p>\n<\/p>\n selector=dict(mode='markers'))<\/p>\n fig.update_layout(title='Interactive Scatter Plot',<\/p>\n xaxis_title='Sepal Width',<\/p>\n yaxis_title='Sepal Length')<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u6839\u636e\u6570\u636e\u7684\u7279\u70b9\u548c\u5206\u6790\u76ee\u7684\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u662f\u7ed8\u5236\u7f8e\u89c2\u7edf\u8ba1\u56fe\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u56fe\u5f62\u7c7b\u578b\u53ca\u5176\u9002\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n \u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\uff0c\u7279\u522b\u662f\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n x = np.linspace(0, 10, 100)<\/p>\n y = np.sin(x)<\/p>\n plt.plot(x, y)<\/p>\n plt.title('Line Plot')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u5206\u7c7b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u6216\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u6570\u503c\u5dee\u5f02\u3002<\/p>\n<\/p>\n categories = ['A', 'B', 'C', 'D']<\/p>\n values = [10, 20, 15, 25]<\/p>\n plt.bar(categories, values)<\/p>\n plt.title('Bar Plot')<\/p>\n plt.xlabel('Categories')<\/p>\n plt.ylabel('Values')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u6563\u70b9\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u7279\u522b\u662f\u67e5\u770b\u76f8\u5173\u6027\u548c\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n x = np.random.rand(50)<\/p>\n y = np.random.rand(50)<\/p>\n plt.scatter(x, y)<\/p>\n plt.title('Scatter Plot')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u7ed8\u5236\u7edf\u8ba1\u56fe\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n \u4f7f\u7528Pandas\u53ef\u4ee5\u8f7b\u677e\u52a0\u8f7d\u548c\u9884\u5904\u7406\u6570\u636e\uff0c\u4f8b\u5982\u8bfb\u53d6CSV\u6587\u4ef6\u3001\u5904\u7406\u7f3a\u5931\u503c\u548c\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u7b49\u3002<\/p>\n<\/p>\n df = pd.read_csv('data.csv')<\/p>\n df = df.dropna()<\/p>\n df['column'] = df['column'].astype(float)<\/p>\n <\/code><\/pre>\n<\/p>\n Pandas\u53ef\u4ee5\u4e0eMatplotlib\u548cSeaborn\u7b49\u7ed8\u56fe\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u76f4\u63a5\u7ed8\u5236DataFrame\u4e2d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n df.plot(x='date', y='value')<\/p>\n plt.title('Line Plot with Pandas')<\/p>\n plt.xlabel('Date')<\/p>\n plt.ylabel('Value')<\/p>\n plt.show()<\/p>\n sns.boxplot(x='category', y='value', data=df)<\/p>\n plt.title('Box Plot with Pandas')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u8272\u5f69\u548c\u6837\u5f0f\u7684\u642d\u914d\u5bf9\u7edf\u8ba1\u56fe\u7684\u7f8e\u89c2\u6027\u6709\u91cd\u8981\u5f71\u54cd\u3002\u5408\u7406\u9009\u62e9\u8272\u5f69\u548c\u6837\u5f0f\uff0c\u53ef\u4ee5\u8ba9\u56fe\u5f62\u66f4\u52a0\u6e05\u6670\u3001\u6613\u8bfb\u548c\u7f8e\u89c2\u3002<\/p>\n<\/p>\n \u9009\u62e9\u5408\u9002\u7684\u8c03\u8272\u677f\u53ef\u4ee5\u63d0\u5347\u56fe\u5f62\u7684\u89c6\u89c9\u6548\u679c\u3002Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u9884\u5b9a\u4e49\u7684\u8c03\u8272\u677f\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u9009\u62e9\u548c\u4f7f\u7528\u3002<\/p>\n<\/p>\n sns.set_palette('husl')<\/p>\n sns.scatterplot(x='total_bill', y='tip', data=tips, hue='day')<\/p>\n plt.title('Scatter Plot with Color Palette')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528\u4e3b\u9898\u6837\u5f0f\u53ef\u4ee5\u7edf\u4e00\u56fe\u5f62\u7684\u6574\u4f53\u98ce\u683c\uff0c\u4f7f\u56fe\u5f62\u770b\u8d77\u6765\u66f4\u52a0\u534f\u8c03\u548c\u4e13\u4e1a\u3002Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u4e3b\u9898\u6837\u5f0f\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8fdb\u884c\u9009\u62e9\u3002<\/p>\n<\/p>\n sns.set_style('darkgrid')<\/p>\n sns.barplot(x='day', y='total_bill', data=tips)<\/p>\n plt.title('Bar Plot with Theme Style')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u5e93\uff0c\u53ef\u4ee5\u5728Python\u4e2d\u7ed8\u5236\u51fa\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u3002\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u3001\u8c03\u6574\u56fe\u5f62\u7684\u7f8e\u89c2\u6027\u3001\u589e\u52a0\u4ea4\u4e92\u6027\u3001\u7ed3\u5408Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u4ee5\u53ca\u6ce8\u91cd\u8272\u5f69\u548c\u6837\u5f0f\u7684\u642d\u914d\uff0c\u90fd\u662f\u63d0\u5347\u7edf\u8ba1\u56fe\u7f8e\u89c2\u6027\u7684\u5173\u952e\u6b65\u9aa4\u3002\u5e0c\u671b\u672c\u6587\u7684\u4ecb\u7ecd\u80fd\u5e2e\u52a9\u8bfb\u8005\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u7ed8\u5236\u51fa\u66f4\u52a0\u4e13\u4e1a\u548c\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n \u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff1f<\/strong> \u5982\u4f55\u4f18\u5316\u7edf\u8ba1\u56fe\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\uff1f<\/strong> \u5982\u4f55\u5728\u7edf\u8ba1\u56fe\u4e2d\u5c55\u793a\u591a\u4e2a\u53d8\u91cf\u7684\u4fe1\u606f\uff1f<\/strong>\u4e00\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u7edf\u8ba1\u56fe<\/h2>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
pyplot<\/code>\u6a21\u5757\u6765\u521b\u5efa\u57fa\u672c\u7684\u56fe\u5f62\uff0c\u4f8b\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
2\u3001\u8c03\u6574\u56fe\u5f62\u7684\u7f8e\u89c2\u6027<\/h3>\n<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n\u4e8c\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u7edf\u8ba1\u56fe<\/h2>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
import seaborn as sns<\/p>\n\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
2\u3001\u8c03\u6574\u56fe\u5f62\u7684\u7f8e\u89c2\u6027<\/h3>\n<\/p>\n
# \u8bbe\u7f6e\u4e3b\u9898\u6837\u5f0f<\/p>\n\u521b\u5efa\u5e26\u6709\u8c03\u8272\u677f\u7684\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u4e09\u3001\u4f7f\u7528Plotly\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/h2>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
plotly.express<\/code>\u6a21\u5757\u6765\u5feb\u901f\u521b\u5efa\u5404\u79cd\u56fe\u5f62\uff0c\u4f8b\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\n\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
2\u3001\u589e\u52a0\u4ea4\u4e92\u6027<\/h3>\n<\/p>\n
fig.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')),<\/p>\n\u56db\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b<\/h2>\n<\/p>\n
1\u3001\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
# \u6570\u636e<\/p>\n\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
2\u3001\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
# \u6570\u636e<\/p>\n\u521b\u5efa\u67f1\u72b6\u56fe<\/strong><\/h2>\n
3\u3001\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
# \u6570\u636e<\/p>\n\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u4e94\u3001\u7ed3\u5408Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h2>\n<\/p>\n
1\u3001\u52a0\u8f7d\u548c\u9884\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
\u8f6c\u6362\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
2\u3001\u4e0e\u7ed8\u56fe\u5e93\u7ed3\u5408\u4f7f\u7528<\/h3>\n<\/p>\n
# \u4f7f\u7528Pandas\u548cMatplotlib\u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n\u4f7f\u7528Pandas\u548cSeaborn\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
\u516d\u3001\u6ce8\u91cd\u8272\u5f69\u548c\u6837\u5f0f\u7684\u642d\u914d<\/h2>\n<\/p>\n
1\u3001\u9009\u62e9\u5408\u9002\u7684\u8c03\u8272\u677f<\/h3>\n<\/p>\n
# \u8bbe\u7f6e\u8c03\u8272\u677f<\/p>\n\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
2\u3001\u4f7f\u7528\u4e3b\u9898\u6837\u5f0f<\/h3>\n<\/p>\n
# \u8bbe\u7f6e\u4e3b\u9898\u6837\u5f0f<\/p>\n\u521b\u5efa\u67f1\u72b6\u56fe<\/strong><\/h2>\n
\u4e03\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002Matplotlib\u662f\u57fa\u7840\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff1bSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff0c\u9002\u5408\u8fdb\u884c\u7edf\u8ba1\u56fe\u7684\u7ed8\u5236\uff1b\u800cPlotly\u5219\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u7528\u6237\u4e0e\u56fe\u8868\u8fdb\u884c\u4ea4\u4e92\u7684\u573a\u666f\u3002\u6839\u636e\u4f60\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u3002<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u7edf\u8ba1\u56fe\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\uff0c\u53ef\u4ee5\u8003\u8651\u4ee5\u4e0b\u51e0\u70b9\uff1a\u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u642d\u914d\uff0c\u907f\u514d\u4f7f\u7528\u8fc7\u4e8e\u9c9c\u8273\u6216\u523a\u773c\u7684\u989c\u8272\uff1b\u4f7f\u7528\u6e05\u6670\u7684\u6807\u7b7e\u548c\u6807\u9898\uff0c\u8ba9\u89c2\u4f17\u4e00\u76ee\u4e86\u7136\uff1b\u5408\u7406\u8c03\u6574\u56fe\u8868\u7684\u5927\u5c0f\u548c\u6bd4\u4f8b\uff0c\u786e\u4fdd\u5404\u90e8\u5206\u5185\u5bb9\u5747\u8861\uff1b\u8003\u8651\u4f7f\u7528\u7f51\u683c\u7ebf\u548c\u6ce8\u91ca\u6765\u7a81\u51fa\u91cd\u8981\u6570\u636e\u70b9\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7edf\u8ba1\u56fe\u66f4\u52a0\u4e13\u4e1a\u548c\u5438\u5f15\u4eba\u3002<\/p>\n
\u5728\u5c55\u793a\u591a\u4e2a\u53d8\u91cf\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528\u5206\u9762\u56fe\u3001\u5806\u53e0\u56fe\u6216\u6c14\u6ce1\u56fe\u7b49\u5f62\u5f0f\u3002\u5206\u9762\u56fe\u53ef\u4ee5\u5c06\u4e0d\u540c\u53d8\u91cf\u7684\u56fe\u8868\u5206\u5f00\u663e\u793a\uff0c\u4fbf\u4e8e\u6bd4\u8f83\uff1b\u5806\u53e0\u56fe\u53ef\u4ee5\u901a\u8fc7\u4e0d\u540c\u989c\u8272\u7684\u533a\u57df\u5c55\u793a\u591a\u4e2a\u53d8\u91cf\u7684\u603b\u548c\u53ca\u5176\u7ec4\u6210\uff1b\u6c14\u6ce1\u56fe\u5219\u901a\u8fc7\u6c14\u6ce1\u7684\u5927\u5c0f\u548c\u4f4d\u7f6e\u5c55\u793a\u4e09\u4e2a\u7ef4\u5ea6\u7684\u4fe1\u606f\u3002\u5728\u9009\u62e9\u65f6\uff0c\u8981\u8003\u8651\u6570\u636e\u7684\u7279\u70b9\u548c\u5c55\u793a\u7684\u76ee\u7684\uff0c\u4ee5\u4fbf\u6709\u6548\u4f20\u8fbe\u4fe1\u606f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u753b\u597d\u770b\u7684\u7edf\u8ba1\u56fe\uff0c\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b […]","protected":false},"author":3,"featured_media":1057695,"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\/1057682"}],"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=1057682"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1057682\/revisions"}],"predecessor-version":[{"id":1057697,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1057682\/revisions\/1057697"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1057695"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1057682"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1057682"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1057682"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}