{"id":1161753,"date":"2025-01-13T19:20:18","date_gmt":"2025-01-13T11:20:18","guid":{"rendered":""},"modified":"2025-01-13T19:20:20","modified_gmt":"2025-01-13T11:20:20","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e5%9b%be%e4%bb%a3%e7%a0%81%e8%ae%be%e7%bd%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1161753.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u56fe\u4ee3\u7801\u8bbe\u7f6e"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u56fe\u4ee3\u7801\u8bbe\u7f6e\"<\/p>\n

\u4f7f\u7528Python\u7ed8\u56fe\u4ee3\u7801\u7684\u8bbe\u7f6e\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u3001Plotly\u5e93\u7b49\u3002Matplotlib\u5e93\u662f\u6700\u57fa\u7840\u4e5f\u662f\u6700\u5e7f\u6cdb\u4f7f\u7528\u7684\u7ed8\u56fe\u5e93\uff0cSeaborn\u5219\u662f\u5728Matplotlib\u57fa\u7840\u4e0a\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u7b80\u5355\u7684\u64cd\u4f5c\uff0cPlotly\u5219\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u60c5\u5f62\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e09\u79cd\u65b9\u6cd5\u7684\u8bbe\u7f6e\u548c\u4f7f\u7528\u3002<\/strong><\/p>\n<\/p>\n

\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u79cd\u5e93\u7ed8\u56fe\uff1a<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u521b\u5efa\u3002<\/p>\n<\/p>\n

\u5b89\u88c5Matplotlib<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u57fa\u672c\u4f7f\u7528<\/h3>\n<\/p>\n
1. \u7ed8\u5236\u7b80\u5355\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u6765\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Line Plot')<\/p>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\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\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6dfb\u52a0\u6807\u9898\u548c\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
2. \u5b9a\u5236\u56fe\u8868<\/h4>\n<\/p>\n
Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5b9a\u5236\u9009\u9879\uff0c\u53ef\u4ee5\u4fee\u6539\u56fe\u8868\u7684\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62\uff0c\u5b9a\u5236\u989c\u8272\u548c\u7ebf\u578b<\/strong><\/h2>\n
plt.plot(x, y, color='green', linestyle='--', marker='o')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Custom Line Plot')<\/p>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5728plt.plot()<\/code>\u51fd\u6570\u4e2d\u6dfb\u52a0\u4e86color<\/code>\u3001linestyle<\/code>\u548cmarker<\/code>\u53c2\u6570\uff0c\u5206\u522b\u7528\u4e8e\u8bbe\u7f6e\u7ebf\u7684\u989c\u8272\u3001\u7ebf\u578b\u548c\u6807\u8bb0\u3002<\/p>\n<\/p>\n
3. \u591a\u4e2a\u5b50\u56fe<\/h4>\n<\/p>\n
Matplotlib\u8fd8\u652f\u6301\u5728\u4e00\u4e2a\u56fe\u5f62\u4e2d\u7ed8\u5236\u591a\u4e2a\u5b50\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y1 = [2, 3, 5, 7, 11]<\/p>\n
y2 = [1, 4, 6, 8, 10]<\/p>\n
\u521b\u5efa\u56fe\u5f62\u548c\u5b50\u56fe<\/strong><\/h2>\n
fig, axs = plt.subplots(2)<\/p>\n
\u7ed8\u5236\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[0].plot(x, y1)<\/p>\n
axs[0].set_title('First Subplot')<\/p>\n
\u7ed8\u5236\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[1].plot(x, y2)<\/p>\n
axs[1].set_title('Second Subplot')<\/p>\n
\u8c03\u6574\u5e03\u5c40<\/strong><\/h2>\n
plt.tight_layout()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.subplots()<\/code>\u51fd\u6570\u521b\u5efa\u4e86\u4e24\u4e2a\u5b50\u56fe\uff0c\u5e76\u5206\u522b\u5728axs[0]<\/code>\u548caxs[1]<\/code>\u4e0a\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u4e8c\u3001SEABORN<\/p>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u7b80\u5355\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Seaborn<\/h3>\n<\/p>\n
\u5728\u5f00\u59cb\u4f7f\u7528Seaborn\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u57fa\u672c\u4f7f\u7528<\/h3>\n<\/p>\n
1. \u7ed8\u5236\u7b80\u5355\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u6765\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
sns.lineplot(x=x, y=y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Line Plot')<\/p>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528sns.lineplot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6dfb\u52a0\u6807\u9898\u548c\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528\u5185\u7f6e\u6570\u636e\u96c6<\/h4>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5185\u7f6e\u7684\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(x='day', y='total_bill', data=tips)<\/p>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
plt.title('Box Plot of Total Bill by Day')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Seaborn\u7684\u5185\u7f6e\u6570\u636e\u96c6tips<\/code>\uff0c\u5e76\u4f7f\u7528sns.boxplot()<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u7bb1\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
3. \u8bbe\u7f6e\u6837\u5f0f<\/h4>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86\u591a\u79cd\u6837\u5f0f\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bbe\u7f6e\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u6837\u5f0f<\/strong><\/h2>\n
sns.set(style='whitegrid')<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
sns.lineplot(x=x, y=y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Line Plot with Whitegrid Style')<\/p>\n
plt.xlabel('X Axis')<\/p>\n
plt.ylabel('Y Axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528sns.set()<\/code>\u51fd\u6570\u5c06\u6837\u5f0f\u8bbe\u7f6e\u4e3awhitegrid<\/code>\u3002<\/p>\n<\/p>\n
\u4e09\u3001PLOTLY<\/p>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u9002\u7528\u4e8e\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u60c5\u5f62\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Plotly<\/h3>\n<\/p>\n
\u5728\u5f00\u59cb\u4f7f\u7528Plotly\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u57fa\u672c\u4f7f\u7528<\/h3>\n<\/p>\n
1. \u7ed8\u5236\u7b80\u5355\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u6765\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
import plotly.offline as pyo<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
trace = go.Scatter(x=x, y=y, mode='lines+markers')<\/p>\n
data = [trace]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
layout = go.Layout(title='Simple Line Plot', xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=data, layout=layout)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
pyo.plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528go.Scatter()<\/code>\u51fd\u6570\u521b\u5efa\u4e86\u6298\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7go.Layout()<\/code>\u51fd\u6570\u8bbe\u7f6e\u4e86\u56fe\u5f62\u7684\u5e03\u5c40\u3002<\/p>\n<\/p>\n
2. \u521b\u5efa\u591a\u4e2a\u56fe\u5f62<\/h4>\n<\/p>\n
Plotly\u8fd8\u652f\u6301\u5728\u4e00\u4e2a\u9875\u9762\u4e2d\u663e\u793a\u591a\u4e2a\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
import plotly.offline as pyo<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y1 = [2, 3, 5, 7, 11]<\/p>\n
y2 = [1, 4, 6, 8, 10]<\/p>\n
\u521b\u5efa\u7b2c\u4e00\u4e2a\u6298\u7ebf\u56fe<\/strong><\/h2>\n
trace1 = go.Scatter(x=x, y=y1, mode='lines+markers', name='First Plot')<\/p>\n
\u521b\u5efa\u7b2c\u4e8c\u4e2a\u6298\u7ebf\u56fe<\/strong><\/h2>\n
trace2 = go.Scatter(x=x, y=y2, mode='lines+markers', name='Second Plot')<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
data = [trace1, trace2]<\/p>\n
layout = go.Layout(title='Multiple Line Plots', xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=data, layout=layout)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
pyo.plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e24\u4e2a\u6298\u7ebf\u56fe\uff0c\u5e76\u5c06\u5b83\u4eec\u6dfb\u52a0\u5230\u540c\u4e00\u4e2a\u56fe\u5f62\u4e2d\u663e\u793a\u3002<\/p>\n<\/p>\n
3. \u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
Plotly\u7684\u4e00\u4e2a\u5f3a\u5927\u529f\u80fd\u662f\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
import plotly.offline as pyo<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
trace = go.Scatter(x=x, y=y, mode='lines+markers', text=['A', 'B', 'C', 'D', 'E'])<\/p>\n
data = [trace]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
layout = go.Layout(title='Interactive Line Plot', xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=data, layout=layout)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
pyo.plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7text<\/code>\u53c2\u6570\u6dfb\u52a0\u4e86\u6570\u636e\u70b9\u7684\u6807\u7b7e\uff0c\u751f\u6210\u4e86\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u5e93\u8fdb\u884cPython\u7ed8\u56fe\u4ee3\u7801\u7684\u8bbe\u7f6e\u3002\u6bcf\u79cd\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u7279\u70b9\u548c\u4f18\u52bf\uff0c\u9009\u62e9\u9002\u5408\u81ea\u5df1\u7684\u5e93\u6765\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u5c55\u793a\u3002<\/p>\n<\/p>\n
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Python\u4e2d\u6709\u591a\u4e2a\u7ed8\u56fe\u5e93\u53ef\u4f9b\u9009\u62e9\uff0c\u5e38\u89c1\u7684\u6709Matplotlib\u3001Seaborn\u3001Plotly\u548cBokeh\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u4e3b\u8981\u53d6\u51b3\u4e8e\u4f60\u7684\u9700\u6c42\uff0c\u4f8b\u5982\uff0cMatplotlib\u9002\u5408\u57fa\u672c\u7684\u7ed8\u56fe\u9700\u6c42\uff0cSeaborn\u5219\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u800cPlotly\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u5982\u679c\u4f60\u5e0c\u671b\u5b9e\u73b0\u590d\u6742\u7684\u53ef\u89c6\u5316\u6548\u679c\uff0cBokeh\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/p>\n
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\u81ea\u5b9a\u4e49\u56fe\u8868\u6837\u5f0f\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\u3002\u4f7f\u7528Matplotlib\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7ebf\u6761\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u6837\u5f0f\u7b49\u5c5e\u6027\u6765\u8c03\u6574\u56fe\u8868\u7684\u5916\u89c2\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528Seaborn\u7684\u4e3b\u9898\u529f\u80fd\u5feb\u901f\u6539\u53d8\u6574\u4f53\u98ce\u683c\u3002\u65e0\u8bba\u4f7f\u7528\u54ea\u4e2a\u5e93\uff0c\u8c03\u6574\u5b57\u4f53\u3001\u80cc\u666f\u8272\u548c\u7f51\u683c\u7ebf\u7b49\u5143\u7d20\u90fd\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002<\/p>\n
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